climate models robustly: Topics by Science.gov

Sample records for climate models robustly

Computer models and the evidence of anthropogenic climate change: An epistemology of variety-of-evidence inferences and robustness analysis.

PubMed

Vezér, Martin A

2016-04-01

To study climate change, scientists employ computer models, which approximate target systems with various levels of skill. Given the imperfection of climate models, how do scientists use simulations to generate knowledge about the causes of observed climate change? Addressing a similar question in the context of biological modelling, Levins (1966) proposed an account grounded in robustness analysis. Recent philosophical discussions dispute the confirmatory power of robustness, raising the question of how the results of computer modelling studies contribute to the body of evidence supporting hypotheses about climate change. Expanding on Staley's (2004) distinction between evidential strength and security, and Lloyd's (2015) argument connecting variety-of-evidence inferences and robustness analysis, I address this question with respect to recent challenges to the epistemology robustness analysis. Applying this epistemology to case studies of climate change, I argue that, despite imperfections in climate models, and epistemic constraints on variety-of-evidence reasoning and robustness analysis, this framework accounts for the strength and security of evidence supporting climatological inferences, including the finding that global warming is occurring and its primary causes are anthropogenic. Copyright © 2016 Elsevier Ltd. All rights reserved.
Evaluating the robustness of conceptual rainfall-runoff models under climate variability in northern Tunisia

NASA Astrophysics Data System (ADS)

Dakhlaoui, H.; Ruelland, D.; Tramblay, Y.; Bargaoui, Z.

2017-07-01

To evaluate the impact of climate change on water resources at the catchment scale, not only future projections of climate are necessary but also robust rainfall-runoff models that must be fairly reliable under changing climate conditions. The aim of this study was thus to assess the robustness of three conceptual rainfall-runoff models (GR4j, HBV and IHACRES) on five basins in northern Tunisia under long-term climate variability, in the light of available future climate scenarios for this region. The robustness of the models was evaluated using a differential split sample test based on a climate classification of the observation period that simultaneously accounted for precipitation and temperature conditions. The study catchments include the main hydrographical basins in northern Tunisia, which produce most of the surface water resources in the country. A 30-year period (1970-2000) was used to capture a wide range of hydro-climatic conditions. The calibration was based on the Kling-Gupta Efficiency (KGE) criterion, while model transferability was evaluated based on the Nash-Sutcliffe efficiency criterion and volume error. The three hydrological models were shown to behave similarly under climate variability. The models simulated the runoff pattern better when transferred to wetter and colder conditions than to drier and warmer ones. It was shown that their robustness became unacceptable when climate conditions involved a decrease of more than 25% in annual precipitation and an increase of more than +1.75 °C in annual mean temperatures. The reduction in model robustness may be partly due to the climate dependence of some parameters. When compared to precipitation and temperature projections in the region, the limits of transferability obtained in this study are generally respected for short and middle term. For long term projections under the most pessimistic emission gas scenarios, the limits of transferability are generally not respected, which may hamper the use of conceptual models for hydrological projections in northern Tunisia.
Model robustness as a confirmatory virtue: The case of climate science.

PubMed

Lloyd, Elisabeth A

2015-02-01

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independently-supported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists' discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.
Robust Spring Drying in the Southwestern U.S. and Seasonal Migration of Wet/Dry Patterns in a Warmer Climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Yang; Leung, Lai-Yung R.; Lu, Jian

2014-03-16

This study compares climate simulations over the United States produced by a regional climate model with the driving global climate simulations as well as a large multi-model ensemble of global climate simulations to investigate robust changes in water availability (precipitation (P) – evapotranspiration (E)). A robust spring dry signal across multiple models is identified in the Southwest that results from a decrease in P and an increase in E in the future. In the boreal winter and summer, the prominent changes in P – E are associated with a north – south dipole pattern, while in spring, the prominent changesmore » in P – E appear as an east – west dipole pattern. The progression of the north – south and east – west dipole patterns through the seasons manifests clearly as a seasonal “clockwise” migration of wet/dry patterns, which is shown to be a robust feature of water availability changes in the US consistent across regional and global climate simulations.« less
Many-objective robust decision making for water allocation under climate change.

PubMed

Yan, Dan; Ludwig, Fulco; Huang, He Qing; Werners, Saskia E

2017-12-31

Water allocation is facing profound challenges due to climate change uncertainties. To identify adaptive water allocation strategies that are robust to climate change uncertainties, a model framework combining many-objective robust decision making and biophysical modeling is developed for large rivers. The framework was applied to the Pearl River basin (PRB), China where sufficient flow to the delta is required to reduce saltwater intrusion in the dry season. Before identifying and assessing robust water allocation plans for the future, the performance of ten state-of-the-art MOEAs (multi-objective evolutionary algorithms) is evaluated for the water allocation problem in the PRB. The Borg multi-objective evolutionary algorithm (Borg MOEA), which is a self-adaptive optimization algorithm, has the best performance during the historical periods. Therefore it is selected to generate new water allocation plans for the future (2079-2099). This study shows that robust decision making using carefully selected MOEAs can help limit saltwater intrusion in the Pearl River Delta. However, the framework could perform poorly due to larger than expected climate change impacts on water availability. Results also show that subjective design choices from the researchers and/or water managers could potentially affect the ability of the model framework, and cause the most robust water allocation plans to fail under future climate change. Developing robust allocation plans in a river basin suffering from increasing water shortage requires the researchers and water managers to well characterize future climate change of the study regions and vulnerabilities of their tools. Copyright © 2017 Elsevier B.V. All rights reserved.
Bias and robustness of uncertainty components estimates in transient climate projections

NASA Astrophysics Data System (ADS)

Hingray, Benoit; Blanchet, Juliette; Jean-Philippe, Vidal

2016-04-01

A critical issue in climate change studies is the estimation of uncertainties in projections along with the contribution of the different uncertainty sources, including scenario uncertainty, the different components of model uncertainty and internal variability. Quantifying the different uncertainty sources faces actually different problems. For instance and for the sake of simplicity, an estimate of model uncertainty is classically obtained from the empirical variance of the climate responses obtained for the different modeling chains. These estimates are however biased. Another difficulty arises from the limited number of members that are classically available for most modeling chains. In this case, the climate response of one given chain and the effect of its internal variability may be actually difficult if not impossible to separate. The estimate of scenario uncertainty, model uncertainty and internal variability components are thus likely to be not really robust. We explore the importance of the bias and the robustness of the estimates for two classical Analysis of Variance (ANOVA) approaches: a Single Time approach (STANOVA), based on the only data available for the considered projection lead time and a time series based approach (QEANOVA), which assumes quasi-ergodicity of climate outputs over the whole available climate simulation period (Hingray and Saïd, 2014). We explore both issues for a simple but classical configuration where uncertainties in projections are composed of two single sources: model uncertainty and internal climate variability. The bias in model uncertainty estimates is explored from theoretical expressions of unbiased estimators developed for both ANOVA approaches. The robustness of uncertainty estimates is explored for multiple synthetic ensembles of time series projections generated with MonteCarlo simulations. For both ANOVA approaches, when the empirical variance of climate responses is used to estimate model uncertainty, the bias is always positive. It can be especially high with STANOVA. In the most critical configurations, when the number of members available for each modeling chain is small (< 3) and when internal variability explains most of total uncertainty variance (75% or more), the overestimation is higher than 100% of the true model uncertainty variance. The bias can be considerably reduced with a time series ANOVA approach, owing to the multiple time steps accounted for. The longer the transient time period used for the analysis, the larger the reduction. When a quasi-ergodic ANOVA approach is applied to decadal data for the whole 1980-2100 period, the bias is reduced by a factor 2.5 to 20 depending on the projection lead time. In all cases, the bias is likely to be not negligible for a large number of climate impact studies resulting in a likely large overestimation of the contribution of model uncertainty to total variance. For both approaches, the robustness of all uncertainty estimates is higher when more members are available, when internal variability is smaller and/or the response-to-uncertainty ratio is higher. QEANOVA estimates are much more robust than STANOVA ones: QEANOVA simulated confidence intervals are roughly 3 to 5 times smaller than STANOVA ones. Excepted for STANOVA when less than 3 members is available, the robustness is rather high for total uncertainty and moderate for internal variability estimates. For model uncertainty or response-to-uncertainty ratio estimates, the robustness is conversely low for QEANOVA to very low for STANOVA. In the most critical configurations (small number of member, large internal variability), large over- or underestimation of uncertainty components is very thus likely. To propose relevant uncertainty analyses and avoid misleading interpretations, estimates of uncertainty components should be therefore bias corrected and ideally come with estimates of their robustness. This work is part of the COMPLEX Project (European Collaborative Project FP7-ENV-2012 number: 308601; http://www.complex.ac.uk/). Hingray, B., Saïd, M., 2014. Partitioning internal variability and model uncertainty components in a multimodel multireplicate ensemble of climate projections. J.Climate. doi:10.1175/JCLI-D-13-00629.1 Hingray, B., Blanchet, J. (revision) Unbiased estimators for uncertainty components in transient climate projections. J. Climate Hingray, B., Blanchet, J., Vidal, J.P. (revision) Robustness of uncertainty components estimates in climate projections. J.Climate
Is the ozone climate penalty robust in Europe?

NASA Astrophysics Data System (ADS)

Colette, Augustin; Andersson, Camilla; Baklanov, Alexander; Bessagnet, Bertrand; Brandt, Jørgen; Christensen, Jesper H.; Doherty, Ruth; Engardt, Magnuz; Geels, Camilla; Giannakopoulos, Christos; Hedegaard, Gitte B.; Katragkou, Eleni; Langner, Joakim; Lei, Hang; Manders, Astrid; Melas, Dimitris; Meleux, Frédérik; Rouïl, Laurence; Sofiev, Mikhail; Soares, Joana; Stevenson, David S.; Tombrou-Tzella, Maria; Varotsos, Konstantinos V.; Young, Paul

2015-08-01

Ozone air pollution is identified as one of the main threats bearing upon human health and ecosystems, with 25 000 deaths in 2005 attributed to surface ozone in Europe (IIASA 2013 TSAP Report #10). In addition, there is a concern that climate change could negate ozone pollution mitigation strategies, making them insufficient over the long run and jeopardising chances to meet the long term objective set by the European Union Directive of 2008 (Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008) (60 ppbv, daily maximum). This effect has been termed the ozone climate penalty. One way of assessing this climate penalty is by driving chemistry-transport models with future climate projections while holding the ozone precursor emissions constant (although the climate penalty may also be influenced by changes in emission of precursors). Here we present an analysis of the robustness of the climate penalty in Europe across time periods and scenarios by analysing the databases underlying 11 articles published on the topic since 2007, i.e. a total of 25 model projections. This substantial body of literature has never been explored to assess the uncertainty and robustness of the climate ozone penalty because of the use of different scenarios, time periods and ozone metrics. Despite the variability of model design and setup in this database of 25 model projection, the present meta-analysis demonstrates the significance and robustness of the impact of climate change on European surface ozone with a latitudinal gradient from a penalty bearing upon large parts of continental Europe and a benefit over the North Atlantic region of the domain. Future climate scenarios present a penalty for summertime (JJA) surface ozone by the end of the century (2071-2100) of at most 5 ppbv. Over European land surfaces, the 95% confidence interval of JJA ozone change is [0.44; 0.64] and [0.99; 1.50] ppbv for the 2041-2070 and 2071-2100 time windows, respectively.
The integrated effects of future climate and hydrologic uncertainty on sustainable flood risk management

NASA Astrophysics Data System (ADS)

Steinschneider, S.; Wi, S.; Brown, C. M.

2013-12-01

Flood risk management performance is investigated within the context of integrated climate and hydrologic modeling uncertainty to explore system robustness. The research question investigated is whether structural and hydrologic parameterization uncertainties are significant relative to other uncertainties such as climate change when considering water resources system performance. Two hydrologic models are considered, a conceptual, lumped parameter model that preserves the water balance and a physically-based model that preserves both water and energy balances. In the conceptual model, parameter and structural uncertainties are quantified and propagated through the analysis using a Bayesian modeling framework with an innovative error model. Mean climate changes and internal climate variability are explored using an ensemble of simulations from a stochastic weather generator. The approach presented can be used to quantify the sensitivity of flood protection adequacy to different sources of uncertainty in the climate and hydrologic system, enabling the identification of robust projects that maintain adequate performance despite the uncertainties. The method is demonstrated in a case study for the Coralville Reservoir on the Iowa River, where increased flooding over the past several decades has raised questions about potential impacts of climate change on flood protection adequacy.
Simulated discharge trends indicate robustness of hydrological models in a changing climate

NASA Astrophysics Data System (ADS)

Addor, Nans; Nikolova, Silviya; Seibert, Jan

2016-04-01

Assessing the robustness of hydrological models under contrasted climatic conditions should be part any hydrological model evaluation. Robust models are particularly important for climate impact studies, as models performing well under current conditions are not necessarily capable of correctly simulating hydrological perturbations caused by climate change. A pressing issue is the usually assumed stationarity of parameter values over time. Modeling experiments using conceptual hydrological models revealed that assuming transposability of parameters values in changing climatic conditions can lead to significant biases in discharge simulations. This raises the question whether parameter values should to be modified over time to reflect changes in hydrological processes induced by climate change. Such a question denotes a focus on the contribution of internal processes (i.e., catchment processes) to discharge generation. Here we adopt a different perspective and explore the contribution of external forcing (i.e., changes in precipitation and temperature) to changes in discharge. We argue that in a robust hydrological model, discharge variability should be induced by changes in the boundary conditions, and not by changes in parameter values. In this study, we explore how well the conceptual hydrological model HBV captures transient changes in hydrological signatures over the period 1970-2009. Our analysis focuses on research catchments in Switzerland undisturbed by human activities. The precipitation and temperature forcing are extracted from recently released 2km gridded data sets. We use a genetic algorithm to calibrate HBV for the whole 40-year period and for the eight successive 5-year periods to assess eventual trends in parameter values. Model calibration is run multiple times to account for parameter uncertainty. We find that in alpine catchments showing a significant increase of winter discharge, this trend can be captured reasonably well with constant parameter values over the whole reference period. Further, preliminary results suggest that some trends in parameter values do not reflect changes in hydrological processes, as reported by others previously, but instead might stem from a modeling artifact related to the parameterization of evapotranspiration, which is overly sensitive to temperature increase. We adopt a trading-space-for-time approach to better understand whether robust relationships between parameter values and forcing can be established, and to critically explore the rationale behind time-dependent parameter values in conceptual hydrological models.
Robustness for slope stability modelling under deep uncertainty

NASA Astrophysics Data System (ADS)

Almeida, Susana; Holcombe, Liz; Pianosi, Francesca; Wagener, Thorsten

2015-04-01

Landslides can have large negative societal and economic impacts, such as loss of life and damage to infrastructure. However, the ability of slope stability assessment to guide management is limited by high levels of uncertainty in model predictions. Many of these uncertainties cannot be easily quantified, such as those linked to climate change and other future socio-economic conditions, restricting the usefulness of traditional decision analysis tools. Deep uncertainty can be managed more effectively by developing robust, but not necessarily optimal, policies that are expected to perform adequately under a wide range of future conditions. Robust strategies are particularly valuable when the consequences of taking a wrong decision are high as is often the case of when managing natural hazard risks such as landslides. In our work a physically based numerical model of hydrologically induced slope instability (the Combined Hydrology and Stability Model - CHASM) is applied together with robust decision making to evaluate the most important uncertainties (storm events, groundwater conditions, surface cover, slope geometry, material strata and geotechnical properties) affecting slope stability. Specifically, impacts of climate change on long-term slope stability are incorporated, accounting for the deep uncertainty in future climate projections. Our findings highlight the potential of robust decision making to aid decision support for landslide hazard reduction and risk management under conditions of deep uncertainty.
Low fidelity of CORDEX and their driving experiments indicates future climatic uncertainty over Himalayan watersheds of Indus basin

NASA Astrophysics Data System (ADS)

Hasson, Shabeh ul; Böhner, Jürgen; Chishtie, Farrukh

2018-03-01

Assessment of future water availability from the Himalayan watersheds of Indus Basin (Jhelum, Kabul and upper Indus basin—UIB) is a growing concern for safeguarding the sustainable socioeconomic wellbeing downstream. This requires, before all, robust climate change information from the present-day state-of-the-art climate models. However, the robustness of climate change projections highly depends upon the fidelity of climate modeling experiments. Hence, this study assesses the fidelity of seven dynamically refined (0.44° ) experiments, performed under the framework of the coordinated regional climate downscaling experiment for South Asia (CX-SA), and additionally, their six coarse-resolution driving datasets participating in the coupled model intercomparison project phase 5 (CMIP5). We assess fidelity in terms of reproducibility of the observed climatology of temperature and precipitation, and the seasonality of the latter for the historical period (1971-2005). Based on the model fidelity results, we further assess the robustness or uncertainty of the far future climate (2061-2095), as projected under the extreme-end warming scenario of the representative concentration pathway (RCP) 8.5. Our results show that the CX-SA and their driving CMIP5 experiments consistently feature low fidelity in terms of the chosen skill metrics, suggesting substantial cold (6-10 ° C) and wet (up to 80%) biases and underestimation of observed precipitation seasonality. Surprisingly, the CX-SA are unable to outperform their driving datasets. Further, the biases of CX-SA and of their driving CMIP5 datasets are higher in magnitude than their projected changes under RCP8.5—and hence under less extreme RCPs—by the end of 21st century, indicating uncertain future climates for the Indus Basin watersheds. Higher inter-dataset disagreements of both CMIP5 and CX-SA for their simulated historical precipitation and for its projected changes reinforce uncertain future wet/dry conditions whereas the CMIP5 projected warming is less robust owing to higher historical period uncertainty. Interestingly, a better agreement among those CX-SA experiments that have been obtained through downscaling different CMIP5 experiments with the same regional climate model (RCM) indicates the RCMs' ability of modulating the influence of lateral boundary conditions over a large domain. These findings, instead of suggesting the usual skill-based identification of 'reasonable' global or regional low fidelity experiments, rather emphasize on a paradigm shift towards improving their fidelity by exploiting the potential of meso-to-local scale climate models—preferably of those that can solely resolve global-to-local scale climatic processes—in terms of microphysics, resolution and explicitly resolved convections. Additionally, an extensive monitoring of the nival regime within the Himalayan watersheds will reduce the observational uncertainty, allowing for a more robust fidelity assessment of the climate modeling experiments.
A bargaining game analysis of international climate negotiations

NASA Astrophysics Data System (ADS)

Smead, Rory; Sandler, Ronald L.; Forber, Patrick; Basl, John

2014-06-01

Climate negotiations under the United Nations Framework Convention on Climate Change have so far failed to achieve a robust international agreement to reduce greenhouse gas emissions. Game theory has been used to investigate possible climate negotiation solutions and strategies for accomplishing them. Negotiations have been primarily modelled as public goods games such as the Prisoner's Dilemma, though coordination games or games of conflict have also been used. Many of these models have solutions, in the form of equilibria, corresponding to possible positive outcomes--that is, agreements with the requisite emissions reduction commitments. Other work on large-scale social dilemmas suggests that it should be possible to resolve the climate problem. It therefore seems that equilibrium selection may be a barrier to successful negotiations. Here we use an N-player bargaining game in an agent-based model with learning dynamics to examine the past failures of and future prospects for a robust international climate agreement. The model suggests reasons why the desirable solutions identified in previous game-theoretic models have not yet been accomplished in practice and what mechanisms might be used to achieve these solutions.
Addressing Climate Change in Long-Term Water Planning Using Robust Decisionmaking

NASA Astrophysics Data System (ADS)

Groves, D. G.; Lempert, R.

2008-12-01

Addressing climate change in long-term natural resource planning is difficult because future management conditions are deeply uncertain and the range of possible adaptation options are so extensive. These conditions pose challenges to standard optimization decision-support techniques. This talk will describe a methodology called Robust Decisionmaking (RDM) that can complement more traditional analytic approaches by utilizing screening-level water management models to evaluate large numbers of strategies against a wide range of plausible future scenarios. The presentation will describe a recent application of the methodology to evaluate climate adaptation strategies for the Inland Empire Utilities Agency in Southern California. This project found that RDM can provide a useful way for addressing climate change uncertainty and identify robust adaptation strategies.
Robust features of future climate change impacts on sorghum yields in West Africa

NASA Astrophysics Data System (ADS)

Sultan, B.; Guan, K.; Kouressy, M.; Biasutti, M.; Piani, C.; Hammer, G. L.; McLean, G.; Lobell, D. B.

2014-10-01

West Africa is highly vulnerable to climate hazards and better quantification and understanding of the impact of climate change on crop yields are urgently needed. Here we provide an assessment of near-term climate change impacts on sorghum yields in West Africa and account for uncertainties both in future climate scenarios and in crop models. Towards this goal, we use simulations of nine bias-corrected CMIP5 climate models and two crop models (SARRA-H and APSIM) to evaluate the robustness of projected crop yield impacts in this area. In broad agreement with the full CMIP5 ensemble, our subset of bias-corrected climate models projects a mean warming of +2.8 °C in the decades of 2031-2060 compared to a baseline of 1961-1990 and a robust change in rainfall in West Africa with less rain in the Western part of the Sahel (Senegal, South-West Mali) and more rain in Central Sahel (Burkina Faso, South-West Niger). Projected rainfall deficits are concentrated in early monsoon season in the Western part of the Sahel while positive rainfall changes are found in late monsoon season all over the Sahel, suggesting a shift in the seasonality of the monsoon. In response to such climate change, but without accounting for direct crop responses to CO2, mean crop yield decreases by about 16-20% and year-to-year variability increases in the Western part of the Sahel, while the eastern domain sees much milder impacts. Such differences in climate and impacts projections between the Western and Eastern parts of the Sahel are highly consistent across the climate and crop models used in this study. We investigate the robustness of impacts for different choices of cultivars, nutrient treatments, and crop responses to CO2. Adverse impacts on mean yield and yield variability are lowest for modern cultivars, as their short and nearly fixed growth cycle appears to be more resilient to the seasonality shift of the monsoon, thus suggesting shorter season varieties could be considered a potential adaptation to ongoing climate changes. Easing nitrogen stress via increasing fertilizer inputs would increase absolute yields, but also make the crops more responsive to climate stresses, thus enhancing the negative impacts of climate change in a relative sense. Finally, CO2 fertilization would significantly offset the negative climate impacts on sorghum yields by about 10%, with drier regions experiencing the largest benefits, though the net impacts of climate change remain negative even after accounting for CO2.
Understanding uncertainty in precipitation changes in a balanced perturbed-physics ensemble under multiple climate forcings

NASA Astrophysics Data System (ADS)

Millar, R.; Ingram, W.; Allen, M. R.; Lowe, J.

2013-12-01

Temperature and precipitation patterns are the climate variables with the greatest impacts on both natural and human systems. Due to the small spatial scales and the many interactions involved in the global hydrological cycle, in general circulation models (GCMs) representations of precipitation changes are subject to considerable uncertainty. Quantifying and understanding the causes of uncertainty (and identifying robust features of predictions) in both global and local precipitation change is an essential challenge of climate science. We have used the huge distributed computing capacity of the climateprediction.net citizen science project to examine parametric uncertainty in an ensemble of 20,000 perturbed-physics versions of the HadCM3 general circulation model. The ensemble has been selected to have a control climate in top-of-atmosphere energy balance [Yamazaki et al. 2013, J.G.R.]. We force this ensemble with several idealised climate-forcing scenarios including carbon dioxide step and transient profiles, solar radiation management geoengineering experiments with stratospheric aerosols, and short-lived climate forcing agents. We will present the results from several of these forcing scenarios under GCM parametric uncertainty. We examine the global mean precipitation energy budget to understand the robustness of a simple non-linear global precipitation model [Good et al. 2012, Clim. Dyn.] as a better explanation of precipitation changes in transient climate projections under GCM parametric uncertainty than a simple linear tropospheric energy balance model. We will also present work investigating robust conclusions about precipitation changes in a balanced ensemble of idealised solar radiation management scenarios [Kravitz et al. 2011, Atmos. Sci. Let.].
Solar irradiance reduction to counteract radiative forcing from a quadrupling of CO2: climate responses simulated by four earth system models

NASA Astrophysics Data System (ADS)

Schmidt, H.; Alterskjær, K.; Karam, D. Bou; Boucher, O.; Jones, A.; Kristjánsson, J. E.; Niemeier, U.; Schulz, M.; Aaheim, A.; Benduhn, F.; Lawrence, M.; Timmreck, C.

2012-06-01

In this study we compare the response of four state-of-the-art Earth system models to climate engineering under scenario G1 of two model intercomparison projects: GeoMIP (Geoengineering Model Intercomparison Project) and IMPLICC (EU project "Implications and risks of engineering solar radiation to limit climate change"). In G1, the radiative forcing from an instantaneous quadrupling of the CO2 concentration, starting from the preindustrial level, is balanced by a reduction of the solar constant. Model responses to the two counteracting forcings in G1 are compared to the preindustrial climate in terms of global means and regional patterns and their robustness. While the global mean surface air temperature in G1 remains almost unchanged compared to the control simulation, the meridional temperature gradient is reduced in all models. Another robust response is the global reduction of precipitation with strong effects in particular over North and South America and northern Eurasia. In comparison to the climate response to a quadrupling of CO2 alone, the temperature responses are small in experiment G1. Precipitation responses are, however, in many regions of comparable magnitude but globally of opposite sign.
Future Simulated Intensification of Precipitation Extremes, CMIP5 Model Uncertainties and Dependencies

NASA Astrophysics Data System (ADS)

Bador, M.; Donat, M.; Geoffroy, O.; Alexander, L. V.

2017-12-01

Precipitation intensity during extreme events is expected to increase with climate change. Throughout the 21st century, CMIP5 climate models project a general increase in annual extreme precipitation in most regions. We investigate how robust this future increase is across different models, regions and seasons. We find that there is strong similarity in extreme precipitation changes between models that share atmospheric physics, reducing the ensemble of 27 models to 14 independent projections. We find that future simulated extreme precipitation increases in most models in the majority of land grid cells located in the dry, intermediate and wet regions according to each model's precipitation climatology. These increases significantly exceed the range of natural variability estimated from long equilibrium control runs. The intensification of extreme precipitation across the entire spectrum of dry to wet regions is particularly robust in the extra-tropics in both wet and dry season, whereas uncertainties are larger in the tropics. The CMIP5 ensemble therefore indicates robust future intensification of annual extreme rainfall in particular in extra-tropical regions. Generally, the CMIP5 robustness is higher during the dry season compared to the wet season and the annual scale, but inter-model uncertainties in the tropics remain important.
Robust signals of future projections of Indian summer monsoon rainfall by IPCC AR5 climate models: Role of seasonal cycle and interannual variability

NASA Astrophysics Data System (ADS)

Jayasankar, C. B.; Surendran, Sajani; Rajendran, Kavirajan

2015-05-01

Coupled Model Intercomparison Project phase 5 (Fifth Assessment Report of Intergovernmental Panel on Climate Change) coupled global climate model Representative Concentration Pathway 8.5 simulations are analyzed to derive robust signals of projected changes in Indian summer monsoon rainfall (ISMR) and its variability. Models project clear future temperature increase but diverse changes in ISMR with substantial intermodel spread. Objective measures of interannual variability (IAV) yields nearly equal chance for future increase or decrease. This leads to discrepancy in quantifying changes in ISMR and variability. However, based primarily on the physical association between mean changes in ISMR and its IAV, and objective methods such as k-means clustering with Dunn's validity index, mean seasonal cycle, and reliability ensemble averaging, projections fall into distinct groups. Physically consistent groups of models with the highest reliability project future reduction in the frequency of light rainfall but increase in high to extreme rainfall and thereby future increase in ISMR by 0.74 ± 0.36 mm d-1, along with increased future IAV. These robust estimates of future changes are important for useful impact assessments.
Robustness and Uncertainty: Applications for Policy in Climate and Hydrological Modeling

NASA Astrophysics Data System (ADS)

Fields, A. L., III

2015-12-01

Policymakers must often decide how to proceed when presented with conflicting simulation data from hydrological, climatological, and geological models. While laboratory sciences often appeal to the reproducibility of results to argue for the validity of their conclusions, simulations cannot use this strategy for a number of pragmatic and methodological reasons. However, robustness of predictions and causal structures can serve the same function for simulations as reproducibility does for laboratory experiments and field observations in either adjudicating between conflicting results or showing that there is insufficient justification to externally validate the results. Additionally, an interpretation of the argument from robustness is presented that involves appealing to the convergence of many well-built and diverse models rather than the more common version which involves appealing to the probability that one of a set of models is likely to be true. This interpretation strengthens the case for taking robustness as an additional requirement for the validation of simulation results and ultimately supports the idea that computer simulations can provide information about the world that is just as trustworthy as data from more traditional laboratory studies and field observations. Understanding the importance of robust results for the validation of simulation data is especially important for policymakers making decisions on the basis of potentially conflicting models. Applications will span climate, hydrological, and hydroclimatological models.
Climate Verification Using Running Mann Whitney Z Statistics

USDA-ARS?s Scientific Manuscript database

A robust method previously used to detect observed intra- to multi-decadal (IMD) climate regimes was adapted to test whether climate models could reproduce IMD variations in U.S. surface temperatures during 1919-2008. This procedure, called the running Mann Whitney Z (MWZ) method, samples data ranki...

The Model Intercomparison Project on the Climatic Response to Volcanic Forcing (VolMIP): Experimental Design and Forcing Input Data for CMIP6

NASA Technical Reports Server (NTRS)

Zanchettin, Davide; Khodri, Myriam; Timmreck, Claudia; Toohey, Matthew; Schmidt, Anja; Gerber, Edwin P.; Hegerl, Gabriele; Robock, Alan; Pausata, Francesco; Ball, William T.;

2016-01-01

The enhancement of the stratospheric aerosol layer by volcanic eruptions induces a complex set of responses causing global and regional climate effects on a broad range of timescales. Uncertainties exist regarding the climatic response to strong volcanic forcing identified in coupled climate simulations that contributed to the fifth phase of the Coupled Model Intercomparison Project (CMIP5). In order to better understand the sources of these model diversities, the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP) has defined a coordinated set of idealized volcanic perturbation experiments to be carried out in alignment with the CMIP6 protocol. VolMIP provides a common stratospheric aerosol data set for each experiment to minimize differences in the applied volcanic forcing. It defines a set of initial conditions to assess how internal climate variability contributes to determining the response. VolMIP will assess to what extent volcanically forced responses of the coupled ocean-atmosphere system are robustly simulated by state-of-the-art coupled climate models and identify the causes that limit robust simulated behavior, especially differences in the treatment of physical processes. This paper illustrates the design of the idealized volcanic perturbation experiments in the VolMIP protocol and describes the common aerosol forcing input data sets to be used.

Land-Use and Climate : first results from the LUCID experiments ; implications for experimental design in IPCC-AR5

NASA Astrophysics Data System (ADS)

de Noblet, N.; Pitman, A.; Participants, Lucid

2009-04-01

The project "Land-Use and Climate, IDentification of robust impacts" (LUCID) was conceived under the auspices of IGBP-iLEAPS and GEWEX-GLASS, to address the robustness of 'local' and possible remote impacts of land-use induced land-cover changes (LCC). LUCID explores, using methodologies that major climate modelling groups recognise, those impacts of LCC that are robust - that is, above the noise generated by model variability and consistent across a suite of climate models. To start with, seven climate models were run, in ensemble mode (5 realisations per 31-years long experiment), with prescribed observed sea-surface temperatures (SSTs) and sea ice extent (SIc). Pre-industrial and present-day simulations were used to explore the impacts of biogeophysical impacts of human-induced land cover change. The imposed LCC perturbation led to statistically significant changes in latent heat flux and near-surface temperature over the regions of land cover change, but few significant changes in precipitation. Our results show no common remote impacts of land cover change. They also highlight a dilemma for both historical hind-casts and future projections; land cover change is regionally important, but it is not feasible within the time frame of the next IPCC (AR5) assessment to implement this change commonly across multiple models. Further analysis are in progress and will be presented to identify the continental regions where changes in LCC may have been more important than the combined changes in SSTs, SIc and CO2 between the pre-industrial times and nowadays.
An index-based robust decision making framework for watershed management in a changing climate.

PubMed

Kim, Yeonjoo; Chung, Eun-Sung

2014-03-01

This study developed an index-based robust decision making framework for watershed management dealing with water quantity and quality issues in a changing climate. It consists of two parts of management alternative development and analysis. The first part for alternative development consists of six steps: 1) to understand the watershed components and process using HSPF model, 2) to identify the spatial vulnerability ranking using two indices: potential streamflow depletion (PSD) and potential water quality deterioration (PWQD), 3) to quantify the residents' preferences on water management demands and calculate the watershed evaluation index which is the weighted combinations of PSD and PWQD, 4) to set the quantitative targets for water quantity and quality, 5) to develop a list of feasible alternatives and 6) to eliminate the unacceptable alternatives. The second part for alternative analysis has three steps: 7) to analyze all selected alternatives with a hydrologic simulation model considering various climate change scenarios, 8) to quantify the alternative evaluation index including social and hydrologic criteria with utilizing multi-criteria decision analysis methods and 9) to prioritize all options based on a minimax regret strategy for robust decision. This framework considers the uncertainty inherent in climate models and climate change scenarios with utilizing the minimax regret strategy, a decision making strategy under deep uncertainty and thus this procedure derives the robust prioritization based on the multiple utilities of alternatives from various scenarios. In this study, the proposed procedure was applied to the Korean urban watershed, which has suffered from streamflow depletion and water quality deterioration. Our application shows that the framework provides a useful watershed management tool for incorporating quantitative and qualitative information into the evaluation of various policies with regard to water resource planning and management. Copyright © 2013 Elsevier B.V. All rights reserved.
New methods in hydrologic modeling and decision support for culvert flood risk under climate change

NASA Astrophysics Data System (ADS)

Rosner, A.; Letcher, B. H.; Vogel, R. M.; Rees, P. S.

2015-12-01

Assessing culvert flood vulnerability under climate change poses an unusual combination of challenges. We seek a robust method of planning for an uncertain future, and therefore must consider a wide range of plausible future conditions. Culverts in our case study area, northwestern Massachusetts, USA, are predominantly found in small, ungaged basins. The need to predict flows both at numerous sites and under numerous plausible climate conditions requires a statistical model with low data and computational requirements. We present a statistical streamflow model that is driven by precipitation and temperature, allowing us to predict flows without reliance on reference gages of observed flows. The hydrological analysis is used to determine each culvert's risk of failure under current conditions. We also explore the hydrological response to a range of plausible future climate conditions. These results are used to determine the tolerance of each culvert to future increases in precipitation. In a decision support context, current flood risk as well as tolerance to potential climate changes are used to provide a robust assessment and prioritization for culvert replacements.
Assessing the Robustness of Green Infrastructure under Stochastic Design Storms and Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Chui, T. F. M.; Yang, Y.

2017-12-01

Green infrastructures (GI) have been widely used to mitigate flood risk, improve surface water quality, and to restore predevelopment hydrologic regimes. Commonly-used GI include, bioretention system, porous pavement and green roof, etc. They are normally sized to fulfil different design criteria (e.g. providing certain storage depths, limiting peak surface flow rates) that are formulated for current climate conditions. While GI commonly have long lifespan, the sensitivity of their performance to climate change is however unclear. This study first proposes a method to formulate suitable design criteria to meet different management interests (e.g. different levels of first flush reduction and peak flow reduction). Then typical designs of GI are proposed. In addition, a high resolution stochastic design storm generator using copulas and random cascade model is developed, which is calibrated using recorded rainfall time series. Then, few climate change scenarios are generated by varying the duration and depth of design storms, and changing the parameters of the calibrated storm generator. Finally, the performance of GI with typical designs under the random synthesized design storms are then assessed using numerical modeling. The robustness of the designs is obtained by the comparing their performance in the future scenarios to the current one. This study overall examines the robustness of the current GI design criteria under uncertain future climate conditions, demonstrating whether current GI design criteria should be modified to account for climate change.
Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting

NASA Astrophysics Data System (ADS)

Cannon, A. J.; Hsieh, W. W.

2008-02-01

Robust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasonal climate forecasts. The neural network model architecture of standard NLCCA is kept intact, but the cost functions used to set the model parameters are replaced with more robust variants. The Pearson product-moment correlation in the double-barreled network is replaced by the biweight midcorrelation, and the mean squared error (mse) in the inverse mapping networks can be replaced by the mean absolute error (mae). Robust variants of NLCCA are demonstrated on a synthetic dataset and are used to forecast sea surface temperatures in the tropical Pacific Ocean based on the sea level pressure field. Results suggest that adoption of the biweight midcorrelation can lead to improved performance, especially when a strong, common event exists in both predictor/predictand datasets. Replacing the mse by the mae leads to improved performance on the synthetic dataset, but not on the climate dataset except at the longest lead time, which suggests that the appropriate cost function for the inverse mapping networks is more problem dependent.
Methodology for qualitative uncertainty assessment of climate impact indicators

NASA Astrophysics Data System (ADS)

Otto, Juliane; Keup-Thiel, Elke; Rechid, Diana; Hänsler, Andreas; Pfeifer, Susanne; Roth, Ellinor; Jacob, Daniela

2016-04-01

The FP7 project "Climate Information Portal for Copernicus" (CLIPC) is developing an integrated platform of climate data services to provide a single point of access for authoritative scientific information on climate change and climate change impacts. In this project, the Climate Service Center Germany (GERICS) has been in charge of the development of a methodology on how to assess the uncertainties related to climate impact indicators. Existing climate data portals mainly treat the uncertainties in two ways: Either they provide generic guidance and/or express with statistical measures the quantifiable fraction of the uncertainty. However, none of the climate data portals give the users a qualitative guidance how confident they can be in the validity of the displayed data. The need for such guidance was identified in CLIPC user consultations. Therefore, we aim to provide an uncertainty assessment that provides the users with climate impact indicator-specific guidance on the degree to which they can trust the outcome. We will present an approach that provides information on the importance of different sources of uncertainties associated with a specific climate impact indicator and how these sources affect the overall 'degree of confidence' of this respective indicator. To meet users requirements in the effective communication of uncertainties, their feedback has been involved during the development process of the methodology. Assessing and visualising the quantitative component of uncertainty is part of the qualitative guidance. As visual analysis method, we apply the Climate Signal Maps (Pfeifer et al. 2015), which highlight only those areas with robust climate change signals. Here, robustness is defined as a combination of model agreement and the significance of the individual model projections. Reference Pfeifer, S., Bülow, K., Gobiet, A., Hänsler, A., Mudelsee, M., Otto, J., Rechid, D., Teichmann, C. and Jacob, D.: Robustness of Ensemble Climate Projections Analyzed with Climate Signal Maps: Seasonal and Extreme Precipitation for Germany, Atmosphere (Basel)., 6(5), 677-698, doi:10.3390/atmos6050677, 2015.
Assessing the robustness and uncertainties of projected changes in temperature and precipitation in AR4 Global Climate Models over the Arabian Peninsula

NASA Astrophysics Data System (ADS)

Almazroui, Mansour; Saeed, Fahad; Islam, Md. Nazrul; Alkhalaf, A. K.

2016-12-01

An ensemble from different climate projections is essential for attaining robust climate change information in a particular region. To achieve this purpose, the results of an ensemble combining the Global Climate Models data from Couple Model Intercomparison Project 3 (CMIP3), have been employed for the Arabian Peninsula region. Different analysis methods comprising spatial plots with robustness analysis, bar plots with likelihood ranges, as well as line plots with likelihood spread along with decadal trend analysis have been carried out at annual as well as seasonal time scales for temperature and precipitation. Results of CMIP3 data for the B1, A1B and A2 scenarios indicate robust changes in temperature and precipitation in the future climate. Spatial plots show a robust summer temperature increase over the whole Peninsula which is higher in the summer season as compared to the winter. The Northern Arabian Peninsula (NAP) region also shows a higher temperature increase in comparison with the Southern Arabian Peninsula (SAP) during the summer season. Moreover the NAP region, which generally comes under the influence of disturbances originating from the Mediterranean Sea region during the winter season, has shown a robust decrease in precipitation during the winter season. Contrarily the SAP region, which remains dry during the winter season and comes under the influence of South Asian Summer Monsoon in the summer season, indicates a robust increase in precipitation during the summer season. This behavior is also obvious from bar plots, which show a gradual decrease (increase) in median precipitation values towards the end of the 21st century for all the three scenarios over the NAP (SAP) region during the winter (summer) season. Moreover, smaller lengths of full as well as likely ranges in the bar plot for NAP (SAP) shows that these precipitation projections are less uncertain as compared to SAP (NAP) for the winter (summer) season. Furthermore from the line plots, a consistent decreasing trend in precipitation (1.35% per decade, significant at 99%) can be observed, while SAP shows an increasing trend in precipitation (1.21% per decade, significant at 99%). Similarly for the case of temperature, a significant (99% level) increase is projected over NAP and SAP regions with values of 0.37 and 0.35 °C per decade respectively. Considering the vulnerability of the region to climate change impacts, these results call for immediate actions in developing the long-term strategies to deal with the adverse impacts of climate change up-to the end of the 21st century at a regional level.
Collaborative Research: Robust Climate Projections and Stochastic Stability of Dynamical Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghil, Michael; McWilliams, James; Neelin, J. David

The project was completed along the lines of the original proposal, with additional elements arising as new results were obtained. The originally proposed three thrusts were expanded to include an additional, fourth one. (i) The e ffects of stochastic perturbations on climate models have been examined at the fundamental level by using the theory of deterministic and random dynamical systems, in both nite and in nite dimensions. (ii) The theoretical results have been implemented first on a delay-diff erential equation (DDE) model of the El-Nino/Southern-Oscillation (ENSO) phenomenon. (iii) More detailed, physical aspects of model robustness have been considered, as proposed,more » within the stripped-down ICTP-AGCM (formerly SPEEDY) climate model. This aspect of the research has been complemented by both observational and intermediate-model aspects of mid-latitude and tropical climate. (iv) An additional thrust of the research relied on new and unexpected results of (i) and involved reduced-modeling strategies and associated prediction aspects have been tested within the team's empirical model reduction (EMR) framework. Finally, more detailed, physical aspects have been considered within the stripped-down SPEEDY climate model. The results of each of these four complementary e fforts are presented in the next four sections, organized by topic and by the team members concentrating on the topic under discussion.« less
Can a reduction of solar irradiance counteract CO2-induced climate change? - Results from four Earth system models

NASA Astrophysics Data System (ADS)

Schmidt, H.; Alterskjær, K.; Karam, D. Bou; Boucher, O.; Jones, A.; Kristjansson, J. E.; Niemeier, U.; Schulz, M.; Aaheim, A.; Benduhn, F.; Lawrence, M.; Timmreck, C.

2012-01-01

In this study we compare the response of four state-of-the-art Earth system models to climate engineering under scenario G1 of the GeoMIP and IMPLICC model intercomparison projects. In G1, the radiative forcing from an instantaneous quadrupling of the CO2 concentration, starting from the preindustrial level, is balanced by a reduction of the solar constant. Model responses to the two counteracting forcings in G1 are compared to the preindustrial climate in terms of global means and regional patterns and their robustness. While the global mean surface air temperature in G1 remains almost unchanged, the meridional temperature gradient is reduced in all models compared to the control simulation. Another robust response is the global reduction of precipitation with strong effects in particular over North and South America and northern Eurasia. It is shown that this reduction is only partly compensated by a reduction in evaporation so that large continental regions are drier in the engineered climate. In comparison to the climate response to a quadrupling of CO2 alone the temperature responses are small in experiment G1. Precipitation responses are, however, of comparable magnitude but in many regions of opposite sign.
Relevance of Regional Hydro-Climatic Projection Data for Hydrodynamics and Water Quality Modelling of the Baltic Sea

NASA Astrophysics Data System (ADS)

Goldenberg, R.; Vigouroux, G.; Chen, Y.; Bring, A.; Kalantari, Z.; Prieto, C.; Destouni, G.

2017-12-01

The Baltic Sea, located in Northern Europe, is one of the world's largest body of brackish water, enclosed and surrounded by nine different countries. The magnitude of climate change may be particularly large in northern regions, and identifying its impacts on vulnerable inland waters and their runoff and nutrient loading to the Baltic Sea is an important and complex task. Exploration of such hydro-climatic impacts is needed to understand potential future changes in physical, ecological and water quality conditions in the regional coastal and marine waters. In this study, we investigate hydro-climatic changes and impacts on the Baltic Sea by synthesizing multi-model climate projection data from the CORDEX regional downscaling initiative (EURO- and Arctic- CORDEX domains, http://www.cordex.org/). We identify key hydro-climatic variable outputs of these models and assess model performance with regard to their projected temporal and spatial change behavior and impacts on different scales and coastal-marine parts, up to the whole Baltic Sea. Model spreading, robustness and impact implications for the Baltic Sea system are investigated for and through further use in simulations of coastal-marine hydrodynamics and water quality based on these key output variables and their change projections. Climate model robustness in this context is assessed by inter-model spreading analysis and observation data comparisons, while projected change implications are assessed by forcing of linked hydrodynamic and water quality modeling of the Baltic Sea based on relevant hydro-climatic outputs for inland water runoff and waterborne nutrient loading to the Baltic sea, as well as for conditions in the sea itself. This focused synthesis and analysis of hydro-climatically relevant output data of regional climate models facilitates assessment of reliability and uncertainty in projections of driver-impact changes of key importance for Baltic Sea physical, water quality and ecological conditions and their future evolution.
Application of stakeholder-based and modelling approaches for supporting robust adaptation decision making under future climatic uncertainty and changing urban-agricultural water demand

NASA Astrophysics Data System (ADS)

Bhave, Ajay; Dessai, Suraje; Conway, Declan; Stainforth, David

2016-04-01

Deep uncertainty in future climate change and socio-economic conditions necessitates the use of assess-risk-of-policy approaches over predict-then-act approaches for adaptation decision making. Robust Decision Making (RDM) approaches embody this principle and help evaluate the ability of adaptation options to satisfy stakeholder preferences under wide-ranging future conditions. This study involves the simultaneous application of two RDM approaches; qualitative and quantitative, in the Cauvery River Basin in Karnataka (population ~23 million), India. The study aims to (a) determine robust water resources adaptation options for the 2030s and 2050s and (b) compare the usefulness of a qualitative stakeholder-driven approach with a quantitative modelling approach. For developing a large set of future scenarios a combination of climate narratives and socio-economic narratives was used. Using structured expert elicitation with a group of climate experts in the Indian Summer Monsoon, climatic narratives were developed. Socio-economic narratives were developed to reflect potential future urban and agricultural water demand. In the qualitative RDM approach, a stakeholder workshop helped elicit key vulnerabilities, water resources adaptation options and performance criteria for evaluating options. During a second workshop, stakeholders discussed and evaluated adaptation options against the performance criteria for a large number of scenarios of climatic and socio-economic change in the basin. In the quantitative RDM approach, a Water Evaluation And Planning (WEAP) model was forced by precipitation and evapotranspiration data, coherent with the climatic narratives, together with water demand data based on socio-economic narratives. We find that compared to business-as-usual conditions options addressing urban water demand satisfy performance criteria across scenarios and provide co-benefits like energy savings and reduction in groundwater depletion, while options reducing agricultural water demand significantly affect downstream water availability. Water demand options demonstrate potential to improve environmental flow conditions and satisfy legal water supply requirements for downstream riparian states. On the other hand, currently planned large scale infrastructural projects demonstrate reduced value in certain scenarios, illustrating the impacts of lock-in effects of large scale infrastructure. From a methodological perspective, we find that while the stakeholder-driven approach revealed robust options in a resource-light manner and helped initiate much needed interaction amongst stakeholders, the modelling approach provides complementary quantitative information. The study reveals robust adaptation options for this important basin and provides a strong methodological basis for carrying out future studies that support adaptation decision making.
Climate model uncertainty in impact assessments for agriculture: A multi-ensemble case study on maize in sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Dale, Amy; Fant, Charles; Strzepek, Kenneth; Lickley, Megan; Solomon, Susan

2017-03-01

We present maize production in sub-Saharan Africa as a case study in the exploration of how uncertainties in global climate change, as reflected in projections from a range of climate model ensembles, influence climate impact assessments for agriculture. The crop model AquaCrop-OS (Food and Agriculture Organization of the United Nations) was modified to run on a 2° × 2° grid and coupled to 122 climate model projections from multi-model ensembles for three emission scenarios (Coupled Model Intercomparison Project Phase 3 [CMIP3] SRES A1B and CMIP5 Representative Concentration Pathway [RCP] scenarios 4.5 and 8.5) as well as two "within-model" ensembles (NCAR CCSM3 and ECHAM5/MPI-OM) designed to capture internal variability (i.e., uncertainty due to chaos in the climate system). In spite of high uncertainty, most notably in the high-producing semi-arid zones, we observed robust regional and sub-regional trends across all ensembles. In agreement with previous work, we project widespread yield losses in the Sahel region and Southern Africa, resilience in Central Africa, and sub-regional increases in East Africa and at the southern tip of the continent. Spatial patterns of yield losses corresponded with spatial patterns of aridity increases, which were explicitly evaluated. Internal variability was a major source of uncertainty in both within-model and between-model ensembles and explained the majority of the spatial distribution of uncertainty in yield projections. Projected climate change impacts on maize production in different regions and nations ranged from near-zero or positive (upper quartile estimates) to substantially negative (lower quartile estimates), highlighting a need for risk management strategies that are adaptive and robust to uncertainty.
A climate robust integrated modelling framework for regional impact assessment of climate change

NASA Astrophysics Data System (ADS)

Janssen, Gijs; Bakker, Alexander; van Ek, Remco; Groot, Annemarie; Kroes, Joop; Kuiper, Marijn; Schipper, Peter; van Walsum, Paul; Wamelink, Wieger; Mol, Janet

2013-04-01

Decision making towards climate proofing the water management of regional catchments can benefit greatly from the availability of a climate robust integrated modelling framework, capable of a consistent assessment of climate change impacts on the various interests present in the catchments. In the Netherlands, much effort has been devoted to developing state-of-the-art regional dynamic groundwater models with a very high spatial resolution (25x25 m2). Still, these models are not completely satisfactory to decision makers because the modelling concepts do not take into account feedbacks between meteorology, vegetation/crop growth, and hydrology. This introduces uncertainties in forecasting the effects of climate change on groundwater, surface water, agricultural yields, and development of groundwater dependent terrestrial ecosystems. These uncertainties add to the uncertainties about the predictions on climate change itself. In order to create an integrated, climate robust modelling framework, we coupled existing model codes on hydrology, agriculture and nature that are currently in use at the different research institutes in the Netherlands. The modelling framework consists of the model codes MODFLOW (groundwater flow), MetaSWAP (vadose zone), WOFOST (crop growth), SMART2-SUMO2 (soil-vegetation) and NTM3 (nature valuation). MODFLOW, MetaSWAP and WOFOST are coupled online (i.e. exchange information on time step basis). Thus, changes in meteorology and CO2-concentrations affect crop growth and feedbacks between crop growth, vadose zone water movement and groundwater recharge are accounted for. The model chain WOFOST-MetaSWAP-MODFLOW generates hydrological input for the ecological prediction model combination SMART2-SUMO2-NTM3. The modelling framework was used to support the regional water management decision making process in the 267 km2 Baakse Beek-Veengoot catchment in the east of the Netherlands. Computations were performed for regionalized 30-year climate change scenarios developed by KNMI for precipitation and reference evapotranspiration according to Penman-Monteith. Special focus in the project was on the role of uncertainty. How valid is the information that is generated by this modelling framework? What are the most important uncertainties of the input data, how do they affect the results of the model chain and how can the uncertainties of the data, results, and model concepts be quantified and communicated? Besides these technical issues, an important part of the study was devoted to the perception of stakeholders. Stakeholder analysis and additional working sessions yielded insight into how the models, their results and the uncertainties are perceived, how the modelling framework and results connect to the stakeholders' information demands and what kind of additional information is needed for adequate support on decision making.
Agricultural Adaptations to Climate Changes in West Africa

NASA Astrophysics Data System (ADS)

Guan, K.; Sultan, B.; Lobell, D. B.; Biasutti, M.; Piani, C.; Hammer, G. L.; McLean, G.

2014-12-01

Agricultural production in West Africa is highly vulnerable to climate variability and change and a fast growing demand for food adds yet another challenge. Assessing possible adaptation strategies of crop production in West Africa under climate change is thus critical for ensuring regional food security and improving human welfare. Our previous efforts have identified as the main features of climate change in West Africa a robust increase in temperature and a complex shift in the rainfall pattern (i.e. seasonality delay and total amount change). Unaddressed, these robust climate changes would reduce regional crop production by up to 20%. In the current work, we use two well-validated crop models (APSIM and SARRA-H) to comprehensively assess different crop adaptation options under future climate scenarios. Particularly, we assess adaptations in both the choice of crop types and management strategies. The expected outcome of this study is to provide West Africa with region-specific adaptation recommendations that take into account both climate variability and climate change.
Evolution of plant–pollinator mutualisms in response to climate change

PubMed Central

Gilman, R Tucker; Fabina, Nicholas S; Abbott, Karen C; Rafferty, Nicole E

2012-01-01

Climate change has the potential to desynchronize the phenologies of interdependent species, with potentially catastrophic effects on mutualist populations. Phenologies can evolve, but the role of evolution in the response of mutualisms to climate change is poorly understood. We developed a model that explicitly considers both the evolution and the population dynamics of a plant–pollinator mutualism under climate change. How the populations evolve, and thus whether the populations and the mutualism persist, depends not only on the rate of climate change but also on the densities and phenologies of other species in the community. Abundant alternative mutualist partners with broad temporal distributions can make a mutualism more robust to climate change, while abundant alternative partners with narrow temporal distributions can make a mutualism less robust. How community composition and the rate of climate change affect the persistence of mutualisms is mediated by two-species Allee thresholds. Understanding these thresholds will help researchers to identify those mutualisms at highest risk owing to climate change. PMID:25568025
iTree-Hydro: Snow hydrology update for the urban forest hydrology model

Treesearch

Yang Yang; Theodore A. Endreny; David J. Nowak

2011-01-01

This article presents snow hydrology updates made to iTree-Hydro, previously called the Urban Forest EffectsâHydrology model. iTree-Hydro Version 1 was a warm climate model developed by the USDA Forest Service to provide a process-based planning tool with robust water quantity and quality predictions given data limitations common to most urban areas. Cold climate...
Towards quantifying uncertainty in predictions of Amazon 'dieback'.

PubMed

Huntingford, Chris; Fisher, Rosie A; Mercado, Lina; Booth, Ben B B; Sitch, Stephen; Harris, Phil P; Cox, Peter M; Jones, Chris D; Betts, Richard A; Malhi, Yadvinder; Harris, Glen R; Collins, Mat; Moorcroft, Paul

2008-05-27

Simulations with the Hadley Centre general circulation model (HadCM3), including carbon cycle model and forced by a 'business-as-usual' emissions scenario, predict a rapid loss of Amazonian rainforest from the middle of this century onwards. The robustness of this projection to both uncertainty in physical climate drivers and the formulation of the land surface scheme is investigated. We analyse how the modelled vegetation cover in Amazonia responds to (i) uncertainty in the parameters specified in the atmosphere component of HadCM3 and their associated influence on predicted surface climate. We then enhance the land surface description and (ii) implement a multilayer canopy light interception model and compare with the simple 'big-leaf' approach used in the original simulations. Finally, (iii) we investigate the effect of changing the method of simulating vegetation dynamics from an area-based model (TRIFFID) to a more complex size- and age-structured approximation of an individual-based model (ecosystem demography). We find that the loss of Amazonian rainforest is robust across the climate uncertainty explored by perturbed physics simulations covering a wide range of global climate sensitivity. The introduction of the refined light interception model leads to an increase in simulated gross plant carbon uptake for the present day, but, with altered respiration, the net effect is a decrease in net primary productivity. However, this does not significantly affect the carbon loss from vegetation and soil as a consequence of future simulated depletion in soil moisture; the Amazon forest is still lost. The introduction of the more sophisticated dynamic vegetation model reduces but does not halt the rate of forest dieback. The potential for human-induced climate change to trigger the loss of Amazon rainforest appears robust within the context of the uncertainties explored in this paper. Some further uncertainties should be explored, particularly with respect to the representation of rooting depth.
Robust increases in severe thunderstorm environments in response to greenhouse forcing

PubMed Central

Diffenbaugh, Noah S.; Scherer, Martin; Trapp, Robert J.

2013-01-01

Although severe thunderstorms are one of the primary causes of catastrophic loss in the United States, their response to elevated greenhouse forcing has remained a prominent source of uncertainty for climate change impacts assessment. We find that the Coupled Model Intercomparison Project, Phase 5, global climate model ensemble indicates robust increases in the occurrence of severe thunderstorm environments over the eastern United States in response to further global warming. For spring and autumn, these robust increases emerge before mean global warming of 2 °C above the preindustrial baseline. We also find that days with high convective available potential energy (CAPE) and strong low-level wind shear increase in occurrence, suggesting an increasing likelihood of atmospheric conditions that contribute to the most severe events, including tornadoes. In contrast, whereas expected decreases in mean wind shear have been used to argue for a negative influence of global warming on severe thunderstorms, we find that decreases in shear are in fact concentrated in days with low CAPE and therefore do not decrease the total occurrence of severe environments. Further, we find that the shift toward high CAPE is most concentrated in days with low convective inhibition, increasing the occurrence of high-CAPE/low-convective inhibition days. The fact that the projected increases in severe environments are robust across a suite of climate models, emerge in response to relatively moderate global warming, and result from robust physical changes suggests that continued increases in greenhouse forcing are likely to increase severe thunderstorm occurrence, thereby increasing the risk of thunderstorm-related damage. PMID:24062439
Robust increases in severe thunderstorm environments in response to greenhouse forcing.

PubMed

Diffenbaugh, Noah S; Scherer, Martin; Trapp, Robert J

2013-10-08

Although severe thunderstorms are one of the primary causes of catastrophic loss in the United States, their response to elevated greenhouse forcing has remained a prominent source of uncertainty for climate change impacts assessment. We find that the Coupled Model Intercomparison Project, Phase 5, global climate model ensemble indicates robust increases in the occurrence of severe thunderstorm environments over the eastern United States in response to further global warming. For spring and autumn, these robust increases emerge before mean global warming of 2 °C above the preindustrial baseline. We also find that days with high convective available potential energy (CAPE) and strong low-level wind shear increase in occurrence, suggesting an increasing likelihood of atmospheric conditions that contribute to the most severe events, including tornadoes. In contrast, whereas expected decreases in mean wind shear have been used to argue for a negative influence of global warming on severe thunderstorms, we find that decreases in shear are in fact concentrated in days with low CAPE and therefore do not decrease the total occurrence of severe environments. Further, we find that the shift toward high CAPE is most concentrated in days with low convective inhibition, increasing the occurrence of high-CAPE/low-convective inhibition days. The fact that the projected increases in severe environments are robust across a suite of climate models, emerge in response to relatively moderate global warming, and result from robust physical changes suggests that continued increases in greenhouse forcing are likely to increase severe thunderstorm occurrence, thereby increasing the risk of thunderstorm-related damage.

Comparison of the timings between abrupt climate changes in Greenland, Antarctica, China and Japan based on robust correlation using Lake Suigetsu as a template.

NASA Astrophysics Data System (ADS)

Nakagawa, T.

2014-12-01

High-resolution pollen-derived climate records from Lake Suigetsu varved sediment core were compared with climate archives from other regions and revealed a particular spatio-temporal structure of the monsoon climate change during so-called D-O events. Leads and lags of the climate change between different regions hold the key to understand the climate system. However, robust assessment of the relative timing of the climate change is often very challenging because correlation of the climatic archives from different regions often has inevitable uncertainties. Greenland and Cariaco basin, for example, provide two of the most frequently sited palaeoclimatic proxy data representative of the high- and low-latitudinal Atlantic regions. However, robust correlation of the records from those regions is difficult because of the uncertainties in layer countings, lack of the radiocarbon age control from ice cores, marine reservoir age of the Cariaco sediments, and the absence of the tephra layers shared by both cites. Similarly, Speleothem and ice core records are not robustly correlated to each other, either for the dead carbon fraction in the speleothems and lack of reliable correlation markers. The generally accepted hypothesis of synchronous climate change between China and the Greenland is, therefore, essentially hypothetical. Lake Suigetsu provides solution to this problem. The lake Suigetsu chronology is supposed to be coherent to the speleothems' U-Th age scale. Suigetsu's semi-continuous radiocarbon dataset, which constitutes major component of the IntCal13 radiocarbon calibration model, also provides opportunity to correlate lake Suigetsu and the Greenland and Antarctic ice cores using cosmogenic isotopes as the correlation key. Results of the correlation and timing comparison, which cast new lights to the mechanism of the monsoon change, will be presented.
Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma E.; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart J. J. M.

2018-06-01

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—`noise', intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM-GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability.
Robust Engineering Designs for Infrastructure Adaptation to a Changing Climate

NASA Astrophysics Data System (ADS)

Samaras, C.; Cook, L.

2015-12-01

Infrastructure systems are expected to be functional, durable and safe over long service lives - 50 to over 100 years. Observations and models of climate science show that greenhouse gas emissions resulting from human activities have changed climate, weather and extreme events. Projections of future changes (albeit with uncertainties caused by inadequacies of current climate/weather models) can be made based on scenarios for future emissions, but actual future emissions are themselves uncertain. Most current engineering standards and practices for infrastructure assume that the probabilities of future extreme climate and weather events will match those of the past. Climate science shows that this assumption is invalid, but is unable, at present, to define these probabilities over the service lives of existing and new infrastructure systems. Engineering designs, plans, and institutions and regulations will need to be adaptable for a range of future conditions (conditions of climate, weather and extreme events, as well as changing societal demands for infrastructure services). For their current and future projects, engineers should: Involve all stakeholders (owners, financers, insurance, regulators, affected public, climate/weather scientists, etc.) in key decisions; Use low regret, adaptive strategies, such as robust decision making and the observational method, comply with relevant standards and regulations, and exceed their requirements where appropriate; Publish design studies and performance/failure investigations to extend the body of knowledge for advancement of practice. The engineering community should conduct observational and modeling research with climate/weather/social scientists and the concerned communities and account rationally for climate change in revised engineering standards and codes. This presentation presents initial research on decisionmaking under uncertainty for climate resilient infrastructure design.
Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma E.; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart J. J. M.

2017-09-01

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—`noise', intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM-GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability.
Designing Flood Management Systems for Joint Economic and Ecological Robustness

NASA Astrophysics Data System (ADS)

Spence, C. M.; Grantham, T.; Brown, C. M.; Poff, N. L.

2015-12-01

Freshwater ecosystems across the United States are threatened by hydrologic change caused by water management operations and non-stationary climate trends. Nonstationary hydrology also threatens flood management systems' performance. Ecosystem managers and flood risk managers need tools to design systems that achieve flood risk reduction objectives while sustaining ecosystem functions and services in an uncertain hydrologic future. Robust optimization is used in water resources engineering to guide system design under climate change uncertainty. Using principles introduced by Eco-Engineering Decision Scaling (EEDS), we extend robust optimization techniques to design flood management systems that meet both economic and ecological goals simultaneously across a broad range of future climate conditions. We use three alternative robustness indices to identify flood risk management solutions that preserve critical ecosystem functions in a case study from the Iowa River, where recent severe flooding has tested the limits of the existing flood management system. We seek design modifications to the system that both reduce expected cost of flood damage while increasing ecologically beneficial inundation of riparian floodplains across a wide range of plausible climate futures. The first robustness index measures robustness as the fraction of potential climate scenarios in which both engineering and ecological performance goals are met, implicitly weighting each climate scenario equally. The second index builds on the first by using climate projections to weight each climate scenario, prioritizing acceptable performance in climate scenarios most consistent with climate projections. The last index measures robustness as mean performance across all climate scenarios, but penalizes scenarios with worse performance than average, rewarding consistency. Results stemming from alternate robustness indices reflect implicit assumptions about attitudes toward risk and reveal the tradeoffs between using structural and non-structural flood management strategies to ensure economic and ecological robustness.
A Robust, Scalable Framework for Conducting Climate Change Susceptibility Analyses

DTIC Science & Technology

2014-05-01

for identifying areas of heightened risk from varying forms of climate forcings is needed. Based on global climate model projections, deviations from...framework provides an opportunity to easily combine multiple data sources — that are often freely available from many federal, state, and global ...Climate change and extreme weather events: implications for food production, plant diseases, and pests. Global Change and Human Health 2:90–104. ERDC/EL
Climate change on the Colorado River: a method to search for robust management strategies

NASA Astrophysics Data System (ADS)

Keefe, R.; Fischbach, J. R.

2010-12-01

The Colorado River is a principal source of water for the seven Basin States, providing approximately 16.5 maf per year to users in the southwestern United States and Mexico. Though the dynamics of the river ensure Upper Basin users a reliable supply of water, the three Lower Basin states (California, Nevada, and Arizona) are in danger of delivery interruptions as Upper Basin demand increases and climate change threatens to reduce future streamflows. In light of the recent drought and uncertain effects of climate change on Colorado River flows, we evaluate the performance of a suite of policies modeled after the shortage sharing agreement adopted in December 2007 by the Department of the Interior. We build on the current literature by using a simplified model of the Lower Colorado River to consider future streamflow scenarios given climate change uncertainty. We also generate different scenarios of parametric consumptive use growth in the Upper Basin and evaluate alternate management strategies in light of these uncertainties. Uncertainty associated with climate change is represented with a multi-model ensemble from the literature, using a nearest neighbor perturbation to increase the size of the ensemble. We use Robust Decision Making to compare near-term or long-term management strategies across an ensemble of plausible future scenarios with the goal of identifying one or more approaches that are robust to alternate assumptions about the future. This method entails using search algorithms to quantitatively identify vulnerabilities that may threaten a given strategy (including the current operating policy) and characterize key tradeoffs between strategies under different scenarios.
Robust and Heterogeneous Hydrological Changes under Global Warming

NASA Astrophysics Data System (ADS)

Kumar, S.; Zwiers, F. W.; Dirmeyer, P.; Lawrence, D. M.; Shrestha, R. R.; Werner, A. T.

2015-12-01

The Intergovernmental Panel on Climate Change (IPCC) has continued to find it difficult to make clear assessments of streamflow changes [Assessment Report 5, Working Group II, Chapter 3] in large part because of the heterogeneity of observed and projected hydrological changes. While prior studies have found some evidence of human influence on precipitation changes, the detection of streamflow changes is not robust. Here, we show that the terrestrial branch of the hydrological cycle, namely the partitioning of precipitation into evapotranspiration and runoff, is an important piece of the puzzle that may explain the apparent disconnect between the detectability of precipitation and streamflow changes. We apply Budyko framework to quantify sensitivity of hydrological changes to climate driven changes in water balance regionally. We demonstrate that the hydrological sensitivity is 3 times greater in regions where the hydrological cycle is energy limited (wet regions) than water limited (dry regions), and therefore the detectability of streamflow changes is also greater by 30-40% in wet regions. Evidence from observations in western North America and an analysis of Coupled Model Intercomparison Project Phase 5 climate models at global scales indicate that use of the Budyko framework can help identify robust and spatially heterogeneous hydrological responses to external forcing on the climate system.
Broken Robustness Analysis: How to make proper climate change conclusions in contradictory multimodal measurement contexts.

NASA Astrophysics Data System (ADS)

Keyser, V.

2015-12-01

Philosophers of science discuss how multiple modes of measurement can generate evidence for the existence and character of a phenomenon (Horwich 1982; Hacking 1983; Franklin and Howson 1984; Collins 1985; Sober 1989; Trout 1993; Culp 1995; Keeley 2002; Staley 2004; Weber 2005; Keyser 2012). But how can this work systematically in climate change measurement? Additionally, what conclusions can scientists and policy-makers draw when different modes of measurement fail to be robust by producing contradictory results? First, I present a new technical account of robust measurement (RAMP) that focuses on the physical independence of measurement processes. I detail how physically independent measurement processes "check each other's results." (This account is in contrast to philosophical accounts of robustness analysis that focus on independent model assumptions or independent measurement products or results.) Second, I present a puzzle about contradictory and divergent climate change measures, which has consistently re-emerged in climate measurement. This discussion will focus on land, drilling, troposphere, and computer simulation measures. Third, to systematically solve this climate measurement puzzle, I use RAMP in the context of drought measurement in order to generate a classification of measurement processes. Here, I discuss how multimodal precipitation measures—e.g., measures of precipitation deficit like the Standard Precipitation Index vs. air humidity measures like the Standardized Relative Humidity Index--can help with the classification scheme of climate change measurement processes. Finally, I discuss how this classification of measures can help scientists and policy-makers draw effective conclusions in contradictory multimodal climate change measurement contexts.
Evaluating historical climate and hydrologic trends in the Central Appalachian region of the United States

NASA Astrophysics Data System (ADS)

Gaertner, B. A.; Zegre, N.

2015-12-01

Climate change is surfacing as one of the most important environmental and social issues of the 21st century. Over the last 100 years, observations show increasing trends in global temperatures and intensity and frequency of precipitation events such as flooding, drought, and extreme storms. Global circulation models (GCM) show similar trends for historic and future climate indicators, albeit with geographic and topographic variability at regional and local scale. In order to assess the utility of GCM projections for hydrologic modeling, it is important to quantify how robust GCM outputs are compared to robust historical observations at finer spatial scales. Previous research in the United States has primarily focused on the Western and Northeastern regions due to dominance of snow melt for runoff and aquifer recharge but the impact of climate warming in the mountainous central Appalachian Region is poorly understood. In this research, we assess the performance of GCM-generated historical climate compared to historical observations primarily in the context of forcing data for macro-scale hydrologic modeling. Our results show significant spatial heterogeneity of modeled climate indices when compared to observational trends at the watershed scale. Observational data is showing considerable variability within maximum temperature and precipitation trends, with consistent increases in minimum temperature. The geographic, temperature, and complex topographic gradient throughout the central Appalachian region is likely the contributing factor in temperature and precipitation variability. Variable climate changes are leading to more severe and frequent climate events such as temperature extremes and storm events, which can have significant impacts on our drinking water supply, infrastructure, and health of all downstream communities.
Changing precipitation in western Europe, climate change or natural variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart

2017-04-01

Multi-model RCM-GCM ensembles provide high resolution climate projections, valuable for among others climate impact assessment studies. While the application of multiple models (both GCMs and RCMs) provides a certain robustness with respect to model uncertainty, the interpretation of differences between ensemble members - the combined result of model uncertainty and natural variability of the climate system - is not straightforward. Natural variability is intrinsic to the climate system, and a potentially large source of uncertainty in climate change projections, especially for projections on the local to regional scale. To quantify the natural variability and get a robust estimate of the forced climate change response (given a certain model and forcing scenario), large ensembles of climate model simulations of the same model provide essential information. While for global climate models (GCMs) a number of such large single model ensembles exists and have been analyzed, for regional climate models (RCMs) the number and size of single model ensembles is limited, and the predictability of the forced climate response at the local to regional scale is still rather uncertain. We present a regional downscaling of a 16-member single model ensemble over western Europe and the Alps at a resolution of 0.11 degrees (˜12km), similar to the highest resolution EURO-CORDEX simulations. This 16-member ensemble was generated by the GCM EC-EARTH, which was downscaled with the RCM RACMO for the period 1951-2100. This single model ensemble has been investigated in terms of the ensemble mean response (our estimate of the forced climate response), as well as the difference between the ensemble members, which measures natural variability. We focus on the response in seasonal mean and extreme precipitation (seasonal maxima and extremes with a return period up to 20 years) for the near to far future. For most precipitation indices we can reliably determine the climate change signal, given the applied model chain and forcing scenario. However, the analysis also shows how limited the information in single ensemble members is on the local scale forced climate response, even for high levels of global warming when the forced response has emerged from natural variability. Analysis and application of multi-model ensembles like EURO-CORDEX should go hand-in-hand with single model ensembles, like the one presented here, to be able to correctly interpret the fine-scale information in terms of a forced signal and random noise due to natural variability.
Present-day constraint for tropical Pacific precipitation changes due to global warming in CMIP5 models

NASA Astrophysics Data System (ADS)

Ham, Yoo-Geun; Kug, Jong-Seong

2016-11-01

The sensitivity of the precipitation responses to greenhouse warming can depend on the present-day climate. In this study, a robust linkage between the present-day precipitation climatology and precipitation change owing to global warming is examined in inter-model space. A model with drier climatology in the present-day simulation tends to simulate an increase in climatological precipitation owing to global warming. Moreover, the horizontal gradient of the present-day precipitation climatology plays an important role in determining the precipitation changes. On the basis of these robust relationships, future precipitation changes are calibrated by removing the impact of the present-day precipitation bias in the climate models. To validate this result, the perfect model approach is adapted, which treats a particular model's precipitation change as an observed change. The results suggest that the precipitation change pattern can be generally improved by applying the present statistical approach.
A Robust Response of Precipitation to Global Warming from CMIP5 Models

NASA Technical Reports Server (NTRS)

Lau, K. -M.; Wu, H. -T.; Kim, K. -M.

2012-01-01

How precipitation responds to global warming is a major concern to society and a challenge to climate change research. Based on analyses of rainfall probability distribution functions of 14 state-of-the-art climate models, we find a robust, canonical global rainfall response to a triple CO2 warming scenario, featuring 100 250% more heavy rain, 5-10% less moderate rain, and 10-15% more very light or no-rain events. Regionally, a majority of the models project a consistent response with more heavy rain events over climatologically wet regions of the deep tropics, and more dry events over subtropical and tropical land areas. Results suggest that increased CO2 emissions induce basic structural changes in global rain systems, increasing risks of severe floods and droughts in preferred geographic locations worldwide.
Surprisingly robust projections of soil temperature and moisture for North American drylands in the 21st century

NASA Astrophysics Data System (ADS)

Bradford, J. B.; Schlaepfer, D.; Palmquist, K. A.; Lauenroth, W.

2017-12-01

Climate projections for western North America suggest temperature increases that are relatively consistent across climate models. However, precipitation projections are less consistent, especially in the Southwest, promoting uncertainty about the future of soil moisture and drought. We utilized a daily time-step ecosystem water balance model to characterize soil temperature and moisture patterns at a 10-km resolution across western North America for historical (1980-2010), mid-century (2020-2050), and late century (2070-2100). We simulated soil moisture and temperature under two representative concentration pathways and eleven climate models (selected strategically to represent the range of variability in projections among the full set of models in the CMIP5 database and perform well in hind-cast comparisons for the region), and we use the results to identify areas with robust projections, e.g. areas where the large majority of models agree in the direction of change in long-term average soil moisture or temperature. Rising air temperatures will increase average soil temperatures across western North America and expand the area of mesic and thermic soil temperature regimes while decreasing the area of cryic and frigid regimes. Future soil moisture conditions are relatively consistent across climate models for much of the region, including many areas with variable precipitation trajectories. Consistent projections for drier soils are expected in most of Arizona and New Mexico, similar to previous studies. Other regions with projections for declining soil moisture include the central and southern U.S. Great Plains and large parts of southern British Columbia. By contrast, areas with robust projections for increasing soil moisture include northeastern Montana, southern Alberta and Saskatchewan, and many areas in the intermountain west dominated by big sagebrush. In addition, seasonal moisture patterns in much of the western US drylands are expected to shift toward cool-season water availability, with potentially important consequences for ecosystem structure and function. These results provide a framework for coping with variability in climate projections and assessing climate change impacts on dryland ecosystems.
Changes in the width of the tropical belt due to simple radiative forcing changes in the GeoMIP simulations

NASA Astrophysics Data System (ADS)

Davis, Nicholas A.; Seidel, Dian J.; Birner, Thomas; Davis, Sean M.; Tilmes, Simone

2016-08-01

Model simulations of future climates predict a poleward expansion of subtropical arid climates at the edges of Earth's tropical belt, which would have significant environmental and societal impacts. This expansion may be related to the poleward shift of the Hadley cell edges, where subsidence stabilizes the atmosphere and suppresses precipitation. Understanding the primary drivers of tropical expansion is hampered by the myriad forcing agents in most model projections of future climate. While many previous studies have examined the response of idealized models to simplified climate forcings and the response of comprehensive climate models to more complex climate forcings, few have examined how comprehensive climate models respond to simplified climate forcings. To shed light on robust processes associated with tropical expansion, here we examine how the tropical belt width, as measured by the Hadley cell edges, responds to simplified forcings in the Geoengineering Model Intercomparison Project (GeoMIP). The tropical belt expands in response to a quadrupling of atmospheric carbon dioxide concentrations and contracts in response to a reduction in the solar constant, with a range of a factor of 3 in the response among nine models. Models with more surface warming and an overall stronger temperature response to quadrupled carbon dioxide exhibit greater tropical expansion, a robust result in spite of inter-model differences in the mean Hadley cell width, parameterizations, and numerical schemes. Under a scenario where the solar constant is reduced to offset an instantaneous quadrupling of carbon dioxide, the Hadley cells remain at their preindustrial width, despite the residual stratospheric cooling associated with elevated carbon dioxide levels. Quadrupled carbon dioxide produces greater tropical belt expansion in the Southern Hemisphere than in the Northern Hemisphere. This expansion is strongest in austral summer and autumn. Ozone depletion has been argued to cause this pattern of changes in observations and model experiments, but the results here indicate that seasonally and hemispherically asymmetric tropical expansion can be a basic response of the general circulation to climate forcings.
Catchments as non-linear filters: evaluating data-driven approaches for spatio-temporal predictions in ungauged basins

NASA Astrophysics Data System (ADS)

Bellugi, D. G.; Tennant, C.; Larsen, L.

2016-12-01

Catchment and climate heterogeneity complicate prediction of runoff across time and space, and resulting parameter uncertainty can lead to large accumulated errors in hydrologic models, particularly in ungauged basins. Recently, data-driven modeling approaches have been shown to avoid the accumulated uncertainty associated with many physically-based models, providing an appealing alternative for hydrologic prediction. However, the effectiveness of different methods in hydrologically and geomorphically distinct catchments, and the robustness of these methods to changing climate and changing hydrologic processes remain to be tested. Here, we evaluate the use of machine learning techniques to predict daily runoff across time and space using only essential climatic forcing (e.g. precipitation, temperature, and potential evapotranspiration) time series as model input. Model training and testing was done using a high quality dataset of daily runoff and climate forcing data for 25+ years for 600+ minimally-disturbed catchments (drainage area range 5-25,000 km2, median size 336 km2) that cover a wide range of climatic and physical characteristics. Preliminary results using Support Vector Regression (SVR) suggest that in some catchments this nonlinear-based regression technique can accurately predict daily runoff, while the same approach fails in other catchments, indicating that the representation of climate inputs and/or catchment filter characteristics in the model structure need further refinement to increase performance. We bolster this analysis by using Sparse Identification of Nonlinear Dynamics (a sparse symbolic regression technique) to uncover the governing equations that describe runoff processes in catchments where SVR performed well and for ones where it performed poorly, thereby enabling inference about governing processes. This provides a robust means of examining how catchment complexity influences runoff prediction skill, and represents a contribution towards the integration of data-driven inference and physically-based models.
A robust empirical seasonal prediction of winter NAO and surface climate.

PubMed

Wang, L; Ting, M; Kushner, P J

2017-03-21

A key determinant of winter weather and climate in Europe and North America is the North Atlantic Oscillation (NAO), the dominant mode of atmospheric variability in the Atlantic domain. Skilful seasonal forecasting of the surface climate in both Europe and North America is reflected largely in how accurately models can predict the NAO. Most dynamical models, however, have limited skill in seasonal forecasts of the winter NAO. A new empirical model is proposed for the seasonal forecast of the winter NAO that exhibits higher skill than current dynamical models. The empirical model provides robust and skilful prediction of the December-January-February (DJF) mean NAO index using a multiple linear regression (MLR) technique with autumn conditions of sea-ice concentration, stratospheric circulation, and sea-surface temperature. The predictability is, for the most part, derived from the relatively long persistence of sea ice in the autumn. The lower stratospheric circulation and sea-surface temperature appear to play more indirect roles through a series of feedbacks among systems driving NAO evolution. This MLR model also provides skilful seasonal outlooks of winter surface temperature and precipitation over many regions of Eurasia and eastern North America.
Uncertainties in Future Regional Sea Level Trends: How to Deal with the Internal Climate Variability?

NASA Astrophysics Data System (ADS)

Becker, M.; Karpytchev, M.; Hu, A.; Deser, C.; Lennartz-Sassinek, S.

2017-12-01

Today, the Climate models (CM) are the main tools for forecasting sea level rise (SLR) at global and regional scales. The CM forecasts are accompanied by inherent uncertainties. Understanding and reducing these uncertainties is becoming a matter of increasing urgency in order to provide robust estimates of SLR impact on coastal societies, which need sustainable choices of climate adaptation strategy. These CM uncertainties are linked to structural model formulation, initial conditions, emission scenario and internal variability. The internal variability is due to complex non-linear interactions within the Earth Climate System and can induce diverse quasi-periodic oscillatory modes and long-term persistences. To quantify the effects of internal variability, most studies used multi-model ensembles or sea level projections from a single model ran with perturbed initial conditions. However, large ensembles are not generally available, or too small, and computationally expensive. In this study, we use a power-law scaling of sea level fluctuations, as observed in many other geophysical signals and natural systems, which can be used to characterize the internal climate variability. From this specific statistical framework, we (1) use the pre-industrial control run of the National Center for Atmospheric Research Community Climate System Model (NCAR-CCSM) to test the robustness of the power-law scaling hypothesis; (2) employ the power-law statistics as a tool for assessing the spread of regional sea level projections due to the internal climate variability for the 21st century NCAR-CCSM; (3) compare the uncertainties in predicted sea level changes obtained from a NCAR-CCSM multi-member ensemble simulations with estimates derived for power-law processes, and (4) explore the sensitivity of spatial patterns of the internal variability and its effects on regional sea level projections.
A transient stochastic weather generator incorporating climate model uncertainty

NASA Astrophysics Data System (ADS)

Glenis, Vassilis; Pinamonti, Valentina; Hall, Jim W.; Kilsby, Chris G.

2015-11-01

Stochastic weather generators (WGs), which provide long synthetic time series of weather variables such as rainfall and potential evapotranspiration (PET), have found widespread use in water resources modelling. When conditioned upon the changes in climatic statistics (change factors, CFs) predicted by climate models, WGs provide a useful tool for climate impacts assessment and adaption planning. The latest climate modelling exercises have involved large numbers of global and regional climate models integrations, designed to explore the implications of uncertainties in the climate model formulation and parameter settings: so called 'perturbed physics ensembles' (PPEs). In this paper we show how these climate model uncertainties can be propagated through to impact studies by testing multiple vectors of CFs, each vector derived from a different sample from a PPE. We combine this with a new methodology to parameterise the projected time-evolution of CFs. We demonstrate how, when conditioned upon these time-dependent CFs, an existing, well validated and widely used WG can be used to generate non-stationary simulations of future climate that are consistent with probabilistic outputs from the Met Office Hadley Centre's Perturbed Physics Ensemble. The WG enables extensive sampling of natural variability and climate model uncertainty, providing the basis for development of robust water resources management strategies in the context of a non-stationary climate.
An increase in aerosol burden due to the land-sea warming contrast

NASA Astrophysics Data System (ADS)

Hassan, T.; Allen, R.; Randles, C. A.

2017-12-01

Climate models simulate an increase in most aerosol species in response to warming, particularly over the tropics and Northern Hemisphere midlatitudes. This increase in aerosol burden is related to a decrease in wet removal, primarily due to reduced large-scale precipitation. Here, we show that the increase in aerosol burden, and the decrease in large-scale precipitation, is related to a robust climate change phenomenon—the land/sea warming contrast. Idealized simulations with two state of the art climate models, the National Center for Atmospheric Research Community Atmosphere Model version 5 (NCAR CAM5) and the Geophysical Fluid Dynamics Laboratory Atmospheric Model 3 (GFDL AM3), show that muting the land-sea warming contrast negates the increase in aerosol burden under warming. This is related to smaller decreases in near-surface relative humidity over land, and in turn, smaller decreases in large-scale precipitation over land—especially in the NH midlatitudes. Furthermore, additional idealized simulations with an enhanced land/sea warming contrast lead to the opposite result—larger decreases in relative humidity over land, larger decreases in large-scale precipitation, and larger increases in aerosol burden. Our results, which relate the increase in aerosol burden to the robust climate projection of enhanced land warming, adds confidence that a warmer world will be associated with a larger aerosol burden.

Seasonal to interannual Arctic sea ice predictability in current global climate models

NASA Astrophysics Data System (ADS)

Tietsche, S.; Day, J. J.; Guemas, V.; Hurlin, W. J.; Keeley, S. P. E.; Matei, D.; Msadek, R.; Collins, M.; Hawkins, E.

2014-02-01

We establish the first intermodel comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea ice extent and volume, there is potential predictive skill for lead times of up to 3 years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.
Evolution of carbon sinks in a changing climate.

PubMed

Fung, Inez Y; Doney, Scott C; Lindsay, Keith; John, Jasmin

2005-08-09

Climate change is expected to influence the capacities of the land and oceans to act as repositories for anthropogenic CO2 and hence provide a feedback to climate change. A series of experiments with the National Center for Atmospheric Research-Climate System Model 1 coupled carbon-climate model shows that carbon sink strengths vary with the rate of fossil fuel emissions, so that carbon storage capacities of the land and oceans decrease and climate warming accelerates with faster CO2 emissions. Furthermore, there is a positive feedback between the carbon and climate systems, so that climate warming acts to increase the airborne fraction of anthropogenic CO2 and amplify the climate change itself. Globally, the amplification is small at the end of the 21st century in this model because of its low transient climate response and the near-cancellation between large regional changes in the hydrologic and ecosystem responses. Analysis of our results in the context of comparable models suggests that destabilization of the tropical land sink is qualitatively robust, although its degree is uncertain.
Evolution of carbon sinks in a changing climate

PubMed Central

Fung, Inez Y.; Doney, Scott C.; Lindsay, Keith; John, Jasmin

2005-01-01

Climate change is expected to influence the capacities of the land and oceans to act as repositories for anthropogenic CO2 and hence provide a feedback to climate change. A series of experiments with the National Center for Atmospheric Research–Climate System Model 1 coupled carbon–climate model shows that carbon sink strengths vary with the rate of fossil fuel emissions, so that carbon storage capacities of the land and oceans decrease and climate warming accelerates with faster CO2 emissions. Furthermore, there is a positive feedback between the carbon and climate systems, so that climate warming acts to increase the airborne fraction of anthropogenic CO2 and amplify the climate change itself. Globally, the amplification is small at the end of the 21st century in this model because of its low transient climate response and the near-cancellation between large regional changes in the hydrologic and ecosystem responses. Analysis of our results in the context of comparable models suggests that destabilization of the tropical land sink is qualitatively robust, although its degree is uncertain. PMID:16061800
Low robustness of increasing reservoir capacity for adaptation to climate change: A case study for an agricultural river basin

NASA Astrophysics Data System (ADS)

Kim, Daeha; Eum, Hyung-Il

2017-04-01

With growing concerns of the uncertain climate change, investments in water infrastructures are considered as adaptation policies for water managers and stakeholders despite their negative impacts on the environment. Particularly in regions with limited water availability or conflicting demands, building reservoirs and/or augmenting their storage capacity were already adopted for alleviating influences of the climate change. This study provides a probabilistic assessment of climate change impacts on water scarcity in a river system regulated by an agricultural reservoir in South Korea, which already increased its storage capacity for water supply. For the assessment, we developed the climate response functions (CRFs) defined as relationships between bi-decadal system performance indicators (reservoir reliability and vulnerability) and corresponding climatic conditions, using hydrological models with 10,000-year long stochastic generation of daily precipitation and temperatures. The climate change impacts were assessed by plotting 52 downscaled climate projections of general circulation models (GCMs) on the CRFs. Results indicated that augmented reservoir capacity makes the reservoir system more sensitive to changes in long-term averages of precipitation and temperatures despite improved system performances. Increasing reservoir capacity is unlikely to be "no regret" adaptation policy for the river system. On the other hand, converting the planting strategy from transplanting to direct sowing (i.e., a demand control) could be a more robust to bi-decadal climatic changes based on CRFs and thus could be good to be a no-regret policy.
Estimating the impact of internal climate variability on ice sheet model simulations

NASA Astrophysics Data System (ADS)

Tsai, C. Y.; Forest, C. E.; Pollard, D.

2016-12-01

Rising sea level threatens human societies and coastal habitats and melting ice sheets are a major contributor to sea level rise (SLR). Thus, understanding uncertainty of both forcing and variability within the climate system is essential for assessing long-term risk of SLR given their impact on ice sheet evolution. The predictability of polar climate is limited by uncertainties from the given forcing, the climate model response to this forcing, and the internal variability from feedbacks within the fully coupled climate system. Among those sources of uncertainty, the impact of internal climate variability on ice sheet changes has not yet been robustly assessed. Here we investigate how internal variability affects ice sheet projections using climate fields from two Community Earth System Model (CESM) large-ensemble (LE) experiments to force a three-dimensional ice sheet model. Each ensemble member in an LE experiment undergoes the same external forcings but with unique initial conditions. We find that for both LEs, 2m air temperature variability over Greenland ice sheet (GrIS) can lead to significantly different ice sheet responses. Our results show that the internal variability from two fully coupled CESM LEs can cause about 25 35 mm differences of GrIS's contribution to SLR in 2100 compared to present day (about 20% of the total change), and 100m differences of SLR in 2300. Moreover, only using ensemble-mean climate fields as the forcing in ice sheet model can significantly underestimate the melt of GrIS. As the Arctic region becomes warmer, the role of internal variability is critical given the complex nonlinear interactions between surface temperature and ice sheet. Our results demonstrate that internal variability from coupled atmosphere-ocean general circulation model can affect ice sheet simulations and the resulting sea-level projections. This study highlights an urgent need to reassess associated uncertainties of projecting ice sheet loss over the next few centuries to obtain robust estimates of the contribution of ice sheet melt to SLR.
Assessing Climate Change Risks Using a Multi-Model Approach

NASA Astrophysics Data System (ADS)

Knorr, W.; Scholze, M.; Prentice, C.

2007-12-01

We quantify the risks of climate-induced changes in key ecosystem processes during the 21st century by forcing a dynamic global vegetation model with multiple scenarios from the IPCC AR4 data archive using 16 climate models and mapping the proportions of model runs showing exceedance of natural variability in wildfire frequency and freshwater supply or shifts in vegetation cover. Our analysis does not assign probabilities to scenarios. Instead, we consider the distribution of outcomes within three sets of model runs grouped according to the amount of global warming they simulate: < 2 degree C (including committed climate change simulations), 2-3 degree C, and >3 degree C. Here, we are contrasting two different methods for calculating the risks: first we use an equal weighting approach giving every model within one of the three sets the same weight, and second, we weight the models according to their ability to model ENSO. The differences are underpinning the need for the development of more robust performance metrics for global climate models.
Robust Decision Making to Support Water Quality Climate Adaptation: a Case Study in the Chesapeake Bay Watershed

NASA Astrophysics Data System (ADS)

Fischbach, J. R.; Lempert, R. J.; Molina-Perez, E.

2017-12-01

The U.S. Environmental Protection Agency (USEPA), together with state and local partners, develops watershed implementation plans designed to meet water quality standards. Climate uncertainty, along with uncertainty about future land use changes or the performance of water quality best management practices (BMPs), may make it difficult for these implementation plans to meet water quality goals. In this effort, we explored how decision making under deep uncertainty (DMDU) methods such as Robust Decision Making (RDM) could help USEPA and its partners develop implementation plans that are more robust to future uncertainty. The study focuses on one part of the Chesapeake Bay watershed, the Patuxent River, which is 2,479 sq km in area, highly urbanized, and has a rapidly growing population. We simulated the contribution of stormwater contaminants from the Patuxent to the overall Total Maximum Daily Load (TMDL) for the Chesapeake Bay under multiple scenarios reflecting climate and other uncertainties. Contaminants considered included nitrogen, phosphorus, and sediment loads. The assessment included a large set of scenario simulations using the USEPA Chesapeake Bay Program's Phase V watershed model. Uncertainties represented in the analysis included 18 downscaled climate projections (based on 6 general circulation models and 3 emissions pathways), 12 land use scenarios with different population projections and development patterns, and alternative assumptions about BMP performance standards and efficiencies associated with different suites of stormwater BMPs. Finally, we developed cost estimates for each of the performance standards and compared cost to TMDL performance as a key tradeoff for future water quality management decisions. In this talk, we describe how this research can help inform climate-related decision support at USEPA's Chesapeake Bay Program, and more generally how RDM and other DMDU methods can support improved water quality management under climate uncertainty.
Robust changes in the socio-climate risk over CONUS by mid 21st century

NASA Astrophysics Data System (ADS)

Ashfaq, M.; Rastogi, D.; Batibeniz, F.; Alifa, M.; Pagán, B. R.; Bonds, B. W.; Pal, J. S.; Diffenbaugh, N. S.; Preston, B. L.

2017-12-01

Using high-resolution near-term ensemble projections of hydro-climatic changes, we investigate impacts of climate change on natural and human systems across the CONUS. Climate projections are based a hybrid downscaling approach where a combination of regional and hydrological models are used to downscales 11 Global Climate Models from the 5th phase of Coupled Model Inter-comparison Project to 4km horizontal grid spacing for 41 years in the historical period (1965-2005) and 41 years in the near-term future period (2010-2050) under Representative Concentration Pathway 8.5. Should emissions continue to rise, climatic changes will likely intensify the regional hydrological cycle over CONUS through the acceleration of the historical trends in cold, warm and wet extremes. Our results show robust changes in the occurrence of severe weather conditions and in the likelihood of ice, freezing rain and snowstorms that may have disruptive impact on large human population across the U.S. More summer like conditions will also drive increase in cooling demands and a net increase in the energy consumption over many regions. We further use an integrated vulnerability index that combines human exposure to different climate extremes (hot, cold, wet and dry) and changes in socioeconomic pathways (due to changes in population and income levels), to reveal that future exposure to potentially damaging climatic conditions will likely increase manifold for population living in major urban centers in California, Texas, Florida, Michigan, Illinois and Northeast. With the current trajectory of emissions, these results warrant that a large human population across the U.S. may feel the impacts of climate change within its lifespan.
Dynamical Core in Atmospheric Model Does Matter in the Simulation of Arctic Climate

NASA Astrophysics Data System (ADS)

Jun, Sang-Yoon; Choi, Suk-Jin; Kim, Baek-Min

2018-03-01

Climate models using different dynamical cores can simulate significantly different winter Arctic climates even if equipped with virtually the same physics schemes. Current climate simulated by the global climate model using cubed-sphere grid with spectral element method (SE core) exhibited significantly warmer Arctic surface air temperature compared to that using latitude-longitude grid with finite volume method core. Compared to the finite volume method core, SE core simulated additional adiabatic warming in the Arctic lower atmosphere, and this was consistent with the eddy-forced secondary circulation. Downward longwave radiation further enhanced Arctic near-surface warming with a higher surface air temperature of about 1.9 K. Furthermore, in the atmospheric response to the reduced sea ice conditions with the same physical settings, only the SE core showed a robust cooling response over North America. We emphasize that special attention is needed in selecting the dynamical core of climate models in the simulation of the Arctic climate and associated teleconnection patterns.
Uncertainty quantification and propagation in a complex human-environment system driven by fire and climate

NASA Astrophysics Data System (ADS)

Terando, A. J.; Reich, B. J.; Pacifici, K.

2013-12-01

Fire is an important disturbance process in many coupled natural-human systems. Changes in the frequency and severity of fires due to anthropogenic climate change could have significant costs to society and the plant and animal communities that are adapted to a particular fire regime Planning for these changes requires a robust model of the relationship between climate and fire that accounts for multiple sources of uncertainty that are present when simulating ecological and climatological processes. Here we model how anthropogenic climate change could affect the wildfire regime for a region in the Southeast US whose natural ecosystems are dependent on frequent, low-intensity fires while humans are at risk from large catastrophic fires. We develop a modeling framework that incorporates three major sources of uncertainty: (1) uncertainty in the ecological drivers of expected monthly area burned, (2) uncertainty in the environmental drivers influencing the probability of an extreme fire event, and (3) structural uncertainty in different downscaled climate models. In addition we use two policy-relevant emission scenarios (climate stabilization and 'business-as-usual') to characterize the uncertainty in future greenhouse gas forcings. We use a Bayesian framework to incorporate different sources of uncertainty including simulation of predictive errors and Stochastic Search Variable Selection. Our results suggest that although the mean process remains stationary, the probability of extreme fires declines through time, owing to the persistence of high atmospheric moisture content during the peak fire season that dampens the effect of increasing temperatures. Including multiple sources of uncertainty leads to wide prediction intervals, but is potentially more useful for decision-makers that will require adaptation strategies that are robust to rapid but uncertain climate and ecological change.
Global Water Cycle Agreement in the Climate Models Assessed in the IPCC AR4

NASA Technical Reports Server (NTRS)

Waliser, D.; Seo, K. -W.; Schubert, S.; Njoku, E.

2007-01-01

This study examines the fidelity of the global water cycle in the climate model simulations assessed in the IPCC Fourth Assessment Report. The results demonstrate good model agreement in quantities that have had a robust global observational basis and that are physically unambiguous. The worst agreement occurs for quantities that have both poor observational constraints and whose model representations can be physically ambiguous. In addition, components involving water vapor (frozen water) typically exhibit the best (worst) agreement, and fluxes typically exhibit better agreement than reservoirs. These results are discussed in relation to the importance of obtaining accurate model representation of the water cycle and its role in climate change. Recommendations are also given for facilitating the needed model improvements.
Extra-Tropical Cyclones at Climate Scales: Comparing Models to Observations

NASA Astrophysics Data System (ADS)

Tselioudis, G.; Bauer, M.; Rossow, W.

2009-04-01

Climate is often defined as the accumulation of weather, and weather is not the concern of climate models. Justification for this latter sentiment has long been hidden behind coarse model resolutions and blunt validation tools based on climatological maps. The spatial-temporal resolutions of today's climate models and observations are converging onto meteorological scales, however, which means that with the correct tools we can test the largely unproven assumption that climate model weather is correct enough that its accumulation results in a robust climate simulation. Towards this effort we introduce a new tool for extracting detailed cyclone statistics from observations and climate model output. These include the usual cyclone characteristics (centers, tracks), but also adaptive cyclone-centric composites. We have created a novel dataset, the MAP Climatology of Mid-latitude Storminess (MCMS), which provides a detailed 6 hourly assessment of the areas under the influence of mid-latitude cyclones, using a search algorithm that delimits the boundaries of each system from the outer-most closed SLP contour. Using this we then extract composites of cloud, radiation, and precipitation properties from sources such as ISCCP and GPCP to create a large comparative dataset for climate model validation. A demonstration of the potential usefulness of these tools in process-based climate model evaluation studies will be shown.
Water resources planning under climate change: Assessing the robustness of real options for the Blue Nile

NASA Astrophysics Data System (ADS)

Jeuland, Marc; Whittington, Dale

2014-03-01

This article presents a methodology for planning new water resources infrastructure investments and operating strategies in a world of climate change uncertainty. It combines a real options (e.g., options to defer, expand, contract, abandon, switch use, or otherwise alter a capital investment) approach with principles drawn from robust decision-making (RDM). RDM comprises a class of methods that are used to identify investment strategies that perform relatively well, compared to the alternatives, across a wide range of plausible future scenarios. Our proposed framework relies on a simulation model that includes linkages between climate change and system hydrology, combined with sensitivity analyses that explore how economic outcomes of investments in new dams vary with forecasts of changing runoff and other uncertainties. To demonstrate the framework, we consider the case of new multipurpose dams along the Blue Nile in Ethiopia. We model flexibility in design and operating decisions—the selection, sizing, and sequencing of new dams, and reservoir operating rules. Results show that there is no single investment plan that performs best across a range of plausible future runoff conditions. The decision-analytic framework is then used to identify dam configurations that are both robust to poor outcomes and sufficiently flexible to capture high upside benefits if favorable future climate and hydrological conditions should arise. The approach could be extended to explore design and operating features of development and adaptation projects other than dams.
Climate Change and Crop Exposure to Adverse Weather: Changes to Frost Risk and Grapevine Flowering Conditions.

PubMed

Mosedale, Jonathan R; Wilson, Robert J; Maclean, Ilya M D

2015-01-01

The cultivation of grapevines in the UK and many other cool climate regions is expected to benefit from the higher growing season temperatures predicted under future climate scenarios. Yet the effects of climate change on the risk of adverse weather conditions or events at key stages of crop development are not always captured by aggregated measures of seasonal or yearly climates, or by downscaling techniques that assume climate variability will remain unchanged under future scenarios. Using fine resolution projections of future climate scenarios for south-west England and grapevine phenology models we explore how risks to cool-climate vineyard harvests vary under future climate conditions. Results indicate that the risk of adverse conditions during flowering declines under all future climate scenarios. In contrast, the risk of late spring frosts increases under many future climate projections due to advancement in the timing of budbreak. Estimates of frost risk, however, were highly sensitive to the choice of phenology model, and future frost exposure declined when budbreak was calculated using models that included a winter chill requirement for dormancy break. The lack of robust phenological models is a major source of uncertainty concerning the impacts of future climate change on the development of cool-climate viticulture in historically marginal climatic regions.
Climate Change and Crop Exposure to Adverse Weather: Changes to Frost Risk and Grapevine Flowering Conditions

PubMed Central

Mosedale, Jonathan R.; Wilson, Robert J.; Maclean, Ilya M. D.

2015-01-01

The cultivation of grapevines in the UK and many other cool climate regions is expected to benefit from the higher growing season temperatures predicted under future climate scenarios. Yet the effects of climate change on the risk of adverse weather conditions or events at key stages of crop development are not always captured by aggregated measures of seasonal or yearly climates, or by downscaling techniques that assume climate variability will remain unchanged under future scenarios. Using fine resolution projections of future climate scenarios for south-west England and grapevine phenology models we explore how risks to cool-climate vineyard harvests vary under future climate conditions. Results indicate that the risk of adverse conditions during flowering declines under all future climate scenarios. In contrast, the risk of late spring frosts increases under many future climate projections due to advancement in the timing of budbreak. Estimates of frost risk, however, were highly sensitive to the choice of phenology model, and future frost exposure declined when budbreak was calculated using models that included a winter chill requirement for dormancy break. The lack of robust phenological models is a major source of uncertainty concerning the impacts of future climate change on the development of cool-climate viticulture in historically marginal climatic regions. PMID:26496127
Visualizing projected Climate Changes - the CMIP5 Multi-Model Ensemble

NASA Astrophysics Data System (ADS)

Böttinger, Michael; Eyring, Veronika; Lauer, Axel; Meier-Fleischer, Karin

2017-04-01

Large ensembles add an additional dimension to climate model simulations. Internal variability of the climate system can be assessed for example by multiple climate model simulations with small variations in the initial conditions or by analyzing the spread in large ensembles made by multiple climate models under common protocols. This spread is often used as a measure of uncertainty in climate projections. In the context of the fifth phase of the WCRP's Coupled Model Intercomparison Project (CMIP5), more than 40 different coupled climate models were employed to carry out a coordinated set of experiments. Time series of the development of integral quantities such as the global mean temperature change for all models visualize the spread in the multi-model ensemble. A similar approach can be applied to 2D-visualizations of projected climate changes such as latitude-longitude maps showing the multi-model mean of the ensemble by adding a graphical representation of the uncertainty information. This has been demonstrated for example with static figures in chapter 12 of the last IPCC report (AR5) using different so-called stippling and hatching techniques. In this work, we focus on animated visualizations of multi-model ensemble climate projections carried out within CMIP5 as a way of communicating climate change results to the scientific community as well as to the public. We take a closer look at measures of robustness or uncertainty used in recent publications suitable for animated visualizations. Specifically, we use the ESMValTool [1] to process and prepare the CMIP5 multi-model data in combination with standard visualization tools such as NCL and the commercial 3D visualization software Avizo to create the animations. We compare different visualization techniques such as height fields or shading with transparency for creating animated visualization of ensemble mean changes in temperature and precipitation including corresponding robustness measures. [1] Eyring, V., Righi, M., Lauer, A., Evaldsson, M., Wenzel, S., Jones, C., Anav, A., Andrews, O., Cionni, I., Davin, E. L., Deser, C., Ehbrecht, C., Friedlingstein, P., Gleckler, P., Gottschaldt, K.-D., Hagemann, S., Juckes, M., Kindermann, S., Krasting, J., Kunert, D., Levine, R., Loew, A., Mäkelä, J., Martin, G., Mason, E., Phillips, A. S., Read, S., Rio, C., Roehrig, R., Senftleben, D., Sterl, A., van Ulft, L. H., Walton, J., Wang, S., and Williams, K. D.: ESMValTool (v1.0) - a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP, Geosci. Model Dev., 9, 1747-1802, doi:10.5194/gmd-9-1747-2016, 2016.
Simulation-based coefficients for adjusting climate impact on energy consumption of commercial buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Na; Makhmalbaf, Atefe; Srivastava, Viraj

This paper presents a new technique for and the results of normalizing building energy consumption to enable a fair comparison among various types of buildings located near different weather stations across the U.S. The method was developed for the U.S. Building Energy Asset Score, a whole-building energy efficiency rating system focusing on building envelope, mechanical systems, and lighting systems. The Asset Score is calculated based on simulated energy use under standard operating conditions. Existing weather normalization methods such as those based on heating and cooling degrees days are not robust enough to adjust all climatic factors such as humidity andmore » solar radiation. In this work, over 1000 sets of climate coefficients were developed to separately adjust building heating, cooling, and fan energy use at each weather station in the United States. This paper also presents a robust, standardized weather station mapping based on climate similarity rather than choosing the closest weather station. This proposed simulated-based climate adjustment was validated through testing on several hundreds of thousands of modeled buildings. Results indicated the developed climate coefficients can isolate and adjust for the impacts of local climate for asset rating.« less
Ocean waves from tropical cyclones in the Gulf of Mexico and the effect of climate change

NASA Astrophysics Data System (ADS)

Appendini, C. M.; Pedrozo-Acuña, A.; Meza-Padilla, R.; Torres-Freyermuth, A.; Cerezo-Mota, R.; López-González, J.

2016-12-01

To generate projections of wave climate associated to tropical cyclones is a challenge due to their short historical record of events, their low occurrence, and the poor wind field resolution in General Circulation Models. Synthetic tropical cyclones provide an alternative to overcome such limitations, improving robust statistics under present and future climates. We use synthetic events to characterize present and future wave climate associated with tropical cyclones in the Gulf of Mexico. The NCEP/NCAR atmospheric reanalysis and the Coupled Model Intercomparison Project Phase 5 models NOAA/GFDL CM3 and UK Met Office HADGEM2-ES, were used to derive present and future wave climate under RCPs 4.5 and 8.5. The results suggest an increase in wave activity for the future climate, particularly for the GFDL model that shows less bias in the present climate, although some areas are expected to decrease the wave energy. The practical implications of determining the future wave climate is exemplified by means of the 100-year design wave, where the use of the present climate may result in under/over design of structures, since the lifespan of a structure includes the future wave climate period.
A real-time Global Warming Index.

PubMed

Haustein, K; Allen, M R; Forster, P M; Otto, F E L; Mitchell, D M; Matthews, H D; Frame, D J

2017-11-13

We propose a simple real-time index of global human-induced warming and assess its robustness to uncertainties in climate forcing and short-term climate fluctuations. This index provides improved scientific context for temperature stabilisation targets and has the potential to decrease the volatility of climate policy. We quantify uncertainties arising from temperature observations, climate radiative forcings, internal variability and the model response. Our index and the associated rate of human-induced warming is compatible with a range of other more sophisticated methods to estimate the human contribution to observed global temperature change.
Challenges in Quantifying Pliocene Terrestrial Warming Revealed by Data-Model Discord

NASA Technical Reports Server (NTRS)

Salzmann, Ulrich; Dolan, Aisling M.; Haywood, Alan M.; Chan, Wing-Le; Voss, Jochen; Hill, Daniel J.; Abe-Ouchi, Ayako; Otto-Bliesner, Bette; Bragg, Frances J.; Chandler, Mark A.;

2013-01-01

Comparing simulations of key warm periods in Earth history with contemporaneous geological proxy data is a useful approach for evaluating the ability of climate models to simulate warm, high-CO2 climates that are unprecedented in the more recent past. Here we use a global data set of confidence-assessed, proxy-based temperature estimates and biome reconstructions to assess the ability of eight models to simulate warm terrestrial climates of the Pliocene epoch. The Late Pliocene, 3.6-2.6 million years ago, is an accessible geological interval to understand climate processes of a warmer world4. We show that model-predicted surface air temperatures reveal a substantial cold bias in the Northern Hemisphere. Particularly strong data-model mismatches in mean annual temperatures (up to 18 C) exist in northern Russia. Our model sensitivity tests identify insufficient temporal constraints hampering the accurate configuration of model boundary conditions as an important factor impacting on data- model discrepancies. We conclude that to allow a more robust evaluation of the ability of present climate models to predict warm climates, future Pliocene data-model comparison studies should focus on orbitally defined time slices.

The Impact of US SO2 Emissions on Clouds and the Hydrological Cycle at Global and Regional Scales in Three Coupled Chemistry-Climate Models

NASA Astrophysics Data System (ADS)

Westervelt, D. M.

2016-12-01

It is widely expected that global and regional emissions of atmospheric aerosols and their precursors will decrease strongly throughout the remainder of the 21st century, due to emission reduction policies enacted to protect human health. Although there is some evidence that these aerosol reductions may lead to significant regional and global climate impacts, we currently lack a full understanding of the magnitude, spatial and temporal pattern, and statistical significance of these influences, especially for clouds and precipitation. Further, we often lack robust understanding of the processes by which regional aerosols influence local and remote climate. Here, we aim to quantify systematically the cloud and hydrological cycle response to regional changes in aerosols through model simulations using three fully coupled chemistry-climate models: NOAA Geophysical Fluid Dynamics Laboratory Coupled Model 3 (GFDL-CM3), NCAR Community Earth System Model (NCAR-CESM1), and NASA Goddard Institute for Space Studies ModelE2 (GISS-E2). The central approach we use is to contrast a long control experiment (400 years) with a collection of long individual perturbation experiments ( 200 years). We perturb emissions of sulfur dioxide (SO2; precursor to sulfate aerosol) in the United States and determine which responses are significant relative to internal variability and robust across the three models. Initial results show robust, statistically significant decreases in cloud droplet number and liquid water path in the source region across the three models due to decreases in sulfate aerosols. Setting SO2 emissions to zero over the U.S. causes both local and remote impacts in precipitation, with notable significant increases in Sahel and Arctic precipitation. In 13 of the 15 regions we analyze, the precipitation response to zero U.S. SO2 emissions agrees in sign, with agreement in magnitude to within one standard deviation in many of those regions. U.S. sulfate also impacts the timing of the arrival of the Sahel rainy season. Our approach enables us to develop a basis for understanding the response of regional emissions of aerosols and their precursors, and will be expanded to other regions and aerosol species in future work.
Radiative-convective equilibrium model intercomparison project

NASA Astrophysics Data System (ADS)

Wing, Allison A.; Reed, Kevin A.; Satoh, Masaki; Stevens, Bjorn; Bony, Sandrine; Ohno, Tomoki

2018-03-01

RCEMIP, an intercomparison of multiple types of models configured in radiative-convective equilibrium (RCE), is proposed. RCE is an idealization of the climate system in which there is a balance between radiative cooling of the atmosphere and heating by convection. The scientific objectives of RCEMIP are three-fold. First, clouds and climate sensitivity will be investigated in the RCE setting. This includes determining how cloud fraction changes with warming and the role of self-aggregation of convection in climate sensitivity. Second, RCEMIP will quantify the dependence of the degree of convective aggregation and tropical circulation regimes on temperature. Finally, by providing a common baseline, RCEMIP will allow the robustness of the RCE state across the spectrum of models to be assessed, which is essential for interpreting the results found regarding clouds, climate sensitivity, and aggregation, and more generally, determining which features of tropical climate a RCE framework is useful for. A novel aspect and major advantage of RCEMIP is the accessibility of the RCE framework to a variety of models, including cloud-resolving models, general circulation models, global cloud-resolving models, single-column models, and large-eddy simulation models.
Interdependency in Multimodel Climate Projections: Component Replication and Result Similarity

NASA Astrophysics Data System (ADS)

Boé, Julien

2018-03-01

Multimodel ensembles are the main way to deal with model uncertainties in climate projections. However, the interdependencies between models that often share entire components make it difficult to combine their results in a satisfactory way. In this study, how the replication of components (atmosphere, ocean, land, and sea ice) between climate models impacts the proximity of their results is quantified precisely, in terms of climatological means and future changes. A clear relationship exists between the number of components shared by climate models and the proximity of their results. Even the impact of a single shared component is generally visible. These conclusions are true at both the global and regional scales. Given available data, it cannot be robustly concluded that some components are more important than others. Those results provide ways to estimate model interdependencies a priori rather than a posteriori based on their results, in order to define independence weights.
Is carbon storage enough? Can plants adapt? New questions in climate change research.

Treesearch

Sally Duncan

2002-01-01

As it becomes increasingly apparent that human activities are partly responsible for global warming, the focus of climate change research is shifting from the churning out of assessments to the pursuit of science that can test the robustness of existing models. The questions now being addressed are becoming more challenging: Can water-use efficiency of plants keep up...
Assess Climate Change's Impact on Coastal Rivers using a Coupled Climate-Hydrology Model

NASA Astrophysics Data System (ADS)

Xue, Z. G.; Gochis, D.; Yu, W.; Zang, Z.; Sampson, K. M.; Keim, B. D.

2016-12-01

In this study we present a coupled climate-hydrological model reproducing the water cycle of three coastal river basins along the northern Gulf of Mexico for the past three decades (1985-2014). Model simulated climate condition, surface physics, and streamflow were well validated against in situ data and satellite-derived products, giving us the confidence that the newly developed WRF-Hydro model can be a robust tool for evaluating climate change's impact on hydrological regime. Trend analysis of model simulated monthly and annual time series indicates that local climate is getting hotter and dryer, specifically during the growing season. Wavelet analysis reveals that local evapotranspiration is strongly correlated with temperature, while soil moisture, water surplus, and streamflow are coupled with precipitation. In addition, local climate is closely correlated with large-scale climate dynamics such as AMO and ENSO. A possible change-point is detected around year 2004, after which, the monthly precipitation decreased by 14.2%, evapotranspiration increased by 2.9%, and water surplus decreased by 36.5%. The implication of the difference between the water surplus (runoff) calculated using the classic Thornthwaite method and river discharge estimated using streamflow records to the coastal environment is also discussed.
Assessing climate change-robustness of protected area management plans-The case of Germany.

PubMed

Geyer, Juliane; Kreft, Stefan; Jeltsch, Florian; Ibisch, Pierre L

2017-01-01

Protected areas are arguably the most important instrument of biodiversity conservation. To keep them fit under climate change, their management needs to be adapted to address related direct and indirect changes. In our study we focus on the adaptation of conservation management planning, evaluating management plans of 60 protected areas throughout Germany with regard to their climate change-robustness. First, climate change-robust conservation management was defined using 11 principles and 44 criteria, which followed an approach similar to sustainability standards. We then evaluated the performance of individual management plans concerning the climate change-robustness framework. We found that climate change-robustness of protected areas hardly exceeded 50 percent of the potential performance, with most plans ranking in the lower quarter. Most Natura 2000 protected areas, established under conservation legislation of the European Union, belong to the sites with especially poor performance, with lower values in smaller areas. In general, the individual principles showed very different rates of accordance with our principles, but similarly low intensity. Principles with generally higher performance values included holistic knowledge management, public accountability and acceptance as well as systemic and strategic coherence. Deficiencies were connected to dealing with the future and uncertainty. Lastly, we recommended the presented principles and criteria as essential guideposts that can be used as a checklist for working towards more climate change-robust planning.
Assessing climate change-robustness of protected area management plans—The case of Germany

PubMed Central

Geyer, Juliane; Kreft, Stefan; Jeltsch, Florian; Ibisch, Pierre L.

2017-01-01

Protected areas are arguably the most important instrument of biodiversity conservation. To keep them fit under climate change, their management needs to be adapted to address related direct and indirect changes. In our study we focus on the adaptation of conservation management planning, evaluating management plans of 60 protected areas throughout Germany with regard to their climate change-robustness. First, climate change-robust conservation management was defined using 11 principles and 44 criteria, which followed an approach similar to sustainability standards. We then evaluated the performance of individual management plans concerning the climate change-robustness framework. We found that climate change-robustness of protected areas hardly exceeded 50 percent of the potential performance, with most plans ranking in the lower quarter. Most Natura 2000 protected areas, established under conservation legislation of the European Union, belong to the sites with especially poor performance, with lower values in smaller areas. In general, the individual principles showed very different rates of accordance with our principles, but similarly low intensity. Principles with generally higher performance values included holistic knowledge management, public accountability and acceptance as well as systemic and strategic coherence. Deficiencies were connected to dealing with the future and uncertainty. Lastly, we recommended the presented principles and criteria as essential guideposts that can be used as a checklist for working towards more climate change-robust planning. PMID:28982187
Predicting climate change: Uncertainties and prospects for surmounting them

NASA Astrophysics Data System (ADS)

Ghil, Michael

2008-03-01

General circulation models (GCMs) are among the most detailed and sophisticated models of natural phenomena in existence. Still, the lack of robust and efficient subgrid-scale parametrizations for GCMs, along with the inherent sensitivity to initial data and the complex nonlinearities involved, present a major and persistent obstacle to narrowing the range of estimates for end-of-century warming. Estimating future changes in the distribution of climatic extrema is even more difficult. Brute-force tuning the large number of GCM parameters does not appear to help reduce the uncertainties. Andronov and Pontryagin (1937) proposed structural stability as a way to evaluate model robustness. Unfortunately, many real-world systems proved to be structurally unstable. We illustrate these concepts with a very simple model for the El Niño--Southern Oscillation (ENSO). Our model is governed by a differential delay equation with a single delay and periodic (seasonal) forcing. Like many of its more or less detailed and realistic precursors, this model exhibits a Devil's staircase. We study the model's structural stability, describe the mechanisms of the observed instabilities, and connect our findings to ENSO phenomenology. In the model's phase-parameter space, regions of smooth dependence on parameters alternate with rough, fractal ones. We then apply the tools of random dynamical systems and stochastic structural stability to the circle map and a torus map. The effect of noise with compact support on these maps is fairly intuitive: it is the most robust structures in phase-parameter space that survive the smoothing introduced by the noise. The nature of the stochastic forcing matters, thus suggesting that certain types of stochastic parametrizations might be better than others in achieving GCM robustness. This talk represents joint work with M. Chekroun, E. Simonnet and I. Zaliapin.
Assessment of simulated and projected climate change in Pakistan using IPCC AR4-based AOGCMs

NASA Astrophysics Data System (ADS)

Saeed, F.; Athar, H.

2017-11-01

A detailed spatio-temporal assessment of two basic climatic parameters (temperature and precipitation) is carried out using 22 Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4)-based atmospheric oceanic general circulation models (AOGCMs) over data-sparse and climatically vulnerable region of Pakistan (20°-37° N and 60°-78° E), for the first time, for the baseline period (1975-1999), as well as for the three projected periods during the twenty-first century centered at 2025-2049, 2050-2074, and 2075-2099, respectively, both on seasonal and on annual bases, under three Special Report on Emission Scenarios (SRES): A2, A1B, and B1. An ensemble-based approach consisting of the IPCC AR4-based AOGCMs indicates that during the winter season (from December to March), 66% of the models display robust projected increase of winter precipitation by about 10% relative to the baseline period, irrespective of emission scenario and projection period, in the upper northern subregion of Pakistan (latitude > 35° N). The projected robust changes in the temperature by the end of twenty-first century are in the range of 3 to 4 ° C during the winter season and on an annual basis, in the central and western regions of Punjab province, especially in A2 and A1B emission scenarios. In particular, the IPCC AR4 models project a progressive increase in temperature throughout Pakistan, in contrast to spatial distribution of precipitation, where spatially less uniform and robust results for projected periods are obtained on sign of change. In general, changes in both precipitation and temperature are larger in the summer season (JAS) as compared to the winter season in the coming decades, relative to the baseline period. This may require comprehensive long-term strategic policies to adapt and mitigate climate change in Pakistan, in comparison to what is currently envisaged.
North Atlantic Jet Variability in PMIP3 LGM Simulations

NASA Astrophysics Data System (ADS)

Hezel, P.; Li, C.

2017-12-01

North Atlantic jet variability in glacial climates has been shown inmodelling studies to be strongly influenced by upstream ice sheettopography. We analyze the results of 8 models from the PMIP3simulations, forced with a hybrid Laurentide Ice Sheet topography, andcompare them to the PMIP2 simulations which were forced with theICE-5G topography, to develop a general understanding of the NorthAtlantic jet and jet variability. The strengthening of the jet andreduced spatial variability is a robust feature of the last glacialmaximum (LGM) simulations compared to the pre-industrial state.However, the canonical picture of the LGM North Atlantic jet as beingmore zonal and elongated compared to pre-industrial climate states isnot a robust result across models, and may have arisen in theliterature as a function of multiple studies performed with the samemodel.
Designing Dynamic Adaptive Policy Pathways using Many-Objective Robust Decision Making

NASA Astrophysics Data System (ADS)

Kwakkel, Jan; Haasnoot, Marjolijn

2017-04-01

Dealing with climate risks in water management requires confronting a wide variety of deeply uncertain factors, while navigating a many dimensional space of trade-offs amongst objectives. There is an emerging body of literature on supporting this type of decision problem, under the label of decision making under deep uncertainty. Two approaches within this literature are Many-Objective Robust Decision Making, and Dynamic Adaptive Policy Pathways. In recent work, these approaches have been compared. One of the main conclusions of this comparison was that they are highly complementary. Many-Objective Robust Decision Making is a model based decision support approach, while Dynamic Adaptive Policy Pathways is primarily a conceptual framework for the design of flexible strategies that can be adapted over time in response to how the future is actually unfolding. In this research we explore this complementarity in more detail. Specifically, we demonstrate how Many-Objective Robust Decision Making can be used to design adaptation pathways. We demonstrate this combined approach using a water management problem, in the Netherlands. The water level of Lake IJselmeer, the main fresh water resource of the Netherlands, is currently managed through discharge by gravity. Due to climate change, this won't be possible in the future, unless water levels are changed. Changing the water level has undesirable flood risk and spatial planning consequences. The challenge is to find promising adaptation pathways that balance objectives related to fresh water supply, flood risk, and spatial issues, while accounting for uncertain climatic and land use change. We conclude that the combination of Many-Objective Robust Decision Making and Dynamic Adaptive Policy Pathways is particularly suited for dealing with deeply uncertain climate risks.
Future hotspots of increasing temperature variability in tropical countries

NASA Astrophysics Data System (ADS)

Bathiany, S.; Dakos, V.; Scheffer, M.; Lenton, T. M.

2017-12-01

Resolving how climate variability will change in future is crucial to determining how challenging it will be for societies and ecosystems to adapt to climate change. We show that the largest increases in temperature variability - that are robust between state-of-the art climate models - are concentrated in tropical countries. On average, temperature variability increases by 15% per degree of global warming in Amazonia and Southern Africa during austral summer, and by up to 10% °C-1 in the Sahel, India and South East Asia. Southern hemisphere changes can be explained by drying soils, whereas shifts in atmospheric structure play a more important role in the Northern hemisphere. These robust regional changes in variability are associated with monthly timescale events, whereas uncertain changes in inter-annual modes of variability make the response of global temperature variability uncertain. Our results suggest that regional changes in temperature variability will create new inequalities in climate change impacts between rich and poor nations.
Validation and quantification of uncertainty in coupled climate models using network analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bracco, Annalisa

We developed a fast, robust and scalable methodology to examine, quantify, and visualize climate patterns and their relationships. It is based on a set of notions, algorithms and metrics used in the study of graphs, referred to as complex network analysis. This approach can be applied to explain known climate phenomena in terms of an underlying network structure and to uncover regional and global linkages in the climate system, while comparing general circulation models outputs with observations. The proposed method is based on a two-layer network representation, and is substantially new within the available network methodologies developed for climate studies.more » At the first layer, gridded climate data are used to identify ‘‘areas’’, i.e., geographical regions that are highly homogeneous in terms of the given climate variable. At the second layer, the identified areas are interconnected with links of varying strength, forming a global climate network. The robustness of the method (i.e. the ability to separate between topological distinct fields, while identifying correctly similarities) has been extensively tested. It has been proved that it provides a reliable, fast framework for comparing and ranking the ability of climate models of reproducing observed climate patterns and their connectivity. We further developed the methodology to account for lags in the connectivity between climate patterns and refined our area identification algorithm to account for autocorrelation in the data. The new methodology based on complex network analysis has been applied to state-of-the-art climate model simulations that participated to the last IPCC (International Panel for Climate Change) assessment to verify their performances, quantify uncertainties, and uncover changes in global linkages between past and future projections. Network properties of modeled sea surface temperature and rainfall over 1956–2005 have been constrained towards observations or reanalysis data sets, and their differences quantified using two metrics. Projected changes from 2051 to 2300 under the scenario with the highest representative and extended concentration pathways (RCP8.5 and ECP8.5) have then been determined. The network of models capable of reproducing well major climate modes in the recent past, changes little during this century. In contrast, among those models the uncertainties in the projections after 2100 remain substantial, and primarily associated with divergences in the representation of the modes of variability, particularly of the El Niño Southern Oscillation (ENSO), and their connectivity, and therefore with their intrinsic predictability, more so than with differences in the mean state evolution. Additionally, we evaluated the relation between the size and the ‘strength’ of the area identified by the network analysis as corresponding to ENSO noting that only a small subset of models can reproduce realistically the observations.« less
Cluster analysis of Southeastern U.S. climate stations

NASA Astrophysics Data System (ADS)

Stooksbury, D. E.; Michaels, P. J.

1991-09-01

A two-step cluster analysis of 449 Southeastern climate stations is used to objectively determine general climate clusters (groups of climate stations) for eight southeastern states. The purpose is objectively to define regions of climatic homogeneity that should perform more robustly in subsequent climatic impact models. This type of analysis has been successfully used in many related climate research problems including the determination of corn/climate districts in Iowa (Ortiz-Valdez, 1985) and the classification of synoptic climate types (Davis, 1988). These general climate clusters may be more appropriate for climate research than the standard climate divisions (CD) groupings of climate stations, which are modifications of the agro-economic United States Department of Agriculture crop reporting districts. Unlike the CD's, these objectively determined climate clusters are not restricted by state borders and thus have reduced multicollinearity which makes them more appropriate for the study of the impact of climate and climatic change.
Estimating daily climatologies for climate indices derived from climate model data and observations

PubMed Central

Mahlstein, Irina; Spirig, Christoph; Liniger, Mark A; Appenzeller, Christof

2015-01-01

Climate indices help to describe the past, present, and the future climate. They are usually closer related to possible impacts and are therefore more illustrative to users than simple climate means. Indices are often based on daily data series and thresholds. It is shown that the percentile-based thresholds are sensitive to the method of computation, and so are the climatological daily mean and the daily standard deviation, which are used for bias corrections of daily climate model data. Sample size issues of either the observed reference period or the model data lead to uncertainties in these estimations. A large number of past ensemble seasonal forecasts, called hindcasts, is used to explore these sampling uncertainties and to compare two different approaches. Based on a perfect model approach it is shown that a fitting approach can improve substantially the estimates of daily climatologies of percentile-based thresholds over land areas, as well as the mean and the variability. These improvements are relevant for bias removal in long-range forecasts or predictions of climate indices based on percentile thresholds. But also for climate change studies, the method shows potential for use. Key Points More robust estimates of daily climate characteristics Statistical fitting approach Based on a perfect model approach PMID:26042192
Water Vapor Winds and Their Application to Climate Change Studies

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Lerner, Jeffrey A.

2000-01-01

The retrieval of satellite-derived winds and moisture from geostationary water vapor imagery has matured to the point where it may be applied to better understanding longer term climate changes that were previously not possible using conventional measurements or model analysis in data-sparse regions. In this paper, upper-tropospheric circulation features and moisture transport covering ENSO periods are presented and discussed. Precursors and other detectable interannual climate change signals are analyzed and compared to model diagnosed features. Estimates of winds and humidity over data-rich regions are used to show the robustness of the data and its value over regions that have previously eluded measurement.
Human health impacts avoided under the Paris Agreement on climate change

NASA Astrophysics Data System (ADS)

Mitchell, Dann

2017-04-01

This analyses makes use of the experiments and model data from the Half a degree Additional warming; Prognosis and Projected Impacts (HAPPI; www.happimip.org) analysis (Mitchell et al, 2016a). HAPPI is unique in that it is specifically designed to address the Paris Agreement priorities on climate impacts, by using equilibrated climates and super-ensembles, thereby enabling robust analysis of extremes. Here we first look at extreme hot and cold spells, and then make use of the most recent heat-mortality models, and heat stress metrics to look at any differences between 1.5C and 2C worlds compared to normal.
Multi-model analysis of the Atlantic influence on Southern Amazon rainfall

DOE PAGES

Yoon, Jin -Ho

2015-12-07

Amazon rainfall is subject to year-to-year fluctuation resulting in drought and flood in various intensities. A major climatic driver of the interannual variation of the Amazon rainfall is El Niño/Southern Oscillation. Also, the Sea Surface Temperature over the Atlantic Ocean is identified as an important climatic driver on the Amazon water cycle. Previously, observational datasets were used to support the Atlantic influence on Amazon rainfall. Furthermore, it is found that multiple global climate models do reproduce the Atlantic-Amazon link robustly. However, there exist differences in rainfall response, which primarily depends on the climatological rainfall amount.
Student Development of Model-Based Reasoning about Carbon Cycling and Climate Change in a Socio-Scientific Issues Unit

ERIC Educational Resources Information Center

Zangori, Laura; Peel, Amanda; Kinslow, Andrew; Friedrichsen, Patricia; Sadler, Troy D.

2017-01-01

Carbon cycling is a key natural system that requires robust science literacy to understand how and why climate change is occurring. Studies show that students tend to compartmentalize carbon movement within plants and animals and are challenged to make sense of how carbon cycles on a global scale. Studies also show that students hold faulty models…
Scientific Uncertainties in Climate Change Detection and Attribution Studies

NASA Astrophysics Data System (ADS)

Santer, B. D.

2017-12-01

It has been claimed that the treatment and discussion of key uncertainties in climate science is "confined to hushed sidebar conversations at scientific conferences". This claim is demonstrably incorrect. Climate change detection and attribution studies routinely consider key uncertainties in observational climate data, as well as uncertainties in model-based estimates of natural variability and the "fingerprints" in response to different external forcings. The goal is to determine whether such uncertainties preclude robust identification of a human-caused climate change fingerprint. It is also routine to investigate the impact of applying different fingerprint identification strategies, and to assess how detection and attribution results are impacted by differences in the ability of current models to capture important aspects of present-day climate. The exploration of the uncertainties mentioned above will be illustrated using examples from detection and attribution studies with atmospheric temperature and moisture.

Hydroclimatic trends in simulations over the CORDEX North America region

NASA Astrophysics Data System (ADS)

Arritt, Raymond; Groisman, Pavel; Daniel, Ariele; Schillerberg, Tayler

2015-04-01

An increase in the occurrence of heavy precipitation has been one of the most pronounced climate change signals for the central United States. We study this trend by using the RegCM4 regional climate model to dynamically downscale CMIP5 global projections for 1950-2099 over the CORDEX North America domain. We examine the robustness of the results by driving the regional model with two different global models, by performing simulations at both 50 km and 25 km grid spacing, and by using different convective parameterizations in RegCM4. The global models sample the range of climate sensitivity in CMIP5: HadGEM2-ES has the highest equilibrium climate sensitivity of the CMIP5 models, while GFDL-ESM2M has one of the lowest sensitivities. RegCM4 results show increases in heavy precipitation (> 50 mm/day) over the central United States for the period 1951-2005 similar to observed trends. This trend is predicted to accelerate so that by the end of the 21st century incidence of heavy precipitation increases by a factor of 2 to 3. The trend is robust in that it is produced regardless of the driving global model or the configuration of the regional model. Results also show a modest increase in the number of dry days and a marked increase in the number of long runs of dry days (16 or more consecutive dry days). The combination of heavier events and longer runs of dry days has implications for sectors such as agriculture and water quality. This research was sponsored by USDA NIFA under the Earth System Modeling program and as part of a regional collaborative project.
Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions

USDA-ARS?s Scientific Manuscript database

Despite widespread application in studying climate change impacts, most crop models ignore complex interactions among air temperature, crop and soil water status, CO2 concentration and atmospheric conditions that influence crop canopy temperature. The current study extended previous studies by evalu...
Controlled comparison of species- and community-level models across novel climates and communities

PubMed Central

Maguire, Kaitlin C.; Blois, Jessica L.; Fitzpatrick, Matthew C.; Williams, John W.; Ferrier, Simon; Lorenz, David J.

2016-01-01

Species distribution models (SDMs) assume species exist in isolation and do not influence one another's distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossil-pollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa. PMID:26962143
Robust Performance of Marginal Pacific Coral Reef Habitats in Future Climate Scenarios.

PubMed

Freeman, Lauren A

2015-01-01

Coral reef ecosystems are under dual threat from climate change. Increasing sea surface temperatures and thermal stress create environmental limits at low latitudes, and decreasing aragonite saturation state creates environmental limits at high latitudes. This study examines the response of unique coral reef habitats to climate change in the remote Pacific, using the National Center for Atmospheric Research Community Earth System Model version 1 alongside the species distribution algorithm Maxent. Narrow ranges of physico-chemical variables are used to define unique coral habitats and their performance is tested in future climate scenarios. General loss of coral reef habitat is expected in future climate scenarios and has been shown in previous studies. This study found exactly that for most of the predominant physico-chemical environments. However, certain coral reef habitats considered marginal today at high latitude, along the equator and in the eastern tropical Pacific were found to be quite robust in climate change scenarios. Furthermore, an environmental coral reef refuge previously identified in the central south Pacific near French Polynesia was further reinforced. Studying the response of specific habitats showed that the prevailing conditions of this refuge during the 20th century shift to a new set of conditions, more characteristic of higher latitude coral reefs in the 20th century, in future climate scenarios projected to 2100.
Tropical Pacific climate during the Medieval Climate Anomaly: progress and pitfalls

NASA Astrophysics Data System (ADS)

Cobb, K. M.; Westphal, N.; Charles, C.; Sayani, H. R.; Edwards, R. L.; Cheng, H.; Grothe, P. R.; Chen, T.; Hitt, N. T.; O'Connor, G.; Atwood, A. R.

2016-12-01

A vast trove of paleoclimate records indicates that the Medieval Climate Anomaly (MCA; 900-1200AD) was characterized by relative warmth throughout the Northern Hemisphere and significant hydroclimate anomalies - particularly well-resolved over North America - that posed a challenge to human populations. The global-scale nature of the climate anomalies has driven speculation that the tropical Pacific, with its rich spectrum of natural variability and far-reaching impact, may have undergone a prolonged reorganization during the MCA. While some key records from across the tropical Pacific document significant changes in temperature and/or hydrology, a dynamically-consistent picture of the MCA tropical Pacific climate state has proven elusive. In particular, there are few if any robust paleoclimate constraints from the central Pacific, where even modest changes in ocean temperature translate into distinct patterns of global atmospheric teleconnections. Here, we present a new collection of fossil coral multi-proxy records from Christmas Island (2N, 157W) that provide robust constraints on both temperature and hydrological changes during the MCA. We employ both modern coral data, instrumental climate data, and climate model output in developing a framework for quantifying the uncertainties associated with the new fossil coral data. In doing so, we illustrate the clear benefits of modern environmental monitoring campaigns that inform the generation of paleoclimate pseudo-proxies.
Land Cover Applications, Landscape Dynamics, and Global Change

USGS Publications Warehouse

Tieszen, Larry L.

2007-01-01

The Land Cover Applications, Landscape Dynamics, and Global Change project at U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) seeks to integrate remote sensing and simulation models to better understand and seek solutions to national and global issues. Modeling processes related to population impacts, natural resource management, climate change, invasive species, land use changes, energy development, and climate mitigation all pose significant scientific opportunities. The project activities use remotely sensed data to support spatial monitoring, provide sensitivity analyses across landscapes and large regions, and make the data and results available on the Internet with data access and distribution, decision support systems, and on-line modeling. Applications support sustainable natural resource use, carbon cycle science, biodiversity conservation, climate change mitigation, and robust simulation modeling approaches that evaluate ecosystem and landscape dynamics.
Flood risk assessment and robust management under deep uncertainty: Application to Dhaka City

NASA Astrophysics Data System (ADS)

Mojtahed, Vahid; Gain, Animesh Kumar; Giupponi, Carlo

2014-05-01

The socio-economic changes as well as climatic changes have been the main drivers of uncertainty in environmental risk assessment and in particular flood. The level of future uncertainty that researchers face when dealing with problems in a future perspective with focus on climate change is known as Deep Uncertainty (also known as Knightian uncertainty), since nobody has already experienced and undergone those changes before and our knowledge is limited to the extent that we have no notion of probabilities, and therefore consolidated risk management approaches have limited potential.. Deep uncertainty is referred to circumstances that analysts and experts do not know or parties to decision making cannot agree on: i) the appropriate models describing the interaction among system variables, ii) probability distributions to represent uncertainty about key parameters in the model 3) how to value the desirability of alternative outcomes. The need thus emerges to assist policy-makers by providing them with not a single and optimal solution to the problem at hand, such as crisp estimates for the costs of damages of natural hazards considered, but instead ranges of possible future costs, based on the outcomes of ensembles of assessment models and sets of plausible scenarios. Accordingly, we need to substitute optimality as a decision criterion with robustness. Under conditions of deep uncertainty, the decision-makers do not have statistical and mathematical bases to identify optimal solutions, while instead they should prefer to implement "robust" decisions that perform relatively well over all conceivable outcomes out of all future unknown scenarios. Under deep uncertainty, analysts cannot employ probability theory or other statistics that usually can be derived from observed historical data and therefore, we turn to non-statistical measures such as scenario analysis. We construct several plausible scenarios with each scenario being a full description of what may happen in future and based on a meaningful synthesis of parameters' values with control of their correlations for maintaining internal consistencies. This paper aims at incorporating a set of data mining and sampling tools to assess uncertainty of model outputs under future climatic and socio-economic changes for Dhaka city and providing a decision support system for robust flood management and mitigation policies. After constructing an uncertainty matrix to identify the main sources of uncertainty for Dhaka City, we identify several hazard and vulnerability maps based on future climatic and socio-economic scenarios. The vulnerability of each flood management alternative under different set of scenarios is determined and finally the robustness of each plausible solution considered is defined based on the above assessment.
Quantifying the economic risks of climate change

NASA Astrophysics Data System (ADS)

Diaz, Delavane; Moore, Frances

2017-11-01

Understanding the value of reducing greenhouse-gas emissions matters for policy decisions and climate risk management, but quantification is challenging because of the complex interactions and uncertainties in the Earth and human systems, as well as normative ethical considerations. Current modelling approaches use damage functions to parameterize a simplified relationship between climate variables, such as temperature change, and economic losses. Here we review and synthesize the limitations of these damage functions and describe how incorporating impacts, adaptation and vulnerability research advances and empirical findings could substantially improve damage modelling and the robustness of social cost of carbon values produced. We discuss the opportunities and challenges associated with integrating these research advances into cost-benefit integrated assessment models, with guidance for future work.
Modeling snow accumulation and ablation processes in forested environments

NASA Astrophysics Data System (ADS)

Andreadis, Konstantinos M.; Storck, Pascal; Lettenmaier, Dennis P.

2009-05-01

The effects of forest canopies on snow accumulation and ablation processes can be very important for the hydrology of midlatitude and high-latitude areas. A mass and energy balance model for snow accumulation and ablation processes in forested environments was developed utilizing extensive measurements of snow interception and release in a maritime mountainous site in Oregon. The model was evaluated using 2 years of weighing lysimeter data and was able to reproduce the snow water equivalent (SWE) evolution throughout winters both beneath the canopy and in the nearby clearing, with correlations to observations ranging from 0.81 to 0.99. Additionally, the model was evaluated using measurements from a Boreal Ecosystem-Atmosphere Study (BOREAS) field site in Canada to test the robustness of the canopy snow interception algorithm in a much different climate. Simulated SWE was relatively close to the observations for the forested sites, with discrepancies evident in some cases. Although the model formulation appeared robust for both types of climates, sensitivity to parameters such as snow roughness length and maximum interception capacity suggested the magnitude of improvements of SWE simulations that might be achieved by calibration.
Will current probabilistic climate change information, as such, improve adaptation?

NASA Astrophysics Data System (ADS)

Lopez, A.; Smith, L. A.

2012-04-01

Probabilistic climate scenarios are currently being provided to end users, to employ as probabilities in adaptation decision making, with the explicit suggestion that they quantify the impacts of climate change relevant to a variety of sectors. These "probabilities" are, however, rather sensitive to the assumptions in, and the structure of the modelling approaches used to generate them. It is often argued that stakeholders require probabilistic climate change information to adequately evaluate and plan adaptation pathways. On the other hand, some circumstantial evidence suggests that on the ground decision making rarely uses well defined probability distributions of climate change as inputs. Nevertheless it is within this context of probability distributions of climate change that we discuss possible drawbacks of supplying information that, while presented as robust and decision relevant, , is in fact unlikely to be so due to known flaws both in the underlying models and in the methodology used to "account for" those known flaws. How might one use a probability forecast that is expected to change in the future, not due to a refinement in our information but due to fundamental flaws in its construction? What then are the alternatives? While the answer will depend on the context of the problem at hand, a good approach will be strongly informed by the timescale of the given planning decision, and the consideration of all the non-climatic factors that have to be taken into account in the corresponding risk assessment. Using a water resources system as an example, we illustrate an alternative approach to deal with these challenges and make robust adaptation decisions today.
Quantifying the importance of model-to-model variability in integrated assessments of 21st century climate

NASA Astrophysics Data System (ADS)

Bond-Lamberty, B. P.; Jones, A. D.; Shi, X.; Calvin, K. V.

2016-12-01

The C4MIP and CMIP5 model intercomparison projects (MIPs) highlighted uncertainties in climate projections, driven to a large extent by interactions between the terrestrial carbon cycle and climate feedbacks. In addition, the importance of feedbacks between human (energy and economic) systems and natural (carbon and climate) systems is poorly understood, and not considered in the previous MIP protocols. The experiments conducted under the previous Integrated Earth System Model (iESM) project, which coupled a earth system model with an integrated assessment model (GCAM), found that the inclusion of climate feedbacks on the terrestrial system in an RCP4.5 scenario increased ecosystem productivity, resulting in declines in cropland extent and increases in bioenergy production and forest cover. As a follow-up to these studies and to further understand climate-carbon cycle interactions and feedbacks, we examined the robustness of these results by running a suite of GCAM-only experiments using changes in ecosystem productivity derived from both the CMIP5 archive and the Agricultural Model Intercomparison Project. In our results, the effects of climate on yield in an RCP8.5 scenario tended to be more positive than those of AgMIP, but more negative than those of the other CMIP models. We discuss these results and the implications of model-to-model variability for integrated coupling studies of the future earth system.
New flux based dose-response relationships for ozone for European forest tree species.

PubMed

Büker, P; Feng, Z; Uddling, J; Briolat, A; Alonso, R; Braun, S; Elvira, S; Gerosa, G; Karlsson, P E; Le Thiec, D; Marzuoli, R; Mills, G; Oksanen, E; Wieser, G; Wilkinson, M; Emberson, L D

2015-11-01

To derive O3 dose-response relationships (DRR) for five European forest trees species and broadleaf deciduous and needleleaf tree plant functional types (PFTs), phytotoxic O3 doses (PODy) were related to biomass reductions. PODy was calculated using a stomatal flux model with a range of cut-off thresholds (y) indicative of varying detoxification capacities. Linear regression analysis showed that DRR for PFT and individual tree species differed in their robustness. A simplified parameterisation of the flux model was tested and showed that for most non-Mediterranean tree species, this simplified model led to similarly robust DRR as compared to a species- and climate region-specific parameterisation. Experimentally induced soil water stress was not found to substantially reduce PODy, mainly due to the short duration of soil water stress periods. This study validates the stomatal O3 flux concept and represents a step forward in predicting O3 damage to forests in a spatially and temporally varying climate. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.
Hydrological changes in the tropics: an Holocene perspective

NASA Astrophysics Data System (ADS)

Braconnot, Pascale

2015-04-01

Past climates offer a large set of natural experiences that can be used to better understand the relative role of different climate feedbacks arising from changes in the Earth's global energetics, Earth's hydrological cycle or from the coupling between climate and biogeochemical cycles. In addition, the numerous climate reconstructions from different and independent ice, marine and terrestrial climate archives allow to test how climate models reproduce past changes and to assess their credibility when used for future climate projections. The presentation will review some of the mechanisms affecting the long term trend in the location of the intertropical convergence zone and the Afro-Asian monsoon. Using simulations of the PMIP project, as well as sensitivity experiments with the IPSL model, I'll discuss the role of monsoon changes in the global Earth's energetics and the different feedbacks from ocean and land-surface. The presentation will contrast the conditions in the Early, the mid and late Holocene and show how robust features of monsoon changes can be used to better assess future changes in regions where model results are uncertain, such as West Africa.
Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

DOE PAGES

Monier, Erwan; Xu, Liyi; Snyder, Richard

2016-04-26

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios,more » climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations-although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Lastly, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.« less
Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

NASA Astrophysics Data System (ADS)

Monier, Erwan; Xu, Liyi; Snyder, Richard

2016-05-01

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios, climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations—although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Finally, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.
Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Monier, Erwan; Xu, Liyi; Snyder, Richard

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios,more » climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations-although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Lastly, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.« less
What is the role of historical anthropogenically-induced land-cover change on the surface climate of West Africa? Results from the LUCID intercomparison project

NASA Astrophysics Data System (ADS)

Souleymane, S.

2015-12-01

West Africa has been highlighted as a hot spot of land surface-atmosphere interactions. This study analyses the outputs of the project Land-Use and Climate, IDentification of Robust Impacts (LUCID) over West Africa. LUCID used seven atmosphere-land models with a common experimental design to explore the impacts of Land Use induced Land Cover Change (LULCC) that are robust and consistent across the climate models. Focusing the analysis on Sahel and Guinea, this study shows that, even though the seven climate models use the same atmospheric and land cover forcing, there are significant differences of West African Monsoon variability across the climate models. The magnitude of that variability differs significantly from model to model resulting two major "features": (1) atmosphere dynamics models; (2) how the land-surface functioning is parameterized in the Land surface Model, in particular regarding the evapotranspiration partitioning within the different land-cover types, as well as the role of leaf area index (LAI) in the flux calculations and how strongly the surface is coupled to the atmosphere. The major role that the models'sensitivity to land-cover perturbations plays in the resulting climate impacts of LULCC has been analysed in this study. The climate models show, however, significant differences in the magnitude and the seasonal partitioning of the temperature change. The LULCC induced cooling is directed by decreases in net shortwave radiation that reduced the available energy (QA) (related to changes in land-cover properties other than albedo, such as LAI and surface roughness), which decreases during most part of the year. The biophysical impacts of LULCC were compared to the impact of elevated greenhouse gases resulting changes in sea surface temperatures and sea ice extent (CO2SST). The results show that the surface cooling (related a decrease in QA) induced by the biophysical effects of LULCC are insignificant compared to surface warming (related an increase in QA), which is induced by the regional significance effect of CO2SST due to a small LULCC imposed. In contrast, the decrease of surface water balance resulting from LULCC effect is a similar sign to those resulting from CO2SST but the signal resulting of the biophysical effects of LULCC is stronger than the regional CO2SST impact.
Carbon-climate-human interactions in an integrated human-Earth system model

NASA Astrophysics Data System (ADS)

Calvin, K. V.; Bond-Lamberty, B. P.; Jones, A. D.; Shi, X.

2016-12-01

The C4MIP and CMIP5 results highlighted large uncertainties in climate projections, driven to a large extent by limited understanding of the interactions between terrestrial carbon-cycle and climate feedbacks, and their associated uncertainties. These feedbacks are dominated by uncertainties in soil processes, disturbance dynamics, ecosystem response to climate change, and agricultural productivity, and land-use change. This research addresses three questions: (1) how do terrestrial feedbacks vary across different levels of climate change, (2) what is the relative contribution of CO2 fertilization and climate change, and (3) how robust are the results across different models and methods? We used a coupled modeling framework that integrates an Integrated Assessment Model (modeling economic and energy activity) with an Earth System Model (modeling the natural earth system) to examine how business-as-usual (RCP 8.5) climate change will affect ecosystem productivity, cropland extent, and other aspects of the human-Earth system. We find that higher levels of radiative forcing result in higher productivity growth, that increases in CO2 concentrations are the dominant contributors to that growth, and that our productivity increases fall in the middle of the range when compared to other CMIP5 models and the AgMIP models. These results emphasize the importance of examining both the anthropogenic and natural components of the earth system, and their long-term interactive feedbacks.
Directed International Technological Change and Climate Policy: New Methods for Identifying Robust Policies Under Conditions of Deep Uncertainty

NASA Astrophysics Data System (ADS)

Molina-Perez, Edmundo

It is widely recognized that international environmental technological change is key to reduce the rapidly rising greenhouse gas emissions of emerging nations. In 2010, the United Nations Framework Convention on Climate Change (UNFCCC) Conference of the Parties (COP) agreed to the creation of the Green Climate Fund (GCF). This new multilateral organization has been created with the collective contributions of COP members, and has been tasked with directing over USD 100 billion per year towards investments that can enhance the development and diffusion of clean energy technologies in both advanced and emerging nations (Helm and Pichler, 2015). The landmark agreement arrived at the COP 21 has reaffirmed the key role that the GCF plays in enabling climate mitigation as it is now necessary to align large scale climate financing efforts with the long-term goals agreed at Paris 2015. This study argues that because of the incomplete understanding of the mechanics of international technological change, the multiplicity of policy options and ultimately the presence of climate and technological change deep uncertainty, climate financing institutions such as the GCF, require new analytical methods for designing long-term robust investment plans. Motivated by these challenges, this dissertation shows that the application of new analytical methods, such as Robust Decision Making (RDM) and Exploratory Modeling (Lempert, Popper and Bankes, 2003) to the study of international technological change and climate policy provides useful insights that can be used for designing a robust architecture of international technological cooperation for climate change mitigation. For this study I developed an exploratory dynamic integrated assessment model (EDIAM) which is used as the scenario generator in a large computational experiment. The scope of the experimental design considers an ample set of climate and technological scenarios. These scenarios combine five sources of uncertainty: climate change, elasticity of substitution between renewable and fossil energy and three different sources of technological uncertainty (i.e. R&D returns, innovation propensity and technological transferability). The performance of eight different GCF and non-GCF based policy regimes is evaluated in light of various end-of-century climate policy targets. Then I combine traditional scenario discovery data mining methods (Bryant and Lempert, 2010) with high dimensional stacking methods (Suzuki, Stem and Manzocchi, 2015; Taylor et al., 2006; LeBlanc, Ward and Wittels, 1990) to quantitatively characterize the conditions under which it is possible to stabilize greenhouse gas emissions and keep temperature rise below 2°C before the end of the century. Finally, I describe a method by which it is possible to combine the results of scenario discovery with high-dimensional stacking to construct a dynamic architecture of low cost technological cooperation. This dynamic architecture consists of adaptive pathways (Kwakkel, Haasnoot and Walker, 2014; Haasnoot et al., 2013) which begin with carbon taxation across both regions as a critical near term action. Then in subsequent phases different forms of cooperation are triggered depending on the unfolding climate and technological conditions. I show that there is no single policy regime that dominates over the entire uncertainty space. Instead I find that it is possible to combine these different architectures into a dynamic framework for technological cooperation across regions that can be adapted to unfolding climate and technological conditions which can lead to a greater rate of success and to lower costs in meeting the end-of-century climate change objectives agreed at the 2015 Paris Conference of the Parties. Keywords: international technological change, emerging nations, climate change, technological uncertainties, Green Climate Fund.
A computational approach to climate science education with CLIMLAB

NASA Astrophysics Data System (ADS)

Rose, B. E. J.

2017-12-01

CLIMLAB is a Python-based software toolkit for interactive, process-oriented climate modeling for use in education and research. It is motivated by the need for simpler tools and more reproducible workflows with which to "fill in the gaps" between blackboard-level theory and the results of comprehensive climate models. With CLIMLAB you can interactively mix and match physical model components, or combine simpler process models together into a more comprehensive model. I use CLIMLAB in the classroom to put models in the hands of students (undergraduate and graduate), and emphasize a hierarchical, process-oriented approach to understanding the key emergent properties of the climate system. CLIMLAB is equally a tool for climate research, where the same needs exist for more robust, process-based understanding and reproducible computational results. I will give an overview of CLIMLAB and an update on recent developments, including: a full-featured, well-documented, interactive implementation of a widely-used radiation model (RRTM) packaging with conda-forge for compiler-free (and hassle-free!) installation on Mac, Windows and Linux interfacing with xarray for i/o and graphics with gridded model data a rich and growing collection of examples and self-computing lecture notes in Jupyter notebook format

Assessing climate adaptation options and uncertainties for cereal systems in West Africa

NASA Astrophysics Data System (ADS)

Guan, K.; Sultan, B.; Biasutti, M.; Lobell, D. B.

2015-12-01

The already fragile agriculture production system in West Africa faces further challenges in meeting food security in the coming decades, primarily due to a fast increasing population and risks of climate change. Successful adaptation of agriculture should not only benefit in the current climate but should also reduce negative (or enhance positive) impacts for climate change. Assessment of various possible adaptation options and their uncertainties provides key information for prioritizing adaptation investments. Here, based on the several robust aspects of climate projections in this region (i.e. temperature increases and rainfall pattern shifts), we use two well-validated crop models (i.e. APSIM and SARRA-H) and an ensemble of downscaled climate forcing to assess five possible and realistic adaptation options (late sowing, intensification, thermal time increase, water harvesting and increased resilience to heat stress) in West Africa for the staple crop production of sorghum. We adopt a new assessment framework to account for both the impacts of adaptation options in current climate and their ability to reduce impacts of future climate change, and also consider changes in both mean yield and its variability. Our results reveal that most proposed "adaptation options" are not more beneficial in the future than in the current climate, i.e. not really reduce the climate change impacts. Increased temperature resilience during grain number formation period is the main adaptation that emerges. We also find that changing from the traditional to modern cultivar, and later sowing in West Sahel appear to be robust adaptations.
FACE-IT. A Science Gateway for Food Security Research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Montella, Raffaele; Kelly, David; Xiong, Wei

Progress in sustainability science is hindered by challenges in creating and managing complex data acquisition, processing, simulation, post-processing, and intercomparison pipelines. To address these challenges, we developed the Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE-IT) for crop and climate impact assessments. This integrated data processing and simulation framework enables data ingest from geospatial archives; data regridding, aggregation, and other processing prior to simulation; large-scale climate impact simulations with agricultural and other models, leveraging high-performance and cloud computing; and post-processing to produce aggregated yields and ensemble variables needed for statistics, for model intercomparison, and to connectmore » biophysical models to global and regional economic models. FACE-IT leverages the capabilities of the Globus Galaxies platform to enable the capture of workflows and outputs in well-defined, reusable, and comparable forms. We describe FACE-IT and applications within the Agricultural Model Intercomparison and Improvement Project and the Center for Robust Decision-making on Climate and Energy Policy.« less
Identification of robust statistical downscaling methods based on a comprehensive suite of performance metrics for South Korea

NASA Astrophysics Data System (ADS)

Eum, H. I.; Cannon, A. J.

2015-12-01

Climate models are a key provider to investigate impacts of projected future climate conditions on regional hydrologic systems. However, there is a considerable mismatch of spatial resolution between GCMs and regional applications, in particular a region characterized by complex terrain such as Korean peninsula. Therefore, a downscaling procedure is an essential to assess regional impacts of climate change. Numerous statistical downscaling methods have been used mainly due to the computational efficiency and simplicity. In this study, four statistical downscaling methods [Bias-Correction/Spatial Disaggregation (BCSD), Bias-Correction/Constructed Analogue (BCCA), Multivariate Adaptive Constructed Analogs (MACA), and Bias-Correction/Climate Imprint (BCCI)] are applied to downscale the latest Climate Forecast System Reanalysis data to stations for precipitation, maximum temperature, and minimum temperature over South Korea. By split sampling scheme, all methods are calibrated with observational station data for 19 years from 1973 to 1991 are and tested for the recent 19 years from 1992 to 2010. To assess skill of the downscaling methods, we construct a comprehensive suite of performance metrics that measure an ability of reproducing temporal correlation, distribution, spatial correlation, and extreme events. In addition, we employ Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to identify robust statistical downscaling methods based on the performance metrics for each season. The results show that downscaling skill is considerably affected by the skill of CFSR and all methods lead to large improvements in representing all performance metrics. According to seasonal performance metrics evaluated, when TOPSIS is applied, MACA is identified as the most reliable and robust method for all variables and seasons. Note that such result is derived from CFSR output which is recognized as near perfect climate data in climate studies. Therefore, the ranking of this study may be changed when various GCMs are downscaled and evaluated. Nevertheless, it may be informative for end-users (i.e. modelers or water resources managers) to understand and select more suitable downscaling methods corresponding to priorities on regional applications.
Transient regional climate change: analysis of the summer climate response in a high-resolution, century-scale, ensemble experiment over the continental United States

PubMed Central

Diffenbaugh, Noah S.; Ashfaq, Moetasim; Scherer, Martin

2013-01-01

Integrating the potential for climate change impacts into policy and planning decisions requires quantification of the emergence of sub-regional climate changes that could occur in response to transient changes in global radiative forcing. Here we report results from a high-resolution, century-scale, ensemble simulation of climate in the United States, forced by atmospheric constituent concentrations from the Special Report on Emissions Scenarios (SRES) A1B scenario. We find that 21st century summer warming permanently emerges beyond the baseline decadal-scale variability prior to 2020 over most areas of the continental U.S. Permanent emergence beyond the baseline annual-scale variability shows much greater spatial heterogeneity, with emergence occurring prior to 2030 over areas of the southwestern U.S., but not prior to the end of the 21st century over much of the southcentral and southeastern U.S. The pattern of emergence of robust summer warming contrasts with the pattern of summer warming magnitude, which is greatest over the central U.S. and smallest over the western U.S. In addition to stronger warming, the central U.S. also exhibits stronger coupling of changes in surface air temperature, precipitation, and moisture and energy fluxes, along with changes in atmospheric circulation towards increased anticylonic anomalies in the mid-troposphere and a poleward shift in the mid-latitude jet aloft. However, as a fraction of the baseline variability, the transient warming over the central U.S. is smaller than the warming over the southwestern or northeastern U.S., delaying the emergence of the warming signal over the central U.S. Our comparisons with observations and the Coupled Model Intercomparison Project Phase 3 (CMIP3) ensemble of global climate model experiments suggest that near-term global warming is likely to cause robust sub-regional-scale warming over areas that exhibit relatively little baseline variability. In contrast, where there is greater variability in the baseline climate dynamics, there can be greater variability in the response to elevated greenhouse forcing, decreasing the robustness of the transient warming signal. PMID:24307747
DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, Jin -Ho

Amazon rainfall is subject to year-to-year fluctuation resulting in drought and flood in various intensities. A major climatic driver of the interannual variation of the Amazon rainfall is El Niño/Southern Oscillation. Also, the Sea Surface Temperature over the Atlantic Ocean is identified as an important climatic driver on the Amazon water cycle. Previously, observational datasets were used to support the Atlantic influence on Amazon rainfall. Furthermore, it is found that multiple global climate models do reproduce the Atlantic-Amazon link robustly. However, there exist differences in rainfall response, which primarily depends on the climatological rainfall amount.
Two takes on the ecosystem impacts of climate change and fishing: Comparing a size-based and a species-based ecosystem model in the central North Pacific

NASA Astrophysics Data System (ADS)

Woodworth-Jefcoats, Phoebe A.; Polovina, Jeffrey J.; Howell, Evan A.; Blanchard, Julia L.

2015-11-01

We compare two ecosystem model projections of 21st century climate change and fishing impacts in the central North Pacific. Both a species-based and a size-based ecosystem modeling approach are examined. While both models project a decline in biomass across all sizes in response to climate change and a decline in large fish biomass in response to increased fishing mortality, the models vary significantly in their handling of climate and fishing scenarios. For example, based on the same climate forcing the species-based model projects a 15% decline in catch by the end of the century while the size-based model projects a 30% decline. Disparities in the models' output highlight the limitations of each approach by showing the influence model structure can have on model output. The aspects of bottom-up change to which each model is most sensitive appear linked to model structure, as does the propagation of interannual variability through the food web and the relative impact of combined top-down and bottom-up change. Incorporating integrated size- and species-based ecosystem modeling approaches into future ensemble studies may help separate the influence of model structure from robust projections of ecosystem change.
Which climate change path are we following? Bad news from Scots pine

PubMed Central

D’Andrea, Ettore; Rezaie, Negar; Cammarano, Mario; Matteucci, Giorgio

2017-01-01

Current expectations on future climate derive from coordinated experiments, which compile many climate models for sampling the entire uncertainty related to emission scenarios, initial conditions, and modelling process. Quantifying this uncertainty is important for taking decisions that are robust under a wide range of possible future conditions. Nevertheless, if uncertainty is too large, it can prevent from planning specific and effective measures. For this reason, reducing the spectrum of the possible scenarios to a small number of one or a few models that actually represent the climate pathway influencing natural ecosystems would substantially increase our planning capacity. Here we adopt a multidisciplinary approach based on the comparison of observed and expected spatial patterns of response to climate change in order to identify which specific models, among those included in the CMIP5, catch the real climate variation driving the response of natural ecosystems. We used dendrochronological analyses for determining the geographic pattern of recent growth trends for three European species of trees. At the same time, we modelled the climatic niche for the same species and forecasted the suitability variation expected across Europe under each different GCM. Finally, we estimated how well each GCM explains the real response of ecosystems, by comparing the expected variation with the observed growth trends. Doing this, we identified four climatic models that are coherent with the observed trends. These models are close to the highest range limit of the climatic variations expected by the ensemble of the CMIP5 models, suggesting that current predictions of climate change impacts on ecosystems could be underestimated. PMID:29252985
Which climate change path are we following? Bad news from Scots pine.

PubMed

Bombi, Pierluigi; D'Andrea, Ettore; Rezaie, Negar; Cammarano, Mario; Matteucci, Giorgio

2017-01-01

Current expectations on future climate derive from coordinated experiments, which compile many climate models for sampling the entire uncertainty related to emission scenarios, initial conditions, and modelling process. Quantifying this uncertainty is important for taking decisions that are robust under a wide range of possible future conditions. Nevertheless, if uncertainty is too large, it can prevent from planning specific and effective measures. For this reason, reducing the spectrum of the possible scenarios to a small number of one or a few models that actually represent the climate pathway influencing natural ecosystems would substantially increase our planning capacity. Here we adopt a multidisciplinary approach based on the comparison of observed and expected spatial patterns of response to climate change in order to identify which specific models, among those included in the CMIP5, catch the real climate variation driving the response of natural ecosystems. We used dendrochronological analyses for determining the geographic pattern of recent growth trends for three European species of trees. At the same time, we modelled the climatic niche for the same species and forecasted the suitability variation expected across Europe under each different GCM. Finally, we estimated how well each GCM explains the real response of ecosystems, by comparing the expected variation with the observed growth trends. Doing this, we identified four climatic models that are coherent with the observed trends. These models are close to the highest range limit of the climatic variations expected by the ensemble of the CMIP5 models, suggesting that current predictions of climate change impacts on ecosystems could be underestimated.
Lagged correlations between the NAO and the 11-year solar cycle: forced response or internal variability?

NASA Astrophysics Data System (ADS)

Oehrlein, J.; Chiodo, G.; Polvani, L. M.; Smith, A. K.

2017-12-01

Recently, the North Atlantic Oscillation has been suggested to respond to the 11-year solar cycle with a lag of a few years. The solar/NAO relationship provides a potential pathway for solar activity to modulate surface climate. However, a short observational record paired with the strong internal variability of the NAO raises questions about the robustness of the claimed solar/NAO relationship. For the first time, we investigate the robustness of the solar/NAO signal in four different reanalysis data sets and long integrations from an ocean-coupled chemistry-climate model forced with the 11-year solar cycle. The signal appears to be robust in the different reanalysis datasets. We also show, for the first time, that many features of the observed signal, such as amplitude, spatial pattern, and lag of 2/3 years, can be accurately reproduced in our model simulations. However, in both the reanalysis and model simulations, we find that this signal is non-stationary. A lagged NAO/solar signal can also be reproduced in two sets of model integrations without the 11-year solar cycle. This suggests that the correlation found in observational data could be the result of internal decadal variability in the NAO and not a response to the solar cycle. This has wide implications towards the interpretation of solar signals in observational data.
An event-based approach to understanding decadal fluctuations in the Atlantic meridional overturning circulation

NASA Astrophysics Data System (ADS)

Allison, Lesley; Hawkins, Ed; Woollings, Tim

2015-01-01

Many previous studies have shown that unforced climate model simulations exhibit decadal-scale fluctuations in the Atlantic meridional overturning circulation (AMOC), and that this variability can have impacts on surface climate fields. However, the robustness of these surface fingerprints across different models is less clear. Furthermore, with the potential for coupled feedbacks that may amplify or damp the response, it is not known whether the associated climate signals are linearly related to the strength of the AMOC changes, or if the fluctuation events exhibit nonlinear behaviour with respect to their strength or polarity. To explore these questions, we introduce an objective and flexible method for identifying the largest natural AMOC fluctuation events in multicentennial/multimillennial simulations of a variety of coupled climate models. The characteristics of the events are explored, including their magnitude, meridional coherence and spatial structure, as well as links with ocean heat transport and the horizontal circulation. The surface fingerprints in ocean temperature and salinity are examined, and compared with the results of linear regression analysis. It is found that the regressions generally provide a good indication of the surface changes associated with the largest AMOC events. However, there are some exceptions, including a nonlinear change in the atmospheric pressure signal, particularly at high latitudes, in HadCM3. Some asymmetries are also found between the changes associated with positive and negative AMOC events in the same model. Composite analysis suggests that there are signals that are robust across the largest AMOC events in each model, which provides reassurance that the surface changes associated with one particular event will be similar to those expected from regression analysis. However, large differences are found between the AMOC fingerprints in different models, which may hinder the prediction and attribution of such events in reality.
A Field Guide to Extra-Tropical Cyclones: Comparing Models to Observations

NASA Astrophysics Data System (ADS)

Bauer, M.

2008-12-01

Climate it is said is the accumulation of weather. And weather is not the concern of climate models. Justification for this latter sentiment has long hidden behind coarse model resolutions and blunt validation tools based on climatological maps and the like. The spatial-temporal resolutions of today's models and observations are converging onto meteorological scales however, which means that with the correct tools we can test the largely unproven assumption that climate model weather is correct enough, or at least lacks perverting biases, such that its accumulation does in fact result in a robust climate prediction. Towards this effort we introduce a new tool for extracting detailed cyclone statistics from climate model output. These include the usual cyclone distribution statistics (maps, histograms), but also adaptive cyclone- centric composites. We have also created a complementary dataset, The MAP Climatology of Mid-latitude Storminess (MCMS), which provides a detailed 6 hourly assessment of the areas under the influence of mid- latitude cyclones based on Reanalysis products. Using this we then extract complimentary composites from sources such as ISCCP and GPCP to create a large comparative dataset for climate model validation. A demonstration of the potential usefulness of these tools will be shown. dime.giss.nasa.gov/mcms/mcms.html
Hunter-gatherer postcranial robusticity relative to patterns of mobility, climatic adaptation, and selection for tissue economy.

PubMed

Stock, J T

2006-10-01

Human skeletal robusticity is influenced by a number of factors, including habitual behavior, climate, and physique. Conflicting evidence as to the relative importance of these factors complicates our ability to interpret variation in robusticity in the past. It remains unclear how the pattern of robusticity in the skeleton relates to adaptive constraints on skeletal morphology. This study investigates variation in robusticity in claviculae, humeri, ulnae, femora, and tibiae among human foragers, relative to climate and habitual behavior. Cross-sectional geometric properties of the diaphyses are compared among hunter-gatherers from southern Africa (n = 83), the Andaman Islands (n = 32), Tierra del Fuego (n = 34), and the Great Lakes region (n = 15). The robusticity of both proximal and distal limb segments correlates negatively with climate and positively with patterns of terrestrial and marine mobility among these groups. However, the relative correspondence between robusticity and these factors varies throughout the body. In the lower limb, partial correlations between polar second moment of area (J(0.73)) and climate decrease from proximal to distal section locations, while this relationship increases from proximal to distal in the upper limb. Patterns of correlation between robusticity and mobility, either terrestrial or marine, generally increase from proximal to distal in the lower and upper limbs, respectively. This suggests that there may be a stronger relationship between observed patterns of diaphyseal hypertrophy and behavioral differences between populations in distal elements. Despite this trend, strength circularity indices at the femoral midshaft show the strongest correspondence with terrestrial mobility, particularly among males.
What Climate Information Do Water Managers Need to Make Robust, Long-Term Plans?

NASA Astrophysics Data System (ADS)

Duran, R.; Lempert, R.; Groves, D.

2008-12-01

What climate information do water managers need to respond to threat of climate change? Southern California's Inland Empire Utilities Agency (IEUA) completed a long-range water resource management plan in 2005 that addressed expected economic and population growth in their service region, but did not consider the potential impacts of climate change. Using a robust decision making (RDM) approach for policy under deep uncertainty, we recently worked with IEUA to conduct a climate-change vulnerability and response options analysis of the agency's long-range plans. This analysis suggests that IEUA is vulnerable to future climate change, but can significantly reduce this vulnerability by increasing their near-term conservation programs and careful monitoring and updating to adjust their plan in the years ahead. In addition to helping IEUA, this analysis provides important guidance on the types of climate and other information that can be most useful for water managers as they attempt to take robust, near-term actions to increaase their resilience to climate change.
The Added Value to Global Model Projections of Climate Change by Dynamical Downscaling: A Case Study over the Continental U.S. using the GISS-ModelE2 and WRF Models

NASA Technical Reports Server (NTRS)

Racherla, P. N.; Shindell, D. T.; Faluvegi, G. S.

2012-01-01

Dynamical downscaling is being increasingly used for climate change studies, wherein the climates simulated by a coupled atmosphere-ocean general circulation model (AOGCM) for a historical and a future (projected) decade are used to drive a regional climate model (RCM) over a specific area. While previous studies have demonstrated that RCMs can add value to AOGCM-simulated climatologies over different world regions, it is unclear as to whether or not this translates to a better reproduction of the observed climate change therein. We address this issue over the continental U.S. using the GISS-ModelE2 and WRF models, a state-of-the-science AOGCM and RCM, respectively. As configured here, the RCM does not effect holistic improvement in the seasonally and regionally averaged surface air temperature or precipitation for the individual historical decades. Insofar as the climate change between the two decades is concerned, the RCM does improve upon the AOGCM when nudged in the domain proper, but only modestly so. Further, the analysis indicates that there is not a strong relationship between skill in capturing climatological means and skill in capturing climate change. Though additional research would be needed to demonstrate the robustness of this finding in AOGCM/RCM models generally, the evidence indicates that, for climate change studies, the most important factor is the skill of the driving global model itself, suggesting that highest priority should be given to improving the long-range climate skill of AOGCMs.
The Community Earth System Model-Polar Climate Working Group and the status of CESM2.

NASA Astrophysics Data System (ADS)

Bailey, D. A.; Holland, M. M.; DuVivier, A. K.

2017-12-01

The Polar Climate Working Group (PCWG) is a consortium of scientists who are interested in modeling and understanding the climate in the Arctic and the Antarctic, and how polar climate processes interact with and influence climate at lower latitudes. Our members come from universities and laboratories, and our interests span all elements of polar climate, from the ocean depths to the top of the atmosphere. In addition to conducting scientific modeling experiments, we are charged with contributing to the development and maintenance of the state-of-the-art sea ice model component (CICE) used in the Community Earth System Model (CESM). A recent priority for the PCWG has been to come up with innovative ways to bring the observational and modeling communities together. This will allow for more robust validation of climate model simulations, the development and implementation of more physically-based model parameterizations, improved data assimilation capabilities, and the better use of models to design and implement field experiments. These have been informed by topical workshops and scientific visitors that we have hosted in these areas. These activities will be discussed and information on how the better integration of observations and models has influenced the new version of the CESM, which is due to be released in late 2017, will be provided. Additionally, we will address how enhanced interactions with the observational community will contribute to model developments and validation moving forward.
Ocean salinities reveal strong global water cycle intensification during 1950 to 2000.

PubMed

Durack, Paul J; Wijffels, Susan E; Matear, Richard J

2012-04-27

Fundamental thermodynamics and climate models suggest that dry regions will become drier and wet regions will become wetter in response to warming. Efforts to detect this long-term response in sparse surface observations of rainfall and evaporation remain ambiguous. We show that ocean salinity patterns express an identifiable fingerprint of an intensifying water cycle. Our 50-year observed global surface salinity changes, combined with changes from global climate models, present robust evidence of an intensified global water cycle at a rate of 8 ± 5% per degree of surface warming. This rate is double the response projected by current-generation climate models and suggests that a substantial (16 to 24%) intensification of the global water cycle will occur in a future 2° to 3° warmer world.
Connectivity planning to address climate change.

PubMed

Nuñez, Tristan A; Lawler, Joshua J; McRae, Brad H; Pierce, D John; Krosby, Meade B; Kavanagh, Darren M; Singleton, Peter H; Tewksbury, Joshua J

2013-04-01

As the climate changes, human land use may impede species from tracking areas with suitable climates. Maintaining connectivity between areas of different temperatures could allow organisms to move along temperature gradients and allow species to continue to occupy the same temperature space as the climate warms. We used a coarse-filter approach to identify broad corridors for movement between areas where human influence is low while simultaneously routing the corridors along present-day spatial gradients of temperature. We modified a cost-distance algorithm to model these corridors and tested the model with data on current land-use and climate patterns in the Pacific Northwest of the United States. The resulting maps identified a network of patches and corridors across which species may move as climates change. The corridors are likely to be robust to uncertainty in the magnitude and direction of future climate change because they are derived from gradients and land-use patterns. The assumptions we applied in our model simplified the stability of temperature gradients and species responses to climate change and land use, but the model is flexible enough to be tailored to specific regions by incorporating other climate variables or movement costs. When used at appropriate resolutions, our approach may be of value to local, regional, and continental conservation initiatives seeking to promote species movements in a changing climate. Planificación de Conectividad para Atender el Cambio Climático. © 2013 Society for Conservation Biology.
Robust Emergent Climate Phenomena Associated with the High-Sensitivity Tail

NASA Astrophysics Data System (ADS)

Boslough, M.; Levy, M.; Backus, G.

2010-12-01

Because the potential effects of climate change are more severe than had previously been thought, increasing focus on uncertainty quantification is required for risk assessment needed by policy makers. Current scientific efforts focus almost exclusively on establishing best estimates of future climate change. However, the greatest consequences occur in the extreme tail of the probability density functions for climate sensitivity (the “high-sensitivity tail”). To this end, we are exploring the impacts of newly postulated, highly uncertain, but high-consequence physical mechanisms to better establish the climate change risk. We define consequence in terms of dramatic change in physical conditions and in the resulting socioeconomic impact (hence, risk) on populations. Although we are developing generally applicable risk assessment methods, we have focused our initial efforts on uncertainty and risk analyses for the Arctic region. Instead of focusing on best estimates, requiring many years of model parameterization development and evaluation, we are focusing on robust emergent phenomena (those that are not necessarily intuitive and are insensitive to assumptions, subgrid-parameterizations, and tunings). For many physical systems, under-resolved models fail to generate such phenomena, which only develop when model resolution is sufficiently high. Our ultimate goal is to discover the patterns of emergent climate precursors (those that cannot be predicted with lower-resolution models) that can be used as a "sensitivity fingerprint" and make recommendations for a climate early warning system that would use satellites and sensor arrays to look for the various predicted high-sensitivity signatures. Our initial simulations are focused on the Arctic region, where underpredicted phenomena such as rapid loss of sea ice are already emerging, and because of major geopolitical implications associated with increasing Arctic accessibility to natural resources, shipping routes, and strategic locations. We anticipate that regional climate will be strongly influenced by feedbacks associated with a seasonally ice-free Arctic, but with unknown emergent phenomena. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy under Contract DE-AC04-94AL85000.
Stress testing hydrologic models using bottom-up climate change assessment

NASA Astrophysics Data System (ADS)

Stephens, C.; Johnson, F.; Marshall, L. A.

2017-12-01

Bottom-up climate change assessment is a promising approach for understanding the vulnerability of a system to potential future changes. The technique has been utilised successfully in risk-based assessments of future flood severity and infrastructure vulnerability. We find that it is also an ideal tool for assessing hydrologic model performance in a changing climate. In this study, we applied bottom-up climate change to compare the performance of two different hydrologic models (an event-based and a continuous model) under increasingly severe climate change scenarios. This allowed us to diagnose likely sources of future prediction error in the two models. The climate change scenarios were based on projections for southern Australia, which indicate drier average conditions with increased extreme rainfall intensities. We found that the key weakness in using the event-based model to simulate drier future scenarios was the model's inability to dynamically account for changing antecedent conditions. This led to increased variability in model performance relative to the continuous model, which automatically accounts for the wetness of a catchment through dynamic simulation of water storages. When considering more intense future rainfall events, representation of antecedent conditions became less important than assumptions around (non)linearity in catchment response. The linear continuous model we applied may underestimate flood risk in a future climate with greater extreme rainfall intensity. In contrast with the recommendations of previous studies, this indicates that continuous simulation is not necessarily the key to robust flood modelling under climate change. By applying bottom-up climate change assessment, we were able to understand systematic changes in relative model performance under changing conditions and deduce likely sources of prediction error in the two models.
High-resolution dynamic downscaling of CMIP5 output over the Tropical Andes

NASA Astrophysics Data System (ADS)

Reichler, Thomas; Andrade, Marcos; Ohara, Noriaki

2015-04-01

Our project is targeted towards making robust predictions of future changes in climate over the tropical part of the South American Andes. This goal is challenging, since tropical lowlands, steep mountains, and snow covered subarctic surfaces meet over relatively short distances, leading to distinct climate regimes within the same domain and pronounced spatial gradients in virtually every climate quantity. We use an innovative approach to solve this problem, including several quadruple nested versions of WRF, a systematic validation strategy to find the version of WRF that best fits our study region, spatial resolutions at the kilometer scale, 20-year-long simulation periods, and bias-corrected output from various CMIP5 simulations that also include the multi-model mean of all CMIP5 models. We show that the simulated changes in climate are consistent with the results from the global climate models and also consistent with two different versions of WRF. We also discuss the expected changes in snow and ice, derived from off-line coupling the regional simulations to a carefully calibrated snow and ice model.

Slowing down of North Pacific climate variability and its implications for abrupt ecosystem change.

PubMed

Boulton, Chris A; Lenton, Timothy M

2015-09-15

Marine ecosystems are sensitive to stochastic environmental variability, with higher-amplitude, lower-frequency--i.e., "redder"--variability posing a greater threat of triggering large ecosystem changes. Here we show that fluctuations in the Pacific Decadal Oscillation (PDO) index have slowed down markedly over the observational record (1900-present), as indicated by a robust increase in autocorrelation. This "reddening" of the spectrum of climate variability is also found in regionally averaged North Pacific sea surface temperatures (SSTs), and can be at least partly explained by observed deepening of the ocean mixed layer. The progressive reddening of North Pacific climate variability has important implications for marine ecosystems. Ecosystem variables that respond linearly to climate forcing will have become prone to much larger variations over the observational record, whereas ecosystem variables that respond nonlinearly to climate forcing will have become prone to more frequent "regime shifts." Thus, slowing down of North Pacific climate variability can help explain the large magnitude and potentially the quick succession of well-known abrupt changes in North Pacific ecosystems in 1977 and 1989. When looking ahead, despite model limitations in simulating mixed layer depth (MLD) in the North Pacific, global warming is robustly expected to decrease MLD. This could potentially reverse the observed trend of slowing down of North Pacific climate variability and its effects on marine ecosystems.
Evaluation of probable maximum snow accumulation: Development of a methodology for climate change studies

NASA Astrophysics Data System (ADS)

Klein, Iris M.; Rousseau, Alain N.; Frigon, Anne; Freudiger, Daphné; Gagnon, Patrick

2016-06-01

Probable maximum snow accumulation (PMSA) is one of the key variables used to estimate the spring probable maximum flood (PMF). A robust methodology for evaluating the PMSA is imperative so the ensuing spring PMF is a reasonable estimation. This is of particular importance in times of climate change (CC) since it is known that solid precipitation in Nordic landscapes will in all likelihood change over the next century. In this paper, a PMSA methodology based on simulated data from regional climate models is developed. Moisture maximization represents the core concept of the proposed methodology; precipitable water being the key variable. Results of stationarity tests indicate that CC will affect the monthly maximum precipitable water and, thus, the ensuing ratio to maximize important snowfall events. Therefore, a non-stationary approach is used to describe the monthly maximum precipitable water. Outputs from three simulations produced by the Canadian Regional Climate Model were used to give first estimates of potential PMSA changes for southern Quebec, Canada. A sensitivity analysis of the computed PMSA was performed with respect to the number of time-steps used (so-called snowstorm duration) and the threshold for a snowstorm to be maximized or not. The developed methodology is robust and a powerful tool to estimate the relative change of the PMSA. Absolute results are in the same order of magnitude as those obtained with the traditional method and observed data; but are also found to depend strongly on the climate projection used and show spatial variability.
Spatial climate patterns explain negligible variation in strength of compensatory density feedbacks in birds and mammals.

PubMed

Herrando-Pérez, Salvador; Delean, Steven; Brook, Barry W; Cassey, Phillip; Bradshaw, Corey J A

2014-01-01

The use of long-term population data to separate the demographic role of climate from density-modified demographic processes has become a major topic of ecological investigation over the last two decades. Although the ecological and evolutionary mechanisms that determine the strength of density feedbacks are now well understood, the degree to which climate gradients shape those processes across taxa and broad spatial scales remains unclear. Intuitively, harsh or highly variable environmental conditions should weaken compensatory density feedbacks because populations are hypothetically unable to achieve or maintain densities at which social and trophic interactions (e.g., competition, parasitism, predation, disease) might systematically reduce population growth. Here we investigate variation in the strength of compensatory density feedback, from long-term time series of abundance over 146 species of birds and mammals, in response to spatial gradients of broad-scale temperature precipitation variables covering 97 localities in 28 countries. We use information-theoretic metrics to rank phylogenetic generalized least-squares regression models that control for sample size (time-series length) and phylogenetic non-independence. Climatic factors explained < 1% of the remaining variation in density-feedback strength across species, with the highest non-control, model-averaged effect sizes related to extreme precipitation variables. We could not link our results directly to other published studies, because ecologists use contrasting responses, predictors and statistical approaches to correlate density feedback and climate--at the expense of comparability in a macroecological context. Censuses of multiple populations within a given species, and a priori knowledge of the spatial scales at which density feedbacks interact with climate, seem to be necessary to determine cross-taxa variation in this phenomenon. Despite the availability of robust modelling tools, the appropriate data have not yet been gathered for most species, meaning that we cannot yet make any robust generalisations about how demographic feedbacks interact with climate.
Spatial Climate Patterns Explain Negligible Variation in Strength of Compensatory Density Feedbacks in Birds and Mammals

PubMed Central

Herrando-Pérez, Salvador; Delean, Steven; Brook, Barry W.; Cassey, Phillip; Bradshaw, Corey J. A.

2014-01-01

The use of long-term population data to separate the demographic role of climate from density-modified demographic processes has become a major topic of ecological investigation over the last two decades. Although the ecological and evolutionary mechanisms that determine the strength of density feedbacks are now well understood, the degree to which climate gradients shape those processes across taxa and broad spatial scales remains unclear. Intuitively, harsh or highly variable environmental conditions should weaken compensatory density feedbacks because populations are hypothetically unable to achieve or maintain densities at which social and trophic interactions (e.g., competition, parasitism, predation, disease) might systematically reduce population growth. Here we investigate variation in the strength of compensatory density feedback, from long-term time series of abundance over 146 species of birds and mammals, in response to spatial gradients of broad-scale temperature precipitation variables covering 97 localities in 28 countries. We use information-theoretic metrics to rank phylogenetic generalized least-squares regression models that control for sample size (time-series length) and phylogenetic non-independence. Climatic factors explained < 1% of the remaining variation in density-feedback strength across species, with the highest non-control, model-averaged effect sizes related to extreme precipitation variables. We could not link our results directly to other published studies, because ecologists use contrasting responses, predictors and statistical approaches to correlate density feedback and climate – at the expense of comparability in a macroecological context. Censuses of multiple populations within a given species, and a priori knowledge of the spatial scales at which density feedbacks interact with climate, seem to be necessary to determine cross-taxa variation in this phenomenon. Despite the availability of robust modelling tools, the appropriate data have not yet been gathered for most species, meaning that we cannot yet make any robust generalisations about how demographic feedbacks interact with climate. PMID:24618822
Regional climate model simulations indicate limited climatic impacts by operational and planned European wind farms.

PubMed

Vautard, Robert; Thais, Françoise; Tobin, Isabelle; Bréon, François-Marie; Devezeaux de Lavergne, Jean-Guy; Colette, Augustin; Yiou, Pascal; Ruti, Paolo Michele

2014-01-01

The rapid development of wind energy has raised concerns about environmental impacts. Temperature changes are found in the vicinity of wind farms and previous simulations have suggested that large-scale wind farms could alter regional climate. However, assessments of the effects of realistic wind power development scenarios at the scale of a continent are missing. Here we simulate the impacts of current and near-future wind energy production according to European Union energy and climate policies. We use a regional climate model describing the interactions between turbines and the atmosphere, and find limited impacts. A statistically significant signal is only found in winter, with changes within ±0.3 °C and within 0-5% for precipitation. It results from the combination of local wind farm effects and changes due to a weak, but robust, anticyclonic-induced circulation over Europe. However, the impacts remain much weaker than the natural climate interannual variability and changes expected from greenhouse gas emissions.
Rainfall Downscaling Conditional on Upper-air Atmospheric Predictors: Improved Assessment of Rainfall Statistics in a Changing Climate

NASA Astrophysics Data System (ADS)

Langousis, Andreas; Mamalakis, Antonis; Deidda, Roberto; Marrocu, Marino

2015-04-01

To improve the level skill of Global Climate Models (GCMs) and Regional Climate Models (RCMs) in reproducing the statistics of rainfall at a basin level and at hydrologically relevant temporal scales (e.g. daily), two types of statistical approaches have been suggested. One is the statistical correction of climate model rainfall outputs using historical series of precipitation. The other is the use of stochastic models of rainfall to conditionally simulate precipitation series, based on large-scale atmospheric predictors produced by climate models (e.g. geopotential height, relative vorticity, divergence, mean sea level pressure). The latter approach, usually referred to as statistical rainfall downscaling, aims at reproducing the statistical character of rainfall, while accounting for the effects of large-scale atmospheric circulation (and, therefore, climate forcing) on rainfall statistics. While promising, statistical rainfall downscaling has not attracted much attention in recent years, since the suggested approaches involved complex (i.e. subjective or computationally intense) identification procedures of the local weather, in addition to demonstrating limited success in reproducing several statistical features of rainfall, such as seasonal variations, the distributions of dry and wet spell lengths, the distribution of the mean rainfall intensity inside wet periods, and the distribution of rainfall extremes. In an effort to remedy those shortcomings, Langousis and Kaleris (2014) developed a statistical framework for simulation of daily rainfall intensities conditional on upper air variables, which accurately reproduces the statistical character of rainfall at multiple time-scales. Here, we study the relative performance of: a) quantile-quantile (Q-Q) correction of climate model rainfall products, and b) the statistical downscaling scheme of Langousis and Kaleris (2014), in reproducing the statistical structure of rainfall, as well as rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e. the Flumendosa catchment, using climate model rainfall and atmospheric data from the ENSEMBLES project (http://ensembleseu.metoffice.com). In doing so, we split the historical rainfall record of mean areal precipitation (MAP) in 15-year calibration and 45-year validation periods, and compare the historical rainfall statistics to those obtained from: a) Q-Q corrected climate model rainfall products, and b) synthetic rainfall series generated by the suggested downscaling scheme. To our knowledge, this is the first time that climate model rainfall and statistically downscaled precipitation are compared to catchment-averaged MAP at a daily resolution. The obtained results are promising, since the proposed downscaling scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the climate model used and the length of the calibration period. This is particularly the case for the yearly rainfall maxima, where direct statistical correction of climate model rainfall outputs shows increased sensitivity to the length of the calibration period and the climate model used. The robustness of the suggested downscaling scheme in modeling rainfall extremes at a daily resolution, is a notable feature that can effectively be used to assess hydrologic risk at a regional level under changing climatic conditions. Acknowledgments The research project is implemented within the framework of the Action «Supporting Postdoctoral Researchers» of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State. CRS4 highly acknowledges the contribution of the Sardinian regional authorities.
High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

NASA Astrophysics Data System (ADS)

Haarsma, Reindert J.; Roberts, Malcolm J.; Vidale, Pier Luigi; Senior, Catherine A.; Bellucci, Alessio; Bao, Qing; Chang, Ping; Corti, Susanna; Fučkar, Neven S.; Guemas, Virginie; von Hardenberg, Jost; Hazeleger, Wilco; Kodama, Chihiro; Koenigk, Torben; Leung, L. Ruby; Lu, Jian; Luo, Jing-Jia; Mao, Jiafu; Mizielinski, Matthew S.; Mizuta, Ryo; Nobre, Paulo; Satoh, Masaki; Scoccimarro, Enrico; Semmler, Tido; Small, Justin; von Storch, Jin-Song

2016-11-01

Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest both the possibility of significant changes in large-scale aspects of circulation as well as improvements in small-scale processes and extremes. However, such high-resolution global simulations at climate timescales, with resolutions of at least 50 km in the atmosphere and 0.25° in the ocean, have been performed at relatively few research centres and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other model intercomparison projects (MIPs). Increases in high-performance computing (HPC) resources, as well as the revised experimental design for CMIP6, now enable a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal-resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950-2050, with the possibility of extending to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulations. HighResMIP thereby focuses on one of the CMIP6 broad questions, "what are the origins and consequences of systematic model biases?", but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.
Impacts of future climate change on river discharge based on hydrological inference: A case study of the Grand River Watershed in Ontario, Canada.

PubMed

Li, Zhong; Huang, Guohe; Wang, Xiuquan; Han, Jingcheng; Fan, Yurui

2016-04-01

Over the recent years, climate change impacts have been increasingly studied at the watershed scale. However, the impact assessment is strongly dependent upon the performance of the climatic and hydrological models. This study developed a two-step method to assess climate change impacts on water resources based on the Providing Regional Climates for Impacts Studies (PRECIS) modeling system and a Hydrological Inference Model (HIM). PRECIS runs provided future temperature and precipitation projections for the watershed under the Intergovernmental Panel on Climate Change SRES A2 and B2 emission scenarios. The HIM based on stepwise cluster analysis is developed to imitate the complex nonlinear relationships between climate input variables and targeted hydrological variables. Its robust mathematical structure and flexibility in predictor selection makes it a desirable tool for fully utilizing various climate modeling outputs. Although PRECIS and HIM cannot fully cover the uncertainties in hydro-climate modeling, they could provide efficient decision support for investigating the impacts of climate change on water resources. The proposed method is applied to the Grand River Watershed in Ontario, Canada. The model performance is demonstrated with comparison to observation data from the watershed during the period 1972-2006. Future river discharge intervals that accommodate uncertainties in hydro-climatic modeling are presented and future river discharge variations are analyzed. The results indicate that even though the total annual precipitation would not change significantly in the future, the inter-annual distribution is very likely to be altered. The water availability is expected to increase in Winter while it is very likely to decrease in Summer over the Grand River Watershed, and adaptation strategies would be necessary. Copyright © 2016 Elsevier B.V. All rights reserved.
Projection of wave conditions in response to climate change: A community approach to global and regional wave downscaling

USGS Publications Warehouse

Erikson, Li H.; Hemer, M.; Lionello, Piero; Mendez, Fernando J.; Mori, Nobuhito; Semedo, Alvaro; Wang, Xiaolan; Wolf, Judith

2015-01-01

Future changes in wind-wave climate have broad implications for coastal geomorphology and management. General circulation models (GCM) are now routinely used for assessing climatological parameters, but generally do not provide parameterizations of ocean wind-waves. To fill this information gap, a growing number of studies use GCM outputs to independently downscale wave conditions to global and regional levels. To consolidate these efforts and provide a robust picture of projected changes, we present strategies from the community-derived multi-model ensemble of wave climate projections (COWCLIP) and an overview of regional contributions. Results and strategies from one contributing regional study concerning changes along the eastern North Pacific coast are presented.
Human-induced changes in the hydrology of the Western United States

USGS Publications Warehouse

Barnett, T.P.; Pierce, D.W.; Hidalgo, H.G.; Bonfils, Celine; Santer, B.D.; Das, T.; Bala, G.; Wood, A.W.; Nozawa, T.; Mirin, A.A.; Cayan, D.R.; Dettinger, M.D.

2008-01-01

Observations have shown that the hydrological cycle of the western United States changed significantly over the last half of the 20th century. We present a regional, multivariable climate change detection and attribution study, using a high-resolution hydrologic model forced by global climate models, focusing on the changes that have already affected this primarily arid region with a large and growing population. The results show that up to 60% of the climate-related trends of river flow, winter air temperature, and snow pack between 1950 and 1999 are human-induced. These results are robust to perturbation of study variates and methods. They portend, in conjunction with previous work, a coming crisis in water supply for the western United States.
Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as `field' or `global' significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Daily precipitation climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.
Thermodynamic and dynamic responses of the hydrological cycle to solar dimming

NASA Astrophysics Data System (ADS)

Smyth, Jane E.; Russotto, Rick D.; Storelvmo, Trude

2017-05-01

The fundamental role of the hydrological cycle in the global climate system motivates a thorough evaluation of its responses to climate change and mitigation. The Geoengineering Model Intercomparison Project (GeoMIP) is a coordinated international effort to assess the climate impacts of solar geoengineering, a proposal to counteract global warming with a reduction in incoming solar radiation. We assess the mechanisms underlying the rainfall response to a simplified simulation of such solar dimming (G1) in the suite of GeoMIP models and identify robust features. While solar geoengineering nearly restores preindustrial temperatures, the global hydrology is altered. Tropical precipitation changes dominate the response across the model suite, and these are driven primarily by shifts of the Hadley circulation cells. We report a damping of the seasonal migration of the Intertropical Convergence Zone (ITCZ) in G1, associated with preferential cooling of the summer hemisphere, and annual mean ITCZ shifts in some models that are correlated with the warming of one hemisphere relative to the other. Dynamical changes better explain the varying tropical rainfall anomalies between models than changes in relative humidity or the Clausius-Clapeyron scaling of precipitation minus evaporation (P - E), given that the relative humidity and temperature responses are robust across the suite. Strong reductions in relative humidity over vegetated land regions are likely related to the CO2 physiological response in plants. The uncertainty in the spatial distribution of tropical P - E changes highlights the need for cautious consideration and continued study before any implementation of solar geoengineering.
Projections of water resources availability in Crete for the 21st century under the global change perspective

NASA Astrophysics Data System (ADS)

Koutroulis, A. G.; Tsanis, I. K.; Jacob, D.

2012-04-01

A robust signal of a warmer and drier climate over the western Mediterranean region is projected from the majority of climate models. This effect appears more pronounced during warm periods, when the seasonal decrease of precipitation can exceed control climatology by 25-30%. The rapid development of Crete in the last 30 years has exerted strong pressures on the natural resources of the region. Urbanization and growth of agriculture, tourism and industry had strong impact on the water resources of island by substantially increasing water demand. The objective of this study is to analyze and assess the impact of global change on the water resources status for the island of Crete for a range of 24 different scenarios of projected hydro-climatological regime, demand and supply potential. Water resources application issues analyzed and facilitated within this study, focusing on a refinement of the future water demands of the island, and comparing with "state of the art" global climate model (GCM) results and an ensemble of regional climate models (RCMs) under three different emission scenarios, to estimate water resources availability, during the 21st century. A robust signal of water scarcity is projected for all the combinations of emission (A2, A1B and B1), demand and infrastructure scenarios. Despite the uncertainty of the assessments, the quantitative impact of the projected changes on water availability indicates that climate change plays an equally important role to water use and management in controlling future water status in a Mediterranean island like the island of Crete. The outcome of this analysis will assist in short and long-term strategic water resources planning by prioritizing water related infrastructure development.
Multi-objective Optimization for the Robust Performance of Drinking Water Treatment Plants under Climate Change and Climate Extremes

NASA Astrophysics Data System (ADS)

Raseman, W. J.; Kasprzyk, J. R.; Rosario-Ortiz, F.; Summers, R. S.; Stewart, J.; Livneh, B.

2016-12-01

To promote public health, the United States Environmental Protection Agency (US EPA), and similar entities around the world enact strict laws to regulate drinking water quality. These laws, such as the Stage 1 and 2 Disinfectants and Disinfection Byproducts (D/DBP) Rules, come at a cost to water treatment plants (WTPs) which must alter their operations and designs to meet more stringent standards and the regulation of new contaminants of concern. Moreover, external factors such as changing influent water quality due to climate extremes and climate change, may force WTPs to adapt their treatment methods. To grapple with these issues, decision support systems (DSSs) have been developed to aid WTP operation and planning. However, there is a critical need to better address long-term decision making for WTPs. In this poster, we propose a DSS framework for WTPs for long-term planning, which improves upon the current treatment of deep uncertainties within the overall potable water system including the impact of climate on influent water quality and uncertainties in treatment process efficiencies. We present preliminary results exploring how a multi-objective evolutionary algorithm (MOEA) search can be coupled with models of WTP processes to identify high-performing plans for their design and operation. This coupled simulation-optimization technique uses Borg MOEA, an auto-adaptive algorithm, and the Water Treatment Plant Model, a simulation model developed by the US EPA to assist in creating the D/DBP Rules. Additionally, Monte Carlo sampling methods were used to study the impact of uncertainty of influent water quality on WTP decision-making and generate plans for robust WTP performance.
Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture

NASA Astrophysics Data System (ADS)

Davis, Tyler W.; Prentice, I. Colin; Stocker, Benjamin D.; Thomas, Rebecca T.; Whitley, Rhys J.; Wang, Han; Evans, Bradley J.; Gallego-Sala, Angela V.; Sykes, Martin T.; Cramer, Wolfgang

2017-02-01

Bioclimatic indices for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios depend on estimates of surface fluxes and other quantities, such as radiation, evapotranspiration and soil moisture, for which direct observations are sparse. These quantities can be derived indirectly from meteorological variables, such as near-surface air temperature, precipitation and cloudiness. Here we present a consolidated set of simple process-led algorithms for simulating habitats (SPLASH) allowing robust approximations of key quantities at ecologically relevant timescales. We specify equations, derivations, simplifications, and assumptions for the estimation of daily and monthly quantities of top-of-the-atmosphere solar radiation, net surface radiation, photosynthetic photon flux density, evapotranspiration (potential, equilibrium, and actual), condensation, soil moisture, and runoff, based on analysis of their relationship to fundamental climatic drivers. The climatic drivers include a minimum of three meteorological inputs: precipitation, air temperature, and fraction of bright sunshine hours. Indices, such as the moisture index, the climatic water deficit, and the Priestley-Taylor coefficient, are also defined. The SPLASH code is transcribed in C++, FORTRAN, Python, and R. A total of 1 year of results are presented at the local and global scales to exemplify the spatiotemporal patterns of daily and monthly model outputs along with comparisons to other model results.
Will Half a Degree Make a Difference? Robust Projections of Indices of Mean and Extreme Climate in Europe Under 1.5°C, 2°C, and 3°C Global Warming

NASA Astrophysics Data System (ADS)

Dosio, Alessandro; Fischer, Erich M.

2018-01-01

Based on high-resolution models, we investigate the change in climate extremes and impact-relevant indicators over Europe under different levels of global warming. We specifically assess the robustness of the changes and the benefits of limiting warming to 1.5°C instead of 2°C. Compared to 1.5°C world, a further 0.5°C warming results in a robust change of minimum summer temperature indices (mean, Tn10p, and Tn900p) over more than 70% of Europe. Robust changes (more than 0.5°C) in maximum temperature affect smaller areas (usually less than 20%). There is a substantial nonlinear change of fixed-threshold indices, with more than 60% increase of the number of tropical nights over southern Europe and more than 50% decrease in the number of frost days over central Europe. The change in mean precipitation due to 0.5°C warming is mostly nonsignificant at the grid point level, but, locally, it is accompanied by a more marked change in extreme rainfall.
Contemporary Drought and Future Effects of Climate Change on the Endangered Blunt-Nosed Leopard Lizard, Gambelia sila.

PubMed

Westphal, Michael F; Stewart, Joseph A E; Tennant, Erin N; Butterfield, H Scott; Sinervo, Barry

2016-01-01

Extreme weather events can provide unique opportunities for testing models that predict the effect of climate change. Droughts of increasing severity have been predicted under numerous models, thus contemporary droughts may allow us to test these models prior to the onset of the more extreme effects predicted with a changing climate. In the third year of an ongoing severe drought, surveys failed to detect neonate endangered blunt-nosed leopard lizards in a subset of previously surveyed populations where we expected to see them. By conducting surveys at a large number of sites across the range of the species over a short time span, we were able to establish a strong positive correlation between winter precipitation and the presence of neonate leopard lizards over geographic space. Our results are consistent with those of numerous longitudinal studies and are in accordance with predictive climate change models. We suggest that scientists can take immediate advantage of droughts while they are still in progress to test patterns of occurrence in other drought-sensitive species and thus provide for more robust models of climate change effects on biodiversity.
Quantifying PM2.5-Meteorology Sensitivities in a Global Climate Model

NASA Technical Reports Server (NTRS)

Westervelt, D. M.; Horowitz, L. W.; Naik, V.; Tai, A. P. K.; Fiore, A. M.; Mauzerall, D. L.

2016-01-01

Climate change can influence fine particulate matter concentrations (PM2.5) through changes in air pollution meteorology. Knowledge of the extent to which climate change can exacerbate or alleviate air pollution in the future is needed for robust climate and air pollution policy decision-making. To examine the influence of climate on PM2.5, we use the Geophysical Fluid Dynamics Laboratory Coupled Model version 3 (GFDL CM3), a fully-coupled chemistry-climate model, combined with future emissions and concentrations provided by the four Representative Concentration Pathways (RCPs). For each of the RCPs, we conduct future simulations in which emissions of aerosols and their precursors are held at 2005 levels while other climate forcing agents evolve in time, such that only climate (and thus meteorology) can influence PM2.5 surface concentrations. We find a small increase in global, annual mean PM2.5 of about 0.21 micro-g/cu m3 (5%) for RCP8.5, a scenario with maximum warming. Changes in global mean PM2.5 are at a maximum in the fall and are mainly controlled by sulfate followed by organic aerosol with minimal influence of black carbon. RCP2.6 is the only scenario that projects a decrease in global PM2.5 with future climate changes, albeit only by -0.06 micro-g/cu m (1.5%) by the end of the 21st century. Regional and local changes in PM2.5 are larger, reaching upwards of 2 micro-g/cu m for polluted (eastern China) and dusty (western Africa) locations on an annually averaged basis in RCP8.5. Using multiple linear regression, we find that future PM2.5 concentrations are most sensitive to local temperature, followed by surface wind and precipitation. PM2.5 concentrations are robustly positively associated with temperature, while negatively related with precipitation and wind speed. Present-day (2006-2015) modeled sensitivities of PM2.5 to meteorological variables are evaluated against observations and found to agree reasonably well with observed sensitivities (within 10e50% over the eastern United States for several variables), although the modeled PM2.5 is less sensitive to precipitation than in the observations due to weaker convective scavenging. We conclude that the hypothesized "climate penalty" of future increases in PM2.5 is relatively minor on a global scale compared to the influence of emissions on PM2.5 concentrations.
Robust Projected Weakening of Winter Monsoon Winds Over the Arabian Sea Under Climate Change

NASA Astrophysics Data System (ADS)

Parvathi, V.; Suresh, I.; Lengaigne, M.; Izumo, T.; Vialard, J.

2017-10-01

The response of the Indian winter monsoon to climate change has received considerably less attention than that of the summer monsoon. We show here that all Coupled Model Intercomparison Project Phase 5 (CMIP5) models display a consistent reduction (of 6.5% for Representative Concentration Pathways 8.5 and 3.5% for 4.5, on an average) of the winter monsoon winds over the Arabian Sea at the end of 21st century. This projected reduction weakens but remains robust when corrected for overestimated winter Arabian Sea winds in CMIP5. This weakening is driven by a reduction in the interhemispheric sea level pressure gradient resulting from enhanced warming of the dry Arabian Peninsula relative to the southern Indian Ocean. The wind weakening reduces winter oceanic heat losses to the atmosphere and deepening of convective mixed layer in the northern Arabian Sea and hence can potentially inhibit the seasonal chlorophyll bloom that contributes substantially to the Arabian Sea annual productivity.
Global warming without global mean precipitation increase?

PubMed Central

Salzmann, Marc

2016-01-01

Global climate models simulate a robust increase of global mean precipitation of about 1.5 to 2% per kelvin surface warming in response to greenhouse gas (GHG) forcing. Here, it is shown that the sensitivity to aerosol cooling is robust as well, albeit roughly twice as large. This larger sensitivity is consistent with energy budget arguments. At the same time, it is still considerably lower than the 6.5 to 7% K−1 decrease of the water vapor concentration with cooling from anthropogenic aerosol because the water vapor radiative feedback lowers the hydrological sensitivity to anthropogenic forcings. When GHG and aerosol forcings are combined, the climate models with a realistic 20th century warming indicate that the global mean precipitation increase due to GHG warming has, until recently, been completely masked by aerosol drying. This explains the apparent lack of sensitivity of the global mean precipitation to the net global warming recently found in observations. As the importance of GHG warming increases in the future, a clear signal will emerge. PMID:27386558

Global warming without global mean precipitation increase?

PubMed

Salzmann, Marc

2016-06-01

Global climate models simulate a robust increase of global mean precipitation of about 1.5 to 2% per kelvin surface warming in response to greenhouse gas (GHG) forcing. Here, it is shown that the sensitivity to aerosol cooling is robust as well, albeit roughly twice as large. This larger sensitivity is consistent with energy budget arguments. At the same time, it is still considerably lower than the 6.5 to 7% K(-1) decrease of the water vapor concentration with cooling from anthropogenic aerosol because the water vapor radiative feedback lowers the hydrological sensitivity to anthropogenic forcings. When GHG and aerosol forcings are combined, the climate models with a realistic 20th century warming indicate that the global mean precipitation increase due to GHG warming has, until recently, been completely masked by aerosol drying. This explains the apparent lack of sensitivity of the global mean precipitation to the net global warming recently found in observations. As the importance of GHG warming increases in the future, a clear signal will emerge.
Seasonal Prediction of Hydro-Climatic Extremes in the Greater Horn of Africa Under Evolving Climate Conditions to Support Adaptation Strategies

NASA Astrophysics Data System (ADS)

Tadesse, T.; Zaitchik, B. F.; Habib, S.; Funk, C. C.; Senay, G. B.; Dinku, T.; Policelli, F. S.; Block, P.; Baigorria, G. A.; Beyene, S.; Wardlow, B.; Hayes, M. J.

2014-12-01

The development of effective strategies to adapt to changes in the character of droughts and floods in Africa will rely on improved seasonal prediction systems that are robust to an evolving climate baseline and can be integrated into disaster preparedness and response. Many efforts have been made to build models to improve seasonal forecasts in the Greater Horn of Africa region (GHA) using satellite and climate data, but these efforts and models must be improved and translated into future conditions under evolving climate conditions. This has considerable social significance, but is challenged by the nature of climate predictability and the adaptability of coupled natural and human systems facing exposure to climate extremes. To address these issues, work is in progress under a project funded by NASA. The objectives of the project include: 1) Characterize and explain large-scale drivers in the ocean-atmosphere-land system associated with years of extreme flood or drought in the GHA. 2) Evaluate the performance of state-of-the-art seasonal forecast methods for prediction of decision-relevant metrics of hydrologic extremes. 3) Apply seasonal forecast systems to prediction of socially relevant impacts on crops, flood risk, and economic outcomes, and assess the value of these predictions to decision makers. 4) Evaluate the robustness of seasonal prediction systems to evolving climate conditions. The National Drought Mitigation Center (University of Nebraska-Lincoln, USA) is leading this project in collaboration with the USGS, Johns Hopkins University, University of Wisconsin-Madison, the International Research Institute for Climate and Society, NASA, and GHA local experts. The project is also designed to have active engagement of end users in various sectors, university researchers, and extension agents in GHA through workshops and/or webinars. This project is expected improve and implement new and existing climate- and remote sensing-based agricultural, meteorological, and hydrologic drought and flood monitoring products (or indicators) that can enhance the preparedness for extreme climate events and climate change adaptation and mitigation strategies in the GHA. Even though this project is in its first year, the preliminary results and future plans to carry out the objectives will be presented.
Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kravitz, Ben; Lynch, Cary; Hartin, Corinne

Pattern scaling is a well-established method for approximating modeled spatial distributions of changes in temperature by assuming a time-invariant pattern that scales with changes in global mean temperature. We compare two methods of pattern scaling for annual mean precipitation (regression and epoch difference) and evaluate which method is better in particular circumstances by quantifying their robustness to interpolation/extrapolation in time, inter-model variations, and inter-scenario variations. Both the regression and epoch-difference methods (the two most commonly used methods of pattern scaling) have good absolute performance in reconstructing the climate model output, measured as an area-weighted root mean square error. We decomposemore » the precipitation response in the RCP8.5 scenario into a CO 2 portion and a non-CO 2 portion. Extrapolating RCP8.5 patterns to reconstruct precipitation change in the RCP2.6 scenario results in large errors due to violations of pattern scaling assumptions when this CO 2-/non-CO 2-forcing decomposition is applied. As a result, the methodologies discussed in this paper can help provide precipitation fields to be utilized in other models (including integrated assessment models or impacts assessment models) for a wide variety of scenarios of future climate change.« less
Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models

DOE PAGES

Kravitz, Ben; Lynch, Cary; Hartin, Corinne; ...

2017-05-12

Pattern scaling is a well-established method for approximating modeled spatial distributions of changes in temperature by assuming a time-invariant pattern that scales with changes in global mean temperature. We compare two methods of pattern scaling for annual mean precipitation (regression and epoch difference) and evaluate which method is better in particular circumstances by quantifying their robustness to interpolation/extrapolation in time, inter-model variations, and inter-scenario variations. Both the regression and epoch-difference methods (the two most commonly used methods of pattern scaling) have good absolute performance in reconstructing the climate model output, measured as an area-weighted root mean square error. We decomposemore » the precipitation response in the RCP8.5 scenario into a CO 2 portion and a non-CO 2 portion. Extrapolating RCP8.5 patterns to reconstruct precipitation change in the RCP2.6 scenario results in large errors due to violations of pattern scaling assumptions when this CO 2-/non-CO 2-forcing decomposition is applied. As a result, the methodologies discussed in this paper can help provide precipitation fields to be utilized in other models (including integrated assessment models or impacts assessment models) for a wide variety of scenarios of future climate change.« less
Comparing estimates of climate change impacts from process-based and statistical crop models

NASA Astrophysics Data System (ADS)

Lobell, David B.; Asseng, Senthold

2017-01-01

The potential impacts of climate change on crop productivity are of widespread interest to those concerned with addressing climate change and improving global food security. Two common approaches to assess these impacts are process-based simulation models, which attempt to represent key dynamic processes affecting crop yields, and statistical models, which estimate functional relationships between historical observations of weather and yields. Examples of both approaches are increasingly found in the scientific literature, although often published in different disciplinary journals. Here we compare published sensitivities to changes in temperature, precipitation, carbon dioxide (CO2), and ozone from each approach for the subset of crops, locations, and climate scenarios for which both have been applied. Despite a common perception that statistical models are more pessimistic, we find no systematic differences between the predicted sensitivities to warming from process-based and statistical models up to +2 °C, with limited evidence at higher levels of warming. For precipitation, there are many reasons why estimates could be expected to differ, but few estimates exist to develop robust comparisons, and precipitation changes are rarely the dominant factor for predicting impacts given the prominent role of temperature, CO2, and ozone changes. A common difference between process-based and statistical studies is that the former tend to include the effects of CO2 increases that accompany warming, whereas statistical models typically do not. Major needs moving forward include incorporating CO2 effects into statistical studies, improving both approaches’ treatment of ozone, and increasing the use of both methods within the same study. At the same time, those who fund or use crop model projections should understand that in the short-term, both approaches when done well are likely to provide similar estimates of warming impacts, with statistical models generally requiring fewer resources to produce robust estimates, especially when applied to crops beyond the major grains.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Ernst, Kathleen M; Van Riemsdijk, Dr. Micheline

This article studies the participation of stakeholders in climate change decision-making in Alaska s National Parks. We place stakeholder participation within literatures on environmental and climate change decision-making. We conducted participant observation and interviews in two planning workshops to investigate the decision-making process, and our findings are three-fold. First, the inclusion of diverse stakeholders expanded climate change decision-making beyond National Park Service (NPS) institutional constraints. Second, workshops of the Climate Change Scenario Planning Project (CCSPP) enhanced institutional understandings of participants attitudes towards climate change and climate change decision-making. Third, the geographical context of climate change influences the decisionmaking process. Asmore » the first regional approach to climate change decision-making within the NPS, the CCSPP serves as a model for future climate change planning in public land agencies. This study shows how the participation of stakeholders can contribute to robust decisions, may move climate change decision-making beyond institutional barriers, and can provide information about attitudes towards climate change decision-making.« less
DOE Office of Scientific and Technical Information (OSTI.GOV)

Ernst, Kathleen M; Van Riemsdijk, Dr. Micheline

This article studies the participation of stakeholders in climate change decision-making in Alaska s National Parks. We place stakeholder participation within literatures on environmental and climate change decision-making. We conducted participant observation and interviews in two planning workshops to investigate the decision-making process, and our findings are three-fold. First, the inclusion of diverse stakeholders expanded climate change decision-making beyond National Park Service (NPS) institutional constraints. Second, workshops of the Climate Change Scenario Planning Project (CCSPP) enhanced institutional understandings of participants attitudes towards climate change and climate change decision-making. Third, the geographical context of climate change influences the decision-making process. Asmore » the first regional approach to climate change decision-making within the NPS, the CCSPP serves as a model for future climate change planning in public land agencies. This study shows how the participation of stakeholders can contribute to robust decisions, may move climate change decision-making beyond institutional barriers, and can provide information about attitudes towards climate change decision-making.« less
Asian droughts in the last millennium: a search for robust impacts of Pacific Ocean surface temperature variabilities

NASA Astrophysics Data System (ADS)

Yu, Entao; King, Martin P.; Sobolowski, Stefan; Otterå, Odd Helge; Gao, Yongqi

2018-06-01

This study investigates the robustness of hydroclimate impacts in Asia due to major drivers of climate variability in the Pacific Ocean, namely the El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Composite analyses are carried out on a tree ring-based Palmer Drought Severity Index as well as on a long coupled global climate model control experiment. El Niño (La Niña) has a robust impact on wet (dry) conditions in West Asia and dry (wet) conditions in South Asia. For the PDO, impacts are found throughout the Asia domain. However, identifying the robust signals due to PDO from these analyses is more challenging due to the limited lengths of the data. Results indicate that West Asia (South and Southeast Asia) experiences wet (dry) conditions during periods of positive PDO. For East Asia, there is indication that positive (negative) PDO is associated with wet (dry) conditions around and southward of 30°N and dry (wet) conditions north of this latitude. This result is consistent with the current understanding of the role of PDO in the "southern-flood northern-drought" phenomenon in China. We suggest that specific extreme events or periods have regional impacts with strong intensities that cannot be fully explained through the composite analysis of ENSO, PDO, or any combination thereof. Two such examples are shown to illustrate this: the Strange Parallel Drought (1756-1768 CE) and the Great Drought (1876-1878 CE). Additionally, during these climate events, ENSO and PDO can be in phases which are not consistent with the required phases of these drivers that explain the concurrent drought and pluvial conditions in Asia. Therefore, not all historical drought and pluvial events in Northeast Asia and northern China can be related back to ENSO or PDO. Finally, we also examine the dynamical characteristics of the reported hydroclimatic impacts in the global climate model experiment. There is moisture transport into (out of) regions that exhibit wet (dry) conditions in a manner consistent with the various ENSO and PDO composites, thereby providing physical explanation of the index-based results.
Climate Modeling and Causal Identification for Sea Ice Predictability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hunke, Elizabeth Clare; Urrego Blanco, Jorge Rolando; Urban, Nathan Mark

This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments inmore » which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidance in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.« less
Building the Capacity for Climate Services: Thoughts on Training Next Generation Climate Science Integrators

NASA Astrophysics Data System (ADS)

Garfin, G. M.; Brugger, J.; Gordon, E. S.; Barsugli, J. J.; Rangwala, I.; Travis, W.

2015-12-01

For more than a decade, stakeholder needs assessments and reports, including the recent National Climate Assessment, have pointed out the need for climate "science translators" or "science integrators" who can help bridge the gap between the cultures and contexts of researchers and decision-makers. Integration is important for exchanging and enhancing knowledge, building capacity to use climate information in decision making, and fostering more robust planning for decision-making in the context of climate change. This talk will report on the characteristics of successful climate science integrators, and a variety of models for training the upcoming generation of climate science integrators. Science integration characteristics identified by an experienced vanguard in the U.S. include maintaining credibility in both the scientific and stakeholder communities, a basic respect for stakeholders demonstrated through active listening, and a deep understanding of the decision-making context. Drawing upon the lessons of training programs for Cooperative Extension, public health professionals, and natural resource managers, we offer ideas about training next generation climate science integrators. Our model combines training and development of skills in interpersonal relations, communication of science, project implementation, education techniques and practices - integrated with a strong foundation in disciplinary knowledge.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Vallis, Geoffrey K.

The project had two main components. The first concerns estimating the climate sensitivity in the presence of forcing uncertainty and natural variability. Climate sensitivity is the increase in the average surface temperature for a given increase in greenhouse gases, for example a doubling of carbon dioxide. We have provided new, probabilistic estimates of climate sensitivity using a simple climate model an the observed warming in the 20th century, in conjunction with ideas in data assimilation and parameter estimation developed in the engineering community. The estimates combine the uncertainty in the anthropogenic aerosols with the uncertainty arising because of natural variability.more » The second component concerns how the atmospheric circulation itself might change with anthropogenic global warming. We have shown that GCMs robustly predict an increase in the length scale of eddies, and we have also explored the dynamical mechanisms whereby there might be a shift in the latitude of the jet stream associated with anthropogenic warming. Such shifts in the jet might cause large changes in regional climate, potentially larger than the globally-averaged signal itself. We have also shown that the tropopause robustly increases in height with global warming, and that the Hadley Cell expands, and that the expansion of the Hadley Cell is correlated with the polewards movement of the mid-latitude jet.« less
Using large hydrological datasets to create a robust, physically based, spatially distributed model for Great Britain

NASA Astrophysics Data System (ADS)

Lewis, Elizabeth; Kilsby, Chris; Fowler, Hayley

2014-05-01

The impact of climate change on hydrological systems requires further quantification in order to inform water management. This study intends to conduct such analysis using hydrological models. Such models are of varying forms, of which conceptual, lumped parameter models and physically-based models are two important types. The majority of hydrological studies use conceptual models calibrated against measured river flow time series in order to represent catchment behaviour. This method often shows impressive results for specific problems in gauged catchments. However, the results may not be robust under non-stationary conditions such as climate change, as physical processes and relationships amenable to change are not accounted for explicitly. Moreover, conceptual models are less readily applicable to ungauged catchments, in which hydrological predictions are also required. As such, the physically based, spatially distributed model SHETRAN is used in this study to develop a robust and reliable framework for modelling historic and future behaviour of gauged and ungauged catchments across the whole of Great Britain. In order to achieve this, a large array of data completely covering Great Britain for the period 1960-2006 has been collated and efficiently stored ready for model input. The data processed include a DEM, rainfall, PE and maps of geology, soil and land cover. A desire to make the modelling system easy for others to work with led to the development of a user-friendly graphical interface. This allows non-experts to set up and run a catchment model in a few seconds, a process that can normally take weeks or months. The quality and reliability of the extensive dataset for modelling hydrological processes has also been evaluated. One aspect of this has been an assessment of error and uncertainty in rainfall input data, as well as the effects of temporal resolution in precipitation inputs on model calibration. SHETRAN has been updated to accept gridded rainfall inputs, and UKCP09 gridded daily rainfall data has been disaggregated using hourly records to analyse the implications of using realistic sub-daily variability. Furthermore, the development of a comprehensive dataset and computationally efficient means of setting up and running catchment models has allowed for examination of how a robust parameter scheme may be derived. This analysis has been based on collective parameterisation of multiple catchments in contrasting hydrological settings and subject to varied processes. 350 gauged catchments all over the UK have been simulated, and a robust set of parameters is being sought by examining the full range of hydrological processes and calibrating to a highly diverse flow data series. The modelling system will be used to generate flow time series based on historical input data and also downscaled Regional Climate Model (RCM) forecasts using the UKCP09 Weather Generator. This will allow for analysis of flow frequency and associated future changes, which cannot be determined from the instrumental record or from lumped parameter model outputs calibrated only to historical catchment behaviour. This work will be based on the existing and functional modelling system described following some further improvements to calibration, particularly regarding simulation of groundwater-dominated catchments.
A network-base analysis of CMIP5 "historical" experiments

NASA Astrophysics Data System (ADS)

Bracco, A.; Foudalis, I.; Dovrolis, C.

2012-12-01

In computer science, "complex network analysis" refers to a set of metrics, modeling tools and algorithms commonly used in the study of complex nonlinear dynamical systems. Its main premise is that the underlying topology or network structure of a system has a strong impact on its dynamics and evolution. By allowing to investigate local and non-local statistical interaction, network analysis provides a powerful, but only marginally explored, framework to validate climate models and investigate teleconnections, assessing their strength, range, and impacts on the climate system. In this work we propose a new, fast, robust and scalable methodology to examine, quantify, and visualize climate sensitivity, while constraining general circulation models (GCMs) outputs with observations. The goal of our novel approach is to uncover relations in the climate system that are not (or not fully) captured by more traditional methodologies used in climate science and often adopted from nonlinear dynamical systems analysis, and to explain known climate phenomena in terms of the network structure or its metrics. Our methodology is based on a solid theoretical framework and employs mathematical and statistical tools, exploited only tentatively in climate research so far. Suitably adapted to the climate problem, these tools can assist in visualizing the trade-offs in representing global links and teleconnections among different data sets. Here we present the methodology, and compare network properties for different reanalysis data sets and a suite of CMIP5 coupled GCM outputs. With an extensive model intercomparison in terms of the climate network that each model leads to, we quantify how each model reproduces major teleconnections, rank model performances, and identify common or specific errors in comparing model outputs and observations.
Consistency and Main Differences Between European Regional Climate Downscaling Intercomparison Results; From PRUDENCE and ENSEMBLES to CORDEX

NASA Astrophysics Data System (ADS)

Christensen, J. H.; Larsen, M. A. D.; Christensen, O. B.; Drews, M.

2017-12-01

For more than 20 years, coordinated efforts to apply regional climate models to downscale GCM simulations for Europe have been pursued by an ever increasing group of scientists. This endeavor showed its first results during EU framework supported projects such as RACCS and MERCURE. Here, the foundation for today's advanced worldwide CORDEX approach was laid out by a core of six research teams, who conducted some of the first coordinated RCM simulations with the aim to assess regional climate change for Europe. However, it was realized at this stage that model bias in GCMs as well as RCMs made this task very challenging. As an immediate outcome, the idea was conceived to make an even more coordinated effort by constructing a well-defined and structured set of common simulations; this lead to the PRUDENCE project (2001-2004). Additional coordinated efforts involving ever increasing numbers of GCMs and RCMs followed in ENSEMBLES (2004-2009) and the ongoing Euro-CORDEX (officially commenced 2011) efforts. Along with the overall coordination, simulations have increased their standard resolution from 50km (PRUDENCE) to about 12km (Euro-CORDEX) and from time slice simulations (PRUDENCE) to transient experiments (ENSEMBLES and CORDEX); from one driving model and emission scenario (PRUDENCE) to several (Euro-CORDEX). So far, this wealth of simulations have been used to assess the potential impacts of future climate change in Europe providing a baseline change as defined by a multi-model mean change with associated uncertainties calculated from model spread in the ensemble. But how has the overall picture of state-of-the-art regional climate change projections changed over this period of almost two decades? Here we compare across scenarios, model resolutions and model vintage the results from PRUDENCE, ENSEMBLES and Euro-CORDEX. By appropriate scaling we identify robust findings about the projected future of European climate expressed by temperature and precipitation changes that confirm the basic findings of PRUDENCE. For parameters such as snow cover and soil moisture availability we also identify major new results, which illustrate that model improvements and higher resolution offer new, physically grounded, robust information that could not have been identified twenty years ago with the approach taken at that time
Future change of climate in South America in the late twenty-first century: intercomparison of scenarios from three regional climate models

NASA Astrophysics Data System (ADS)

Marengo, Jose A.; Ambrizzi, Tercio; Da Rocha, Rosmeri P.; Alves, Lincoln M.; Cuadra, Santiago V.; Valverde, Maria C.; Torres, Roger R.; Santos, Daniel C.; Ferraz, Simone E. T.

2010-11-01

Regional climate change projections for the last half of the twenty-first century have been produced for South America, as part of the CREAS (Cenarios REgionalizados de Clima Futuro da America do Sul) regional project. Three regional climate models RCMs (Eta CCS, RegCM3 and HadRM3P) were nested within the HadAM3P global model. The simulations cover a 30-year period representing present climate (1961-1990) and projections for the IPCC A2 high emission scenario for 2071-2100. The focus was on the changes in the mean circulation and surface variables, in particular, surface air temperature and precipitation. There is a consistent pattern of changes in circulation, rainfall and temperatures as depicted by the three models. The HadRM3P shows intensification and a more southward position of the subtropical Pacific high, while a pattern of intensification/weakening during summer/winter is projected by the Eta CCS/RegCM3. There is a tendency for a weakening of the subtropical westerly jet from the Eta CCS and HadRM3P, consistent with other studies. There are indications that regions such of Northeast Brazil and central-eastern and southern Amazonia may experience rainfall deficiency in the future, while the Northwest coast of Peru-Ecuador and northern Argentina may experience rainfall excesses in a warmer future, and these changes may vary with the seasons. The three models show warming in the A2 scenario stronger in the tropical region, especially in the 5°N-15°S band, both in summer and especially in winter, reaching up to 6-8°C warmer than in the present. In southern South America, the warming in summer varies between 2 and 4°C and in winter between 3 and 5°C in the same region from the 3 models. These changes are consistent with changes in low level circulation from the models, and they are comparable with changes in rainfall and temperature extremes reported elsewhere. In summary, some aspects of projected future climate change are quite robust across this set of model runs for some regions, as the Northwest coast of Peru-Ecuador, northern Argentina, Eastern Amazonia and Northeast Brazil, whereas for other regions they are less robust as in Pantanal region of West Central and southeastern Brazil.
The Dependencies of Ecosystem Pattern, Structure, and Dynamics on Climate, Climate Variability, and Climate Change

NASA Astrophysics Data System (ADS)

Flanagan, S.; Hurtt, G. C.; Fisk, J. P.; Rourke, O.

2012-12-01

A robust understanding of the sensitivity of the pattern, structure, and dynamics of ecosystems to climate, climate variability, and climate change is needed to predict ecosystem responses to current and projected climate change. We present results of a study designed to first quantify the sensitivity of ecosystems to climate through the use of climate and ecosystem data, and then use the results to test the sensitivity of the climate data in a state-of the art ecosystem model. A database of available ecosystem characteristics such as mean canopy height, above ground biomass, and basal area was constructed from sources like the National Biomass and Carbon Dataset (NBCD). The ecosystem characteristics were then paired by latitude and longitude with the corresponding climate characteristics temperature, precipitation, photosynthetically active radiation (PAR) and dew point that were retrieved from the North American Regional Reanalysis (NARR). The average yearly and seasonal means of the climate data, and their associated maximum and minimum values, over the 1979-2010 time frame provided by NARR were constructed and paired with the ecosystem data. The compiled results provide natural patterns of vegetation structure and distribution with regard to climate data. An advanced ecosystem model, the Ecosystem Demography model (ED), was then modified to allow yearly alterations to its mechanistic climate lookup table and used to predict the sensitivities of ecosystem pattern, structure, and dynamics to climate data. The combined ecosystem structure and climate data results were compared to ED's output to check the validity of the model. After verification, climate change scenarios such as those used in the last IPCC were run and future forest structure changes due to climate sensitivities were identified. The results of this study can be used to both quantify and test key relationships for next generation models. The sensitivity of ecosystem characteristics to climate data shown in the database construction and by the model reinforces the need for high-resolution datasets and stresses the importance of understanding and incorporating climate change scenarios into earth system models.
Detecting and Quantifying Paleoseasonality in Stalagmites using Geochemical and Modelling Approaches

NASA Astrophysics Data System (ADS)

Baldini, J. U. L.

2017-12-01

Stalagmites are now well established sources of terrestrial paleoclimate information, providing insights into climate change on a variety of timescales. One of the most exciting aspects of stalagmites as climate archives is their ability to provide information regarding seasonality, a notoriously difficult component of climate change to characterise. However, stalagmite geochemistry may reflect not only the most apparent seasonal signal in external climate parameters, but also cave-specific signals such as seasonal changes in cave air carbon dioxide concentrations, sudden shifts in ventilation, and stochastic hydrological processes. Additionally, analytical bias may dampen or completely obfuscate any paleoseasonality, highlighting the need for appropriate quantification of this issue using simple models. Evidence from stalagmites now suggests that a seasonal signal is extractable from many samples, and that this signal can provide an important extra dimension to paleoclimate interpretations. Additionally, lower resolution annual- to decadal-scale isotope ratio records may also reflect shifts in seasonality, but identifying these is often challenging. Integrating geochemical datasets with models and cave monitoring data can greatly increase the accuracy of climate reconstructions, and yield the most robust records.
Climate change impact on North Sea wave conditions: a consistent analysis of ten projections

NASA Astrophysics Data System (ADS)

Grabemann, Iris; Groll, Nikolaus; Möller, Jens; Weisse, Ralf

2015-02-01

Long-term changes in the mean and extreme wind wave conditions as they may occur in the course of anthropogenic climate change can influence and endanger human coastal and offshore activities. A set of ten wave climate projections derived from time slice and transient simulations of future conditions is analyzed to estimate the possible impact of anthropogenic climate change on mean and extreme wave conditions in the North Sea. This set includes different combinations of IPCC SRES emission scenarios (A2, B2, A1B, and B1), global and regional models, and initial states. A consistent approach is used to provide a more robust assessment of expected changes and uncertainties. While the spatial patterns and the magnitude of the climate change signals vary, some robust features among the ten projections emerge: mean and severe wave heights tend to increase in the eastern parts of the North Sea towards the end of the twenty-first century in nine to ten projections, but the magnitude of the increase in extreme waves varies in the order of decimeters between these projections. For the western parts of the North Sea more than half of the projections suggest a decrease in mean and extreme wave heights. Comparing the different sources of uncertainties due to models, scenarios, and initial conditions, it can be inferred that the influence of the emission scenario on the climate change signal seems to be less important. Furthermore, the transient projections show strong multi-decadal fluctuations, and changes towards the end of the twenty-first century might partly be associated with internal variability rather than with systematic changes.
Projecting Future Water Levels of the Laurentian Great Lakes

NASA Astrophysics Data System (ADS)

Bennington, V.; Notaro, M.; Holman, K.

2013-12-01

The Laurentian Great Lakes are the largest freshwater system on Earth, containing 84% of North America's freshwater. The lakes are a valuable economic and recreational resource, valued at over 62 billion in annual wages and supporting a 7 billion fishery. Shipping, recreation, and coastal property values are significantly impacted by water level variability, with large economic consequences. Great Lakes water levels fluctuate both seasonally and long-term, responding to natural and anthropogenic climate changes. Due to the integrated nature of water levels, a prolonged small change in any one of the net basin supply components: over-lake precipitation, watershed runoff, or evaporation from the lake surface, may result in important trends in water levels. We utilize the Abdus Salam International Centre for Theoretical Physics's Regional Climate Model Version 4.5.6 to dynamically downscale three global global climate models that represent a spread of potential future climate change for the region to determine whether the climate models suggest a robust response of the Laurentian Great Lakes to anthropogenic climate change. The Model for Interdisciplinary Research on Climate Version 5 (MIROC5), the National Centre for Meteorological Research Earth system model (CNRM-CM5), and the Community Climate System Model Version 4 (CCSM4) project different regional temperature increases and precipitation change over the next century and are used as lateral boundary conditions. We simulate the historical (1980-2000) and late-century periods (2080-2100). Upon model evaluation we will present dynamically downscaled projections of net basin supply changes for each of the Laurentian Great Lakes.
Evaluation of the Absolute Regional Temperature Potential

NASA Technical Reports Server (NTRS)

Shindell, D. T.

2012-01-01

The Absolute Regional Temperature Potential (ARTP) is one of the few climate metrics that provides estimates of impacts at a sub-global scale. The ARTP presented here gives the time-dependent temperature response in four latitude bands (90-28degS, 28degS-28degN, 28-60degN and 60-90degN) as a function of emissions based on the forcing in those bands caused by the emissions. It is based on a large set of simulations performed with a single atmosphere-ocean climate model to derive regional forcing/response relationships. Here I evaluate the robustness of those relationships using the forcing/response portion of the ARTP to estimate regional temperature responses to the historic aerosol forcing in three independent climate models. These ARTP results are in good accord with the actual responses in those models. Nearly all ARTP estimates fall within +/-20%of the actual responses, though there are some exceptions for 90-28degS and the Arctic, and in the latter the ARTP may vary with forcing agent. However, for the tropics and the Northern Hemisphere mid-latitudes in particular, the +/-20% range appears to be roughly consistent with the 95% confidence interval. Land areas within these two bands respond 39-45% and 9-39% more than the latitude band as a whole. The ARTP, presented here in a slightly revised form, thus appears to provide a relatively robust estimate for the responses of large-scale latitude bands and land areas within those bands to inhomogeneous radiative forcing and thus potentially to emissions as well. Hence this metric could allow rapid evaluation of the effects of emissions policies at a finer scale than global metrics without requiring use of a full climate model.

Assessing the resilience of Norway spruce forests through a model-based reanalysis of thinning trials☆

PubMed Central

Seidl, Rupert; Vigl, Friedrich; Rössler, Günter; Neumann, Markus; Rammer, Werner

2017-01-01

As a result of a rapidly changing climate the resilience of forests is an increasingly important property for ecosystem management. Recent efforts have improved the theoretical understanding of resilience, yet its operational quantification remains challenging. Furthermore, there is growing awareness that resilience is not only a means to addressing the consequences of climate change but is also affected by it, necessitating a better understanding of the climate sensitivity of resilience. Quantifying current and future resilience is thus an important step towards mainstreaming resilience thinking into ecosystem management. Here, we present a novel approach for quantifying forest resilience from thinning trials, and assess the climate sensitivity of resilience using process-based ecosystem modeling. We reinterpret the wide range of removal intensities and frequencies in thinning trials as an experimental gradient of perturbation, and estimate resilience as the recovery rate after perturbation. Our specific objectives were (i) to determine how resilience varies with stand and site conditions, (ii) to assess the climate sensitivity of resilience across a range of potential future climate scenarios, and (iii) to evaluate the robustness of resilience estimates to different focal indicators and assessment methodologies. We analyzed three long-term thinning trials in Norway spruce (Picea abies (L.) Karst.) forests across an elevation gradient in Austria, evaluating and applying the individual-based process model iLand. The resilience of Norway spruce was highest at the montane site, and decreased at lower elevations. Resilience also decreased with increasing stand age and basal area. The effects of climate change were strongly context-dependent: At the montane site, where precipitation levels were ample even under climate change, warming increased resilience in all scenarios. At lower elevations, however, rising temperatures decreased resilience, particularly at precipitation levels below 750–800 mm. Our results were largely robust to different focal variables and resilience definitions. Based on our findings management can improve the capacity to recover from partial disturbances by avoiding overmature and overstocked conditions. At increasingly water limited sites a strongly decreasing resilience of Norway spruce will require a shift towards tree species better adapted to the expected future conditions. PMID:28860674
Investigating the Capacity of Hydrological Models to Project Impacts of Climate Change in the Context of Water Allocation

NASA Astrophysics Data System (ADS)

Velez, Carlos; Maroy, Edith; Rocabado, Ivan; Pereira, Fernando

2017-04-01

To analyse the impacts of climate changes, hydrological models are used to project the hydrology responds under future conditions that normally differ from those for which they were calibrated. The challenge is to assess the validity of the projected effects when there is not data to validate it. A framework for testing the ability of models to project climate change was proposed by Refsgaard et al., (2014). The authors recommend the use of the differential-split sample test (DSST) in order to build confidence in the model projections. The method follow three steps: 1. A small number of sub-periods are selected according to one climate characteristics, 2. The calibration - validation test is applied on these periods, 3. The validation performances are compered to evaluate whether they vary significantly when climatic characteristics differ between calibration and validation. DSST rely on the existing records of climate and hydrological variables; and performances are estimated based on indicators of error between observed and simulated variables. Other authors suggest that, since climate models are not able to reproduce single events but rather statistical properties describing the climate, this should be reflected when testing hydrological models. Thus, performance criteria such as RMSE should be replaced by for instance flow duration curves or other distribution functions. Using this type of performance criteria, Van Steenbergen and Willems, (2012) proposed a method to test the validity of hydrological models in a climate changing context. The method is based on the evaluation of peak flow increases due to different levels of rainfall increases. In contrast to DSST, this method use the projected climate variability and it is especially useful to compare different modelling tools. In the framework of a water allocation project for the region of Flanders (Belgium) we calibrated three hydrological models: NAM, PDM and VHM; for 67 gauged sub-catchments with approx. 40 years of records. This paper investigates the capacity of the three hydrological models to project the impacts of climate change scenarios. It is proposed a general testing framework which combine the use of the existing information through an adapted form of DSST with the approach proposed by Van Steenbergen and Willems, (2012) adapted to assess statistical properties of flows useful in the context of water allocation. To assess the model we use robustness criteria based on a Log Nash-Sutcliffe, BIAS on cummulative volumes and relative changes based on Q50/Q90 estimated from the duration curve. The three conceptual rainfall-runoff models yielded different results per sub-catchments. A relation was found between robustness criteria and changes in mean rainfall and changes in mean potential evapotranspiration. Biases are greatly affected by changes in precipitation, especially when the climate scenarios involve changes in precipitation volume beyond the range used for calibration. Using the combine approach we were able to classify the modelling tools per sub-catchments and create an ensemble of best models to project the impacts of climate variability for the catchments of 10 main rivers in Flanders. Thus, managers could understand better the usability of the modelling tools and the credibility of its outputs for water allocation applications. References Refsgaard, J.C., Madsen, H., Andréassian, V., Arnbjerg-Nielsen, K., Davidson, T.A., Drews, M., Hamilton, D.P., Jeppesen, E., Kjellström, E., Olesen, J.E., Sonnenborg, T.O., Trolle, D., Willems, P., Christensen, J.H., 2014. A framework for testing the ability of models to project climate change and its impacts. Clim. Change. Van Steenbergen, N., Willems, P., 2012. Method for testing the accuracy of rainfall - runoff models in predicting peak flow changes due to rainfall changes , in a climate changing context. J. Hydrol. 415, 425-434.
Future Directions in Simulating Solar Geoengineering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kravitz, Benjamin S.; Robock, Alan; Boucher, Olivier

2014-08-05

Solar geoengineering is a proposed set of technologies to temporarily alleviate some of the consequences of anthropogenic greenhouse gas emissions. The Geoengineering Model Intercomparison Project (GeoMIP) created a framework of geoengineering simulations in climate models that have been performed by modeling centers throughout the world (B. Kravitz et al., The Geoengineering Model Intercomparison Project (GeoMIP), Atmospheric Science Letters, 12(2), 162-167, doi:10.1002/asl.316, 2011). These experiments use state-of-the-art climate models to simulate solar geoengineering via uniform solar reduction, creation of stratospheric sulfate aerosol layers, or injecting sea spray into the marine boundary layer. GeoMIP has been quite successful in its mission ofmore » revealing robust features and key uncertainties of the modeled effects of solar geoengineering.« less
Ecosystem oceanography for global change in fisheries.

PubMed

Cury, Philippe Maurice; Shin, Yunne-Jai; Planque, Benjamin; Durant, Joël Marcel; Fromentin, Jean-Marc; Kramer-Schadt, Stephanie; Stenseth, Nils Christian; Travers, Morgane; Grimm, Volker

2008-06-01

Overexploitation and climate change are increasingly causing unanticipated changes in marine ecosystems, such as higher variability in fish recruitment and shifts in species dominance. An ecosystem-based approach to fisheries attempts to address these effects by integrating populations, food webs and fish habitats at different scales. Ecosystem models represent indispensable tools to achieve this objective. However, a balanced research strategy is needed to avoid overly complex models. Ecosystem oceanography represents such a balanced strategy that relates ecosystem components and their interactions to climate change and exploitation. It aims at developing realistic and robust models at different levels of organisation and addressing specific questions in a global change context while systematically exploring the ever-increasing amount of biological and environmental data.
Climate Forecasts and Water Resource Management: Applications for a Developing Country

NASA Astrophysics Data System (ADS)

Brown, C.; Rogers, P.

2002-05-01

While the quantity of water on the planet earth is relatively constant, the demand for water is continuously increasing. Population growth leads to linear increases in water demand, and economic growth leads to further demand growth. Strzepek et al. calculate that with a United Nations mean population estimate of 8.5 billion people by 2025 and globally balanced economic growth, water use could increase by 70% over that time (Strzepek et al., 1995). For developing nations especially, supplying water for this growing demand requires the construction of new water supply infrastructure. The prospect of designing and constructing long life-span infrastructure is clouded by the uncertainty of future climate. The availability of future water resources is highly dependent on future climate. With realization of the nonstationarity of climate, responsible design emphasizes resiliency and robustness of water resource systems (IPCC, 1995; Gleick et al., 1999). Resilient systems feature multiple sources and complex transport and distribution systems, and so come at a high economic and environmental price. A less capital-intense alternative to creating resilient and robust water resource systems is the use of seasonal climate forecasts. Such forecasts provide adequate lead time and accuracy to allow water managers and water-based sectors such as agriculture or hydropower to optimize decisions for the expected water supply. This study will assess the use of seasonal climate forecasts from regional climate models as a method to improve water resource management in systems with limited water supply infrastructure
Using Paleo-climate Comparisons to Constrain Future Projections in CMIP5

NASA Technical Reports Server (NTRS)

Schmidt, G. A.; Annan, J D.; Bartlein, P. J.; Cook, B. I.; Guilyardi, E.; Hargreaves, J. C.; Harrison, S. P.; Kageyama, M.; LeGrande, A. N..; Konecky, B.;

2013-01-01

We present a description of the theoretical framework and best practice for using the paleo-climate model component of the Coupled Model Intercomparison Project (Phase 5) (CMIP5) to constrain future projections of climate using the same models. The constraints arise from measures of skill in hindcasting paleo-climate changes from the present over 3 periods: the Last Glacial Maximum (LGM) (21 thousand years before present, ka), the mid-Holocene (MH) (6 ka) and the Last Millennium (LM) (8501850 CE). The skill measures may be used to validate robust patterns of climate change across scenarios or to distinguish between models that have differing outcomes in future scenarios. We find that the multi-model ensemble of paleo-simulations is adequate for addressing at least some of these issues. For example, selected benchmarks for the LGM and MH are correlated to the rank of future projections of precipitationtemperature or sea ice extent to indicate that models that produce the best agreement with paleoclimate information give demonstrably different future results than the rest of the models. We also find that some comparisons, for instance associated with model variability, are strongly dependent on uncertain forcing timeseries, or show time dependent behaviour, making direct inferences for the future problematic. Overall, we demonstrate that there is a strong potential for the paleo-climate simulations to help inform the future projections and urge all the modeling groups to complete this subset of the CMIP5 runs.

Overlooked Role of Mesoscale Winds in Powering Ocean Diapycnal Mixing.

PubMed

Jing, Zhao; Wu, Lixin; Ma, Xiaohui; Chang, Ping

2016-11-16

Diapycnal mixing affects the uptake of heat and carbon by the ocean as well as plays an important role in global ocean circulations and climate. In the thermocline, winds provide an important energy source for furnishing diapycnal mixing primarily through the generation of near-inertial internal waves. However, this contribution is largely missing in the current generation of climate models. In this study, it is found that mesoscale winds at scales of a few hundred kilometers account for more than 65% of near-inertial energy flux into the North Pacific basin and 55% of turbulent kinetic dissipation rate in the thermocline, suggesting their dominance in powering diapycnal mixing in the thermocline. Furthermore, a new parameterization of wind-driven diapycnal mixing in the ocean interior for climate models is proposed, which, for the first time, successfully captures both temporal and spatial variations of wind-driven diapycnal mixing in the thermocline. It is suggested that as mesoscale winds are not resolved by the climate models participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) due to insufficient resolutions, the diapycnal mixing is likely poorly represented, raising concerns about the accuracy and robustness of climate change simulations and projections.
Overlooked Role of Mesoscale Winds in Powering Ocean Diapycnal Mixing

PubMed Central

Jing, Zhao; Wu, Lixin; Ma, Xiaohui; Chang, Ping

2016-01-01

Diapycnal mixing affects the uptake of heat and carbon by the ocean as well as plays an important role in global ocean circulations and climate. In the thermocline, winds provide an important energy source for furnishing diapycnal mixing primarily through the generation of near-inertial internal waves. However, this contribution is largely missing in the current generation of climate models. In this study, it is found that mesoscale winds at scales of a few hundred kilometers account for more than 65% of near-inertial energy flux into the North Pacific basin and 55% of turbulent kinetic dissipation rate in the thermocline, suggesting their dominance in powering diapycnal mixing in the thermocline. Furthermore, a new parameterization of wind-driven diapycnal mixing in the ocean interior for climate models is proposed, which, for the first time, successfully captures both temporal and spatial variations of wind-driven diapycnal mixing in the thermocline. It is suggested that as mesoscale winds are not resolved by the climate models participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) due to insufficient resolutions, the diapycnal mixing is likely poorly represented, raising concerns about the accuracy and robustness of climate change simulations and projections. PMID:27849059
Identifying robust regional precipitation responses to regional aerosol emissions perturbations in three coupled chemistry-climate models

NASA Astrophysics Data System (ADS)

Westervelt, D. M.; Fiore, A. M.; Lamarque, J. F.; Previdi, M. J.; Conley, A. J.; Shindell, D. T.; Mascioli, N. R.; Correa, G. J. P.; Faluvegi, G.; Horowitz, L. W.

2017-12-01

Regional emissions of anthropogenic aerosols and their precursors will likely decrease for the remainder of the 21st century, due to emission reduction policies enacted to protect human health. Although there is some evidence that regional climate effects of aerosols can be significant, we currently lack a robust understanding of the magnitude, spatio-temporal pattern, statistical significance, and physical processes responsible for these influences, especially for precipitation. Here, we aim to quantify systematically the precipitation response to regional changes in aerosols and investigate underlying mechanisms using three fully coupled chemistry-climate models: NOAA Geophysical Fluid Dynamics Laboratory Coupled Model 3 (GFDL-CM3), NCAR Community Earth System Model (CESM), and NASA Goddard Institute for Space Studies ModelE2 (GISS-E2). The central approach we use is to contrast a long control experiment (400 years, run with perpetual year 2000 emissions) with 14 individual aerosol emissions perturbation experiments ( 200 years each). We perturb emissions of sulfur dioxide (SO2) and carbonaceous aerosol (BC and OM) within several world regions and assess which responses are significant relative to internal variability determined by the control run and robust across the three models. Initial results show significant changes in precipitation in several vulnerable regions including the Western Sahel and the Indian subcontinent. SO2 emissions reductions from Europe and the United States have the largest impact on precipitation among most of the selected response regions. The precipitation response to emissions changes from these regions projects onto known modes of variability, such as the North Atlantic Oscillation (NAO) and the El Niño Southern Oscillation (ENSO). Across all perturbation experiments, we find a strong linear relationship between the responses of Sahel precipitation and the interhemispheric temperature difference, suggesting a common mechanism of an anomalous Hadley cell circulation and a shift of the Intertropical Convergence Zone (ITCZ). GFDL-CM3 and CESM1 each show strong changes in regional precipitation in response to the various regional aerosol emissions perturbations, whereas a more modest response occurs in GISS-E2, owing to a weaker aerosol indirect effect.
An Ensemble Approach to Understanding the ENSO Response to Climate Change

NASA Astrophysics Data System (ADS)

Stevenson, S.; Capotondi, A.; Fasullo, J.; Otto-Bliesner, B. L.

2017-12-01

The dynamics of the El Nino/Southern Oscillation (ENSO) are known to be sensitive to changes in background climate conditions, as well as atmosphere/ocean feedbacks. However, the degree to which shifts in ENSO characteristics can be robustly attributed to external climate forcings remains unknown. Efforts to assess these changes in a multi-model framework are subject to uncertainties due to both differing model physics and internal ENSO variability. New community ensembles created at the National Center for Atmospheric Research and the NOAA Geophysical Fluid Dynamics Laboratory are ideally suited to addressing this problem, providing many realizations of the climate of the 850-2100 period with a combination of both natural and anthropogenic climate forcing factors. Here we analyze the impacts of external forcing on El Nino and La Nina evolution using four sets of simulations: the CESM Last Millennium Ensemble (CESM-LME), which covers the 850-2005 period and provides long-term context for forced responses; the Large Ensemble (CESM-LE), which includes 20th century and 21st century (RCP8.5) projections; the Medium Ensemble (CESM-ME), which is composed of 21st century RCP4.5 projections; and a large ensemble with the GFDL ESM2M, which includes 20th century and RCP8.5 projections. In the CESM, ENSO variance increases slightly over the 20th century in all ensembles, with the effects becoming much larger during the 21st. The slower increase in variance over the 20th century is shown to arise from compensating influences from greenhouse gas (GHG) and anthropogenic aerosol emissions, which give way to GHG-dominated effects by 2100. However, the 21st century variance increase is not robust: CESM and the ESM2M differ drastically in their ENSO projections. The mechanisms for these inter-model differences are discussed, as are the implications for the design of future multi-model ENSO projection experiments.
Built Expansion and Global Climate Change Drive Projected Urban Heat: Relative Magnitudes, Interactions, and Mitigation

NASA Astrophysics Data System (ADS)

Krayenhoff, E. S.; Georgescu, M.; Moustaoui, M.

2016-12-01

Surface climates are projected to warm due to global climate change over the course of the 21st century, and demographic projections suggest urban areas in the United States will continue to expand and develop, with associated local climate outcomes. Interactions between these two drivers of urban heat have not been robustly quantified to date. Here, simulations with the Weather Research and Forecasting model (coupled to a Single-Layer Urban Canopy Model) are performed at 20 km resolution over the continental U.S. for two 10-year periods: contemporary (2000-2009) and end-of-century (2090-2099). Present and end of century urban land use are derived from the Environmental Protection Agency's Integrated Climate and Land-Use Scenarios. Modelled effects on urban climates are evaluated regionally. Sensitivity to climate projection (Community Climate System Model 4.0, RCP 4.5 vs. RCP 8.5) and associated urban development scenarios are assessed. Effects on near-surface urban air temperature of RCP8.5 climate change are greater than those attributable to the corresponding urban development in many regions. Interaction effects vary by region, and while of lesser magnitude, are not negligible. Moreover, urban development and its interactions with RCP8.5 climate change modify the distribution of convective precipitation over the eastern US. Interaction effects result from the different meteorological effects of urban areas under current and future climate. Finally, the potential for design implementations such as green roofs and high albedo roofs to offset the projected warming is considered. Impacts of these implementations on precipitation are also assessed.
Introducing the Met Office 2.2-km Europe-wide convection-permitting regional climate simulations

NASA Astrophysics Data System (ADS)

Kendon, Elizabeth J.; Chan, Steven C.; Berthou, Segolene; Fosser, Giorgia; Roberts, Malcolm J.; Fowler, Hayley J.

2017-04-01

The Met Office is currently conducting Europe-wide 2.2-km convection-permitting model (CPM) simulations driven by ERA-Interim reanalysis and present/future-climate GCM simulations. Here, we present the preliminary results of these new European simulations examining daily and sub-daily precipitation outputs in comparison with observations across Europe, 12-km European and 1.5-km UK climate model simulations. As the simulations are not yet complete, we focus on diagnostics that are relatively robust with a limited amount of data; for instance, the diurnal cycle and the probability distribution of daily and sub-daily precipitation intensities. We will also present specific case studies that showcase the benefits of using continental-scale CPM simulations over previously-available small-domain CPM simulations.
Water Planning and Climate Change: Actionable Intelligence Yet?

NASA Astrophysics Data System (ADS)

Milly, P.

2008-05-01

Within a rational planning framework, water planners design major water projects by evaluating tradeoffs of costs, benefits, and risks to life and property. The evaluation is based on anticipated future runoff and streamflow. Generally, planners have invoked the stationarity approximation: they have assumed that hydrologic conditions during the planned lifetime of a project will be similar to those observed in the past. Contemporary anthropogenic climate change arguably makes stationarity untenable. In principle, stationarity-based planning under non- stationarity potentially leads to incorrect assessment of tradeoffs, sub-optimal decisions, and excessive financial and environmental costs (e.g., a reservoir that is too big to ever be filled) and/or insufficient benefits (e.g., levees that are too small to hold back the flood waters). As the reigning default assumption for planning, stationarity is an easy target for criticism; provision of a practical alternative is not so easy. The leading alternative, use of quantitative climate-change projections from global climate models in conjunction with water planners' river-basin models, has serious shortcomings of its own. Climate models (1) neglect some terrestrial processes known to influence runoff and streamflow; (2) do not represent precipitation well at the finer resolved time and space scales; (3) do not resolve any processes at the even finer spatial scale of relevance to much of water planning; and (4) disagree among themselves about some changes. Even setting aside the issue of scale mismatch, for which various "downscaling" methods have been proposed, outputs from climate models generally are not directly transferable to river-basin models, and river-basin models commonly use empiricisms whose historical validity might not extrapolate well under climate change. So climate science is informing water management that stationarity is a flawed assumption, but it has not presented a universally and reliably superior alternative. What is to be done? Is climate-change information of sufficient strength to justify making decisions that differ from those that would be optimal under stationarity? I.e., does climate science provide "actionable intelligence" to water planners? A conservative approach to planning in the presence of climate change would begin with stationarity as a base and then superpose, with quantitative estimates of uncertainties, those model-projected changes that appear to be qualitatively robust. The current state of science suggests that the following changes could be considered robust: (1) reduction in the fraction of precipitation falling as snow and earlier seasonal melting of snow, with consequent seasonal redistribution of runoff and streamflow; (2) gradual sea-level rise with heightened risk of encroachment of saline water into coastal surface- and ground-water-supply sources; and (3) global redistribution of precipitation and resultant runoff, with regional focal points ("hot spots") of desiccation and moistening. Even considering the attendant uncertainties, the available information about these changes can significantly affect the cost-benefit-risk tradeoffs of existing and prospective water projects and, therefore, can rationally inform decisions about future courses of action or inaction.
Some Good Practices for Integration and Outreach and their Implementation in the Community Integrated Assessment System (CIAS) and its associated web portal CLIMASCOPE

NASA Astrophysics Data System (ADS)

Warren, R. F.; Price, J. T.; Goswami, S.

2010-12-01

Successful communication of knowledge to climate change policy makers requires the careful integration of scientific knowledge in an integrated assessment that can be clearly communicated to stakeholders, and which encapsulates the uncertainties in the analysis and conveys the need for using a risk assessment approach. It is important that (i) the system is co-designed with the users (ii) relevant disciplines are included (iii) assumptions made are clear (iv) the robustness of outputs to uncertainties is demonstrated (v) the system is flexible so that it can keep up with changing stakeholder needs and (vi) the results are communicated clearly and are readily accessible. The “Community Integrated Assessment System” (CIAS) is a unique multi-institutional, modular, and flexible integrated assessment system for modeling climate change which fulfils the above six criteria. It differs from other integrated models in being a flexible system allowing various combinations of component modules, to be connected together into alternative integrated assessment models. These modules may be written at different institutions in different computer languages and/or based on different operating systems. Scientists are able determine which particular CIAS coupled model they wish to use through a web portal. This includes the facility to implement Latin hypercube experimental design facilitating formal uncertainty analysis. Further exploration of robustness is possible through the ability to select, for example, alternative hyrdrological or climate models to address the same questions. It has been applied to study future scenarios of climate change mitigation, through for example the AVOIDing dangerous climate change project for DEFRA, in which the avoided impacts (benefits) of alternative climate policies were compared to no-policy baselines. These highlight the potential for mitigation to remove a substantial fraction of the climate change impacts that would otherwise occur; but also show that is not possible to avoid all the impacts, and hence that adaptation will still be required. For example, this has been shown for projections of future European drought. CIAS has also been used for analyses used in the IPCC 4AR and the Stern review. Recent applications include a study of the role of avoided deforestation in climate mitigation, and a study of the impacts of climate change on biodiversity. A second web portal, CLIMASCOPE, is being developed for use by stakeholders, currently focusing on the needs of adaptation planners. This will benefit communication by allowing a wide range of users free access to regional climate change projections in simple manner, yet one which encourages risk assessment through encapsulation of the uncertainties in climate change projection. Examples of CLIMASCOPE output that is being made available to stakeholders will be shown.
Using proxies to explore ensemble uncertainty in climate impact studies: the example of air pollution

NASA Astrophysics Data System (ADS)

Lemaire, V. E. P.; Colette, A.; Menut, L.

2015-10-01

Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to climate change so that, in the future, a climate penalty could jeopardize the expected efficiency of air pollution mitigation measures. A common method to assess the impact of climate on air quality consists in implementing chemistry-transport models forced by climate projection. However, the computing cost of such method requires optimizing ensemble exploration techniques. By using a training dataset of deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for 8 regions in Europe and developed simple statistical models that could be used to predict air pollutant concentrations. The evolution of the key climate variables driving either particulate or gaseous pollution allows concluding on the robustness of the climate impact on air quality. The climate benefit for PM2.5 was confirmed -0.96 (±0.18), -1.00 (±0.37), -1.16 ± (0.23) μg m-3, for resp. Eastern Europe, Mid Europe and Northern Italy and for the Eastern Europe, France, Iberian Peninsula, Mid Europe and Northern Italy regions a climate penalty on ozone was identified 10.11 (±3.22), 8.23 (±2.06), 9.23 (±1.13), 6.41 (±2.14), 7.43 (±2.02) μg m-3. This technique also allows selecting a subset of relevant regional climate model members that should be used in priority for future deterministic projections.
Determinants of the lethality of climate-related disasters in the Caribbean Community (CARICOM): a cross-country analysis

PubMed Central

Andrewin, Aisha N.; Rodriguez-Llanes, Jose M.; Guha-Sapir, Debarati

2015-01-01

Floods and storms are climate-related hazards posing high mortality risk to Caribbean Community (CARICOM) nations. However risk factors for their lethality remain untested. We conducted an ecological study investigating risk factors for flood and storm lethality in CARICOM nations for the period 1980–2012. Lethality - deaths versus no deaths per disaster event- was the outcome. We examined biophysical and social vulnerability proxies and a decadal effect as predictors. We developed our regression model via multivariate analysis using a generalized logistic regression model with quasi-binomial distribution; removal of multi-collinear variables and backward elimination. Robustness was checked through subset analysis. We found significant positive associations between lethality, percentage of total land dedicated to agriculture (odds ratio [OR] 1.032; 95% CI: 1.013–1.053) and percentage urban population (OR 1.029, 95% CI 1.003–1.057). Deaths were more likely in the 2000–2012 period versus 1980–1989 (OR 3.708, 95% CI 1.615–8.737). Robustness checks revealed similar coefficients and directions of association. Population health in CARICOM nations is being increasingly impacted by climate-related disasters connected to increasing urbanization and land use patterns. Our findings support the evidence base for setting sustainable development goals (SDG). PMID:26153115
Making CORDEX accessible to users: case studies in the Middle East

NASA Astrophysics Data System (ADS)

Dubois, Ghislain

2017-04-01

The current demand of long term climate projections corresponds to more applied requests from users: climate data and services are supposed to enable robust decision making in very diversified environments…Issues like uncertainty management (elaborating probabilistic projections based on full ensembles analysis) or tailoring of indicators should be central. However, an assessment of a sample of local, regional and national climate change adaptation strategies, in Europe and in the Med (Stoverinck, Dubois and Amelung 2013) highlighted the frequent insufficient robustness of climate information used to inform policy making. Some initiatives only refer to past climate data, use only one SRES or RCP scenario, one model or a too limited set of downscaling techniques. The CORDEX program (Coordinated regional climate downscaling experiment, coordinated by WCRP) forms the largest effort of climate downscaling so far. Its datasets, initially developed for scientific purposes have strong potential to improve regional and local adaptation policies. They can be considered as reference for the coming years, not only reflecting the improvement of our knowledge of climate, but also offering data in a much more harmonized and accessible way. The PROCLIM initiative (www.pro-clim.org) aims at developing a European climate service, proposing territorialized climate projections, supporting local adaptation frameworks, derived from CORDEX. This encompasses several methodological challenges: understanding users' needs at the European level, specifying indices, selecting relevant geographical domains, correcting systematic biases, selecting sub-ensembles of the CORDEX datasets so as to provide a sound uncertainty analysis, representing results in an user-friendly manner. The presentation will detail some features of PROCLIM, based on two recent experiments: the elaboration of long term climate projections, based on AFRICA-CORDEX, supporting the elaboration of the third national communication on climate change of Jordan; and the provision of high resolution hydro-climatic projections for Israel, Palestine and Jordan, which combined post-processing of CORDEX, and some dedicated runs of WRF, configured in climate mode.
Unexpected Results are Usually Wrong, but Often Interesting

NASA Astrophysics Data System (ADS)

Huber, M.

2014-12-01

In climate modeling, an unexpected result is usually wrong, arising from some sort of mistake. Despite the fact that we all bemoan uncertainty in climate, the field is underlain by a robust, successful body of theory and any properly conducted modeling experiment is posed and conducted within that context. Consequently, if results from a complex climate model disagree with theory or from expectations from simpler models, much skepticism is in order. But, this exposes the fundamental tension of using complex, sophisticated models. If simple models and theory were perfect there would be no reason for complex models--the entire point of sophisticated models is to see if unexpected phenomena arise as emergent properties of the system. In this talk, I will step through some paleoclimate examples, drawn from my own work, of unexpected results that emerge from complex climate models arising from mistakes of two kinds. The first kind of mistake, is what I call a 'smart mistake'; it is an intentional incorporation of assumptions, boundary conditions, or physics that is in violation of theoretical or observational constraints. The second mistake, a 'dumb mistake', is just that, an unintentional violation. Analysis of such mistaken simulations provides some potentially novel and certainly interesting insights into what is possible and right in paleoclimate modeling by forcing the reexamination of well-held assumptions and theories.
The Borderlands and climate change: Chapter 10 in United States-Mexican Borderlands: Facing tomorrow's challenges through USGS science

USGS Publications Warehouse

Fitzpatrick, Joan; Gray, Floyd; Dubiel, Russell; Langman, Jeff; Moring, J. Bruce; Norman, Laura M.; Page, William R.; Parcher, Jean W.

2013-01-01

The prediction of global climate change in response to both natural forces and human activity is one of the defining issues of our times. The unprecedented observational capacity of modern earth-orbiting satellites coupled with the development of robust computational representations (models) of the Earth’s weather and climate systems afford us the opportunity to observe and investigate how these systems work now, how they have worked in the past, and how they will work in the future when forced in specific ways. In the most recent report on global climate change by the Intergovernmental Panel on Climate Change (IPCC; Solomon and others, 2007), analyses using multiple climate models support recent observations that the Earth’s climate is changing in response to a combination of natural and human-induced causes. These changes will be significant in the United States–Mexican border region, where the process of climate change affects all of the Borderlands challenge themes discussed in the preceding chapters. The dual possibilities of both significantly-changed climate and increasing variability in climate make it challenging to take full measure of the potential effects because the Borderlands already experience a high degree of interannual variability and climatological extremes.
System robustness analysis for drought risk management in South Florida

NASA Astrophysics Data System (ADS)

Eilander, D.; Bouwer, L.; Barnes, J.; Mens, M.; Obeysekera, J.

2015-12-01

Drought is a frequently returning natural hazard in Florida, with at least one severe drought to be expected every decade. These droughts have had many impacts such as loss of agricultural products, inadequate public water supply and salt water intrusion into freshwater aquifers. Furthermore, climate change projections for South Florida suggest that dry spells are likely to be more frequent and prolonged, with negative impacts on water supply management for all users. In this study a System Robustness Analysis was conducted in order to analyse the effectiveness of strategies to limit the socio-economic impact of droughts under climate change. System Robustness Analysis (SRA) aims to support decision making by quantifying how well a system, with and without additional measures, can remain functioning under a range of external disturbances. Two system characteristics add up to system robustness: Resistance is the ability to withstand disturbances without responding (zero impact), and resilience is the ability to recover from the response to a disturbance. SRA can help to provide insight into the sensitivity of a system to changing magnitudes of extreme weather events. A regional-scale hydrologic and water management model is used to simulate the effect of changing precipitation and evaporation forcing on agricultural and urban water supply and demand in South Florida. The complex water management operational rules including water use restrictions are simulated in the model. Based on model runs with a various climate scenarios, drought events with a wide range of severity are identified and for each event the socio-economic impacts are determined. Here, a drought is defined as a reduced streamflow in the upstream Kissimmee basin, which contributes most to Lake Okeechobee, the major surface water storage in the system. The drought severity is characterized by the maximum drought deficit volume. Drought impacts are analyzed for several users in Miami Dade County. From the relation between drought severity and drought impact the resistance and resilience of the system for hydrological droughts are found. This relation is investigated for an array of adaptation measures and strategies in order to find strategies that will effectively increase the system's ability to deal with future drought events.

Carbon-Temperature-Water Change Analysis for Peanut Production Under Climate Change: A Prototype for the AgMIP Coordinated Climate-Crop Modeling Project (C3MP)

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; McDermid, Sonali; Rosenzweig, Cynthia; Baigorria, Guillermo A.; Jones, James W.; Romero, Consuelo C.; Cecil, L. DeWayne

2014-01-01

Climate change is projected to push the limits of cropping systems and has the potential to disrupt the agricultural sector from local to global scales. This article introduces the Coordinated Climate-Crop Modeling Project (C3MP), an initiative of the Agricultural Model Intercomparison and Improvement Project (AgMIP) to engage a global network of crop modelers to explore the impacts of climate change via an investigation of crop responses to changes in carbon dioxide concentration ([CO2]), temperature, and water. As a demonstration of the C3MP protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO-Peanut crop model for Henry County, Alabama, to evaluate responses to the range of plausible [CO2], temperature changes, and precipitation changes projected by climate models out to the end of the 21st century. These sensitivity tests are used to derive crop model emulators that estimate changes in mean yield and the coefficient of variation for seasonal yields across a broad range of climate conditions, reproducing mean yields from sensitivity test simulations with deviations of ca. 2% for rain-fed conditions. We apply these statistical emulators to investigate how peanuts respond to projections from various global climate models, time periods, and emissions scenarios, finding a robust projection of modest (<10%) median yield losses in the middle of the 21st century accelerating to more severe (>20%) losses and larger uncertainty at the end of the century under the more severe representative concentration pathway (RCP8.5). This projection is not substantially altered by the selection of the AgMERRA global gridded climate dataset rather than the local historical observations, differences between the Third and Fifth Coupled Model Intercomparison Project (CMIP3 and CMIP5), or the use of the delta method of climate impacts analysis rather than the C3MP impacts response surface and emulator approach.
Trends and uncertainties in budburst projections of Norway spruce in Northern Europe.

PubMed

Olsson, Cecilia; Olin, Stefan; Lindström, Johan; Jönsson, Anna Maria

2017-12-01

Budburst is regulated by temperature conditions, and a warming climate is associated with earlier budburst. A range of phenology models has been developed to assess climate change effects, and they tend to produce different results. This is mainly caused by different model representations of tree physiology processes, selection of observational data for model parameterization, and selection of climate model data to generate future projections. In this study, we applied (i) Bayesian inference to estimate model parameter values to address uncertainties associated with selection of observational data, (ii) selection of climate model data representative of a larger dataset, and (iii) ensembles modeling over multiple initial conditions, model classes, model parameterizations, and boundary conditions to generate future projections and uncertainty estimates. The ensemble projection indicated that the budburst of Norway spruce in northern Europe will on average take place 10.2 ± 3.7 days earlier in 2051-2080 than in 1971-2000, given climate conditions corresponding to RCP 8.5. Three provenances were assessed separately (one early and two late), and the projections indicated that the relationship among provenance will remain also in a warmer climate. Structurally complex models were more likely to fail predicting budburst for some combinations of site and year than simple models. However, they contributed to the overall picture of current understanding of climate impacts on tree phenology by capturing additional aspects of temperature response, for example, chilling. Model parameterizations based on single sites were more likely to result in model failure than parameterizations based on multiple sites, highlighting that the model parameterization is sensitive to initial conditions and may not perform well under other climate conditions, whether the change is due to a shift in space or over time. By addressing a range of uncertainties, this study showed that ensemble modeling provides a more robust impact assessment than would a single phenology model run.
Relationship of cranial robusticity to cranial form, geography and climate in Homo sapiens.

PubMed

Baab, Karen L; Freidline, Sarah E; Wang, Steven L; Hanson, Timothy

2010-01-01

Variation in cranial robusticity among modern human populations is widely acknowledged but not well-understood. While the use of "robust" cranial traits in hominin systematics and phylogeny suggests that these characters are strongly heritable, this hypothesis has not been tested. Alternatively, cranial robusticity may be a response to differences in diet/mastication or it may be an adaptation to cold, harsh environments. This study quantifies the distribution of cranial robusticity in 14 geographically widespread human populations, and correlates this variation with climatic variables, neutral genetic distances, cranial size, and cranial shape. With the exception of the occipital torus region, all traits were positively correlated with each other, suggesting that they should not be treated as individual characters. While males are more robust than females within each of the populations, among the independent variables (cranial shape, size, climate, and neutral genetic distances), only shape is significantly correlated with inter-population differences in robusticity. Two-block partial least-squares analysis was used to explore the relationship between cranial shape (captured by three-dimensional landmark data) and robusticity across individuals. Weak support was found for the hypothesis that robusticity was related to mastication as the shape associated with greater robusticity was similar to that described for groups that ate harder-to-process diets. Specifically, crania with more prognathic faces, expanded glabellar and occipital regions, and (slightly) longer skulls were more robust than those with rounder vaults and more orthognathic faces. However, groups with more mechanically demanding diets (hunter-gatherers) were not always more robust than groups practicing some form of agriculture.
Climate projections of spatial variations in coastal storm surges along the Gulf of Mexico and U.S. east coast

NASA Astrophysics Data System (ADS)

Yao, Zhigang; Xue, Zuo; He, Ruoying; Bao, Xianwen; Xie, Jun; Ge, Qian

2017-02-01

Using statistically downscaled atmospheric forcing, we performed a numerical investigation to evaluate future climate's impact on storm surges along the Gulf of Mexico and U.S. east coast. The focus is on the impact of climatic changes in wind pattern and surface pressure while neglecting sea level rise and other factors. We adapted the regional ocean model system (ROMS) to the study region with a mesh grid size of 7-10 km in horizontal and 18 vertical layers. The model was validated by a hindcast of the coastal sea levels in the winter of 2008. Model's robustness was confirmed by the good agreement between model-simulated and observed sea levels at 37 tidal gages. Two 10-year forecasts, one for the IPCC Pre-Industry (PI) and the other for the A1FI scenario, were conducted. The differences in model-simulated surge heights under the two climate scenarios were analyzed. We identified three types of responses in extreme surge heights to future climate: a clear decrease in Middle Atlantic Bight, an increase in the western Gulf of Mexico, and non-significant response for the remaining area. Such spatial pattern is also consistent with previous projections of sea surface winds and ocean wave heights.
Livestock Helminths in a Changing Climate: Approaches and Restrictions to Meaningful Predictions.

PubMed

Fox, Naomi J; Marion, Glenn; Davidson, Ross S; White, Piran C L; Hutchings, Michael R

2012-03-06

Climate change is a driving force for livestock parasite risk. This is especially true for helminths including the nematodes Haemonchus contortus, Teladorsagia circumcincta, Nematodirus battus, and the trematode Fasciola hepatica, since survival and development of free-living stages is chiefly affected by temperature and moisture. The paucity of long term predictions of helminth risk under climate change has driven us to explore optimal modelling approaches and identify current bottlenecks to generating meaningful predictions. We classify approaches as correlative or mechanistic, exploring their strengths and limitations. Climate is one aspect of a complex system and, at the farm level, husbandry has a dominant influence on helminth transmission. Continuing environmental change will necessitate the adoption of mitigation and adaptation strategies in husbandry. Long term predictive models need to have the architecture to incorporate these changes. Ultimately, an optimal modelling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modelling, incorporating changes in livestock husbandry and disease control. Irrespective of approach, the principal limitation to parasite predictions is the availability of active surveillance data and empirical data on physiological responses to climate variables. By combining improved empirical data and refined models with a broad view of the livestock system, robust projections of helminth risk can be developed.
Evaluation of Historical and Projected Agricultural Climate Risk Over the Continental US

NASA Astrophysics Data System (ADS)

Zhu, X.; Troy, T. J.; Devineni, N.

2016-12-01

Food demands are rising due to an increasing population with changing food preferences, which places pressure on agricultural systems. In addition, in the past decade climate extremes have highlighted the vulnerability of our agricultural production to climate variability. Quantitative analyses in the climate-agriculture research field have been performed in many studies. However, climate risk still remains difficult to evaluate at large scales yet shows great potential of help us better understand historical climate change impacts and evaluate the future risk given climate projections. In this study, we developed a framework to evaluate climate risk quantitatively by applying statistical methods such as Bayesian regression, distribution fitting, and Monte Carlo simulation. We applied the framework over different climate regions in the continental US both historically and for modeled climate projections. The relative importance of any major growing season climate index, such as maximum dry period or heavy precipitation, was evaluated to determine what climate indices play a role in affecting crop yields. The statistical modeling framework was applied using county yields, with irrigated and rainfed yields separated to evaluate the different risk. This framework provides estimates of the climate risk facing agricultural production in the near-term that account for the full uncertainty of climate occurrences, range of crop response, and spatial correlation in climate. In particular, the method provides robust estimates of importance of irrigation in mitigating agricultural climate risk. The results of this study can contribute to decision making about crop choice and water use in an uncertain climate.
An AgMIP framework for improved agricultural representation in integrated assessment models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agriculturalmore » Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.« less
An AgMIP framework for improved agricultural representation in integrated assessment models

NASA Astrophysics Data System (ADS)

Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold; Boote, Kenneth J.; Elliott, Joshua; Ewert, Frank; Jones, James W.; Martre, Pierre; McDermid, Sonali P.; Müller, Christoph; Snyder, Abigail; Thorburn, Peter J.

2017-12-01

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.
High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

DOE PAGES

Haarsma, Reindert J.; Roberts, Malcolm J.; Vidale, Pier Luigi; ...

2016-11-22

Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest both the possibility of significant changes in large-scale aspects of circulation as well as improvements in small-scale processes and extremes. However, such high-resolution global simulations at climate timescales, with resolutions of at least 50 km in the atmosphere and 0.25° in the ocean, have been performed at relativelymore » few research centres and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other model intercomparison projects (MIPs). Increases in high-performance computing (HPC) resources, as well as the revised experimental design for CMIP6, now enable a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal-resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950–2050, with the possibility of extending to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulations. Lastly, HighResMIP thereby focuses on one of the CMIP6 broad questions, “what are the origins and consequences of systematic model biases?”, but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.« less
Assessing the Role of Climate Variability on Liver Fluke Risk in the UK Through Mechanistic Hydro-Epidemiological Modelling

NASA Astrophysics Data System (ADS)

Beltrame, L.; Dunne, T.; Rose, H.; Walker, J.; Morgan, E.; Vickerman, P.; Wagener, T.

2016-12-01

Liver fluke is a flatworm parasite infecting grazing animals worldwide. In the UK, it causes considerable production losses to cattle and sheep industries and costs farmers millions of pounds each year due to reduced growth rates and lower milk yields. Large part of the parasite life-cycle takes place outside of the host, with its survival and development strongly controlled by climatic and hydrologic conditions. Evidence of climate-driven changes in the distribution and seasonality of fluke disease already exists, as the infection is increasingly expanding to new areas and becoming a year-round problem. Therefore, it is crucial to assess current and potential future impacts of climate variability on the disease to guide interventions at the farm scale and mitigate risk. Climate-based fluke risk models have been available since the 1950s, however, they are based on empirical relationships derived between historical climate and incidence data, and thus are unlikely to be robust for simulating risk under changing conditions. Moreover, they are not dynamic, but estimate risk over large regions in the UK based on monthly average climate conditions, so they do not allow investigating the effects of climate variability for supporting farmers' decisions. In this study, we introduce a mechanistic model for fluke, which represents habitat suitability for disease development at 25m resolution with a daily time step, explicitly linking the parasite life-cycle to key hydro-climate conditions. The model is used on a case study in the UK and sensitivity analysis is performed to better understand the role of climate variability on the space-time dynamics of the disease, while explicitly accounting for uncertainties. Comparisons are presented with experts' knowledge and a widely used empirical model.
Simulating Climate Change in Ireland

NASA Astrophysics Data System (ADS)

Nolan, P.; Lynch, P.

2012-04-01

At the Meteorology & Climate Centre at University College Dublin, we are using the CLM-Community's COSMO-CLM Regional Climate Model (RCM) and the WRF RCM (developed at NCAR) to simulate the climate of Ireland at high spatial resolution. To address the issue of model uncertainty, a Multi-Model Ensemble (MME) approach is used. The ensemble method uses different RCMs, driven by several Global Climate Models (GCMs), to simulate climate change. Through the MME approach, the uncertainty in the RCM projections is quantified, enabling us to estimate the probability density function of predicted changes, and providing a measure of confidence in the predictions. The RCMs were validated by performing a 20-year simulation of the Irish climate (1981-2000), driven by ECMWF ERA-40 global re-analysis data, and comparing the output to observations. Results confirm that the output of the RCMs exhibit reasonable and realistic features as documented in the historical data record. Projections for the future Irish climate were generated by downscaling the Max Planck Institute's ECHAM5 GCM, the UK Met Office HadGEM2-ES GCM and the CGCM3.1 GCM from the Canadian Centre for Climate Modelling. Simulations were run for a reference period 1961-2000 and future period 2021-2060. The future climate was simulated using the A1B, A2, B1, RCP 4.5 & RCP 8.5 greenhouse gas emission scenarios. Results for the downscaled simulations show a substantial overall increase in precipitation and wind speed for the future winter months and a decrease during the summer months. The predicted annual change in temperature is approximately 1.1°C over Ireland. To date, all RCM projections are in general agreement, thus increasing our confidence in the robustness of the results.
How to reduce long-term drift in present-day and deep-time simulations?

NASA Astrophysics Data System (ADS)

Brunetti, Maura; Vérard, Christian

2018-06-01

Climate models are often affected by long-term drift that is revealed by the evolution of global variables such as the ocean temperature or the surface air temperature. This spurious trend reduces the fidelity to initial conditions and has a great influence on the equilibrium climate after long simulation times. Useful insight on the nature of the climate drift can be obtained using two global metrics, i.e. the energy imbalance at the top of the atmosphere and at the ocean surface. The former is an indicator of the limitations within a given climate model, at the level of both numerical implementation and physical parameterisations, while the latter is an indicator of the goodness of the tuning procedure. Using the MIT general circulation model, we construct different configurations with various degree of complexity (i.e. different parameterisations for the bulk cloud albedo, inclusion or not of friction heating, different bathymetry configurations) to which we apply the same tuning procedure in order to obtain control runs for fixed external forcing where the climate drift is minimised. We find that the interplay between tuning procedure and different configurations of the same climate model provides crucial information on the stability of the control runs and on the goodness of a given parameterisation. This approach is particularly relevant for constructing good-quality control runs of the geological past where huge uncertainties are found in both initial and boundary conditions. We will focus on robust results that can be generally applied to other climate models.
Aggression and violence around the world: A model of CLimate, Aggression, and Self-control in Humans (CLASH).

PubMed

Van Lange, Paul A M; Rinderu, Maria I; Bushman, Brad J

2017-01-01

Worldwide there are substantial differences within and between countries in aggression and violence. Although there are various exceptions, a general rule is that aggression and violence increase as one moves closer to the equator, which suggests the important role of climate differences. While this pattern is robust, theoretical explanations for these large differences in aggression and violence within countries and around the world are lacking. Most extant explanations focus on the influence of average temperature as a factor that triggers aggression (The General Aggression Model), or the notion that warm temperature allows for more social interaction situations (Routine Activity Theory) in which aggression is likely to unfold. We propose a new model, CLimate, Aggression, and Self-control in Humans (CLASH), that helps us to understand differences within and between countries in aggression and violence in terms of differences in climate. Lower temperatures, and especially larger degrees of seasonal variation in climate, call for individuals and groups to adopt a slower life history strategy, a greater focus on the future (vs. present), and a stronger focus on self-control. The CLASH model further outlines that slow life strategy, future orientation, and strong self-control are important determinants of inhibiting aggression and violence. We also discuss how CLASH differs from other recently developed models that emphasize climate differences for understanding conflict. We conclude by discussing the theoretical and societal importance of climate in shaping individual and societal differences in aggression and violence.
Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness

PubMed Central

Lenton, T. M.; Livina, V. N.; Dakos, V.; Van Nes, E. H.; Scheffer, M.

2012-01-01

We address whether robust early warning signals can, in principle, be provided before a climate tipping point is reached, focusing on methods that seek to detect critical slowing down as a precursor of bifurcation. As a test bed, six previously analysed datasets are reconsidered, three palaeoclimate records approaching abrupt transitions at the end of the last ice age and three models of varying complexity forced through a collapse of the Atlantic thermohaline circulation. Approaches based on examining the lag-1 autocorrelation function or on detrended fluctuation analysis are applied together and compared. The effects of aggregating the data, detrending method, sliding window length and filtering bandwidth are examined. Robust indicators of critical slowing down are found prior to the abrupt warming event at the end of the Younger Dryas, but the indicators are less clear prior to the Bølling-Allerød warming, or glacial termination in Antarctica. Early warnings of thermohaline circulation collapse can be masked by inter-annual variability driven by atmospheric dynamics. However, rapidly decaying modes can be successfully filtered out by using a long bandwidth or by aggregating data. The two methods have complementary strengths and weaknesses and we recommend applying them together to improve the robustness of early warnings. PMID:22291229
A climate model projection weighting scheme accounting for performance and interdependence

NASA Astrophysics Data System (ADS)

Knutti, Reto; Sedláček, Jan; Sanderson, Benjamin M.; Lorenz, Ruth; Fischer, Erich M.; Eyring, Veronika

2017-02-01

Uncertainties of climate projections are routinely assessed by considering simulations from different models. Observations are used to evaluate models, yet there is a debate about whether and how to explicitly weight model projections by agreement with observations. Here we present a straightforward weighting scheme that accounts both for the large differences in model performance and for model interdependencies, and we test reliability in a perfect model setup. We provide weighted multimodel projections of Arctic sea ice and temperature as a case study to demonstrate that, for some questions at least, it is meaningless to treat all models equally. The constrained ensemble shows reduced spread and a more rapid sea ice decline than the unweighted ensemble. We argue that the growing number of models with different characteristics and considerable interdependence finally justifies abandoning strict model democracy, and we provide guidance on when and how this can be achieved robustly.
Integrating 3D geological information with a national physically-based hydrological modelling system

NASA Astrophysics Data System (ADS)

Lewis, Elizabeth; Parkin, Geoff; Kessler, Holger; Whiteman, Mark

2016-04-01

Robust numerical models are an essential tool for informing flood and water management and policy around the world. Physically-based hydrological models have traditionally not been used for such applications due to prohibitively large data, time and computational resource requirements. Given recent advances in computing power and data availability, a robust, physically-based hydrological modelling system for Great Britain using the SHETRAN model and national datasets has been created. Such a model has several advantages over less complex systems. Firstly, compared with conceptual models, a national physically-based model is more readily applicable to ungauged catchments, in which hydrological predictions are also required. Secondly, the results of a physically-based system may be more robust under changing conditions such as climate and land cover, as physical processes and relationships are explicitly accounted for. Finally, a fully integrated surface and subsurface model such as SHETRAN offers a wider range of applications compared with simpler schemes, such as assessments of groundwater resources, sediment and nutrient transport and flooding from multiple sources. As such, SHETRAN provides a robust means of simulating numerous terrestrial system processes which will add physical realism when coupled to the JULES land surface model. 306 catchments spanning Great Britain have been modelled using this system. The standard configuration of this system performs satisfactorily (NSE > 0.5) for 72% of catchments and well (NSE > 0.7) for 48%. Many of the remaining 28% of catchments that performed relatively poorly (NSE < 0.5) are located in the chalk in the south east of England. As such, the British Geological Survey 3D geology model for Great Britain (GB3D) has been incorporated, for the first time in any hydrological model, to pave the way for improvements to be made to simulations of catchments with important groundwater regimes. This coupling has involved development of software to allow for easy incorporation of geological information into SHETRAN for any model setup. The addition of more realistic subsurface representation following this approach is shown to greatly improve model performance in areas dominated by groundwater processes. The resulting modelling system has great potential to be used as a resource at national, regional and local scales in an array of different applications, including climate change impact assessments, land cover change studies and integrated assessments of groundwater and surface water resources.
Pollen-based continental climate reconstructions at 6 and 21 ka: a global synthesis

USGS Publications Warehouse

Bartlein, P.J.; Harrison, S.P.; Brewer, Sandra; Connor, S.; Davis, B.A.S.; Gajewski, K.; Guiot, J.; Harrison-Prentice, T. I.; Henderson, A.; Peyron, O.; Prentice, I.C.; Scholze, M.; Seppa, H.; Shuman, B.; Sugita, S.; Thompson, R.S.; Viau, A.E.; Williams, J.; Wu, H.

2010-01-01

Subfossil pollen and plant macrofossil data derived from 14C-dated sediment profiles can provide quantitative information on glacial and interglacial climates. The data allow climate variables related to growing-season warmth, winter cold, and plant-available moisture to be reconstructed. Continental-scale reconstructions have been made for the mid-Holocene (MH, around 6 ka) and Last Glacial Maximum (LGM, around 21 ka), allowing comparison with palaeoclimate simulations currently being carried out as part of the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change. The synthesis of the available MH and LGM climate reconstructions and their uncertainties, obtained using modern-analogue, regression and model-inversion techniques, is presented for four temperature variables and two moisture variables. Reconstructions of the same variables based on surface-pollen assemblages are shown to be accurate and unbiased. Reconstructed LGM and MH climate anomaly patterns are coherent, consistent between variables, and robust with respect to the choice of technique. They support a conceptual model of the controls of Late Quaternary climate change whereby the first-order effects of orbital variations and greenhouse forcing on the seasonal cycle of temperature are predictably modified by responses of the atmospheric circulation and surface energy balance.
Rocks and Rain: orographic precipitation and the form of mountain ranges

NASA Astrophysics Data System (ADS)

Roe, G. H.; Anders, A. M.; Durran, D. R.; Montgomery, D. R.; Hallet, B.

2005-12-01

In mountainous landscapes patterns of erosion reflect patterns of precipitation that are, in turn, controlled by the orography. Ultimately therefore, the feedbacks between orography and the climate it creates are responsible for the sculpting of mountain ranges. Key questions concerning these interactions are: 1) how robust are patterns of precipitation on geologic time scales? and 2) how do those patterns affect landscape form? Since climate is by definition the statistics of weather, there is tremendous information to be gleaned from how patterns of precipitation vary between different weather events. However up to now sparse measurements and computational limitations have hampered our knowledge of such variations. For the Olympics in Washington State, a characteristic midlatitude mountain range, we report results from a high-resolution, state-of-the-art numerical weather prediction model and a dense network of precipitation gauges. Down to scales around 10 km, the patterns of precipitation are remarkably robust both storm-by-storm and year-to-year, lending confidence that they are indeed persistent on the relevant time scales. Secondly, the consequences of the coupled interactions are presented using a landscape evolution model coupled with a simple model of orographic precipitation that is able to substantially reproduce the observed precipitation patterns.
Photosynthetic Control of Atmospheric Carbonyl Sulfide during the Growing Season

NASA Technical Reports Server (NTRS)

Campbell, J. Elliott; Carmichael, Gregory R.; Chai, T.; Mena-Carrasco, M.; Tang, Y.; Blake, D. R.; Blake, N. J.; Vay, Stephanie A.; Collatz, G. James; Baker, I.;

2008-01-01

Climate models incorporate photosynthesis-climate feedbacks, yet we lack robust tools for large-scale assessments of these processes. Recent work suggests that carbonyl sulfide (COS), a trace gas consumed by plants, could provide a valuable constraint on photosynthesis. Here we analyze airborne observations of COS and carbon dioxide concentrations during the growing season over North America with a three-dimensional atmospheric transport model. We successfully modeled the persistent vertical drawdown of atmospheric COS using the quantitative relation between COS and photosynthesis that has been measured in plant chamber experiments. Furthermore, this drawdown is driven by plant uptake rather than other continental and oceanic fluxes in the model. These results provide quantitative evidence that COS gradients in the continental growing season may have broad use as a measurement-based photosynthesis tracer.

When 1+1 can be >2: Uncertainties compound when simulating climate, fisheries and marine ecosystems

NASA Astrophysics Data System (ADS)

Evans, Karen; Brown, Jaclyn N.; Sen Gupta, Alex; Nicol, Simon J.; Hoyle, Simon; Matear, Richard; Arrizabalaga, Haritz

2015-03-01

Multi-disciplinary approaches that combine oceanographic, biogeochemical, ecosystem, fisheries population and socio-economic models are vital tools for modelling whole ecosystems. Interpreting the outputs from such complex models requires an appreciation of the many different types of modelling frameworks being used and their associated limitations and uncertainties. Both users and developers of particular model components will often have little involvement or understanding of other components within such modelling frameworks. Failure to recognise limitations and uncertainties associated with components and how these uncertainties might propagate throughout modelling frameworks can potentially result in poor advice for resource management. Unfortunately, many of the current integrative frameworks do not propagate the uncertainties of their constituent parts. In this review, we outline the major components of a generic whole of ecosystem modelling framework incorporating the external pressures of climate and fishing. We discuss the limitations and uncertainties associated with each component of such a modelling system, along with key research gaps. Major uncertainties in modelling frameworks are broadly categorised into those associated with (i) deficient knowledge in the interactions of climate and ocean dynamics with marine organisms and ecosystems; (ii) lack of observations to assess and advance modelling efforts and (iii) an inability to predict with confidence natural ecosystem variability and longer term changes as a result of external drivers (e.g. greenhouse gases, fishing effort) and the consequences for marine ecosystems. As a result of these uncertainties and intrinsic differences in the structure and parameterisation of models, users are faced with considerable challenges associated with making appropriate choices on which models to use. We suggest research directions required to address these uncertainties, and caution against overconfident predictions. Understanding the full impact of uncertainty makes it clear that full comprehension and robust certainty about the systems themselves are not feasible. A key research direction is the development of management systems that are robust to this unavoidable uncertainty.

Foraminifera Models to Interrogate Ostensible Proxy-Model Discrepancies During Late Pliocene

NASA Astrophysics Data System (ADS)

Jacobs, P.; Dowsett, H. J.; de Mutsert, K.

2017-12-01

Planktic foraminifera faunal assemblages have been used in the reconstruction of past oceanic states (e.g. the Last Glacial Maximum, the mid-Piacenzian Warm Period). However these reconstruction efforts have typically relied on inverse modeling using transfer functions or the modern analog technique, which by design seek to translate foraminifera into one or two target oceanic variables, primarily sea surface temperature (SST). These reconstructed SST data have then been used to test the performance of climate models, and discrepancies have been attributed to shortcomings in climate model processes and/or boundary conditions. More recently forward proxy models or proxy system models have been used to leverage the multivariate nature of proxy relationships to their environment, and to "bring models into proxy space". Here we construct ecological models of key planktic foraminifera taxa, calibrated and validated with World Ocean Atlas (WO13) oceanographic data. Multiple modeling methods (e.g. multilayer perceptron neural networks, Mahalanobis distance, logistic regression, and maximum entropy) are investigated to ensure robust results. The resulting models are then driven by a Late Pliocene climate model simulation with biogeochemical as well as temperature variables. Similarities and differences with previous model-proxy comparisons (e.g. PlioMIP) are discussed.
Incorporating climate-system and carbon-cycle uncertainties in integrated assessments of climate change. (Invited)

NASA Astrophysics Data System (ADS)

Rogelj, J.; McCollum, D. L.; Reisinger, A.; Knutti, R.; Riahi, K.; Meinshausen, M.

2013-12-01

The field of integrated assessment draws from a large body of knowledge across a range of disciplines to gain robust insights about possible interactions, trade-offs, and synergies. Integrated assessment of climate change, for example, uses knowledge from the fields of energy system science, economics, geophysics, demography, climate change impacts, and many others. Each of these fields comes with its associated caveats and uncertainties, which should be taken into account when assessing any results. The geophysical system and its associated uncertainties are often represented by models of reduced complexity in integrated assessment modelling frameworks. Such models include simple representations of the carbon-cycle and climate system, and are often based on the global energy balance equation. A prominent example of such model is the 'Model for the Assessment of Greenhouse Gas Induced Climate Change', MAGICC. Here we show how a model like MAGICC can be used for the representation of geophysical uncertainties. Its strengths, weaknesses, and limitations are discussed and illustrated by means of an analysis which attempts to integrate socio-economic and geophysical uncertainties. These uncertainties in the geophysical response of the Earth system to greenhouse gases remains key for estimating the cost of greenhouse gas emission mitigation scenarios. We look at uncertainties in four dimensions: geophysical, technological, social and political. Our results indicate that while geophysical uncertainties are an important factor influencing projections of mitigation costs, political choices that delay mitigation by one or two decades a much more pronounced effect.
Extreme weather and climate events with ecological relevance: a review

PubMed Central

Meehl, Gerald A.

2017-01-01

Robust evidence exists that certain extreme weather and climate events, especially daily temperature and precipitation extremes, have changed in regard to intensity and frequency over recent decades. These changes have been linked to human-induced climate change, while the degree to which climate change impacts an individual extreme climate event (ECE) is more difficult to quantify. Rapid progress in event attribution has recently been made through improved understanding of observed and simulated climate variability, methods for event attribution and advances in numerical modelling. Attribution for extreme temperature events is stronger compared with other event types, notably those related to the hydrological cycle. Recent advances in the understanding of ECEs, both in observations and their representation in state-of-the-art climate models, open new opportunities for assessing their effect on human and natural systems. Improved spatial resolution in global climate models and advances in statistical and dynamical downscaling now provide climatic information at appropriate spatial and temporal scales. Together with the continued development of Earth System Models that simulate biogeochemical cycles and interactions with the biosphere at increasing complexity, these make it possible to develop a mechanistic understanding of how ECEs affect biological processes, ecosystem functioning and adaptation capabilities. Limitations in the observational network, both for physical climate system parameters and even more so for long-term ecological monitoring, have hampered progress in understanding bio-physical interactions across a range of scales. New opportunities for assessing how ECEs modulate ecosystem structure and functioning arise from better scientific understanding of ECEs coupled with technological advances in observing systems and instrumentation. This article is part of the themed issue ‘Behavioural, ecological and evolutionary responses to extreme climatic events’. PMID:28483866
Extreme weather and climate events with ecological relevance: a review.

PubMed

Ummenhofer, Caroline C; Meehl, Gerald A

2017-06-19

Robust evidence exists that certain extreme weather and climate events, especially daily temperature and precipitation extremes, have changed in regard to intensity and frequency over recent decades. These changes have been linked to human-induced climate change, while the degree to which climate change impacts an individual extreme climate event (ECE) is more difficult to quantify. Rapid progress in event attribution has recently been made through improved understanding of observed and simulated climate variability, methods for event attribution and advances in numerical modelling. Attribution for extreme temperature events is stronger compared with other event types, notably those related to the hydrological cycle. Recent advances in the understanding of ECEs, both in observations and their representation in state-of-the-art climate models, open new opportunities for assessing their effect on human and natural systems. Improved spatial resolution in global climate models and advances in statistical and dynamical downscaling now provide climatic information at appropriate spatial and temporal scales. Together with the continued development of Earth System Models that simulate biogeochemical cycles and interactions with the biosphere at increasing complexity, these make it possible to develop a mechanistic understanding of how ECEs affect biological processes, ecosystem functioning and adaptation capabilities. Limitations in the observational network, both for physical climate system parameters and even more so for long-term ecological monitoring, have hampered progress in understanding bio-physical interactions across a range of scales. New opportunities for assessing how ECEs modulate ecosystem structure and functioning arise from better scientific understanding of ECEs coupled with technological advances in observing systems and instrumentation.This article is part of the themed issue 'Behavioural, ecological and evolutionary responses to extreme climatic events'. © 2017 The Author(s).
The proportionality of global warming to cumulative carbon emissions.

PubMed

Matthews, H Damon; Gillett, Nathan P; Stott, Peter A; Zickfeld, Kirsten

2009-06-11

The global temperature response to increasing atmospheric CO(2) is often quantified by metrics such as equilibrium climate sensitivity and transient climate response. These approaches, however, do not account for carbon cycle feedbacks and therefore do not fully represent the net response of the Earth system to anthropogenic CO(2) emissions. Climate-carbon modelling experiments have shown that: (1) the warming per unit CO(2) emitted does not depend on the background CO(2) concentration; (2) the total allowable emissions for climate stabilization do not depend on the timing of those emissions; and (3) the temperature response to a pulse of CO(2) is approximately constant on timescales of decades to centuries. Here we generalize these results and show that the carbon-climate response (CCR), defined as the ratio of temperature change to cumulative carbon emissions, is approximately independent of both the atmospheric CO(2) concentration and its rate of change on these timescales. From observational constraints, we estimate CCR to be in the range 1.0-2.1 degrees C per trillion tonnes of carbon (Tt C) emitted (5th to 95th percentiles), consistent with twenty-first-century CCR values simulated by climate-carbon models. Uncertainty in land-use CO(2) emissions and aerosol forcing, however, means that higher observationally constrained values cannot be excluded. The CCR, when evaluated from climate-carbon models under idealized conditions, represents a simple yet robust metric for comparing models, which aggregates both climate feedbacks and carbon cycle feedbacks. CCR is also likely to be a useful concept for climate change mitigation and policy; by combining the uncertainties associated with climate sensitivity, carbon sinks and climate-carbon feedbacks into a single quantity, the CCR allows CO(2)-induced global mean temperature change to be inferred directly from cumulative carbon emissions.
Representing agriculture in Earth System Models: Approaches and priorities for development

NASA Astrophysics Data System (ADS)

McDermid, S. S.; Mearns, L. O.; Ruane, A. C.

2017-09-01

Earth System Model (ESM) advances now enable improved representations of spatially and temporally varying anthropogenic climate forcings. One critical forcing is global agriculture, which is now extensive in land-use and intensive in management, owing to 20th century development trends. Agriculture and food systems now contribute nearly 30% of global greenhouse gas emissions and require copious inputs and resources, such as fertilizer, water, and land. Much uncertainty remains in quantifying important agriculture-climate interactions, including surface moisture and energy balances and biogeochemical cycling. Despite these externalities and uncertainties, agriculture is increasingly being leveraged to function as a net sink of anthropogenic carbon, and there is much emphasis on future sustainable intensification. Given its significance as a major environmental and climate forcing, there now exist a variety of approaches to represent agriculture in ESMs. These approaches are reviewed herein, and range from idealized representations of agricultural extent to the development of coupled climate-crop models that capture dynamic feedbacks. We highlight the robust agriculture-climate interactions and responses identified by these modeling efforts, as well as existing uncertainties and model limitations. To this end, coordinated and benchmarking assessments of land-use-climate feedbacks can be leveraged for further improvements in ESM's agricultural representations. We suggest key areas for continued model development, including incorporating irrigation and biogeochemical cycling in particular. Last, we pose several critical research questions to guide future work. Our review focuses on ESM representations of climate-surface interactions over managed agricultural lands, rather than on ESMs as an estimation tool for crop yields and productivity.
Changes in U.S. Regional-Scale Air Quality at 2030 Simulated Using RCP 6.0

NASA Astrophysics Data System (ADS)

Nolte, C. G.; Otte, T.; Pinder, R. W.; Faluvegi, G.; Shindell, D. T.

2012-12-01

Recent improvements in air quality in the United States have been due to significant reductions in emissions of ozone and particulate matter (PM) precursors, and these downward emissions trends are expected to continue in the next few decades. To ensure that planned air quality regulations are robust under a range of possible future climates and to consider possible policy actions to mitigate climate change, it is important to characterize and understand the effects of climate change on air quality. Recent work by several research groups using global and regional models has demonstrated that there is a "climate penalty," in which climate change leads to increases in surface ozone levels in polluted continental regions. One approach to simulating future air quality at the regional scale is via dynamical downscaling, in which fields from a global climate model are used as input for a regional climate model, and these regional climate data are subsequently used for chemical transport modeling. However, recent studies using this approach have encountered problems with the downscaled regional climate fields, including unrealistic surface temperatures and misrepresentation of synoptic pressure patterns such as the Bermuda High. We developed a downscaling methodology and showed that it now reasonably simulates regional climate by evaluating it against historical data. In this work, regional climate simulations created by downscaling the NASA/GISS Model E2 global climate model are used as input for the Community Multiscale Air Quality (CMAQ) model. CMAQ simulations over the continental United States are conducted for two 11-year time slices, one representing current climate (1995-2005) and one following Representative Concentration Pathway 6.0 from 2025-2035. Anthropogenic emissions of ozone and PM precursors are held constant at year 2006 levels for both the current and future periods. In our presentation, we will examine the changes in ozone and PM concentrations, with particular focus on exceedances of the current U.S. air quality standards, and attempt to relate the changes in air quality to the projected changes in regional climate.
A Skilful Marine Sclerochronological Network Based Reconstruction of North Atlantic Subpolar Gyre Dynamics

NASA Astrophysics Data System (ADS)

Reynolds, D.; Hall, I. R.; Slater, S. M.; Scourse, J. D.; Wanamaker, A. D.; Halloran, P. R.; Garry, F. K.

2017-12-01

Spatial network analyses of precisely dated, and annually resolved, tree-ring proxy records have facilitated robust reconstructions of past atmospheric climate variability and the associated mechanisms and forcings that drive it. In contrast, a lack of similarly dated marine archives has constrained the use of such techniques in the marine realm, despite the potential for developing a more robust understanding of the role basin scale ocean dynamics play in the global climate system. Here we show that a spatial network of marine molluscan sclerochronological oxygen isotope (δ18Oshell) series spanning the North Atlantic region provides a skilful reconstruction of basin scale North Atlantic sea surface temperatures (SSTs). Our analyses demonstrate that the composite marine series (referred to as δ18Oproxy_PC1) is significantly sensitive to inter-annual variability in North Atlantic SSTs (R=-0.61 P<0.01) and surface air temperatures (SATs; R=-0.67, P<0.01) over the 20th century. Subpolar gyre (SPG) SSTs dominates variability in the δ18Oproxy_PC1 series at sub-centennial frequencies (R=-0.51, P<0.01). Comparison of the δ18Oproxy_PC1 series against variability in the strength of the European Slope Current and maximum North Atlantic meridional overturning circulation derived from numeric climate models (CMIP5), indicates that variability in the SPG region, associated with the strength of the surface currents of the North Atlantic, are playing a significant role in shaping the multi-decadal scale SST variability over the industrial era. These analyses demonstrate that spatial networks developed from sclerochronological archives can provide powerful baseline archives of past ocean variability that can facilitate the development of a quantitative understanding for the role the oceans play in the global climate systems and constraining uncertainties in numeric climate models.
Seasonal climate change patterns due to cumulative CO2 emissions

NASA Astrophysics Data System (ADS)

Partanen, Antti-Ilari; Leduc, Martin; Damon Matthews, H.

2017-07-01

Cumulative CO2 emissions are near linearly related to both global and regional changes in annual-mean surface temperature. These relationships are known as the transient climate response to cumulative CO2 emissions (TCRE) and the regional TCRE (RTCRE), and have been shown to remain approximately constant over a wide range of cumulative emissions. Here, we assessed how well this relationship holds for seasonal patterns of temperature change, as well as for annual-mean and seasonal precipitation patterns. We analyzed an idealized scenario with CO2 concentration growing at an annual rate of 1% using data from 12 Earth system models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Seasonal RTCRE values for temperature varied considerably, with the highest seasonal variation evident in the Arctic, where RTCRE was about 5.5 °C per Tt C for boreal winter and about 2.0 °C per Tt C for boreal summer. Also the precipitation response in the Arctic during boreal winter was stronger than during other seasons. We found that emission-normalized seasonal patterns of temperature change were relatively robust with respect to time, though they were sub-linear with respect to emissions particularly near the Arctic. Moreover, RTCRE patterns for precipitation could not be quantified robustly due to the large internal variability of precipitation. Our results suggest that cumulative CO2 emissions are a useful metric to predict regional and seasonal changes in precipitation and temperature. This extension of the TCRE framework to seasonal and regional climate change is helpful for communicating the link between emissions and climate change to policy-makers and the general public, and is well-suited for impact studies that could make use of estimated regional-scale climate changes that are consistent with the carbon budgets associated with global temperature targets.
Physiological-based modelling of marine fish early life stages provides process knowledge on climate impacts

NASA Astrophysics Data System (ADS)

Peck, M. A.

2016-02-01

Gaining a cause-and-effect understanding of climate-driven changes in marine fish populations at appropriate spatial scales is important for providing robust advice for ecosystem-based fisheries management. Coupling long-term, retrospective analyses and 3-d biophysical, individual-based models (IBMs) shows great potential to reveal mechanism underlying historical changes and to project future changes in marine fishes. IBMs created for marine fish early life stages integrate organismal-level physiological responses and climate-driven changes in marine habitats (from ocean physics to lower trophic level productivity) to test and reveal processes affecting marine fish recruitment. Case studies are provided for hindcasts and future (A1 and B2 projection) simulations performed on some of the most ecologically- and commercially-important pelagic and demersal fishes in the North Sea including European anchovy, Atlantic herring, European sprat and Atlantic cod. We discuss the utility of coupling biophysical IBMs to size-spectrum models to better project indirect (trophodynamic) pathways of climate influence on the early life stages of these and other fishes. Opportunities and challenges are discussed regarding the ability of these physiological-based tools to capture climate-driven changes in living marine resources and food web dynamics of shelf seas.
European climate change at global mean temperature increases of 1.5 and 2 °C above pre-industrial conditions as simulated by the EURO-CORDEX regional climate models

NASA Astrophysics Data System (ADS)

Kjellström, Erik; Nikulin, Grigory; Strandberg, Gustav; Bøssing Christensen, Ole; Jacob, Daniela; Keuler, Klaus; Lenderink, Geert; van Meijgaard, Erik; Schär, Christoph; Somot, Samuel; Sørland, Silje Lund; Teichmann, Claas; Vautard, Robert

2018-05-01

We investigate European regional climate change for time periods when the global mean temperature has increased by 1.5 and 2 °C compared to pre-industrial conditions. Results are based on regional downscaling of transient climate change simulations for the 21st century with global climate models (GCMs) from the fifth-phase Coupled Model Intercomparison Project (CMIP5). We use an ensemble of EURO-CORDEX high-resolution regional climate model (RCM) simulations undertaken at a computational grid of 12.5 km horizontal resolution covering Europe. The ensemble consists of a range of RCMs that have been used for downscaling different GCMs under the RCP8.5 forcing scenario. The results indicate considerable near-surface warming already at the lower 1.5 °C of warming. Regional warming exceeds that of the global mean in most parts of Europe, being the strongest in the northernmost parts of Europe in winter and in the southernmost parts of Europe together with parts of Scandinavia in summer. Changes in precipitation, which are less robust than the ones in temperature, include increases in the north and decreases in the south with a borderline that migrates from a northerly position in summer to a southerly one in winter. Some of these changes are already seen at 1.5 °C of warming but are larger and more robust at 2 °C. Changes in near-surface wind speed are associated with a large spread among individual ensemble members at both warming levels. Relatively large areas over the North Atlantic and some parts of the continent show decreasing wind speed while some ocean areas in the far north show increasing wind speed. The changes in temperature, precipitation and wind speed are shown to be modified by changes in mean sea level pressure, indicating a strong relationship with the large-scale circulation and its internal variability on decade-long timescales. By comparing to a larger ensemble of CMIP5 GCMs we find that the RCMs can alter the results, leading either to attenuation or amplification of the climate change signal in the underlying GCMs. We find that the RCMs tend to produce less warming and more precipitation (or less drying) in many areas in both winter and summer.
Projecting the Hydrologic Impacts of Climate Change on Montane Wetlands.

PubMed

Lee, Se-Yeun; Ryan, Maureen E; Hamlet, Alan F; Palen, Wendy J; Lawler, Joshua J; Halabisky, Meghan

2015-01-01

Wetlands are globally important ecosystems that provide critical services for natural communities and human society. Montane wetland ecosystems are expected to be among the most sensitive to changing climate, as their persistence depends on factors directly influenced by climate (e.g. precipitation, snowpack, evaporation). Despite their importance and climate sensitivity, wetlands tend to be understudied due to a lack of tools and data relative to what is available for other ecosystem types. Here, we develop and demonstrate a new method for projecting climate-induced hydrologic changes in montane wetlands. Using observed wetland water levels and soil moisture simulated by the physically based Variable Infiltration Capacity (VIC) hydrologic model, we developed site-specific regression models relating soil moisture to observed wetland water levels to simulate the hydrologic behavior of four types of montane wetlands (ephemeral, intermediate, perennial, permanent wetlands) in the U. S. Pacific Northwest. The hybrid models captured observed wetland dynamics in many cases, though were less robust in others. We then used these models to a) hindcast historical wetland behavior in response to observed climate variability (1916-2010 or later) and classify wetland types, and b) project the impacts of climate change on montane wetlands using global climate model scenarios for the 2040s and 2080s (A1B emissions scenario). These future projections show that climate-induced changes to key driving variables (reduced snowpack, higher evapotranspiration, extended summer drought) will result in earlier and faster drawdown in Pacific Northwest montane wetlands, leading to systematic reductions in water levels, shortened wetland hydroperiods, and increased probability of drying. Intermediate hydroperiod wetlands are projected to experience the greatest changes. For the 2080s scenario, widespread conversion of intermediate wetlands to fast-drying ephemeral wetlands will likely reduce wetland habitat availability for many species.
Projecting the Hydrologic Impacts of Climate Change on Montane Wetlands

PubMed Central

Hamlet, Alan F.; Palen, Wendy J.; Lawler, Joshua J.; Halabisky, Meghan

2015-01-01

Wetlands are globally important ecosystems that provide critical services for natural communities and human society. Montane wetland ecosystems are expected to be among the most sensitive to changing climate, as their persistence depends on factors directly influenced by climate (e.g. precipitation, snowpack, evaporation). Despite their importance and climate sensitivity, wetlands tend to be understudied due to a lack of tools and data relative to what is available for other ecosystem types. Here, we develop and demonstrate a new method for projecting climate-induced hydrologic changes in montane wetlands. Using observed wetland water levels and soil moisture simulated by the physically based Variable Infiltration Capacity (VIC) hydrologic model, we developed site-specific regression models relating soil moisture to observed wetland water levels to simulate the hydrologic behavior of four types of montane wetlands (ephemeral, intermediate, perennial, permanent wetlands) in the U. S. Pacific Northwest. The hybrid models captured observed wetland dynamics in many cases, though were less robust in others. We then used these models to a) hindcast historical wetland behavior in response to observed climate variability (1916–2010 or later) and classify wetland types, and b) project the impacts of climate change on montane wetlands using global climate model scenarios for the 2040s and 2080s (A1B emissions scenario). These future projections show that climate-induced changes to key driving variables (reduced snowpack, higher evapotranspiration, extended summer drought) will result in earlier and faster drawdown in Pacific Northwest montane wetlands, leading to systematic reductions in water levels, shortened wetland hydroperiods, and increased probability of drying. Intermediate hydroperiod wetlands are projected to experience the greatest changes. For the 2080s scenario, widespread conversion of intermediate wetlands to fast-drying ephemeral wetlands will likely reduce wetland habitat availability for many species. PMID:26331850
Mean-state acceleration of cloud-resolving models and large eddy simulations

DOE PAGES

Jones, C. R.; Bretherton, C. S.; Pritchard, M. S.

2015-10-29

In this study, large eddy simulations and cloud-resolving models (CRMs) are routinely used to simulate boundary layer and deep convective cloud processes, aid in the development of moist physical parameterization for global models, study cloud-climate feedbacks and cloud-aerosol interaction, and as the heart of superparameterized climate models. These models are computationally demanding, placing practical constraints on their use in these applications, especially for long, climate-relevant simulations. In many situations, the horizontal-mean atmospheric structure evolves slowly compared to the turnover time of the most energetic turbulent eddies. We develop a simple scheme to reduce this time scale separation to accelerate themore » evolution of the mean state. Using this approach we are able to accelerate the model evolution by a factor of 2–16 or more in idealized stratocumulus, shallow and deep cumulus convection without substantial loss of accuracy in simulating mean cloud statistics and their sensitivity to climate change perturbations. As a culminating test, we apply this technique to accelerate the embedded CRMs in the Superparameterized Community Atmosphere Model by a factor of 2, thereby showing that the method is robust and stable to realistic perturbations across spatial and temporal scales typical in a GCM.« less
Causes of model dry and warm bias over central U.S. and impact on climate projections.

PubMed

Lin, Yanluan; Dong, Wenhao; Zhang, Minghua; Xie, Yuanyu; Xue, Wei; Huang, Jianbin; Luo, Yong

2017-10-12

Climate models show a conspicuous summer warm and dry bias over the central United States. Using results from 19 climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5), we report a persistent dependence of warm bias on dry bias with the precipitation deficit leading the warm bias over this region. The precipitation deficit is associated with the widespread failure of models in capturing strong rainfall events in summer over the central U.S. A robust linear relationship between the projected warming and the present-day warm bias enables us to empirically correct future temperature projections. By the end of the 21st century under the RCP8.5 scenario, the corrections substantially narrow the intermodel spread of the projections and reduce the projected temperature by 2.5 K, resulting mainly from the removal of the warm bias. Instead of a sharp decrease, after this correction the projected precipitation is nearly neutral for all scenarios.Climate models repeatedly show a warm and dry bias over the central United States, but the origin of this bias remains unclear. Here the authors associate this bias to precipitation deficits in models and after applying a correction, projected precipitation in this region shows no significant changes.
How Sensitive Is the Carbon Budget Approach to Potential Carbon Cycle Changes?

NASA Astrophysics Data System (ADS)

Matthews, D.

2014-12-01

The recent development of global Earth-system models, which include dynamic representations of both physical climate and carbon cycle processes, has led to new insights about how the climate responds to human carbon dioxide emissions. Notably, several model analyses have now shown that global temperature responds linearly to cumulative CO2 emissions across a wide range of emissions scenarios. This implies that the timing of CO2 emissions does not affect the overall climate response, and allows a finite global carbon carbon budget to be defined for a given global temperature target. This linear climate response, however, emerges from the interaction of several non-linear processes and feedbacks involving how carbon sinks respond to changes in atmospheric CO2 and climate. In this presentation, I will give an overview of how carbon sinks and carbon cycle feedbacks contribute to the overall linearity of the climate response to cumulative emissions, and will assess how robust this relationship is to a range of possible changes in the carbon cycle, including (a) potential positive carbon cycle feedbacks that are not well represented in the current generation of Earth-system models and (b) negative emission scenarios resulting from possible technological strategies to remove CO2 from the atmosphere.
Enhanced poleward propagation of storms under climate change

NASA Astrophysics Data System (ADS)

Tamarin-Brodsky, Talia; Kaspi, Yohai

2017-12-01

Earth's midlatitudes are dominated by regions of large atmospheric weather variability—often referred to as storm tracks— which influence the distribution of temperature, precipitation and wind in the extratropics. Comprehensive climate models forced by increased greenhouse gas emissions suggest that under global warming the storm tracks shift poleward. While the poleward shift is a robust response across most models, there is currently no consensus on what the underlying dynamical mechanism is. Here we present a new perspective on the poleward shift, which is based on a Lagrangian view of the storm tracks. We show that in addition to a poleward shift in the genesis latitude of the storms, associated with the shift in baroclinicity, the latitudinal displacement of cyclonic storms increases under global warming. This is achieved by applying a storm-tracking algorithm to an ensemble of CMIP5 models. The increased latitudinal propagation in a warmer climate is shown to be a result of stronger upper-level winds and increased atmospheric water vapour. These changes in the propagation characteristics of the storms can have a significant impact on midlatitude climate.
Robust global ocean cooling trend for the pre-industrial Common Era

NASA Astrophysics Data System (ADS)

McGregor, Helen V.; Evans, Michael N.; Goosse, Hugues; Leduc, Guillaume; Martrat, Belen; Addison, Jason A.; Mortyn, P. Graham; Oppo, Delia W.; Seidenkrantz, Marit-Solveig; Sicre, Marie-Alexandrine; Phipps, Steven J.; Selvaraj, Kandasamy; Thirumalai, Kaustubh; Filipsson, Helena L.; Ersek, Vasile

2015-09-01

The oceans mediate the response of global climate to natural and anthropogenic forcings. Yet for the past 2,000 years -- a key interval for understanding the present and future climate response to these forcings -- global sea surface temperature changes and the underlying driving mechanisms are poorly constrained. Here we present a global synthesis of sea surface temperatures for the Common Era (CE) derived from 57 individual marine reconstructions that meet strict quality control criteria. We observe a cooling trend from 1 to 1800 CE that is robust against explicit tests for potential biases in the reconstructions. Between 801 and 1800 CE, the surface cooling trend is qualitatively consistent with an independent synthesis of terrestrial temperature reconstructions, and with a sea surface temperature composite derived from an ensemble of climate model simulations using best estimates of past external radiative forcings. Climate simulations using single and cumulative forcings suggest that the ocean surface cooling trend from 801 to 1800 CE is not primarily a response to orbital forcing but arises from a high frequency of explosive volcanism. Our results show that repeated clusters of volcanic eruptions can induce a net negative radiative forcing that results in a centennial and global scale cooling trend via a decline in mixed-layer oceanic heat content.
Robust global ocean cooling trend for the pre-industrial Common Era

USGS Publications Warehouse

McGregor, Helen V.; Evans, Michael N.; Goosse, Hugues; Leduc, Guillaume; Martrat, Belen; Addison, Jason A.; Mortyn, P. Graham; Oppo, Delia W.; Seidenkrantz, Marit-Solveig; Sicre, Marie-Alexandrine; Phipps, Steven J.; Selvaraj, Kandasamy; Thirumalai, Kaustubh; Filipsson, Helena L.; Ersek, Vasile

2015-01-01

The oceans mediate the response of global climate to natural and anthropogenic forcings. Yet for the past 2,000 years — a key interval for understanding the present and future climate response to these forcings — global sea surface temperature changes and the underlying driving mechanisms are poorly constrained. Here we present a global synthesis of sea surface temperatures for the Common Era (CE) derived from 57 individual marine reconstructions that meet strict quality control criteria. We observe a cooling trend from 1 to 1800 CEthat is robust against explicit tests for potential biases in the reconstructions. Between 801 and 1800 CE, the surface cooling trend is qualitatively consistent with an independent synthesis of terrestrial temperature reconstructions, and with a sea surface temperature composite derived from an ensemble of climate model simulations using best estimates of past external radiative forcings. Climate simulations using single and cumulative forcings suggest that the ocean surface cooling trend from 801 to 1800 CE is not primarily a response to orbital forcing but arises from a high frequency of explosive volcanism. Our results show that repeated clusters of volcanic eruptions can induce a net negative radiative forcing that results in a centennial and global scale cooling trend via a decline in mixed-layer oceanic heat content.
Heat waves in Africa and India: a multidisciplinary approach.

NASA Astrophysics Data System (ADS)

Janicot, Serge; Moron, Vincent; Oueslati, Boutheina; Pohl, Benjamin; Rome, Sandra; Lalou, Richard; Dos Santos, Stéphanie

2017-04-01

While the heat wave impacts on public health have been widely addressed in developed countries, less effort has been made to detect them and evaluate their impacts in least developed countries, especially in Africa and to a lesser extent in India, where climate is warmer and adaptation capacities are low. Climate and epidemiologic analyses show however that this problem is already present and climate projections indicate that such events should increase in frequency and intensity in the coming decades. However climate models display important temperature and radiative biases over this region, which must be reduced to provide robust information on the future evolution of heat waves. Moreover early warning systems have to face up to institutional malfunctions. This talk lays the elements for a multidisciplinary approach of tackling heat wave occurrences.

Regional Climate Change Impact on Agricultural Land Use in West Africa

NASA Astrophysics Data System (ADS)

Ahmed, K. F.; Wang, G.; You, L.

2014-12-01

Agriculture is a key element of the human-induced land use land cover change (LULCC) that is influenced by climate and can potentially influence regional climate. Temperature and precipitation directly impact the crop yield (by controlling photosynthesis, respiration and other physiological processes) that then affects agricultural land use pattern. In feedback, the resulting changes in land use and land cover play an important role to determine the direction and magnitude of global, regional and local climate change by altering Earth's radiative equilibrium. The assessment of future agricultural land use is, therefore, of great importance in climate change study. In this study, we develop a prototype land use projection model and, using this model, project the changes to land use pattern and future land cover map accounting for climate-induced yield changes for major crops in West Africa. Among the inputs to the land use projection model are crop yield changes simulated by the crop model DSSAT, driven with the climate forcing data from the regional climate model RegCM4.3.4-CLM4.5, which features a projected decrease of future mean crop yield and increase of inter-annual variability. Another input to the land use projection model is the projected changes of food demand in the future. In a so-called "dumb-farmer scenario" without any adaptation, the combined effect of decrease in crop yield and increase in food demand will lead to a significant increase in agricultural land use in future years accompanied by a decrease in forest and grass area. Human adaptation through land use optimization in an effort to minimize agricultural expansion is found to have little impact on the overall areas of agricultural land use. While the choice of the General Circulation Model (GCM) to derive initial and boundary conditions for the regional climate model can be a source of uncertainty in projecting the future LULCC, results from sensitivity experiments indicate that the changes in land use pattern are robust.
Building a foundation for continued dialogue between climate science and water resource communities

NASA Astrophysics Data System (ADS)

Vano, J. A.; Arnold, J.; Clark, M. P.; Gutmann, E. D.; Hamman, J.; Nijssen, B.; Wood, A.

2017-12-01

Research into climate change has led to the development of many global climate models, downscaling techniques, and impacts models. This proliferation of information has resulted in insights into how climate change will impact hydrology that are more robust than any single approach, which is helpful for advancing the science. However, the variety of approaches makes navigating what information to use in water resource planning and management challenging. Each technique has strengths and weaknesses and associated uncertainties, and approaches are always being updated. Here we provide a user-focused, modularly framed guidance that is designed to be expandable and where updates can be targeted. This includes describing dos and don'ts for how to use climate change information in water resource planning and management that can be read at multiple levels. It can provide context for those seeking to understand the general need, opportunities, and challenges of including climate change information. It also provides details (frequently asked questions and examples) and direction to further guidance and resources for those engaged in the technical work. This guidance is intended to provide a foundation for continued dialogue within and between the climate science and application communities, to increase the utility and appropriate use of climate change information.
Collaboration pathway(s) using new tools for optimizing operational climate monitoring from space

NASA Astrophysics Data System (ADS)

Helmuth, Douglas B.; Selva, Daniel; Dwyer, Morgan M.

2014-10-01

Consistently collecting the earth's climate signatures remains a priority for world governments and international scientific organizations. Architecting a solution requires transforming scientific missions into an optimized robust `operational' constellation that addresses the needs of decision makers, scientific investigators and global users for trusted data. The application of new tools offers pathways for global architecture collaboration. Recent (2014) rulebased decision engine modeling runs that targeted optimizing the intended NPOESS architecture, becomes a surrogate for global operational climate monitoring architecture(s). This rule-based systems tools provide valuable insight for Global climate architectures, through the comparison and evaluation of alternatives considered and the exhaustive range of trade space explored. A representative optimization of Global ECV's (essential climate variables) climate monitoring architecture(s) is explored and described in some detail with thoughts on appropriate rule-based valuations. The optimization tools(s) suggest and support global collaboration pathways and hopefully elicit responses from the audience and climate science shareholders.
Idealized climate change simulations with a high-resolution physical model: HadGEM3-GC2

NASA Astrophysics Data System (ADS)

Senior, Catherine A.; Andrews, Timothy; Burton, Chantelle; Chadwick, Robin; Copsey, Dan; Graham, Tim; Hyder, Pat; Jackson, Laura; McDonald, Ruth; Ridley, Jeff; Ringer, Mark; Tsushima, Yoko

2016-06-01

Idealized climate change simulations with a new physical climate model, HadGEM3-GC2 from The Met Office Hadley Centre are presented and contrasted with the earlier MOHC model, HadGEM2-ES. The role of atmospheric resolution is also investigated. The Transient Climate Response (TCR) is 1.9 K/2.1 K at N216/N96 and Effective Climate Sensitivity (ECS) is 3.1 K/3.2 K at N216/N96. These are substantially lower than HadGEM2-ES (TCR: 2.5 K; ECS: 4.6 K) arising from a combination of changes in the size of climate feedbacks. While the change in the net cloud feedback between HadGEM3 and HadGEM2 is relatively small, there is a change in sign of its longwave and a strengthening of its shortwave components. At a global scale, there is little impact of the increase in atmospheric resolution on the future climate change signal and even at a broad regional scale, many features are robust including tropical rainfall changes, however, there are some significant exceptions. For the North Atlantic and western Europe, the tripolar pattern of winter storm changes found in most CMIP5 models is little impacted by resolution but for the most intense storms, there is a larger percentage increase in number at higher resolution than at lower resolution. Arctic sea-ice sensitivity shows a larger dependence on resolution than on atmospheric physics.
To Tip or Not to Tip: The Case of the Congo Basin Rainforest Realm

NASA Astrophysics Data System (ADS)

Pietsch, S.; Bednar, J. E.; Fath, B. D.; Winter, P. A.

2017-12-01

The future response of the Congo basin rainforest, the second largest tropical carbon reservoir, to climate change is still under debate. Different Climate projections exist stating increase and decrease in rainfall and different changes in rainfall patterns. Within this study we assess all options of climate change possibilities to define the climatic thresholds of Congo basin rainforest stability and assess the limiting conditions for rainforest persistence. We use field data from 199 research plots from the Western Congo basin to calibrate and validate a complex BioGeoChemistry model (BGC-MAN) and assess model performance against an array of possible future climates. Next, we analyze the reasons for the occurrence of tipping points, their spatial and temporal probability of occurrence, will present effects of hysteresis and derive probabilistic spatial-temporal resilience landscapes for the region. Additionally, we will analyze attractors of forest growth dynamics and assess common linear measures for early warning signals of sudden shifts in system dynamics for their robustness in the context of the Congo Basin case, and introduce the correlation integral as a nonlinear measure of risk assessment.
Predicting the distributions of Egypt's medicinal plants and their potential shifts under future climate change.

PubMed

Kaky, Emad; Gilbert, Francis

2017-01-01

Climate change is one of the most difficult of challenges to conserving biodiversity, especially for countries with few data on the distributions of their taxa. Species distribution modelling is a modern approach to the assessment of the potential effects of climate change on biodiversity, with the great advantage of being robust to small amounts of data. Taking advantage of a recently validated dataset, we use the medicinal plants of Egypt to identify hotspots of diversity now and in the future by predicting the effect of climate change on the pattern of species richness using species distribution modelling. Then we assess how Egypt's current Protected Area network is likely to perform in protecting plants under climate change. The patterns of species richness show that in most cases the A2a 'business as usual' scenario was more harmful than the B2a 'moderate mitigation' scenario. Predicted species richness inside Protected Areas was higher than outside under all scenarios, indicating that Egypt's PAs are well placed to help conserve medicinal plants.
Robustness of plant-insect herbivore interaction networks to climate change in a fragmented temperate forest landscape.

PubMed

Bähner, K W; Zweig, K A; Leal, I R; Wirth, R

2017-10-01

Forest fragmentation and climate change are among the most severe and pervasive forms of human impact. Yet, their combined effects on plant-insect herbivore interaction networks, essential components of forest ecosystems with respect to biodiversity and functioning, are still poorly investigated, particularly in temperate forests. We addressed this issue by analysing plant-insect herbivore networks (PIHNs) from understories of three managed beech forest habitats: small forest fragments (2.2-145 ha), forest edges and forest interior areas within three continuous control forests (1050-5600 ha) in an old hyper-fragmented forest landscape in SW Germany. We assessed the impact of forest fragmentation, particularly edge effects, on PIHNs and the resulting differences in robustness against climate change by habitat-wise comparison of network topology and biologically realistic extinction cascades of networks following scores of vulnerability to climate change for the food plant species involved. Both the topological network metrics (complexity, nestedness, trophic niche redundancy) and robustness to climate change strongly increased in forest edges and fragments as opposed to the managed forest interior. The nature of the changes indicates that human impacts modify network structure mainly via host plant availability to insect herbivores. Improved robustness of PIHNs in forest edges/small fragments to climate-driven extinction cascades was attributable to an overall higher thermotolerance across plant communities, along with positive effects of network structure. The impoverishment of PIHNs in managed forest interiors and the suggested loss of insect diversity from climate-induced co-extinction highlight the need for further research efforts focusing on adequate silvicultural and conservation approaches.
Evaluation of climatic changes in South-Asia

NASA Astrophysics Data System (ADS)

Kjellstrom, Erik; Rana, Arun; Grigory, Nikulin; Renate, Wilcke; Hansson, Ulf; Kolax, Michael

2016-04-01

Literature has sufficient evidences of climate change impact all over the world and its impact on various sectors. In light of new advancements made in climate modeling, availability of several climate downscaling approaches, the more robust bias correction methods with varying complexities and strengths, in the present study we performed a systematic evaluation of climate change impact over South-Asia region. We have used different Regional Climate Models (RCMs) (from CORDEX domain), (Global Climate Models GCMs) and gridded observations for the study area to evaluate the models in historical/control period (1980-2010) and changes in future period (2010-2099). Firstly, GCMs and RCMs are evaluated against the Gridded observational datasets in the area using precipitation and temperature as indicative variables. Observational dataset are also evaluated against the reliable set of observational dataset, as pointed in literature. Bias, Correlation, and changes (among other statistical measures) are calculated for the entire region and both the variables. Eventually, the region was sub-divided into various smaller domains based on homogenous precipitation zones to evaluate the average changes over time period. Spatial and temporal changes for the region are then finally calculated to evaluate the future changes in the region. Future changes are calculated for 2 Representative Concentration Pathways (RCPs), the middle emission (RCP4.5) and high emission (RCP8.5) and for both climatic variables, precipitation and temperature. Lastly, Evaluation of Extremes is performed based on precipitation and temperature based indices for whole region in future dataset. Results have indicated that the whole study region is under extreme stress in future climate scenarios for both climatic variables i.e. precipitation and temperature. Precipitation variability is dependent on the location in the area leading to droughts and floods in various regions in future. Temperature is hinting towards a constant increase throughout the region regardless of location.
Challenges in identifying sites climatically matched to the native ranges of animal invaders.

PubMed

Rodda, Gordon H; Jarnevich, Catherine S; Reed, Robert N

2011-02-09

Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk.
Challenges in Identifying Sites Climatically Matched to the Native Ranges of Animal Invaders

PubMed Central

Rodda, Gordon H.; Jarnevich, Catherine S.; Reed, Robert N.

2011-01-01

Background Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Methodology/Principal Findings Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. Conclusions/Significance When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk. PMID:21347411
Challenges in identifying sites climatically matched to the native ranges of animal invaders

USGS Publications Warehouse

Rodda, G.H.; Jarnevich, C.S.; Reed, R.N.

2011-01-01

Background: Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Methodology/Principal Findings: Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. Conclusions/Significance: When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk.
Advancing land surface model development with satellite-based Earth observations

NASA Astrophysics Data System (ADS)

Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo

2017-04-01

The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help to improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology, but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability and understanding of climate system feedbacks. Orth, R., E. Dutra, I. F. Trigo, and G. Balsamo (2016): Advancing land surface model development with satellite-based Earth observations. Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-628
Modeling transport of nutrients & sediment loads into Lake Tahoe under climate change

USGS Publications Warehouse

Riverson, John; Coats, Robert; Costa-Cabral, Mariza; Dettinger, Mike; Reuter, John; Sahoo, Goloka; Schladow, Geoffrey

2013-01-01

The outputs from two General Circulation Models (GCMs) with two emissions scenarios were downscaled and bias-corrected to develop regional climate change projections for the Tahoe Basin. For one model—the Geophysical Fluid Dynamics Laboratory or GFDL model—the daily model results were used to drive a distributed hydrologic model. The watershed model used an energy balance approach for computing evapotranspiration and snowpack dynamics so that the processes remain a function of the climate change projections. For this study, all other aspects of the model (i.e. land use distribution, routing configuration, and parameterization) were held constant to isolate impacts of climate change projections. The results indicate that (1) precipitation falling as rain rather than snow will increase, starting at the current mean snowline, and moving towards higher elevations over time; (2) annual accumulated snowpack will be reduced; (3) snowpack accumulation will start later; and (4) snowmelt will start earlier in the year. Certain changes were masked (or counter-balanced) when summarized as basin-wide averages; however, spatial evaluation added notable resolution. While rainfall runoff increased at higher elevations, a drop in total precipitation volume decreased runoff and fine sediment load from the lower elevation meadow areas and also decreased baseflow and nitrogen loads basin-wide. This finding also highlights the important role that the meadow areas could play as high-flow buffers under climatic change. Because the watershed model accounts for elevation change and variable meteorological patterns, it provided a robust platform for evaluating the impacts of projected climate change on hydrology and water quality.
Mixed-phase cloud physics and Southern Ocean cloud feedback in climate models

DOE PAGES

McCoy, Daniel T.; Hartmann, Dennis L.; Zelinka, Mark D.; ...

2015-08-21

Increasing optical depth poleward of 45° is a robust response to warming in global climate models. Much of this cloud optical depth increase has been hypothesized to be due to transitions from ice-dominated to liquid-dominated mixed-phase cloud. In this study, the importance of liquid-ice partitioning for the optical depth feedback is quantified for 19 Coupled Model Intercomparison Project Phase 5 models. All models show a monotonic partitioning of ice and liquid as a function of temperature, but the temperature at which ice and liquid are equally mixed (the glaciation temperature) varies by as much as 40 K across models. Modelsmore » that have a higher glaciation temperature are found to have a smaller climatological liquid water path (LWP) and condensed water path and experience a larger increase in LWP as the climate warms. The ice-liquid partitioning curve of each model may be used to calculate the response of LWP to warming. It is found that the repartitioning between ice and liquid in a warming climate contributes at least 20% to 80% of the increase in LWP as the climate warms, depending on model. Intermodel differences in the climatological partitioning between ice and liquid are estimated to contribute at least 20% to the intermodel spread in the high-latitude LWP response in the mixed-phase region poleward of 45°S. As a result, it is hypothesized that a more thorough evaluation and constraint of global climate model mixed-phase cloud parameterizations and validation of the total condensate and ice-liquid apportionment against observations will yield a substantial reduction in model uncertainty in the high-latitude cloud response to warming.« less
Using Global Climate Data to Inform Long-Term Water Planning Decisions

NASA Astrophysics Data System (ADS)

Groves, D. G.; Lempert, R.

2008-12-01

Water managers throughout the world are working to consider climate change in their long-term water planning processes. The best available information regarding plausible future hydrologic conditions are largely derived from global circulation models and from paleoclimate data. To date there lacks a single approach for (1) utilizing these data in water management planning tools for analysis and (2) evaluating the myriad of possible adaptation options. This talk will describe several approaches being used at RAND to incorporate global projections of climate change into local, regional, and state-wide long-term water planning. It will draw on current work with the California Department of Water Resources and other local Western water agencies, and a recently completed project with the Inland Empire Utilities Agency. Work to date suggests that climate information can be assimilated into local water planning tools to help identify robust climate adaptation water management strategies.
Nonlinear regional warming with increasing CO2 concentrations

NASA Astrophysics Data System (ADS)

Good, Peter; Lowe, Jason A.; Andrews, Timothy; Wiltshire, Andrew; Chadwick, Robin; Ridley, Jeff K.; Menary, Matthew B.; Bouttes, Nathaelle; Dufresne, Jean Louis; Gregory, Jonathan M.; Schaller, Nathalie; Shiogama, Hideo

2015-02-01

When considering adaptation measures and global climate mitigation goals, stakeholders need regional-scale climate projections, including the range of plausible warming rates. To assist these stakeholders, it is important to understand whether some locations may see disproportionately high or low warming from additional forcing above targets such as 2 K (ref. ). There is a need to narrow uncertainty in this nonlinear warming, which requires understanding how climate changes as forcings increase from medium to high levels. However, quantifying and understanding regional nonlinear processes is challenging. Here we show that regional-scale warming can be strongly superlinear to successive CO2 doublings, using five different climate models. Ensemble-mean warming is superlinear over most land locations. Further, the inter-model spread tends to be amplified at higher forcing levels, as nonlinearities grow--especially when considering changes per kelvin of global warming. Regional nonlinearities in surface warming arise from nonlinearities in global-mean radiative balance, the Atlantic meridional overturning circulation, surface snow/ice cover and evapotranspiration. For robust adaptation and mitigation advice, therefore, potentially avoidable climate change (the difference between business-as-usual and mitigation scenarios) and unavoidable climate change (change under strong mitigation scenarios) may need different analysis methods.
North Atlantic summers have warmed more than winters since 1353, and the response of marine zooplankton.

PubMed

Kamenos, Nicholas A

2010-12-28

Modeling and measurements show that Atlantic marine temperatures are rising; however, the low temporal resolution of models and restricted spatial resolution of measurements (i) mask regional details critical for determining the rate and extent of climate variability, and (ii) prevent robust determination of climatic impacts on marine ecosystems. To address both issues for the North East Atlantic, a fortnightly resolution marine climate record from 1353-2006 was constructed for shallow inshore waters and compared to changes in marine zooplankton abundance. For the first time summer marine temperatures are shown to have increased nearly twice as much as winter temperatures since 1353. Additional climatic instability began in 1700 characterized by ∼5-65 year climate oscillations that appear to be a recent phenomenon. Enhanced summer-specific warming reduced the abundance of the copepod Calanus finmarchicus, a key food item of cod, and led to significantly lower projected abundances by 2040 than at present. The faster increase of summer marine temperatures has implications for climate projections and affects abundance, and thus biomass, near the base of the marine food web with potentially significant feedback effects for marine food security.
CO2, the greenhouse effect and global warming: from the pioneering work of Arrhenius and Callendar to today's Earth System Models.

PubMed

Anderson, Thomas R; Hawkins, Ed; Jones, Philip D

2016-09-01

Climate warming during the course of the twenty-first century is projected to be between 1.0 and 3.7°C depending on future greenhouse gas emissions, based on the ensemble-mean results of state-of-the-art Earth System Models (ESMs). Just how reliable are these projections, given the complexity of the climate system? The early history of climate research provides insight into the understanding and science needed to answer this question. We examine the mathematical quantifications of planetary energy budget developed by Svante Arrhenius (1859-1927) and Guy Stewart Callendar (1898-1964) and construct an empirical approximation of the latter, which we show to be successful at retrospectively predicting global warming over the course of the twentieth century. This approximation is then used to calculate warming in response to increasing atmospheric greenhouse gases during the twenty-first century, projecting a temperature increase at the lower bound of results generated by an ensemble of ESMs (as presented in the latest assessment by the Intergovernmental Panel on Climate Change). This result can be interpreted as follows. The climate system is conceptually complex but has at its heart the physical laws of radiative transfer. This basic, or "core" physics is relatively straightforward to compute mathematically, as exemplified by Callendar's calculations, leading to quantitatively robust projections of baseline warming. The ESMs include not only the physical core but also climate feedbacks that introduce uncertainty into the projections in terms of magnitude, but not sign: positive (amplification of warming). As such, the projections of end-of-century global warming by ESMs are fundamentally trustworthy: quantitatively robust baseline warming based on the well-understood physics of radiative transfer, with extra warming due to climate feedbacks. These projections thus provide a compelling case that global climate will continue to undergo significant warming in response to ongoing emissions of CO 2 and other greenhouse gases to the atmosphere. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Climate-driven coral reorganisation influences aggressive behaviour in juvenile coral-reef fishes

NASA Astrophysics Data System (ADS)

Kok, Judith E.; Graham, Nicholas A. J.; Hoogenboom, Mia O.

2016-06-01

Globally, habitat degradation is altering the abundance and diversity of species in a variety of ecosystems. This study aimed to determine how habitat degradation, in terms of changing coral composition under climate change, affected abundance, species richness and aggressive behaviour of juveniles of three damselfishes ( Pomacentrus moluccensis, P. amboinensis and Dischistodus perspicillatus, in order of decreasing reliance on coral). Patch reefs were constructed to simulate two types of reefs: present-day reefs that are vulnerable to climate-induced coral bleaching, and reefs with more bleaching-robust coral taxa, thereby simulating the likely future of coral reefs under a warming climate. Fish communities were allowed to establish naturally on the reefs during the summer recruitment period. Climate-robust reefs had lower total species richness of coral-reef fishes than climate-vulnerable reefs, but total fish abundance was not significantly different between reef types (pooled across all species and life-history stages). The nature of aggressive interactions, measured as the number of aggressive chases, varied according to coral composition; on climate-robust reefs, juveniles used the substratum less often to avoid aggression from competitors, and interspecific aggression became relatively more frequent than intraspecific aggression for juveniles of the coral-obligate P. moluccensis. This study highlights the importance of coral composition as a determinant of behaviour and diversity of coral-reef fishes.
Future Climate Change in the Baltic Sea Area

NASA Astrophysics Data System (ADS)

Bøssing Christensen, Ole; Kjellström, Erik; Zorita, Eduardo; Sonnenborg, Torben; Meier, Markus; Grinsted, Aslak

2015-04-01

Regional climate models have been used extensively since the first assessment of climate change in the Baltic Sea region published in 2008, not the least for studies of Europe (and including the Baltic Sea catchment area). Therefore, conclusions regarding climate model results have a better foundation than was the case for the first BACC report of 2008. This presentation will report model results regarding future climate. What is the state of understanding about future human-driven climate change? We will cover regional models, statistical downscaling, hydrological modelling, ocean modelling and sea-level change as it is projected for the Baltic Sea region. Collections of regional model simulations from the ENSEMBLES project for example, financed through the European 5th Framework Programme and the World Climate Research Programme Coordinated Regional Climate Downscaling Experiment, have made it possible to obtain an increasingly robust estimation of model uncertainty. While the first Baltic Sea assessment mainly used four simulations from the European 5th Framework Programme PRUDENCE project, an ensemble of 13 transient regional simulations with twice the horizontal resolution reaching the end of the 21st century has been available from the ENSEMBLES project; therefore it has been possible to obtain more quantitative assessments of model uncertainty. The literature about future climate change in the Baltic Sea region is largely built upon the ENSEMBLES project. Also within statistical downscaling, a considerable number of papers have been published, encompassing now the application of non-linear statistical models, projected changes in extremes and correction of climate model biases. The uncertainty of hydrological change has received increasing attention since the previous Baltic Sea assessment. Several studies on the propagation of uncertainties originating in GCMs, RCMs, and emission scenarios are presented. The number of studies on uncertainties related to downscaling and impact models is relatively small, but more are emerging. A large number of coupled climate-environmental scenario simulations for the Baltic Sea have been performed within the BONUS+ projects (ECOSUPPORT, INFLOW, AMBER and Baltic-C (2009-2011)), using various combinations of output from GCMs, RCMs, hydrological models and scenarios for load and emission of nutrients as forcing for Baltic Sea models. Such a large ensemble of scenario simulations for the Baltic Sea has never before been produced and enables for the first time an estimation of uncertainties.

Using an ensemble of regional climate models to assess climate change impacts on water scarcity in European river basins.

PubMed

Gampe, David; Nikulin, Grigory; Ludwig, Ralf

2016-12-15

Climate change will likely increase pressure on the water balances of Mediterranean basins due to decreasing precipitation and rising temperatures. To overcome the issue of data scarcity the hydrological relevant variables total runoff, surface evaporation, precipitation and air temperature are taken from climate model simulations. The ensemble applied in this study consists of 22 simulations, derived from different combinations of four General Circulation Models (GCMs) forcing different Regional Climate Models (RCMs) and two Representative Concentration Pathways (RCPs) at ~12km horizontal resolution provided through the EURO-CORDEX initiative. Four river basins (Adige, Ebro, Evrotas and Sava) are selected and climate change signals for the future period 2035-2065 as compared to the reference period 1981-2010 are investigated. Decreased runoff and evaporation indicate increased water scarcity over the Ebro and the Evrotas, as well as the southern parts of the Adige and the Sava, resulting from a temperature increase of 1-3° and precipitation decrease of up to 30%. Most severe changes are projected for the summer months indicating further pressure on the river basins already at least partly characterized by flow intermittency. The widely used Falkenmark indicator is presented and confirms this tendency and shows the necessity for spatially distributed analysis and high resolution projections. Related uncertainties are addressed by the means of a variance decomposition and model agreement to determine the robustness of the projections. The study highlights the importance of high resolution climate projections and represents a feasible approach to assess climate impacts on water scarcity also in regions that suffer from data scarcity. Copyright Â© 2016. Published by Elsevier B.V.
Can we trust climate models to realistically represent severe European windstorms?

NASA Astrophysics Data System (ADS)

Trzeciak, Tomasz M.; Knippertz, Peter; Owen, Jennifer S. R.

2014-05-01

Despite the enormous advances made in climate change research, robust projections of the position and the strength of the North Atlantic stormtrack are not yet possible. In particular with respect to damaging windstorms, this incertitude bears enormous risks to European societies and the (re)insurance industry. Previous studies have addressed the problem of climate model uncertainty through statistical comparisons of simulations of the current climate with (re-)analysis data and found that there is large disagreement between different climate models, different ensemble members of the same model and observed climatologies of intense cyclones. One weakness of such statistical evaluations lies in the difficulty to separate influences of the climate model's basic state from the influence of fast processes on the development of the most intense storms. Compensating effects between the two might conceal errors and suggest higher reliability than there really is. A possible way to separate influences of fast and slow processes in climate projections is through a "seamless" approach of hindcasting historical, severe storms with climate models started from predefined initial conditions and run in a numerical weather prediction mode on the time scale of several days. Such a cost-effective case-study approach, which draws from and expands on the concepts from the Transpose-AMIP initiative, has recently been undertaken in the SEAMSEW project at the University of Leeds funded by the AXA Research Fund. Key results from this work focusing on 20 historical storms and using different lead times and horizontal and vertical resolutions include: (a) Tracks are represented reasonably well by most hindcasts. (b) Sensitivity to vertical resolution is low. (c) There is a systematic underprediction of cyclone depth for a coarse resolution of T63, but surprisingly no systematic bias is found for higher-resolution runs using T127, showing that climate models are in fact able to represent the storm dynamics well, if given the correct initial conditions. Combined with a too low number of deep cyclones in many climate models, this points too an insufficient number of storm-prone initial conditions in free-running climate runs. This question will be addressed in future work.
Transitions between multiple equilibria of paleo climate: a glimpse in to the dynamics of abrupt climate change

NASA Astrophysics Data System (ADS)

Ferreira, David; Marshall, John; Ito, Takamitsu; McGee, David; Moreno-Chamarro, Eduardo

2017-04-01

The dynamics regulating large climatic transitions such as glacial-interglacial cycles or DO events remains a puzzle. Forcings behind these transitions are not robustly identified and potential candidates (e.g. Milankovitch cycles, freshwater perturbations) often appear too weak to explain such dramatic transitions. A potential solution to this long-standing puzzle is that Earth's climate is endowed with multiple equilibrium states of global extent. Such states are commonly found in low-order or conceptual climate models, but it is unclear whether a system as complex as Earth's climate can sustain multiple equilibrium states. Here we report that multiple equilibrium states of the climate system are also possible in a complex, fully dynamical coupled ocean-atmosphere-sea ice GCM with idealized Earth-like geometry, resolved weather systems and a hydrological cycle. In our model, two equilibrium states coexist for the same parameters and external forcings: a Warm climate with a small Northern hemisphere sea ice cap and a large southern one and a Cold climate with large ice caps at both poles. The dynamical states of the Warm and Cold solutions exhibit striking similarities with our present-day climate and the climate of the Last Glacial Maximum, respectively. A carbon cycle model driven by the two dynamical states produces an atmospheric pCO2 draw-down of about 110 pm between the Warm and Cold states, close to Glacial-Interglacial differences found in ice cores. Mechanism controlling the existence of the multiple states and changes in the atmospheric CO2 will be briefly presented. Finally we willdescribe transition experiments from the Cold to the Warm state, focusing on the lead-lags in the system, notably between the Northern and Southern Hemispheres climates.
Assessing climate change and socio-economic uncertainties in long term management of water resources

NASA Astrophysics Data System (ADS)

Jahanshahi, Golnaz; Dawson, Richard; Walsh, Claire; Birkinshaw, Stephen; Glenis, Vassilis

2015-04-01

Long term management of water resources is challenging for decision makers given the range of uncertainties that exist. Such uncertainties are a function of long term drivers of change, such as climate, environmental loadings, demography, land use and other socio economic drivers. Impacts of climate change on frequency of extreme events such as drought make it a serious threat to water resources and water security. The release of probabilistic climate information, such as the UKCP09 scenarios, provides improved understanding of some uncertainties in climate models. This has motivated a more rigorous approach to dealing with other uncertainties in order to understand the sensitivity of investment decisions to future uncertainty and identify adaptation options that are as far as possible robust. We have developed and coupled a system of models that includes a weather generator, simulations of catchment hydrology, demand for water and the water resource system. This integrated model has been applied in the Thames catchment which supplies the city of London, UK. This region is one of the driest in the UK and hence sensitive to water availability. In addition, it is one of the fastest growing parts of the UK and plays an important economic role. Key uncertainties in long term water resources in the Thames catchment, many of which result from earth system processes, are identified and quantified. The implications of these uncertainties are explored using a combination of uncertainty analysis and sensitivity testing. The analysis shows considerable uncertainty in future rainfall, river flow and consequently water resource. For example, results indicate that by the 2050s, low flow (Q95) in the Thames catchment will range from -44 to +9% compared with the control scenario (1970s). Consequently, by the 2050s the average number of drought days are expected to increase 4-6 times relative to the 1970s. Uncertainties associated with urban growth increase these risks further. Adaptation measures, such as new reservoirs can manage these risks to a certain extent, but our sensitivity testing demonstrates that they are less robust to future uncertainties than measures taken to reduce water demand. Keywords: Climate change, Uncertainty, Decision making, Drought, Risk, Water resources management.
A Robust Decision-Making Technique for Water Management under Decadal Scale Climate Variability

NASA Astrophysics Data System (ADS)

Callihan, L.; Zagona, E. A.; Rajagopalan, B.

2013-12-01

Robust decision making, a flexible and dynamic approach to managing water resources in light of deep uncertainties associated with climate variability at inter-annual to decadal time scales, is an analytical framework that detects when a system is in or approaching a vulnerable state. It provides decision makers the opportunity to implement strategies that both address the vulnerabilities and perform well over a wide range of plausible future scenarios. A strategy that performs acceptably over a wide range of possible future states is not likely to be optimal with respect to the actual future state. The degree of success--the ability to avoid vulnerable states and operate efficiently--thus depends on the skill in projecting future states and the ability to select the most efficient strategies to address vulnerabilities. This research develops a robust decision making framework that incorporates new methods of decadal scale projections with selection of efficient strategies. Previous approaches to water resources planning under inter-annual climate variability combining skillful seasonal flow forecasts with climatology for subsequent years are not skillful for medium term (i.e. decadal scale) projections as decision makers are not able to plan adequately to avoid vulnerabilities. We address this need by integrating skillful decadal scale streamflow projections into the robust decision making framework and making the probability distribution of this projection available to the decision making logic. The range of possible future hydrologic scenarios can be defined using a variety of nonparametric methods. Once defined, an ensemble projection of decadal flow scenarios are generated from a wavelet-based spectral K-nearest-neighbor resampling approach using historical and paleo-reconstructed data. This method has been shown to generate skillful medium term projections with a rich variety of natural variability. The current state of the system in combination with the probability distribution of the projected flow ensembles enables the selection of appropriate decision options. This process is repeated for each year of the planning horizon--resulting in system outcomes that can be evaluated on their performance and resiliency. The research utilizes the RiverSMART suite of software modeling and analysis tools developed under the Bureau of Reclamation's WaterSMART initiative and built around the RiverWare modeling environment. A case study is developed for the Gunnison and Upper Colorado River Basins. The ability to mitigate vulnerability using the framework is gauged by system performance indicators that measure the ability of the system to meet various water demands (i.e. agriculture, environmental flows, hydropower etc.). Options and strategies for addressing vulnerabilities include measures such as conservation, reallocation and adjustments to operational policy. In addition to being able to mitigate vulnerabilities, options and strategies are evaluated based on benefits, costs and reliability. Flow ensembles are also simulated to incorporate mean and variance from climate change projections for the planning horizon and the above robust decision-making framework is applied to evaluate its performance under changing climate.
Modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change

USGS Publications Warehouse

Reese, Gordon; Skagen, Susan K.

2017-01-01

To identify areas on the landscape that may contribute to a robust network of conservation areas, we modeled the probabilities of occurrence of several en route migratory shorebirds and wintering waterfowl in the southern Great Plains of North America, including responses to changing climate. We predominantly used data from the eBird citizen-science project to model probabilities of occurrence relative to land-use patterns, spatial distribution of wetlands, and climate. We projected models to potential future climate conditions using five representative general circulation models of the Coupled Model Intercomparison Project 5 (CMIP5). We used Random Forests to model probabilities of occurrence and compared the time periods 1981–2010 (hindcast) and 2041–2070 (forecast) in “model space.” Projected changes in shorebird probabilities of occurrence varied with species-specific general distribution pattern, migration distance, and spatial extent. Species using the western and northern portion of the study area exhibited the greatest likelihoods of decline, whereas species with more easterly occurrences, mostly long-distance migrants, had the greatest projected increases in probability of occurrence. At an ecoregional extent, differences in probabilities of shorebird occurrence ranged from −0.015 to 0.045 when averaged across climate models, with the largest increases occurring early in migration. Spatial shifts are predicted for several shorebird species. Probabilities of occurrence of wintering Mallards and Northern Pintail are predicted to increase by 0.046 and 0.061, respectively, with northward shifts projected for both species. When incorporated into partner land management decision tools, results at ecoregional extents can be used to identify wetland complexes with the greatest potential to support birds in the nonbreeding season under a wide range of future climate scenarios.
Reference aquaplanet climate in the Community Atmosphere Model, Version 5

DOE PAGES

Medeiros, Brian; Williamson, David L.; Olson, Jerry G.

2016-03-18

In this study, fundamental characteristics of the aquaplanet climate simulated by the Community Atmosphere Model, Version 5.3 (CAM5.3) are presented. The assumptions and simplifications of the configuration are described. A 16 year long, perpetual equinox integration with prescribed SST using the model’s standard 18 grid spacing is presented as a reference simulation. Statistical analysis is presented that shows similar aquaplanet configurations can be run for about 2 years to obtain robust climatological structures, including global and zonal means, eddy statistics, and precipitation distributions. Such a simulation can be compared to the reference simulation to discern differences in the climate, includingmore » an assessment of confidence in the differences. To aid such comparisons, the reference simulation has been made available via earthsystemgrid.org. Examples are shown comparing the reference simulation with simulations from the CAM5 series that make different microphysical assumptions and use a different dynamical core.« less
Seasonality of eddy kinetic energy in an eddy permitting global climate model

NASA Astrophysics Data System (ADS)

Uchida, Takaya; Abernathey, Ryan; Smith, Shafer

2017-10-01

We examine the seasonal cycle of upper-ocean mesoscale turbulence in a high resolution CESM climate simulation. The ocean model component (POP) has 0.1° resolution, mesoscale resolving at low and middle latitudes. Seasonally and regionally resolved wavenumber power spectra are calculated for sea-surface eddy kinetic energy (EKE). Although the interpretation of the spectral slopes in terms of turbulence theory is complicated by the strong presence of dissipation and the narrow inertial range, the EKE spectra consistently show higher power at small scales during winter throughout the ocean. Potential hypotheses for this seasonality are investigated. Diagnostics of baroclinc energy conversion rates and evidence from linear quasigeostrophic stability analysis indicate that seasonally varying mixed-layer instability is responsible for the seasonality in EKE. The ability of this climate model, which is not considered submesoscale resolving, to produce mixed layer instability although damped by dissipation, demonstrates the ubiquity and robustness of this process for modulating upper ocean EKE.
Can we trust climate models to realistically represent severe European windstorms?

NASA Astrophysics Data System (ADS)

Trzeciak, Tomasz M.; Knippertz, Peter; Pirret, Jennifer S. R.; Williams, Keith D.

2016-06-01

Cyclonic windstorms are one of the most important natural hazards for Europe, but robust climate projections of the position and the strength of the North Atlantic storm track are not yet possible, bearing significant risks to European societies and the (re)insurance industry. Previous studies addressing the problem of climate model uncertainty through statistical comparisons of simulations of the current climate with (re-)analysis data show large disagreement between different climate models, different ensemble members of the same model and observed climatologies of intense cyclones. One weakness of such evaluations lies in the difficulty to separate influences of the climate model's basic state from the influence of fast processes on the development of the most intense storms, which could create compensating effects and therefore suggest higher reliability than there really is. This work aims to shed new light into this problem through a cost-effective "seamless" approach of hindcasting 20 historical severe storms with the two global climate models, ECHAM6 and GA4 configuration of the Met Office Unified Model, run in a numerical weather prediction mode using different lead times, and horizontal and vertical resolutions. These runs are then compared to re-analysis data. The main conclusions from this work are: (a) objectively identified cyclone tracks are represented satisfactorily by most hindcasts; (b) sensitivity to vertical resolution is low; (c) cyclone depth is systematically under-predicted for a coarse resolution of T63 by both climate models; (d) no systematic bias is found for the higher resolution of T127 out to about three days, demonstrating that climate models are in fact able to represent the complex dynamics of explosively deepening cyclones well, if given the correct initial conditions; (e) an analysis using a recently developed diagnostic tool based on the surface pressure tendency equation points to too weak diabatic processes, mainly latent heating, as the main source for the under-prediction in the coarse-resolution runs. Finally, an interesting implication of these results is that the too low number of deep cyclones in many free-running climate simulations may therefore be related to an insufficient number of storm-prone initial conditions. This question will be addressed in future work.
Particulate matter air pollution in Europe in a +2 °C warming world

NASA Astrophysics Data System (ADS)

Lacressonnière, Gwendoline; Watson, Laura; Gauss, Michael; Engardt, Magnuz; Andersson, Camilla; Beekmann, Matthias; Colette, Augustin; Foret, Gilles; Josse, Béatrice; Marécal, Virginie; Nyiri, Agnes; Siour, Guillaume; Sobolowski, Stefan; Vautard, Robert

2017-04-01

In the framework of the IMPACT2C project, we have evaluated the future European particulate matter concentrations under the influence of climate change and anthropogenic emission reductions. To do so, 30-year simulations for present and future scenarios were performed with an ensemble of four regional Chemical Transport Models. +2 °C scenarios were issued from different regional climate simulations belonging to the CORDEX experiment (RCP4.5 scenario). Comparing present day simulations to observations shows that these simulations meet the requested quality criteria even if some biases do exist. Also, we showed that using regional climate models instead of meteorological reanalysis was not critical for the quality of our simulations. Present day as well as future scenarios show the large variability between models associated with different meteorology and process parameterizations. Future projections of PM concentrations show a large reduction of PM10 and PM2.5 concentrations in a +2 °C climate over the European continent (especially over Benelux), which can be mostly attributed to emission reduction policies. Under a current legislation scenario, annual PM10 could be reduced by between 1.8 and 2.9 μg m-3 (14.1-20.4%). If maximum technologically feasible emission reductions were implemented, further reductions of 1.4-1.9 μg m-3 (18.6-20.9%) are highlighted. Changes due to a +2 °C warming, in isolation from emission changes, are in general much weaker (-1.1 to +0.4 μg m-3,-0.3 to +5.1% for annual PM10 averaged over the European domain). Even if large differences exist between models, we have determined that the decrease of PM over Europe associated with emission reduction is a robust result. The patterns of PM changes resulting from climate change (for example the increase of PM over Spain and southern France and the decrease of PM10 over eastern Europe) are also robustly predicted even if its amplitude remains weak compared to changes associated with emission reductions.
Simulated crop yield in response to changes in climate and agricultural practices: results from a simple process based model

NASA Astrophysics Data System (ADS)

Caldararu, S.; Smith, M. J.; Purves, D.; Emmott, S.

2013-12-01

Global agriculture will, in the future, be faced with two main challenges: climate change and an increase in global food demand driven by an increase in population and changes in consumption habits. To be able to predict both the impacts of changes in climate on crop yields and the changes in agricultural practices necessary to respond to such impacts we currently need to improve our understanding of crop responses to climate and the predictive capability of our models. Ideally, what we would have at our disposal is a modelling tool which, given certain climatic conditions and agricultural practices, can predict the growth pattern and final yield of any of the major crops across the globe. We present a simple, process-based crop growth model based on the assumption that plants allocate above- and below-ground biomass to maintain overall carbon optimality and that, to maintain this optimality, the reproductive stage begins at peak nitrogen uptake. The model includes responses to available light, water, temperature and carbon dioxide concentration as well as nitrogen fertilisation and irrigation. The model is data constrained at two sites, the Yaqui Valley, Mexico for wheat and the Southern Great Plains flux site for maize and soybean, using a robust combination of space-based vegetation data (including data from the MODIS and Landsat TM and ETM+ instruments), as well as ground-based biomass and yield measurements. We show a number of climate response scenarios, including increases in temperature and carbon dioxide concentrations as well as responses to irrigation and fertiliser application.
Reservoir adaptive operating rules based on both of historical streamflow and future projections

NASA Astrophysics Data System (ADS)

Zhang, Wei; Liu, Pan; Wang, Hao; Chen, Jie; Lei, Xiaohui; Feng, Maoyuan

2017-10-01

Climate change is affecting hydrological variables and consequently is impacting water resources management. Historical strategies are no longer applicable under climate change. Therefore, adaptive management, especially adaptive operating rules for reservoirs, has been developed to mitigate the possible adverse effects of climate change. However, to date, adaptive operating rules are generally based on future projections involving uncertainties under climate change, yet ignoring historical information. To address this, we propose an approach for deriving adaptive operating rules considering both historical information and future projections, namely historical and future operating rules (HAFOR). A robustness index was developed by comparing benefits from HAFOR with benefits from conventional operating rules (COR). For both historical and future streamflow series, maximizations of both average benefits and the robustness index were employed as objectives, and four trade-offs were implemented to solve the multi-objective problem. Based on the integrated objective, the simulation-based optimization method was used to optimize the parameters of HAFOR. Using the Dongwushi Reservoir in China as a case study, HAFOR was demonstrated to be an effective and robust method for developing adaptive operating rules under the uncertain changing environment. Compared with historical or projected future operating rules (HOR or FPOR), HAFOR can reduce the uncertainty and increase the robustness for future projections, especially regarding results of reservoir releases and volumes. HAFOR, therefore, facilitates adaptive management in the context that climate change is difficult to predict accurately.
How uncertain are climate model projections of water availability indicators across the Middle East?

PubMed

Hemming, Debbie; Buontempo, Carlo; Burke, Eleanor; Collins, Mat; Kaye, Neil

2010-11-28

The projection of robust regional climate changes over the next 50 years presents a considerable challenge for the current generation of climate models. Water cycle changes are particularly difficult to model in this area because major uncertainties exist in the representation of processes such as large-scale and convective rainfall and their feedback with surface conditions. We present climate model projections and uncertainties in water availability indicators (precipitation, run-off and drought index) for the 1961-1990 and 2021-2050 periods. Ensembles from two global climate models (GCMs) and one regional climate model (RCM) are used to examine different elements of uncertainty. Although all three ensembles capture the general distribution of observed annual precipitation across the Middle East, the RCM is consistently wetter than observations, especially over the mountainous areas. All future projections show decreasing precipitation (ensemble median between -5 and -25%) in coastal Turkey and parts of Lebanon, Syria and Israel and consistent run-off and drought index changes. The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) GCM ensemble exhibits drying across the north of the region, whereas the Met Office Hadley Centre work Quantifying Uncertainties in Model ProjectionsAtmospheric (QUMP-A) GCM and RCM ensembles show slight drying in the north and significant wetting in the south. RCM projections also show greater sensitivity (both wetter and drier) and a wider uncertainty range than QUMP-A. The nature of these uncertainties suggests that both large-scale circulation patterns, which influence region-wide drying/wetting patterns, and regional-scale processes, which affect localized water availability, are important sources of uncertainty in these projections. To reduce large uncertainties in water availability projections, it is suggested that efforts would be well placed to focus on the understanding and modelling of both large-scale processes and their teleconnections with Middle East climate and localized processes involved in orographic precipitation.
Pollen-based continental climate reconstructions at 6 and 21 ka: A global synthesis

USGS Publications Warehouse

Bartlein, P.J.; Harrison, S.P.; Brewer, Sandra; Connor, S.; Davis, B.A.S.; Gajewski, K.; Guiot, J.; Harrison-Prentice, T. I.; Henderson, A.; Peyron, O.; Prentice, I.C.; Scholze, M.; Seppa, H.; Shuman, B.; Sugita, S.; Thompson, R.S.; Viau, A.E.; Williams, J.; Wu, H.

2011-01-01

Subfossil pollen and plant macrofossil data derived from 14C-dated sediment profiles can provide quantitative information on glacial and interglacial climates. The data allow climate variables related to growing-season warmth, winter cold, and plant-available moisture to be reconstructed. Continental-scale reconstructions have been made for the mid-Holocene (MH, around 6 ka) and Last Glacial Maximum (LGM, around 21 ka), allowing comparison with palaeoclimate simulations currently being carried out as part of the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change. The synthesis of the available MH and LGM climate reconstructions and their uncertainties, obtained using modern-analogue, regression and model-inversion techniques, is presented for four temperature variables and two moisture variables. Reconstructions of the same variables based on surface-pollen assemblages are shown to be accurate and unbiased. Reconstructed LGM and MH climate anomaly patterns are coherent, consistent between variables, and robust with respect to the choice of technique. They support a conceptual model of the controls of Late Quaternary climate change whereby the first-order effects of orbital variations and greenhouse forcing on the seasonal cycle of temperature are predictably modified by responses of the atmospheric circulation and surface energy balance. ?? 2010 The Author(s).
Robust climate policies under uncertainty: a comparison of robust decision making and info-gap methods.

PubMed

Hall, Jim W; Lempert, Robert J; Keller, Klaus; Hackbarth, Andrew; Mijere, Christophe; McInerney, David J

2012-10-01

This study compares two widely used approaches for robustness analysis of decision problems: the info-gap method originally developed by Ben-Haim and the robust decision making (RDM) approach originally developed by Lempert, Popper, and Bankes. The study uses each approach to evaluate alternative paths for climate-altering greenhouse gas emissions given the potential for nonlinear threshold responses in the climate system, significant uncertainty about such a threshold response and a variety of other key parameters, as well as the ability to learn about any threshold responses over time. Info-gap and RDM share many similarities. Both represent uncertainty as sets of multiple plausible futures, and both seek to identify robust strategies whose performance is insensitive to uncertainties. Yet they also exhibit important differences, as they arrange their analyses in different orders, treat losses and gains in different ways, and take different approaches to imprecise probabilistic information. The study finds that the two approaches reach similar but not identical policy recommendations and that their differing attributes raise important questions about their appropriate roles in decision support applications. The comparison not only improves understanding of these specific methods, it also suggests some broader insights into robustness approaches and a framework for comparing them. © 2012 RAND Corporation.
Reconciling the Observed and Modeled Southern Hemisphere Circulation Response to Volcanic Eruptions

NASA Astrophysics Data System (ADS)

McGraw, M. C.; Barnes, E. A.; Deser, C.

2016-12-01

Confusion exists regarding the tropospheric circulation response to volcanic eruptions, with models and observations seeming to disagree on the sign of the response. The forced Southern Hemisphere circulation response to the eruptions of Pinatubo and El Chichon is shown to be a robust positive annular mode, using over 200 ensemble members from 38 climate models. It is demonstrated that the models and observations are not at odds, but rather, internal climate variability is large and can overwhelm the forced response. It is further argued that the state of ENSO can at least partially explain the sign of the observed anomalies, and may account for the perceived discrepancy between model and observational studies. The eruptions of both El Chichon and Pinatubo occurred during El Nino events, and it is demonstrated that the SAM anomalies following volcanic eruptions are weaker during El Nino events compared to La Nina events.
El Nino-like Teleconnection Increases California Precipitation in Response to Warming

NASA Astrophysics Data System (ADS)

Allen, R.

2017-12-01

Future California (CA) precipitation projections, including those from the most recent Climate Model Intercomparison Project (CMIP5), remain uncertain. This uncertainty is related to several factors, including relatively large internal climate variability, model shortcomings, and because CA lies within a transition zone, where mid-latitude regions are expected to become wetter and subtropical regions drier. Here, we use a multitude of models to show CA may receive more precipitation in the future under a business-as-usual scenario. The boreal winter season-when most of the CA precipitation increase occurs-is associated with robust changes in the mean circulation reminiscent of an El Nino teleconnection. Using idealized simulations with two different models, we further show that warming of tropical Pacific sea surface temperatures accounts for these changes. Models that better simulate the observed El Nino-CA precipitation teleconnection yield larger, and more consistent increases in CA precipitation through the twenty-first century.
Beyond Prediction: the Many Ways in which Climate Science can Inform Adaptation Decisions

NASA Astrophysics Data System (ADS)

Lempert, R. J.

2017-12-01

Climate science provides an increasingly rich understanding of current and future climate, but this understanding is often not fully incorporated into climate adaptation decisions. In particular, the provision of climate information is still trapped in a narrow prediction-based framework, which envisions a sequential process that begins with model-based forecasts of future climate and decision makers then acting on those forecasts. Among its challenges, this framework can discourage action when climate predictions are deemed too uncertain, encourage overconfidence when climate scientists and decision makers fail to focus on decision-relevant but poorly understood extreme events, and offers a too-narrow communication path among climate scientists and decision makers. This talk will describe how robust decision approaches, organized around the idea of stress testing proposed adaptation decisions over a wide range of futures, can enable a richer flow information among climate scientists and decision makers. The talk illustrates these themes with two examples: 1) conservation management that explores the tradeoffs among alternative climate information products with different combinations of ensemble size and spatial resolution and 2) water quality implementation planning that focuses on the handling of extremes.
Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions

DOE PAGES

Huntzinger, D. N.; Michalak, A. M.; Schwalm, C.; ...

2017-07-06

Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO 2, and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO 2 which shows almost twice the variability in cumulative landmore » uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO 2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.« less
Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huntzinger, D. N.; Michalak, A. M.; Schwalm, C.

2017-07-06

Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO2, and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO2 which shows almost twice the variability in cumulative land uptake sincemore » 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.« less

Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huntzinger, D. N.; Michalak, A. M.; Schwalm, C.

Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO 2, and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO 2 which shows almost twice the variability in cumulative landmore » uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO 2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.« less
DOE Office of Scientific and Technical Information (OSTI.GOV)

McCoy, Daniel T.; Hartmann, Dennis L.; Zelinka, Mark D.

Increasing optical depth poleward of 45° is a robust response to warming in global climate models. Much of this cloud optical depth increase has been hypothesized to be due to transitions from ice-dominated to liquid-dominated mixed-phase cloud. In this study, the importance of liquid-ice partitioning for the optical depth feedback is quantified for 19 Coupled Model Intercomparison Project Phase 5 models. All models show a monotonic partitioning of ice and liquid as a function of temperature, but the temperature at which ice and liquid are equally mixed (the glaciation temperature) varies by as much as 40 K across models. Modelsmore » that have a higher glaciation temperature are found to have a smaller climatological liquid water path (LWP) and condensed water path and experience a larger increase in LWP as the climate warms. The ice-liquid partitioning curve of each model may be used to calculate the response of LWP to warming. It is found that the repartitioning between ice and liquid in a warming climate contributes at least 20% to 80% of the increase in LWP as the climate warms, depending on model. Intermodel differences in the climatological partitioning between ice and liquid are estimated to contribute at least 20% to the intermodel spread in the high-latitude LWP response in the mixed-phase region poleward of 45°S. As a result, it is hypothesized that a more thorough evaluation and constraint of global climate model mixed-phase cloud parameterizations and validation of the total condensate and ice-liquid apportionment against observations will yield a substantial reduction in model uncertainty in the high-latitude cloud response to warming.« less
Regionalizing Africa: Patterns of Precipitation Variability in Observations and Global Climate Models

NASA Technical Reports Server (NTRS)

Badr, Hamada S.; Dezfuli, Amin K.; Zaitchik, Benjamin F.; Peters-Lidard, Christa D.

2016-01-01

Many studies have documented dramatic climatic and environmental changes that have affected Africa over different time scales. These studies often raise questions regarding the spatial extent and regional connectivity of changes inferred from observations and proxies and/or derived from climate models. Objective regionalization offers a tool for addressing these questions. To demonstrate this potential, applications of hierarchical climate regionalizations of Africa using observations and GCM historical simulations and future projections are presented. First, Africa is regionalized based on interannual precipitation variability using Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data for the period 19812014. A number of data processing techniques and clustering algorithms are tested to ensure a robust definition of climate regions. These regionalization results highlight the seasonal and even month-to-month specificity of regional climate associations across the continent, emphasizing the need to consider time of year as well as research question when defining a coherent region for climate analysis. CHIRPS regions are then compared to those of five GCMs for the historic period, with a focus on boreal summer. Results show that some GCMs capture the climatic coherence of the Sahel and associated teleconnections in a manner that is similar to observations, while other models break the Sahel into uncorrelated subregions or produce a Sahel-like region of variability that is spatially displaced from observations. Finally, shifts in climate regions under projected twenty-first-century climate change for different GCMs and emissions pathways are examined. A projected change is found in the coherence of the Sahel, in which the western and eastern Sahel become distinct regions with different teleconnections. This pattern is most pronounced in high-emissions scenarios.
Nutrient pollution mitigation measures across Europe are resilient under future climate

NASA Astrophysics Data System (ADS)

Wade, Andrew; Skeffington, Richard; Couture, Raoul; Erlandsson, Martin; Groot, Simon; Halliday, Sarah; Harezlak, Valesca; Hejzlar, Joseph; Jackson-Blake, Leah; Lepistö, Ahti; Papastergiadou, Eva; Psaltopoulos, Demetrios; Riera, Joan; Rankinen, Katri; Skuras, Dimitris; Trolle, Dennis; Whitehead, Paul; Dunn, Sarah; Bucak, Tuba

2016-04-01

The key results from the application of catchment-scale biophysical models to assess the likely effectiveness of nutrient pollution mitigation measures set in the context of projected land management and climate change are presented. The assessment is based on the synthesis of modelled outputs of daily river flow, river and lake nitrogen and phosphorus concentrations, and lake chlorophyll-a, for baseline (1981-2010) and scenario (2031-2060) periods for nine study sites across Europe. Together the nine sites represent a sample of key climate and land management types. The robustness and uncertainty in the daily, seasonal and long-term modelled outputs was assessed prior to the scenario runs. Credible scenarios of land-management changes were provided by social scientists and economists familiar with each study site, whilst likely mitigation measures were derived from local stakeholder consultations and cost-effectiveness assessments. Modelled mitigation options were able to reduce nutrient concentrations, and there was no evidence here that they were less effective under future climate. With less certainty, mitigation options could affect the ecological status of waters at these sites in a positive manner, leading to improvement in Water Framework Directive status at some sites. However, modelled outcomes for sites in southern Europe highlighted that increased evaporation and decreased precipitation will cause much lower flows leading to adverse impacts of river and lake ecology. Uncertainties in the climate models, as represented by three GCM-RCM combinations, did not affect this overall picture much.
Assessing the Assessment Methods: Climate Change and Hydrologic Impacts

NASA Astrophysics Data System (ADS)

Brekke, L. D.; Clark, M. P.; Gutmann, E. D.; Mizukami, N.; Mendoza, P. A.; Rasmussen, R.; Ikeda, K.; Pruitt, T.; Arnold, J. R.; Rajagopalan, B.

2014-12-01

The Bureau of Reclamation, the U.S. Army Corps of Engineers, and other water management agencies have an interest in developing reliable, science-based methods for incorporating climate change information into longer-term water resources planning. Such assessments must quantify projections of future climate and hydrology, typically relying on some form of spatial downscaling and bias correction to produce watershed-scale weather information that subsequently drives hydrology and other water resource management analyses (e.g., water demands, water quality, and environmental habitat). Water agencies continue to face challenging method decisions in these endeavors: (1) which downscaling method should be applied and at what resolution; (2) what observational dataset should be used to drive downscaling and hydrologic analysis; (3) what hydrologic model(s) should be used and how should these models be configured and calibrated? There is a critical need to understand the ramification of these method decisions, as they affect the signal and uncertainties produced by climate change assessments and, thus, adaptation planning. This presentation summarizes results from a three-year effort to identify strengths and weaknesses of widely applied methods for downscaling climate projections and assessing hydrologic conditions. Methods were evaluated from two perspectives: historical fidelity, and tendency to modulate a global climate model's climate change signal. On downscaling, four methods were applied at multiple resolutions: statistically using Bias Correction Spatial Disaggregation, Bias Correction Constructed Analogs, and Asynchronous Regression; dynamically using the Weather Research and Forecasting model. Downscaling results were then used to drive hydrologic analyses over the contiguous U.S. using multiple models (VIC, CLM, PRMS), with added focus placed on case study basins within the Colorado Headwaters. The presentation will identify which types of climate changes are expressed robustly across methods versus those that are sensitive to method choice; which method choices seem relatively more important; and where strategic investments in research and development can substantially improve guidance on climate change provided to water managers.
Compromise-based Robust Prioritization of Climate Change Adaptation Strategies for Watershed Management

NASA Astrophysics Data System (ADS)

Kim, Y.; Chung, E. S.

2014-12-01

This study suggests a robust prioritization framework for climate change adaptation strategies under multiple climate change scenarios with a case study of selecting sites for reusing treated wastewater (TWW) in a Korean urban watershed. The framework utilizes various multi-criteria decision making techniques, including the VIKOR method and the Shannon entropy-based weights. In this case study, the sustainability of TWW use is quantified with indicator-based approaches with the DPSIR framework, which considers both hydro-environmental and socio-economic aspects of the watershed management. Under the various climate change scenarios, the hydro-environmental responses to reusing TWW in potential alternative sub-watersheds are determined using the Hydrologic Simulation Program in Fortran (HSPF). The socio-economic indicators are obtained from the statistical databases. Sustainability scores for multiple scenarios are estimated individually and then integrated with the proposed approach. At last, the suggested framework allows us to prioritize adaptation strategies in a robust manner with varying levels of compromise between utility-based and regret-based strategies.
(Non-) robustness of vulnerability assessments to climate change: An application to New Zealand.

PubMed

Fernandez, Mario Andres; Bucaram, Santiago; Renteria, Willington

2017-12-01

Assessments of vulnerability to climate change are a key element to inform climate policy and research. Assessments based on the aggregation of indicators have a strong appeal for their simplicity but are at risk of over-simplification and uncertainty. This paper explores the non-robustness of indicators-based assessments to changes in assumptions on the degree of substitution or compensation between indicators. Our case study is a nationwide assessment for New Zealand. We found that the ranking of geographic areas is sensitive to different parameterisations of the aggregation function, that is, areas that are categorised as highly vulnerable may switch to the least vulnerable category even with respect to the same climate hazards and population groups. Policy implications from the assessments are then compromised. Though indicators-based approaches may help on identifying drivers of vulnerability, there are weak grounds to use them to recommend mitigation or adaptation decisions given the high level of uncertainty because of non-robustness. Copyright © 2017 Elsevier Ltd. All rights reserved.
Forest management under climatic and social uncertainty: trade-offs between reducing climate change impacts and fostering adaptive capacity.

PubMed

Seidl, Rupert; Lexer, Manfred J

2013-01-15

The unabated continuation of anthropogenic greenhouse gas emissions and the lack of an international consensus on a stringent climate change mitigation policy underscore the importance of adaptation for coping with the all but inevitable changes in the climate system. Adaptation measures in forestry have particularly long lead times. A timely implementation is thus crucial for reducing the considerable climate vulnerability of forest ecosystems. However, since future environmental conditions as well as future societal demands on forests are inherently uncertain, a core requirement for adaptation is robustness to a wide variety of possible futures. Here we explicitly address the roles of climatic and social uncertainty in forest management, and tackle the question of robustness of adaptation measures in the context of multi-objective sustainable forest management (SFM). We used the Austrian Federal Forests (AFF) as a case study, and employed a comprehensive vulnerability assessment framework based on ecosystem modeling, multi-criteria decision analysis, and practitioner participation. We explicitly considered climate uncertainty by means of three climate change scenarios, and accounted for uncertainty in future social demands by means of three societal preference scenarios regarding SFM indicators. We found that the effects of climatic and social uncertainty on the projected performance of management were in the same order of magnitude, underlining the notion that climate change adaptation requires an integrated social-ecological perspective. Furthermore, our analysis of adaptation measures revealed considerable trade-offs between reducing adverse impacts of climate change and facilitating adaptive capacity. This finding implies that prioritization between these two general aims of adaptation is necessary in management planning, which we suggest can draw on uncertainty analysis: Where the variation induced by social-ecological uncertainty renders measures aiming to reduce climate change impacts statistically insignificant (i.e., for approximately one third of the investigated management units of the AFF case study), fostering adaptive capacity is suggested as the preferred pathway for adaptation. We conclude that climate change adaptation needs to balance between anticipating expected future conditions and building the capacity to address unknowns and surprises. Copyright © 2012 Elsevier Ltd. All rights reserved.
How Hot was Africa during the Mid-Holocene? Reexamining Africa's Thermal History via integrated Climate and Proxy System Modeling

NASA Astrophysics Data System (ADS)

Dee, S.; Russell, J. M.; Morrill, C.

2017-12-01

Climate models predict Africa will warm by up to 5°C in the coming century. Reconstructions of African temperature since the Last Glacial Maximum (LGM) have made fundamental contributions to our understanding of past, present, and future climate and can help constrain predictions from general circulation models (GCMs). However, many of these reconstructions are based on proxies of lake temperature, so the confounding influences of lacustrine processes may complicate our interpretations of past changes in tropical climate. These proxy-specific uncertainties require robust methodology for data-model comparison. We develop a new proxy system model (PSM) for paleolimnology to facilitate data-model comparison and to fully characterize uncertainties in climate reconstructions. Output from GCMs are used to force the PSM to simulate lake temperature, hydrology, and associated proxy uncertainties. We compare reconstructed East African lake and air temperatures in individual records and in a stack of 9 lake records to those predicted by our PSM forced with Paleoclimate Model Intercomparison Project (PMIP3) simulations, focusing on the mid-Holocene (6 kyr BP). We additionally employ single-forcing transient climate simulations from TraCE (10 kyr to 4 kyr B.P. and historical), as well as 200-yr time slice simulations from CESM1.0 to run the lake PSM. We test the sensitivity of African climate change during the mid-Holocene to orbital, greenhouse gas, and ice-sheet forcing in single-forcing simulations, and investigate dynamical hypotheses for these changes. Reconstructions of tropical African temperature indicate 1-2ºC warming during the mid-Holocene relative to the present, similar to changes predicted in the coming decades. However, most climate models underestimate the warming observed in these paleoclimate data (Fig. 1, 6kyr B.P.). We investigate this discrepancy using the new lake PSM and climate model simulations, with attention to the (potentially non-stationary) relationship between lake surface temperature and air temperature. The data-model comparison helps partition the impacts of lake-specific processes such as energy balance, mixing, sedimentation and bioturbation. We provide new insight into the patterns, amplitudes, sensitivity, and mechanisms of African temperature change.
Strategies for Teaching Regional Climate Modeling: Online Professional Development for Scientists and Decision Makers

NASA Astrophysics Data System (ADS)

Walton, P.; Yarker, M. B.; Mesquita, M. D. S.; Otto, F. E. L.

2014-12-01

There is a clear role for climate science in supporting decision making at a range of scales and in a range of contexts: from Global to local, from Policy to Industry. However, clear a role climate science can play, there is also a clear discrepancy in the understanding of how to use the science and associated tools (such as climate models). Despite there being a large body of literature on the science there is clearly a need to provide greater support in how to apply appropriately. However, access to high quality professional development courses can be problematic, due to geographic, financial and time constraints. In attempt to address this gap we independently developed two online professional courses that focused on helping participants use and apply two regional climate models, WRF and PRECIS. Both courses were designed to support participants' learning through tutor led programs that covered the basic climate scientific principles of regional climate modeling and how to apply model outputs. The fundamental differences between the two courses are: 1) the WRF modeling course expected participants to design their own research question that was then run on a version of the model, whereas 2) the PRECIS course concentrated on the principles of regional modeling and how the climate science informed the modeling process. The two courses were developed to utilise the cost and time management benefits associated with eLearning, with the recognition that this mode of teaching can also be accessed internationally, providing professional development courses in countries that may not be able to provide their own. The development teams saw it as critical that the courses reflected sound educational theory, to ensure that participants had the maximum opportunity to learn successfully. In particular, the role of reflection is central to both course structures to help participants make sense of the science in relation to their own situation. This paper details the different structures of both courses, evaluating the advantages and disadvantages of each, along with the educational approaches used. We conclude by proposing a framework for the develop of educationally robust online professional development programs that actively supports decision makers in understanding, developing and applying regional climate models.
Future Midwest Heat Waves in WRF

NASA Astrophysics Data System (ADS)

Huber, M.; Buzan, J. R.; Yoo, J.

2017-12-01

We present heat stress results for the upper Midwest derived from convection resolving Weather Research and Forecasting (WRF) model simulations carried out for the RCP 8.5 Scenario and driven by Community Earth System Model (CESM) boundary conditions as part of the Indiana Climate Change Assessment. Using this modeling system we find widespread and severe increases in moist heat stress metrics in the Midwest by end of century. We detail scaling arguments that suggest our results are robust and not model dependent and describe potential health, welfare, and productivity implications of these results.
Revisiting the generation and interpretation of climate models experiments for adaptation decision-making (Invited)

NASA Astrophysics Data System (ADS)

Ranger, N.; Millner, A.; Niehoerster, F.

2010-12-01

Traditionally, climate change risk assessments have taken a roughly four-stage linear ‘chain’ of moving from socioeconomic projections, to climate projections, to primary impacts and then finally onto economic and social impact assessment. Adaptation decisions are then made on the basis of these outputs. The escalation of uncertainty through this chain is well known; resulting in an ‘explosion’ of uncertainties in the final risk and adaptation assessment. The space of plausible future risk scenarios is growing ever wider with the application of new techniques which aim to explore uncertainty ever more deeply; such as those used in the recent ‘probabilistic’ UK Climate Projections 2009, and the stochastic integrated assessment models, for example PAGE2002. This explosion of uncertainty can make decision-making problematic, particularly given that the uncertainty information communicated can not be treated as strictly probabilistic and therefore, is not an easy fit with standard decision-making under uncertainty approaches. Additional problems can arise from the fact that the uncertainty estimated for different components of the ‘chain’ is rarely directly comparable or combinable. Here, we explore the challenges and limitations of using current projections for adaptation decision-making. We report the findings of a recent report completed for the UK Adaptation Sub-Committee on approaches to deal with these challenges and make robust adaptation decisions today. To illustrate these approaches, we take a number of illustrative case studies, including a case of adaptation to hurricane risk on the US Gulf Coast. This is a particularly interesting case as it involves urgent adaptation of long-lived infrastructure but requires interpreting highly uncertain climate change science and modelling; i.e. projections of Atlantic basin hurricane activity. An approach we outline is reversing the linear chain of assessments to put the economics and decision-making first. Such an approach forces one to focus on the information of greatest value for the specific decision. We suggest that such an approach will help to accommodate the uncertainties in the chain and facilitate robust decision-making. Initial findings of these case studies will be presented with the aim of raising open questions and promoting discussion of the methodology. Finally, we reflect on the implications for the design of climate model experiments.
On the appropriate definition of soil profile configuration and initial conditions for land surface-hydrology models in cold regions

NASA Astrophysics Data System (ADS)

Sapriza-Azuri, Gonzalo; Gamazo, Pablo; Razavi, Saman; Wheater, Howard S.

2018-06-01

Arctic and subarctic regions are amongst the most susceptible regions on Earth to global warming and climate change. Understanding and predicting the impact of climate change in these regions require a proper process representation of the interactions between climate, carbon cycle, and hydrology in Earth system models. This study focuses on land surface models (LSMs) that represent the lower boundary condition of general circulation models (GCMs) and regional climate models (RCMs), which simulate climate change evolution at the global and regional scales, respectively. LSMs typically utilize a standard soil configuration with a depth of no more than 4 m, whereas for cold, permafrost regions, field experiments show that attention to deep soil profiles is needed to understand and close the water and energy balances, which are tightly coupled through the phase change. To address this gap, we design and run a series of model experiments with a one-dimensional LSM, called CLASS (Canadian Land Surface Scheme), as embedded in the MESH (Modélisation Environmentale Communautaire - Surface and Hydrology) modelling system, to (1) characterize the effect of soil profile depth under different climate conditions and in the presence of parameter uncertainty; (2) assess the effect of including or excluding the geothermal flux in the LSM at the bottom of the soil column; and (3) develop a methodology for temperature profile initialization in permafrost regions, where the system has an extended memory, by the use of paleo-records and bootstrapping. Our study area is in Norman Wells, Northwest Territories of Canada, where measurements of soil temperature profiles and historical reconstructed climate data are available. Our results demonstrate a dominant role for parameter uncertainty, that is often neglected in LSMs. Considering such high sensitivity to parameter values and dependency on the climate condition, we show that a minimum depth of 20 m is essential to adequately represent the temperature dynamics. We further show that our proposed initialization procedure is effective and robust to uncertainty in paleo-climate reconstructions and that more than 300 years of reconstructed climate time series are needed for proper model initialization.
Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios

DOE PAGES

Kim, John B.; Monier, Erwan; Sohngen, Brent; ...

2017-03-28

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomesmore » of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world’s forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO 2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Finally, our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO 2 fertilization effects may considerably reduce the range of projections.« less
Decomposing climate-induced temperature and water effects on the expansion and operation of the US electricity system

NASA Astrophysics Data System (ADS)

Sun, Y.; Eurek, K.; Macknick, J.; Steinberg, D. C.; Averyt, K.; Badger, A.; Livneh, B.

2017-12-01

Climate change has the potential to affect the supply and demands of the U.S. power sector. Rising air temperatures can affect the seasonal and total demand for electricity, alter the thermal efficiency of power plants, and lower the maximum capacity of electric transmission lines. Changes in hydrology can affect seasonal and total availability of water used for power plant operations. Prior studies have examined some climate impacts on the electricity sector, but there has been no systematic study quantifying and comparing the importance of these climate-induced effects in isolation and in combination. Here, we perform a systematic assessment using the Regional Energy Deployment System (ReEDS) electricity sector model in combination with downscaled climate results from four models in the CMIP5 archive that provide contrasting temperature and precipitation trends for key regions in the U.S. The ReEDS model captures dynamic climate and hydrological resource data .when choosing the cost optimal mix of generation resources necessary to balance supply and demand for electricity. We examine how different climate-induced changes in air temperature and water availability, considered in isolation and in combination, may affect energy and economic outcomes at a regional and national level from the present through 2050. Results indicate that temperature-induced impacts on electricity consumption show consistent trends nationwide across all climate scenarios. Hydrological impacts and variability differ by model and tend to have a minor effect on national electricity trends, but can be important determinants regionally. Taken together, this suggests that isolated climate change impacts on the electricity system depend on the geographic scale of interest - the effect of rising temperatures on demand, which is qualitatively robust to the choice of climate model, largely determines impacts on generation, capacity and cost at the national level, whereas other impact pathways may dominate at regional level.
Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios

NASA Astrophysics Data System (ADS)

Kim, John B.; Monier, Erwan; Sohngen, Brent; Pitts, G. Stephen; Drapek, Ray; McFarland, James; Ohrel, Sara; Cole, Jefferson

2017-04-01

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomes of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world’s forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO2 fertilization effects may considerably reduce the range of projections.
Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, John B.; Monier, Erwan; Sohngen, Brent

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomesmore » of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world’s forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO 2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Finally, our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO 2 fertilization effects may considerably reduce the range of projections.« less
Quantitative Decision Support Requires Quantitative User Guidance

NASA Astrophysics Data System (ADS)

Smith, L. A.

2009-12-01

Is it conceivable that models run on 2007 computer hardware could provide robust and credible probabilistic information for decision support and user guidance at the ZIP code level for sub-daily meteorological events in 2060? In 2090? Retrospectively, how informative would output from today’s models have proven in 2003? or the 1930’s? Consultancies in the United Kingdom, including the Met Office, are offering services to “future-proof” their customers from climate change. How is a US or European based user or policy maker to determine the extent to which exciting new Bayesian methods are relevant here? or when a commercial supplier is vastly overselling the insights of today’s climate science? How are policy makers and academic economists to make the closely related decisions facing them? How can we communicate deep uncertainty in the future at small length-scales without undermining the firm foundation established by climate science regarding global trends? Three distinct aspects of the communication of the uses of climate model output targeting users and policy makers, as well as other specialist adaptation scientists, are discussed. First, a brief scientific evaluation of the length and time scales at which climate model output is likely to become uninformative is provided, including a note on the applicability the latest Bayesian methodology to current state-of-the-art general circulation models output. Second, a critical evaluation of the language often employed in communication of climate model output, a language which accurately states that models are “better”, have “improved” and now “include” and “simulate” relevant meteorological processed, without clearly identifying where the current information is thought to be uninformative and misleads, both for the current climate and as a function of the state of the (each) climate simulation. And thirdly, a general approach for evaluating the relevance of quantitative climate model output for a given problem is presented. Based on climate science, meteorology, and the details of the question in hand, this approach identifies necessary (never sufficient) conditions required for the rational use of climate model output in quantitative decision support tools. Inasmuch as climate forecasting is a problem of extrapolation, there will always be harsh limits on our ability to establish where a model is fit for purpose, this does not, however, limit us from identifying model noise as such, and thereby avoiding some cases of the misapplication and over interpretation of model output. It is suggested that failure to clearly communicate the limits of today’s climate model in providing quantitative decision relevant climate information to today’s users of climate information, would risk the credibility of tomorrow’s climate science and science based policy more generally.
Ensemble projections of wildfire activity and carbonaceous aerosol concentrations over the western United States in the mid-21st century

PubMed Central

Yue, Xu; Mickley, Loretta J.; Logan, Jennifer A.; Kaplan, Jed O.

2013-01-01

We estimate future wildfire activity over the western United States during the mid-21st century (2046–2065), based on results from 15 climate models following the A1B scenario. We develop fire prediction models by regressing meteorological variables from the current and previous years together with fire indexes onto observed regional area burned. The regressions explain 0.25–0.60 of the variance in observed annual area burned during 1980–2004, depending on the ecoregion. We also parameterize daily area burned with temperature, precipitation, and relative humidity. This approach explains ~0.5 of the variance in observed area burned over forest ecoregions but shows no predictive capability in the semi-arid regions of Nevada and California. By applying the meteorological fields from 15 climate models to our fire prediction models, we quantify the robustness of our wildfire projections at mid-century. We calculate increases of 24–124% in area burned using regressions and 63–169% with the parameterization. Our projections are most robust in the southwestern desert, where all GCMs predict significant (p<0.05) meteorological changes. For forested ecoregions, more GCMs predict significant increases in future area burned with the parameterization than with the regressions, because the latter approach is sensitive to hydrological variables that show large inter-model variability in the climate projections. The parameterization predicts that the fire season lengthens by 23 days in the warmer and drier climate at mid-century. Using a chemical transport model, we find that wildfire emissions will increase summertime surface organic carbon aerosol over the western United States by 46–70% and black carbon by 20–27% at midcentury, relative to the present day. The pollution is most enhanced during extreme episodes: above the 84th percentile of concentrations, OC increases by ~90% and BC by ~50%, while visibility decreases from 130 km to 100 km in 32 Federal Class 1 areas in Rocky Mountains Forest. PMID:24015109
Projected increase in El Niño-driven tropical cyclone frequency in the Pacific

NASA Astrophysics Data System (ADS)

Chand, Savin S.; Tory, Kevin J.; Ye, Hua; Walsh, Kevin J. E.

2017-02-01

The El Niño/Southern Oscillation (ENSO) drives substantial variability in tropical cyclone (TC) activity around the world. However, it remains uncertain how the projected future changes in ENSO under greenhouse warming will affect TC activity, apart from an expectation that the overall frequency of TCs is likely to decrease for most ocean basins. Here we show robust changes in ENSO-driven variability in TC occurrence by the late twenty-first century. In particular, we show that TCs become more frequent (~20-40%) during future-climate El Niño events compared with present-climate El Niño events--and less frequent during future-climate La Niña events--around a group of small island nations (for example, Fiji, Vanuatu, Marshall Islands and Hawaii) in the Pacific. We examine TCs across 20 models from the Coupled Model Intercomparison Project phase 5 database, forced under historical and greenhouse warming conditions. The 12 most realistic models identified show a strong consensus on El Niño-driven changes in future-climate large-scale environmental conditions that modulate development of TCs over the off-equatorial western Pacific and the central North Pacific regions. These results have important implications for climate change and adaptation pathways for the vulnerable Pacific island nations.

Building a Framework in Improving Drought Monitoring and Early Warning Systems in Africa

NASA Astrophysics Data System (ADS)

Tadesse, T.; Wall, N.; Haigh, T.; Shiferaw, A. S.; Beyene, S.; Demisse, G. B.; Zaitchik, B.

2015-12-01

Decision makers need a basic understanding of the prediction models and products of hydro-climatic extremes and their suitability in time and space for strategic resource and development planning to develop mitigation and adaptation strategies. Advances in our ability to assess and predict climate extremes (e.g., droughts and floods) under evolving climate change suggest opportunity to improve management of climatic/hydrologic risk in agriculture and water resources. In the NASA funded project entitled, "Seasonal Prediction of Hydro-Climatic Extremes in the Greater Horn of Africa (GHA) under Evolving Climate Conditions to Support Adaptation Strategies," we are attempting to develop a framework that uses dialogue between managers and scientists on how to enhance the use of models' outputs and prediction products in the GHA as well as improve the delivery of this information in ways that can be easily utilized by managers. This process is expected to help our multidisciplinary research team obtain feedback on the models and forecast products. In addition, engaging decision makers is essential in evaluating the use of drought and flood prediction models and products for decision-making processes in drought and flood management. Through this study, we plan to assess information requirements to implement a robust Early Warning Systems (EWS) by engaging decision makers in the process. This participatory process could also help the existing EWSs in Africa and to develop new local and regional EWSs. In this presentation, we report the progress made in the past two years of the NASA project.
Water Resource Planning Under Future Climate and Socioeconomic Uncertainty in the Cauvery River Basin in Karnataka, India

PubMed Central

Conway, Declan; Dessai, Suraje; Stainforth, David A.

2018-01-01

Abstract Decision‐Making Under Uncertainty (DMUU) approaches have been less utilized in developing countries than developed countries for water resources contexts. High climate vulnerability and rapid socioeconomic change often characterize developing country contexts, making DMUU approaches relevant. We develop an iterative multi‐method DMUU approach, including scenario generation, coproduction with stakeholders and water resources modeling. We apply this approach to explore the robustness of adaptation options and pathways against future climate and socioeconomic uncertainties in the Cauvery River Basin in Karnataka, India. A water resources model is calibrated and validated satisfactorily using observed streamflow. Plausible future changes in Indian Summer Monsoon (ISM) precipitation and water demand are used to drive simulations of water resources from 2021 to 2055. Two stakeholder‐identified decision‐critical metrics are examined: a basin‐wide metric comprising legal instream flow requirements for the downstream state of Tamil Nadu, and a local metric comprising water supply reliability to Bangalore city. In model simulations, the ability to satisfy these performance metrics without adaptation is reduced under almost all scenarios. Implementing adaptation options can partially offset the negative impacts of change. Sequencing of options according to stakeholder priorities into Adaptation Pathways affects metric satisfaction. Early focus on agricultural demand management improves the robustness of pathways but trade‐offs emerge between intrabasin and basin‐wide water availability. We demonstrate that the fine balance between water availability and demand is vulnerable to future changes and uncertainty. Despite current and long‐term planning challenges, stakeholders in developing countries may engage meaningfully in coproduction approaches for adaptation decision‐making under deep uncertainty. PMID:29706676
Water Resource Planning Under Future Climate and Socioeconomic Uncertainty in the Cauvery River Basin in Karnataka, India.

PubMed

Bhave, Ajay Gajanan; Conway, Declan; Dessai, Suraje; Stainforth, David A

2018-02-01

Decision-Making Under Uncertainty (DMUU) approaches have been less utilized in developing countries than developed countries for water resources contexts. High climate vulnerability and rapid socioeconomic change often characterize developing country contexts, making DMUU approaches relevant. We develop an iterative multi-method DMUU approach, including scenario generation, coproduction with stakeholders and water resources modeling. We apply this approach to explore the robustness of adaptation options and pathways against future climate and socioeconomic uncertainties in the Cauvery River Basin in Karnataka, India. A water resources model is calibrated and validated satisfactorily using observed streamflow. Plausible future changes in Indian Summer Monsoon (ISM) precipitation and water demand are used to drive simulations of water resources from 2021 to 2055. Two stakeholder-identified decision-critical metrics are examined: a basin-wide metric comprising legal instream flow requirements for the downstream state of Tamil Nadu, and a local metric comprising water supply reliability to Bangalore city. In model simulations, the ability to satisfy these performance metrics without adaptation is reduced under almost all scenarios. Implementing adaptation options can partially offset the negative impacts of change. Sequencing of options according to stakeholder priorities into Adaptation Pathways affects metric satisfaction. Early focus on agricultural demand management improves the robustness of pathways but trade-offs emerge between intrabasin and basin-wide water availability. We demonstrate that the fine balance between water availability and demand is vulnerable to future changes and uncertainty. Despite current and long-term planning challenges, stakeholders in developing countries may engage meaningfully in coproduction approaches for adaptation decision-making under deep uncertainty.
Water Resource Planning Under Future Climate and Socioeconomic Uncertainty in the Cauvery River Basin in Karnataka, India

NASA Astrophysics Data System (ADS)

Bhave, Ajay Gajanan; Conway, Declan; Dessai, Suraje; Stainforth, David A.

2018-02-01

Decision-Making Under Uncertainty (DMUU) approaches have been less utilized in developing countries than developed countries for water resources contexts. High climate vulnerability and rapid socioeconomic change often characterize developing country contexts, making DMUU approaches relevant. We develop an iterative multi-method DMUU approach, including scenario generation, coproduction with stakeholders and water resources modeling. We apply this approach to explore the robustness of adaptation options and pathways against future climate and socioeconomic uncertainties in the Cauvery River Basin in Karnataka, India. A water resources model is calibrated and validated satisfactorily using observed streamflow. Plausible future changes in Indian Summer Monsoon (ISM) precipitation and water demand are used to drive simulations of water resources from 2021 to 2055. Two stakeholder-identified decision-critical metrics are examined: a basin-wide metric comprising legal instream flow requirements for the downstream state of Tamil Nadu, and a local metric comprising water supply reliability to Bangalore city. In model simulations, the ability to satisfy these performance metrics without adaptation is reduced under almost all scenarios. Implementing adaptation options can partially offset the negative impacts of change. Sequencing of options according to stakeholder priorities into Adaptation Pathways affects metric satisfaction. Early focus on agricultural demand management improves the robustness of pathways but trade-offs emerge between intrabasin and basin-wide water availability. We demonstrate that the fine balance between water availability and demand is vulnerable to future changes and uncertainty. Despite current and long-term planning challenges, stakeholders in developing countries may engage meaningfully in coproduction approaches for adaptation decision-making under deep uncertainty.
Future summer mega-heatwave and record-breaking temperatures in a warmer France climate

NASA Astrophysics Data System (ADS)

Bador, Margot; Terray, Laurent; Boé, Julien; Somot, Samuel; Alias, Antoinette; Gibelin, Anne-Laure; Dubuisson, Brigitte

2017-07-01

This study focuses on future very hot summers associated with severe heatwaves and record-breaking temperatures in France. Daily temperature observations and a pair of historical and scenario (greenhouse gas radiative concentration pathway 8.5) simulations with the high-resolution (∼12.5 km) ALADIN regional climate model provide a robust framework to examine the spatial distribution of these extreme events and their 21st century evolution. Five regions are identified with an extreme event spatial clustering algorithm applied to observed temperatures. They are used to diagnose the 21st century heatwave spatial patterns. In the 2070s, we find a simulated mega-heatwave as severe as the 2003 observed heatwave relative to its contemporaneous climate. A 20-member initial condition ensemble is used to assess the sensitivity of this future heatwave to the internal variability in the regional climate model and to pre-existing land surface conditions. Even in a much warmer and drier climate in France, late spring dry land conditions may lead to a significant amplification of summer extreme temperatures and heatwave intensity through limitations in evapotranspiration. By 2100, the increase in summer temperature maxima exhibits a range from 6 °C to almost 13 °C in the five regions in France, relative to historical maxima. These projections are comparable with the estimates given by a large number of global climate models.
Tropospheric jet response to Antarctic ozone depletion: An update with Chemistry-Climate Model Initiative (CCMI) models

NASA Astrophysics Data System (ADS)

Son, Seok-Woo; Han, Bo-Reum; Garfinkel, Chaim I.; Kim, Seo-Yeon; Park, Rokjin; Abraham, N. Luke; Akiyoshi, Hideharu; Archibald, Alexander T.; Butchart, N.; Chipperfield, Martyn P.; Dameris, Martin; Deushi, Makoto; Dhomse, Sandip S.; Hardiman, Steven C.; Jöckel, Patrick; Kinnison, Douglas; Michou, Martine; Morgenstern, Olaf; O’Connor, Fiona M.; Oman, Luke D.; Plummer, David A.; Pozzer, Andrea; Revell, Laura E.; Rozanov, Eugene; Stenke, Andrea; Stone, Kane; Tilmes, Simone; Yamashita, Yousuke; Zeng, Guang

2018-05-01

The Southern Hemisphere (SH) zonal-mean circulation change in response to Antarctic ozone depletion is re-visited by examining a set of the latest model simulations archived for the Chemistry-Climate Model Initiative (CCMI) project. All models reasonably well reproduce Antarctic ozone depletion in the late 20th century. The related SH-summer circulation changes, such as a poleward intensification of westerly jet and a poleward expansion of the Hadley cell, are also well captured. All experiments exhibit quantitatively the same multi-model mean trend, irrespective of whether the ocean is coupled or prescribed. Results are also quantitatively similar to those derived from the Coupled Model Intercomparison Project phase 5 (CMIP5) high-top model simulations in which the stratospheric ozone is mostly prescribed with monthly- and zonally-averaged values. These results suggest that the ozone-hole-induced SH-summer circulation changes are robust across the models irrespective of the specific chemistry-atmosphere-ocean coupling.
Multi crop model climate risk country-level management design: case study on the Tanzanian maize production system

NASA Astrophysics Data System (ADS)

Chavez, E.

2015-12-01

Future climate projections indicate that a very serious consequence of post-industrial anthropogenic global warming is the likelihood of the greater frequency and intensity of extreme hydrometeorological events such as heat waves, droughts, storms, and floods. The design of national and international policies targeted at building more resilient and environmentally sustainable food systems needs to rely on access to robust and reliable data which is largely absent. In this context, the improvement of the modelling of current and future agricultural production losses using the unifying language of risk is paramount. In this study, we use a methodology that allows the integration of the current understanding of the various interacting systems of climate, agro-environment, crops, and the economy to determine short to long-term risk estimates of crop production loss, in different environmental, climate, and adaptation scenarios. This methodology is applied to Tanzania to assess optimum risk reduction and maize production increase paths in different climate scenarios. The simulations carried out use inputs from three different crop models (DSSAT, APSIM, WRSI) run in different technological scenarios and thus allowing to estimate crop model-driven risk exposure estimation bias. The results obtained also allow distinguishing different region-specific optimum climate risk reduction policies subject to historical as well as RCP2.5 and RCP8.5 climate scenarios. The region-specific risk profiles obtained provide a simple framework to determine cost-effective risk management policies for Tanzania and allow to optimally combine investments in risk reduction and risk transfer.
Climate projections and extremes in dynamically downscaled CMIP5 model outputs over the Bengal delta: a quartile based bias-correction approach with new gridded data

NASA Astrophysics Data System (ADS)

Hasan, M. Alfi; Islam, A. K. M. Saiful; Akanda, Ali Shafqat

2017-11-01

In the era of global warning, the insight of future climate and their changing extremes is critical for climate-vulnerable regions of the world. In this study, we have conducted a robust assessment of Regional Climate Model (RCM) results in a monsoon-dominated region within the new Coupled Model Intercomparison Project Phase 5 (CMIP5) and the latest Representative Concentration Pathways (RCP) scenarios. We have applied an advanced bias correction approach to five RCM simulations in order to project future climate and associated extremes over Bangladesh, a critically climate-vulnerable country with a complex monsoon system. We have also generated a new gridded product that performed better in capturing observed climatic extremes than existing products. The bias-correction approach provided a notable improvement in capturing the precipitation extremes as well as mean climate. The majority of projected multi-model RCMs indicate an increase of rainfall, where one model shows contrary results during the 2080s (2071-2100) era. The multi-model mean shows that nighttime temperatures will increase much faster than daytime temperatures and the average annual temperatures are projected to be as hot as present-day summer temperatures. The expected increase of precipitation and temperature over the hilly areas are higher compared to other parts of the country. Overall, the projected extremities of future rainfall are more variable than temperature. According to the majority of the models, the number of the heavy rainy days will increase in future years. The severity of summer-day temperatures will be alarming, especially over hilly regions, where winters are relatively warm. The projected rise of both precipitation and temperature extremes over the intense rainfall-prone northeastern region of the country creates a possibility of devastating flash floods with harmful impacts on agriculture. Moreover, the effect of bias-correction, as presented in probable changes of both bias-corrected and uncorrected extremes, can be considered in future policy making.
The End-to-end Demonstrator for improved decision making in the water sector in Europe (EDgE)

NASA Astrophysics Data System (ADS)

Wood, Eric; Wanders, Niko; Pan, Ming; Sheffield, Justin; Samaniego, Luis; Thober, Stephan; Kumar, Rohinni; Prudhomme, Christel; Houghton-Carr, Helen

2017-04-01

High-resolution simulations of water resources from hydrological models are vital to supporting important climate services. Apart from a high level of detail, both spatially and temporally, it is important to provide simulations that consistently cover a range of timescales, from historical reanalysis to seasonal forecast and future projections. In the new EDgE project commissioned by the ECMWF (C3S) we try to fulfill these requirements. EDgE is a proof-of-concept project which combines climate data and state-of-the-art hydrological modelling to demonstrate a water-oriented information system implemented through a web application. EDgE is working with key European stakeholders representative of private and public sectors to jointly develop and tailor approaches and techniques. With these tools, stakeholders are assisted in using improved climate information in decision-making, and supported in the development of climate change adaptation and mitigation policies. Here, we present the first results of the EDgE modelling chain, which is divided into three main processes: 1) pre-processing and downscaling; 2) hydrological modelling; 3) post-processing. Consistent downscaling and bias corrections for historical simulations, seasonal forecasts and climate projections ensure that the results across scales are robust. The daily temporal resolution and 5km spatial resolution ensure locally relevant simulations. With the use of four hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP), uncertainty between models is properly addressed, while consistency is guaranteed by using identical input data for static land surface parameterizations. The forecast results are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs) that have been created in collaboration with the end-user community of the EDgE project. The final product of this project is composed of 15 years of seasonal forecast and 10 climate change projections, all combined with four hydrological models. These unique high-resolution climate information simulations in the EDgE project provide an unprecedented information system for decision-making over Europe.
Regional Climate Variability Under Model Simulations of Solar Geoengineering

NASA Astrophysics Data System (ADS)

Dagon, Katherine; Schrag, Daniel P.

2017-11-01

Solar geoengineering has been shown in modeling studies to successfully mitigate global mean surface temperature changes from greenhouse warming. Changes in land surface hydrology are complicated by the direct effect of carbon dioxide (CO2) on vegetation, which alters the flux of water from the land surface to the atmosphere. Here we investigate changes in boreal summer climate variability under solar geoengineering using multiple ensembles of model simulations. We find that spatially uniform solar geoengineering creates a strong meridional gradient in the Northern Hemisphere temperature response, with less consistent patterns in precipitation, evapotranspiration, and soil moisture. Using regional summertime temperature and precipitation results across 31-member ensembles, we show a decrease in the frequency of heat waves and consecutive dry days under solar geoengineering relative to a high-CO2 world. However in some regions solar geoengineering of this amount does not completely reduce summer heat extremes relative to present day climate. In western Russia and Siberia, an increase in heat waves is connected to a decrease in surface soil moisture that favors persistent high temperatures. Heat waves decrease in the central United States and the Sahel, while the hydrologic response increases terrestrial water storage. Regional changes in soil moisture exhibit trends over time as the model adjusts to solar geoengineering, particularly in Siberia and the Sahel, leading to robust shifts in climate variance. These results suggest potential benefits and complications of large-scale uniform climate intervention schemes.
A multiresolution method for climate system modeling: application of spherical centroidal Voronoi tessellations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ringler, Todd; Ju, Lili; Gunzburger, Max

2008-11-14

During the next decade and beyond, climate system models will be challenged to resolve scales and processes that are far beyond their current scope. Each climate system component has its prototypical example of an unresolved process that may strongly influence the global climate system, ranging from eddy activity within ocean models, to ice streams within ice sheet models, to surface hydrological processes within land system models, to cloud processes within atmosphere models. These new demands will almost certainly result in the develop of multiresolution schemes that are able, at least regionally, to faithfully simulate these fine-scale processes. Spherical centroidal Voronoimore » tessellations (SCVTs) offer one potential path toward the development of a robust, multiresolution climate system model components. SCVTs allow for the generation of high quality Voronoi diagrams and Delaunay triangulations through the use of an intuitive, user-defined density function. In each of the examples provided, this method results in high-quality meshes where the quality measures are guaranteed to improve as the number of nodes is increased. Real-world examples are developed for the Greenland ice sheet and the North Atlantic ocean. Idealized examples are developed for ocean–ice shelf interaction and for regional atmospheric modeling. In addition to defining, developing, and exhibiting SCVTs, we pair this mesh generation technique with a previously developed finite-volume method. Our numerical example is based on the nonlinear, shallow water equations spanning the entire surface of the sphere. This example is used to elucidate both the potential benefits of this multiresolution method and the challenges ahead.« less
Towards a more efficient and robust representation of subsurface hydrological processes in Earth System Models

NASA Astrophysics Data System (ADS)

Rosolem, R.; Rahman, M.; Kollet, S. J.; Wagener, T.

2017-12-01

Understanding the impacts of land cover and climate changes on terrestrial hydrometeorology is important across a range of spatial and temporal scales. Earth System Models (ESMs) provide a robust platform for evaluating these impacts. However, current ESMs lack the representation of key hydrological processes (e.g., preferential water flow, and direct interactions with aquifers) in general. The typical "free drainage" conceptualization of land models can misrepresent the magnitude of those interactions, consequently affecting the exchange of energy and water at the surface as well as estimates of groundwater recharge. Recent studies show the benefits of explicitly simulating the interactions between subsurface and surface processes in similar models. However, such parameterizations are often computationally demanding resulting in limited application for large/global-scale studies. Here, we take a different approach in developing a novel parameterization for groundwater dynamics. Instead of directly adding another complex process to an established land model, we examine a set of comprehensive experimental scenarios using a very robust and establish three-dimensional hydrological model to develop a simpler parameterization that represents the aquifer to land surface interactions. The main goal of our developed parameterization is to simultaneously maximize the computational gain (i.e., "efficiency") while minimizing simulation errors in comparison to the full 3D model (i.e., "robustness") to allow for easy implementation in ESMs globally. Our study focuses primarily on understanding both the dynamics for groundwater recharge and discharge, respectively. Preliminary results show that our proposed approach significantly reduced the computational demand while model deviations from the full 3D model are considered to be small for these processes.
Mechanistic species distribution modelling as a link between physiology and conservation.

PubMed

Evans, Tyler G; Diamond, Sarah E; Kelly, Morgan W

2015-01-01

Climate change conservation planning relies heavily on correlative species distribution models that estimate future areas of occupancy based on environmental conditions encountered in present-day ranges. The approach benefits from rapid assessment of vulnerability over a large number of organisms, but can have poor predictive power when transposed to novel environments and reveals little in the way of causal mechanisms that define changes in species distribution or abundance. Having conservation planning rely largely on this single approach also increases the risk of policy failure. Mechanistic models that are parameterized with physiological information are expected to be more robust when extrapolating distributions to future environmental conditions and can identify physiological processes that set range boundaries. Implementation of mechanistic species distribution models requires knowledge of how environmental change influences physiological performance, and because this information is currently restricted to a comparatively small number of well-studied organisms, use of mechanistic modelling in the context of climate change conservation is limited. In this review, we propose that the need to develop mechanistic models that incorporate physiological data presents an opportunity for physiologists to contribute more directly to climate change conservation and advance the field of conservation physiology. We begin by describing the prevalence of species distribution modelling in climate change conservation, highlighting the benefits and drawbacks of both mechanistic and correlative approaches. Next, we emphasize the need to expand mechanistic models and discuss potential metrics of physiological performance suitable for integration into mechanistic models. We conclude by summarizing other factors, such as the need to consider demography, limiting broader application of mechanistic models in climate change conservation. Ideally, modellers, physiologists and conservation practitioners would work collaboratively to build models, interpret results and consider conservation management options, and articulating this need here may help to stimulate collaboration.
A probabilistic method for streamflow projection and associated uncertainty analysis in a data sparse alpine region

NASA Astrophysics Data System (ADS)

Ren, Weiwei; Yang, Tao; Shi, Pengfei; Xu, Chong-yu; Zhang, Ke; Zhou, Xudong; Shao, Quanxi; Ciais, Philippe

2018-06-01

Climate change imposes profound influence on regional hydrological cycle and water security in many alpine regions worldwide. Investigating regional climate impacts using watershed scale hydrological models requires a large number of input data such as topography, meteorological and hydrological data. However, data scarcity in alpine regions seriously restricts evaluation of climate change impacts on water cycle using conventional approaches based on global or regional climate models, statistical downscaling methods and hydrological models. Therefore, this study is dedicated to development of a probabilistic model to replace the conventional approaches for streamflow projection. The probabilistic model was built upon an advanced Bayesian Neural Network (BNN) approach directly fed by the large-scale climate predictor variables and tested in a typical data sparse alpine region, the Kaidu River basin in Central Asia. Results show that BNN model performs better than the general methods across a number of statistical measures. The BNN method with flexible model structures by active indicator functions, which reduce the dependence on the initial specification for the input variables and the number of hidden units, can work well in a data limited region. Moreover, it can provide more reliable streamflow projections with a robust generalization ability. Forced by the latest bias-corrected GCM scenarios, streamflow projections for the 21st century under three RCP emission pathways were constructed and analyzed. Briefly, the proposed probabilistic projection approach could improve runoff predictive ability over conventional methods and provide better support to water resources planning and management under data limited conditions as well as enable a facilitated climate change impact analysis on runoff and water resources in alpine regions worldwide.
The Poleward Shift of Storm Tracks Under Climate Change: Tracking Cyclones in CMIP5

NASA Astrophysics Data System (ADS)

Kaspi, Y.; Tamarin, T.

2017-12-01

Extratropical cyclones dominate the distribution of precipitation and wind in the midlatitudes, and therefore their frequency, intensity, and paths have a significant effect on weather and climate. Comprehensive climate models forced by enhanced greenhouse gas emissions suggest that under a climate change scenario, the latitudinal band of storm tracks would shift poleward. While the poleward shift is a robust response across most models, there is currently no consensus on what is the dominant dynamical mechanism. Here we use a Lagrangian approach to study the poleward shift, by employing a storm-tracking algorithm on an ensemble of CMIP5 models forced by increased CO2 emissions. We demonstrate that in addition to a poleward shift in the latitude of storm genesis, associated with the expansion of the Hadley cell, the averaged cyclonic storm also propagates more poleward until it reaches its maximum intensity. A mechanism for enhanced poleward motion of cyclones in a warmer climate is proposed, supported by idealized global warming experiments, and relates the shift to changes in upper level jet and atmospheric water vapour content. Our results imply that under the RCP8.5 climate change scenario, the averaged latitude of peak cyclone intensity shifts poleward by about 1.2○ (1.0○) in the Atlantic (Pacific) storm track in the Northern Hemisphere (NH), and by about 1.6○ in the Southern Hemisphere (SH) storm track. These changes in cyclone tracks can have a significant impact on midlatitude climate.
Vegetation Fires in the Coupled Human-Earth System Under Future Environmental and Policy Perspectives

NASA Astrophysics Data System (ADS)

le page, Y.; Morton, D. C.; Hurtt, G. C.

2013-12-01

Fires play a major role in terrestrial ecosystems dynamics and the carbon cycle. Potential changes in fire regimes due to climate change, land use change, or human management could have substantial ecological, climatic and socio-economic impacts, and have recently been emphasized as a source of uncertainty for policy-makers and climate mitigation cost estimates. Anticipating these interactions thus entails interdisciplinary models. Here we describe the development of a new fire modeling framework, which features the essential integration of climatic, vegetation and anthropogenic drivers. The model is an attempt to realistically account for ignition, spread and termination processes, on a 12-hour time step and at 1 degree spatial resolution globally. Because the quantitative influence of fire drivers on these processes are often poorly constrained, the framework includes an optimization procedure whereby key parameters (e.g. influence of moisture on fire spread, probability of cloud-to-ground lightning flashes to actually ignite a fire, human ignition frequency as a function of land use density) are determined to maximize the agreement between modeled and observed burned area over the past decade. The model performs surprisingly well across all biomes, and shows good agreement on non-optimized features, such as seasonality and fire size, which suggests some potential for robust projections. We couple the model to an integrated assessment model and explore the consequences of mitigation policies, land use decisions and climate change on future fire regimes with a focus on the Amazon basin. The coupled model future projections show that business-as-usual land use expansion would increase the frequency of escaped fires in the remaining forest, especially when combined with models projecting a drier climate. Inversely, climate mitigation policies as projected in the IPCC RCP4.5 scenario achieve synergistic benefits, with increased forest extent, less fire ignitions, and higher moisture levels.
Projected vegetation changes for the American Southwest: combined dynamic modeling and bioclimatic-envelope approach.

PubMed

Notaro, Michael; Mauss, Adrien; Williams, John W

2012-06-01

This study focuses on potential impacts of 21st century climate change on vegetation in the Southwest United States, based on debiased and interpolated climate projections from 17 global climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Among these models a warming trend is universal, but projected changes in precipitation vary in sign and magnitude. Two independent methods are applied: a dynamic global vegetation model to assess changes in plant functional types and bioclimatic envelope modeling to assess changes in individual tree and shrub species and biodiversity. The former approach investigates broad responses of plant functional types to climate change, while considering competition, disturbances, and carbon fertilization, while the latter approach focuses on the response of individual plant species, and net biodiversity, to climate change. The dynamic model simulates a region-wide reduction in vegetation cover during the 21st century, with a partial replacement of evergreen trees with grasses in the mountains of Colorado and Utah, except at the highest elevations, where tree cover increases. Across southern Arizona, central New Mexico, and eastern Colorado, grass cover declines, in some cases abruptly. Due to the prevalent warming trend among all 17 climate models, vegetation cover declines in the 21st century, with the greatest vegetation losses associated with models that project a drying trend. The inclusion of the carbon fertilization effect largely ameliorates the projected vegetation loss. Based on bioclimatic envelope modeling for the 21st century, the number of tree and shrub species that are expected to experience robust declines in range likely outweighs the number of species that are expected to expand in range. Dramatic shifts in plant species richness are projected, with declines in the high-elevation evergreen forests, increases in the eastern New Mexico prairies, and a northward shift of the Sonoran Desert biodiversity maximum.
Contrasting model complexity under a changing climate in a headwaters catchment.

NASA Astrophysics Data System (ADS)

Foster, L.; Williams, K. H.; Maxwell, R. M.

2017-12-01

Alpine, snowmelt-dominated catchments are the source of water for more than 1/6th of the world's population. These catchments are topographically complex, leading to steep weather gradients and nonlinear relationships between water and energy fluxes. Recent evidence suggests that alpine systems are more sensitive to climate warming, but these regions are vastly simplified in climate models and operational water management tools due to computational limitations. Simultaneously, point-scale observations are often extrapolated to larger regions where feedbacks can both exacerbate or mitigate locally observed changes. It is critical to determine whether projected climate impacts are robust to different methodologies, including model complexity. Using high performance computing and an integrated model of a representative headwater catchment we determined the hydrologic response from 30 projected climate changes to precipitation, temperature and vegetation for the Rocky Mountains. Simulations were run with 100m and 1km resolution, and with and without lateral subsurface flow in order to vary model complexity. We found that model complexity alters nonlinear relationships between water and energy fluxes. Higher-resolution models predicted larger changes per degree of temperature increase than lower resolution models, suggesting that reductions to snowpack, surface water, and groundwater due to warming may be underestimated in simple models. Increases in temperature were found to have a larger impact on water fluxes and stores than changes in precipitation, corroborating previous research showing that mountain systems are significantly more sensitive to temperature changes than to precipitation changes and that increases in winter precipitation are unlikely to compensate for increased evapotranspiration in a higher energy environment. These numerical experiments help to (1) bracket the range of uncertainty in published literature of climate change impacts on headwater hydrology; (2) characterize the role of precipitation and temperature changes on water supply for snowmelt-dominated downstream basins; and (3) identify which climate impacts depend on the scale of simulation.
Evaluating the ClimEx Single Model Large Ensemble in Comparison with EURO-CORDEX Results of Seasonal Means and Extreme Precipitation Indicators

NASA Astrophysics Data System (ADS)

von Trentini, F.; Schmid, F. J.; Braun, M.; Brisette, F.; Frigon, A.; Leduc, M.; Martel, J. L.; Willkofer, F.; Wood, R. R.; Ludwig, R.

2017-12-01

Meteorological extreme events seem to become more frequent in the present and future, and a seperation of natural climate variability and a clear climate change effect on these extreme events gains more and more interest. Since there is only one realisation of historical events, natural variability in terms of very long timeseries for a robust statistical analysis is not possible with observation data. A new single model large ensemble (SMLE), developed for the ClimEx project (Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec) is supposed to overcome this lack of data by downscaling 50 members of the CanESM2 (RCP 8.5) with the Canadian CRCM5 regional model (using the EURO-CORDEX grid specifications) for timeseries of 1950-2099 each, resulting in 7500 years of simulated climate. This allows for a better probabilistic analysis of rare and extreme events than any preceding dataset. Besides seasonal sums, several extreme indicators like R95pTOT, RX5day and others are calculated for the ClimEx ensemble and several EURO-CORDEX runs. This enables us to investigate the interaction between natural variability (as it appears in the CanESM2-CRCM5 members) and a climate change signal of those members for past, present and future conditions. Adding the EURO-CORDEX results to this, we can also assess the role of internal model variability (or natural variability) in climate change simulations. A first comparison shows similar magnitudes of variability of climate change signals between the ClimEx large ensemble and the CORDEX runs for some indicators, while for most indicators the spread of the SMLE is smaller than the spread of different CORDEX models.
Applying Multimodel Ensemble from Regional Climate Models for Improving Runoff Projections on Semiarid Regions of Spain

NASA Astrophysics Data System (ADS)

Garcia Galiano, S. G.; Olmos, P.; Giraldo Osorio, J. D.

2015-12-01

In the Mediterranean area, significant changes on temperature and precipitation are expected throughout the century. These trends could exacerbate the existing conditions in regions already vulnerable to climatic variability, reducing the water availability. Improving knowledge about plausible impacts of climate change on water cycle processes at basin scale, is an important step for building adaptive capacity to the impacts in this region, where severe water shortages are expected for the next decades. RCMs ensemble in combination with distributed hydrological models with few parameters, constitutes a valid and robust methodology to increase the reliability of climate and hydrological projections. For reaching this objective, a novel methodology for building Regional Climate Models (RCMs) ensembles of meteorological variables (rainfall an temperatures), was applied. RCMs ensembles are justified for increasing the reliability of climate and hydrological projections. The evaluation of RCMs goodness-of-fit to build the ensemble is based on empirical probability density functions (PDF) extracted from both RCMs dataset and a highly resolution gridded observational dataset, for the time period 1961-1990. The applied method is considering the seasonal and annual variability of the rainfall and temperatures. The RCMs ensembles constitute the input to a distributed hydrological model at basin scale, for assessing the runoff projections. The selected hydrological model is presenting few parameters in order to reduce the uncertainties involved. The study basin corresponds to a head basin of Segura River Basin, located in the South East of Spain. The impacts on runoff and its trend from observational dataset and climate projections, were assessed. Considering the control period 1961-1990, plausible significant decreases in runoff for the time period 2021-2050, were identified.

Extraction of Urban Morphology Parameters from Generic European Datasets: A Case Study for Antwerp, Berlin and Almada

NASA Astrophysics Data System (ADS)

Stevens, Catherine; Thomas, Bart

2014-05-01

Climate change is driven by global processes such as the global ocean circulation and its variability over time leading to changing weather patterns on regional scales as well as changes in the severity and occurrence of extreme events such as heat waves. The response of urban societies to the evolving climate depends not only on their regional climate characteristics but also on other local factors such as the urban heat island effect. Simulation of this phenomenon with local urban climate models requires comprehensive information about the urban morphology. This study focusses on the extraction of the planar and frontal area indices from detailed 3D city models and their relationship with the European Soil Sealing Level database from the European Environment Agency. These parameters have been calculated on a 1km2 grid and compared with soil sealing values aggregated at the same spatial resolution. The optimal size of the grid is a trade-off between the level of detail and the robustness of the established relationships by reducing the scatter at small scales. Moreover, the transferability of the results to other geographical areas has been investigated. The analyses have been conducted in the framework of the NACLIM FP7 project funded by the European Commission and include the cities of Antwerp (BE), Berlin (DE) and Almada (PT) represented by different climate and urban characteristics. First results show a correlation of 70% between the planar area index and the averaged soil sealing using a linear regression model at a 1km scale. Moreover, a good correspondence has been found between the relationships for Antwerp and Berlin which is promising for urban climate modellers to reduce model complexity and analyse various climate scenarios in an effective way.
Communicating climate information to end-users: an experience driven by the understanding and anticipation of user needs.

NASA Astrophysics Data System (ADS)

Chaumont, Diane; Huard, David; Logan, Travis; Sottile, Marie-France; Brown, Ross; Gauvin St-Denis, Blaise; Grenier, Patrick; Braun, Marco

2013-04-01

Planning and adapting to a changing climate requires credible information about the magnitude and rate of projected changes. Ouranos, a consortium on regional climatology and adaptation to climate change was launched in the Province of Québec, Canada, ten years ago, with the objective of developing and providing climate information and expertise in support to adaption. Ouranos differs from most other climate service centers by integrating climate modeling activities, impacts and adaptation expertise and climate analysis services under one roof. The Climate Scenarios Group operates at the interface between climate modellers and users and is responsible for developing, producing and communicating climate scenarios to end-users in a consistent manner. This process requires close collaboration with users to define, understand and eventually anticipate their needs. The varied scientific expertise of climate scenarios specialists --who also act as communicators-- has proven to be a key element for successful communication. A large amount of effort is spent on the characterization and communication of the uncertainties involved in scenario construction. Two main activities have been put in place by the experts in climate modeling to address this: (1) a training course on climate models and (2) a fact-sheet summarizing the uncertainty and robustness of the climate change scenario provided for each I&A application. The latter tool ensures the transparency, traceability, and accountability of our products, and at the same time, encourages a sense of shared responsibility for the final choice of climate scenarios. In addition to uncertainty, two other main issues have been identified as essential in communication with users: 1) observed natural variability at relevant scales and 2) reconciliation of the projected trend with the recent observed trend. Our group has devoted substantial resources for the advancement of communication with end-users in these particular areas. This presentation will provide an overview of progress in communicating climate information at the Ouranos Consortium. We will discuss success and failures and future plans, in particular the extent to which Ouranos needs to work with users in decision-making activities.
Persistent Cold Air Outbreaks over North America Under Climate Warming

NASA Astrophysics Data System (ADS)

Gao, Y.; Leung, L. R.; Lu, J.

2014-12-01

This study evaluates the change of cold air outbreaks (CAO) over North America using Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble of global climate simulations as well as regional high resolution climate simulations. In future, while robust decrease of CAO duration dominates in most of the North America, the decrease over northwestern U.S. was found to have much smaller magnitude than the surrounding regions. We found statistically significant increase of the sea level pressure over gulf of Alaska, leading to the advection of cold air to northwestern U.S.. By shifting the probability distribution of present temperature towards future warmer conditions, we identified the changes in large scale circulation contribute to about 50% of the enhanced sea level pressure. Using the high resolution regional climate model results, we found that increases of existing snowpack could potentially trigger the increase of CAO in the near future over the southwestern U.S. and Rocky Mountain through surface albedo effects. By the end of this century, the top 5 most extreme historical CAO events may still occur and wind chill warning will continue to have societal impacts over North America in particular over northwestern United States.
Incorporating palaeoclimate data into water security planning and decision making - a case study from southeast Queensland, Australia

NASA Astrophysics Data System (ADS)

Kiem, Anthony; Vance, Tessa; Tozer, Carly; Roberts, Jason

2017-04-01

Decision makers in the water sector need to deal with existing hydroclimatic variability and uncertainty about future changes to climate and catchment conditions. Identifying solutions for hydroclimatic risk adaptation strategies that are both optimal and robust in the presence of variability and uncertainty presents a difficult challenge. A major reason for this challenge is the fact that the instrumental record in Australia is short ( 60-130 years) and fails to encompass enough climate variability to allow the calculation of robust statistics around the baseline risk of extreme events (e.g. multi-year droughts, decadal periods with clustering of major flood events). This climate variability is documented pre-1900 in palaeoclimate records from sources such as corals, tree-rings, freshwater and marine sediments. Despite being remote from Queensland, a high resolution and highly correlated palaeoclimate record from the Law Dome ice cores in Antarctica (Vance et al. 2015) is also now available and has identified eight mega-droughts (lasting from 5-39 years) during 1000-2009 AD. Most importantly, the palaeoclimate information confirms that the post-1900 instrumental period (i.e. the period on which all water resources infrastructure, policy, operation rules and strategies is based) does not capture the full range of variability that has occurred. Other work also clearly shows that, out to 2050 at least, impacts associated with natural variability significantly exceed even the worst-case climate change scenarios (i.e. obtained from Global Climate Models run under the highest emission scenarios). This presentation will demonstrate how the Law Dome ice cores from Antarctica have been used to produce a highly accurate, 1000 year, annual and seasonal resolution, hydroclimate reconstruction (i.e. precipitation and streamflow) for the southeast Queensland region of Australia. We will then show how the palaeoclimate data has been incorporated into the South East Queensland Regional Stochastic Model (SEQRSM) of catchment hydrology to (a) demonstrate the utility of a palaeoclimate proxy approach in producing more robust estimates of hydroclimatic risk under climate variability and change; (b) gain improved insights into the characteristics (e.g. location, duration, frequency, magnitude, spatial extent, sequencing) of hydroclimate extremes for water security planning and (c) deliver optimised solutions for hydroclimatic risk adaptation strategies to water managers (e.g. optimal and sustainable supply of water to meet current and future urban requirements and also to nearby catchments to support irrigation for dairy, vegetable and forage crops).
The Teaching of Anthropogenic Climate Change and Earth Science via Technology-Enabled Inquiry Education

NASA Technical Reports Server (NTRS)

Bush, Drew; Sieber, Renee; Seiler, Gale; Chandler, Mark

2016-01-01

A gap has existed between the tools and processes of scientists working on anthropogenic global climate change (AGCC) and the technologies and curricula available to educators teaching the subject through student inquiry. Designing realistic scientific inquiry into AGCC poses a challenge because research on it relies on complex computer models, globally distributed data sets, and complex laboratory and data collection procedures. Here we examine efforts by the scientific community and educational researchers to design new curricula and technology that close this gap and impart robust AGCC and Earth Science understanding. We find technology-based teaching shows promise in promoting robust AGCC understandings if associated curricula address mitigating factors such as time constraints in incorporating technology and the need to support teachers implementing AGCC and Earth Science inquiry. We recommend the scientific community continue to collaborate with educational researchers to focus on developing those inquiry technologies and curricula that use realistic scientific processes from AGCC research and/or the methods for determining how human society should respond to global change.
Prerequisites for understanding climate-change impacts on northern prairie wetlands

USGS Publications Warehouse

Anteau, Michael J.; Wiltermuth, Mark T.; Post van der Burg, Max; Pearse, Aaron T.

2016-01-01

The Prairie Pothole Region (PPR) contains ecosystems that are typified by an extensive matrix of grasslands and depressional wetlands, which provide numerous ecosystem services. Over the past 150 years the PPR has experienced numerous landscape modifications resulting in agricultural conversion of 75–99 % of native prairie uplands and drainage of 50–90 % of wetlands. There is concern over how and where conservation dollars should be spent within the PPR to protect and restore wetland basins to support waterbird populations that will be robust to a changing climate. However, while hydrological impacts of landscape modifications appear substantial, they are still poorly understood. Previous modeling efforts addressing impacts of climate change on PPR wetlands have yet to fully incorporate interacting or potentially overshadowing impacts of landscape modification. We outlined several information needs for building more informative models to predict climate change effects on PPR wetlands. We reviewed how landscape modification influences wetland hydrology and present a conceptual model to describe how modified wetlands might respond to climate variability. We note that current climate projections do not incorporate cyclical variability in climate between wet and dry periods even though such dynamics have shaped the hydrology and ecology of PPR wetlands. We conclude that there are at least three prerequisite steps to making meaningful predictions about effects of climate change on PPR wetlands. Those evident to us are: 1) an understanding of how physical and watershed characteristics of wetland basins of similar hydroperiods vary across temperature and moisture gradients; 2) a mechanistic understanding of how wetlands respond to climate across a gradient of anthropogenic modifications; and 3) improved climate projections for the PPR that can meaningfully represent potential changes in climate variability including intensity and duration of wet and dry periods. Once these issues are addressed, we contend that modeling efforts will better inform and quantify ecosystem services provided by wetlands to meet needs of waterbird conservation and broader societal interests such as flood control and water quality.
Building climate adaptation capabilities through technology and community

NASA Astrophysics Data System (ADS)

Murray, D.; McWhirter, J.; Intsiful, J. D.; Cozzini, S.

2011-12-01

To effectively plan for adaptation to changes in climate, decision makers require infrastructure and tools that will provide them with timely access to current and future climate information. For example, climate scientists and operational forecasters need to access global and regional model projections and current climate information that they can use to prepare monitoring products and reports and then publish these for the decision makers. Through the UNDP African Adaption Programme, an infrastructure is being built across Africa that will provide multi-tiered access to such information. Web accessible servers running RAMADDA, an open source content management system for geoscience information, will provide access to the information at many levels: from the raw and processed climate model output to real-time climate conditions and predictions to documents and presentation for government officials. Output from regional climate models (e.g. RegCM4) and downscaled global climate models will be accessible through RAMADDA. The Integrated Data Viewer (IDV) is being used by scientists to create visualizations that assist the understanding of climate processes and projections, using the data on these as well as external servers. Since RAMADDA is more than a data server, it is also being used as a publishing platform for the generated material that will be available and searchable by the decision makers. Users can wade through the enormous volumes of information and extract subsets for their region or project of interest. Participants from 20 countries attended workshops at ICTP during 2011. They received training on setting up and installing the servers and necessary software and are now working on deploying the systems in their respective countries. This is the first time an integrated and comprehensive approach to climate change adaptation has been widely applied in Africa. It is expected that this infrastructure will enhance North-South collaboration and improve the delivery of technical support and services. This improved infrastructure will enhance the capacity of countries to provide a wide range of robust products and services in a timely manner.
Climate change hotspots in the CMIP5 global climate model ensemble.

PubMed

Diffenbaugh, Noah S; Giorgi, Filippo

2012-01-10

We use a statistical metric of multi-dimensional climate change to quantify the emergence of global climate change hotspots in the CMIP5 climate model ensemble. Our hotspot metric extends previous work through the inclusion of extreme seasonal temperature and precipitation, which exert critical influence on climate change impacts. The results identify areas of the Amazon, the Sahel and tropical West Africa, Indonesia, and the Tibetan Plateau as persistent regional climate change hotspots throughout the 21 st century of the RCP8.5 and RCP4.5 forcing pathways. In addition, areas of southern Africa, the Mediterranean, the Arctic, and Central America/western North America also emerge as prominent regional climate change hotspots in response to intermediate and high levels of forcing. Comparisons of different periods of the two forcing pathways suggest that the pattern of aggregate change is fairly robust to the level of global warming below approximately 2°C of global warming (relative to the late-20 th -century baseline), but not at the higher levels of global warming that occur in the late-21 st -century period of the RCP8.5 pathway, with areas of southern Africa, the Mediterranean, and the Arctic exhibiting particular intensification of relative aggregate climate change in response to high levels of forcing. Although specific impacts will clearly be shaped by the interaction of climate change with human and biological vulnerabilities, our identification of climate change hotspots can help to inform mitigation and adaptation decisions by quantifying the rate, magnitude and causes of the aggregate climate response in different parts of the world.
Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission.

PubMed

Notz, Dirk; Stroeve, Julienne

2016-11-11

Arctic sea ice is retreating rapidly, raising prospects of a future ice-free Arctic Ocean during summer. Because climate-model simulations of the sea-ice loss differ substantially, we used a robust linear relationship between monthly-mean September sea-ice area and cumulative carbon dioxide (CO 2 ) emissions to infer the future evolution of Arctic summer sea ice directly from the observational record. The observed linear relationship implies a sustained loss of 3 ± 0.3 square meters of September sea-ice area per metric ton of CO 2 emission. On the basis of this sensitivity, Arctic sea ice will be lost throughout September for an additional 1000 gigatons of CO 2 emissions. Most models show a lower sensitivity, which is possibly linked to an underestimation of the modeled increase in incoming longwave radiation and of the modeled transient climate response. Copyright © 2016, American Association for the Advancement of Science.
Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change.

PubMed

Morin, Xavier; Thuiller, Wilfried

2009-05-01

Obtaining reliable predictions of species range shifts under climate change is a crucial challenge for ecologists and stakeholders. At the continental scale, niche-based models have been widely used in the last 10 years to predict the potential impacts of climate change on species distributions all over the world, although these models do not include any mechanistic relationships. In contrast, species-specific, process-based predictions remain scarce at the continental scale. This is regrettable because to secure relevant and accurate predictions it is always desirable to compare predictions derived from different kinds of models applied independently to the same set of species and using the same raw data. Here we compare predictions of range shifts under climate change scenarios for 2100 derived from niche-based models with those of a process-based model for 15 North American boreal and temperate tree species. A general pattern emerged from our comparisons: niche-based models tend to predict a stronger level of extinction and a greater proportion of colonization than the process-based model. This result likely arises because niche-based models do not take phenotypic plasticity and local adaptation into account. Nevertheless, as the two kinds of models rely on different assumptions, their complementarity is revealed by common findings. Both modeling approaches highlight a major potential limitation on species tracking their climatic niche because of migration constraints and identify similar zones where species extirpation is likely. Such convergent predictions from models built on very different principles provide a useful way to offset uncertainties at the continental scale. This study shows that the use in concert of both approaches with their own caveats and advantages is crucial to obtain more robust results and that comparisons among models are needed in the near future to gain accuracy regarding predictions of range shifts under climate change.
The Data Platform for Climate Research and Action: Introducing Climate Watch

NASA Astrophysics Data System (ADS)

Hennig, R. J.; Ge, M.; Friedrich, J.; Lebling, K.; Carlock, G.; Arcipowska, A.; Mangan, E.; Biru, H.; Tankou, A.; Chaudhury, M.

2017-12-01

The Paris Agreement, adopted through Decision 1/CP.21, brings all nations together to take on ambitious efforts to combat climate change. Open access to climate data supporting climate research, advancing knowledge, and informing decision making is key to encourage and strengthen efforts of stakeholders at all levels to address and respond to effects of climate change. Climate Watch is a robust online data platform developed in response to the urgent needs of knowledge and tools to empower climate research and action, including those of researchers, policy makers, the private sector, civil society, and all other non-state actors. Building on the rapid growing technology of open data and information sharing, Climate Watch is equipped with extensive amount of climate data, informative visualizations, concise yet efficient user interface, and connection to resources users need to gather insightful information on national and global progress towards delivering on the objective of the Convention and the Paris Agreement. Climate Watch brings together hundreds of quantitative and qualitative indicators for easy explore, visualize, compare, download at global, national, and sectoral levels: Greenhouse gas (GHG) emissions for more than 190 countries over the1850-2014 time period, covering all seven Kyoto Gases following IPCC source/sink categories; Structured information on over 150 NDCs facilitating the clarity, understanding and transparency of countries' contributions to address climate change; Over 6500 identified linkages between climate actions in NDCs across the 169 targets of the sustainable development goals (SDG); Over 200 indicators describing low carbon pathways from models and scenarios by integrated assessment models (IAMs) and national sources; and Data on vulnerability and risk, policies, finance, and many more. Climate Watch platform is developed as part of the broader efforts within the World Resources Institute, the NDC Partnership, and in collaboration with GIZ, UNFCCC, World Bank, and Climate Analytics.
Climate Change: From Science to Practice.

PubMed

Wheeler, Nicola; Watts, Nick

2018-03-01

Climate change poses a significant threat to human health. Understanding how climate science can be translated into public health practice is an essential first step in enabling robust adaptation and improving resiliency to climate change. Recent research highlights the importance of iterative approaches to public health adaptation to climate change, enabling uncertainties of health impacts and barriers to adaptation to be accounted for. There are still significant barriers to adaptation, which are context-specific and thus present unique challenges to public health practice. The implementation of flexible adaptation approaches, using frameworks targeted for public health, is key to ensuring robust adaptation to climate change in public health practice. The BRACE framework provides an excellent approach for health adaptation to climate change. Combining this with the insights provided and by the adaptation pathways approach allows for more deliberate accounting of long-term uncertainties. The mainstreaming of climate change adaptation into public health practice and planning is important in facilitating this approach and overcoming the significant barriers to effective adaptation. Yet, the immediate and future limits to adaptation provide clear justification for urgent and accelerated efforts to mitigate climate change.
Projecting date palm distribution in Iran under climate change using topography, physicochemical soil properties, soil taxonomy, land use, and climate data

NASA Astrophysics Data System (ADS)

Shabani, Farzin; Kumar, Lalit; Taylor, Subhashni

2014-11-01

This study set out to model potential date palm distribution under current and future climate scenarios using an emission scenario, in conjunction with two different global climate models (GCMs): CSIRO-Mk3.0 (CS), and MIROC-H (MR), and to refine results based on suitability under four nonclimatic parameters. Areas containing suitable physicochemical soil properties and suitable soil taxonomy, together with land slopes of less than 10° and suitable land uses for date palm ( Phoenix dactylifera) were selected as appropriate refining tools to ensure the CLIMEX results were accurate and robust. Results showed that large regions of Iran are projected as likely to become climatically suitable for date palm cultivation based on the projected scenarios for the years 2030, 2050, 2070, and 2100. The study also showed CLIMEX outputs merit refinement by nonclimatic parameters and that the incremental introduction of each additional parameter decreased the disagreement between GCMs. Furthermore, the study indicated that the least amount of disagreement in terms of areas conducive to date palm cultivation resulted from CS and MR GCMs when the locations of suitable physicochemical soil properties and soil taxonomy were used as refinement tools.
Climate projections FAQ

Treesearch

A.E. Daniels; J.F. Morrison; L.A. Joyce; N.L. Crookston; S.C. Chen; S.G. McNulty

2012-01-01

Climate scenarios offer one way to identify and examine the land management challenges posed by climate change. Selecting projections, however, requires careful consideration of the natural resources under study, and where and how they are sensitive to climate. Selection also depends on the robustness of different projections for the resources and geographic area of...
Influence of three common calibration metrics on the diagnosis of climate change impacts on water resources

NASA Astrophysics Data System (ADS)

Seiller, G.; Roy, R.; Anctil, F.

2017-04-01

Uncertainties associated to the evaluation of the impacts of climate change on water resources are broad, from multiple sources, and lead to diagnoses sometimes difficult to interpret. Quantification of these uncertainties is a key element to yield confidence in the analyses and to provide water managers with valuable information. This work specifically evaluates the influence of hydrological modeling calibration metrics on future water resources projections, on thirty-seven watersheds in the Province of Québec, Canada. Twelve lumped hydrologic models, representing a wide range of operational options, are calibrated with three common objective functions derived from the Nash-Sutcliffe efficiency. The hydrologic models are forced with climate simulations corresponding to two RCP, twenty-nine GCM from CMIP5 (Coupled Model Intercomparison Project phase 5) and two post-treatment techniques, leading to future projections in the 2041-2070 period. Results show that the diagnosis of the impacts of climate change on water resources are quite affected by the hydrologic models selection and calibration metrics. Indeed, for the four selected hydrological indicators, dedicated to water management, parameters from the three objective functions can provide different interpretations in terms of absolute and relative changes, as well as projected changes direction and climatic ensemble consensus. The GR4J model and a multimodel approach offer the best modeling options, based on calibration performance and robustness. Overall, these results illustrate the need to provide water managers with detailed information on relative changes analysis, but also absolute change values, especially for hydrological indicators acting as security policy thresholds.
South Asia river-flow projections and their implications for water resources

NASA Astrophysics Data System (ADS)

Mathison, C.; Wiltshire, A. J.; Falloon, P.; Challinor, A. J.

2015-12-01

South Asia is a region with a large and rising population, a high dependence on water intense industries, such as agriculture and a highly variable climate. In recent years, fears over the changing Asian summer monsoon (ASM) and rapidly retreating glaciers together with increasing demands for water resources have caused concern over the reliability of water resources and the potential impact on intensely irrigated crops in this region. Despite these concerns, there is a lack of climate simulations with a high enough resolution to capture the complex orography, and water resource analysis is limited by a lack of observations of the water cycle for the region. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. Two global climate models (GCMs), which represent the ASM reasonably well are downscaled (1960-2100) using a regional climate model (RCM). In the absence of robust observations, ERA-Interim reanalysis is also downscaled providing a constrained estimate of the water balance for the region for comparison against the GCMs (1990-2006). The RCM river flow is routed using a river-routing model to allow analysis of present-day and future river flows through comparison with available river gauge observations. We examine how useful these simulations are for understanding potential changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows but overestimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model. The simulations suggest an increasing trend in annual mean river flows for some of the river gauges in this analysis, in some cases almost doubling by the end of the century. The future maximum river-flow rates still occur during the ASM period, with a magnitude in some cases, greater than the present-day natural variability. Increases in river flow could mean additional water resources for irrigation, the largest usage of water in this region, but has implications in terms of inundation risk. These projected increases could be more than countered by changes in demand due to depleted groundwater, increases in domestic use or expansion of water intense industries. Including missing hydrological processes in the model would make these projections more robust but could also change the sign of the projections.
Landscape fragmentation affects responses of avian communities to climate change.

PubMed

Jarzyna, Marta A; Porter, William F; Maurer, Brian A; Zuckerberg, Benjamin; Finley, Andrew O

2015-08-01

Forecasting the consequences of climate change is contingent upon our understanding of the relationship between biodiversity patterns and climatic variability. While the impacts of climate change on individual species have been well-documented, there is a paucity of studies on climate-mediated changes in community dynamics. Our objectives were to investigate the relationship between temporal turnover in avian biodiversity and changes in climatic conditions and to assess the role of landscape fragmentation in affecting this relationship. We hypothesized that community turnover would be highest in regions experiencing the most pronounced changes in climate and that these patterns would be reduced in human-dominated landscapes. To test this hypothesis, we quantified temporal turnover in avian communities over a 20-year period using data from the New York State Breeding Atlases collected during 1980-1985 and 2000-2005. We applied Bayesian spatially varying intercept models to evaluate the relationship between temporal turnover and temporal trends in climatic conditions and landscape fragmentation. We found that models including interaction terms between climate change and landscape fragmentation were superior to models without the interaction terms, suggesting that the relationship between avian community turnover and changes in climatic conditions was affected by the level of landscape fragmentation. Specifically, we found weaker associations between temporal turnover and climatic change in regions with prevalent habitat fragmentation. We suggest that avian communities in fragmented landscapes are more robust to climate change than communities found in contiguous habitats because they are comprised of species with wider thermal niches and thus are less susceptible to shifts in climatic variability. We conclude that highly fragmented regions are likely to undergo less pronounced changes in composition and structure of faunal communities as a result of climate change, whereas those changes are likely to be greater in contiguous and unfragmented habitats. © 2015 John Wiley & Sons Ltd.
Livestock Helminths in a Changing Climate: Approaches and Restrictions to Meaningful Predictions

PubMed Central

Fox, Naomi J.; Marion, Glenn; Davidson, Ross S.; White, Piran C. L.; Hutchings, Michael R.

2012-01-01

Simple Summary Parasitic helminths represent one of the most pervasive challenges to livestock, and their intensity and distribution will be influenced by climate change. There is a need for long-term predictions to identify potential risks and highlight opportunities for control. We explore the approaches to modelling future helminth risk to livestock under climate change. One of the limitations to model creation is the lack of purpose driven data collection. We also conclude that models need to include a broad view of the livestock system to generate meaningful predictions. Abstract Climate change is a driving force for livestock parasite risk. This is especially true for helminths including the nematodes Haemonchus contortus, Teladorsagia circumcincta, Nematodirus battus, and the trematode Fasciola hepatica, since survival and development of free-living stages is chiefly affected by temperature and moisture. The paucity of long term predictions of helminth risk under climate change has driven us to explore optimal modelling approaches and identify current bottlenecks to generating meaningful predictions. We classify approaches as correlative or mechanistic, exploring their strengths and limitations. Climate is one aspect of a complex system and, at the farm level, husbandry has a dominant influence on helminth transmission. Continuing environmental change will necessitate the adoption of mitigation and adaptation strategies in husbandry. Long term predictive models need to have the architecture to incorporate these changes. Ultimately, an optimal modelling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modelling, incorporating changes in livestock husbandry and disease control. Irrespective of approach, the principal limitation to parasite predictions is the availability of active surveillance data and empirical data on physiological responses to climate variables. By combining improved empirical data and refined models with a broad view of the livestock system, robust projections of helminth risk can be developed. PMID:26486780
Collaborative Research: Robust Climate Projections and Stochastic Stability of Dynamical Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ilya Zaliapin

This project focused on conceptual exploration of El Nino/Southern Oscillation (ENSO) variability and sensitivity using a Delay Differential Equation developed in the project. We have (i) established the existence and continuous dependence of solutions of the model (ii) explored multiple models solutions, and the distribution of solutions extrema, and (iii) established and explored the phase locking phenomenon and the existence of multiple solutions for the same values of model parameters. In addition, we have applied to our model the concept of pullback attractor, which greatly facilitated predictive understanding of the nonlinear model's behavior.
The role of climatic variables in winter cereal yields: a retrospective analysis.

PubMed

Luo, Qunying; Wen, Li

2015-02-01

This study examined the effects of observed climate including [CO2] on winter cereal [winter wheat (Triticum aestivum), barley (Hordeum vulgare) and oat (Avena sativa)] yields by adopting robust statistical analysis/modelling approaches (i.e. autoregressive fractionally integrated moving average, generalised addition model) based on long time series of historical climate data and cereal yield data at three locations (Moree, Dubbo and Wagga Wagga) in New South Wales, Australia. Research results show that (1) growing season rainfall was significantly, positively and non-linearly correlated with crop yield at all locations considered; (2) [CO2] was significantly, positively and non-linearly correlated with crop yields in all cases except wheat and barley yields at Wagga Wagga; (3) growing season maximum temperature was significantly, negatively and non-linearly correlated with crop yields at Dubbo and Moree (except for barley); and (4) radiation was only significantly correlated with oat yield at Wagga Wagga. This information will help to identify appropriate management adaptation options in dealing with the risk and in taking the opportunities of climate change.

Comparing approaches for using climate projections in assessing water resources investments for systems with multiple stakeholder groups

NASA Astrophysics Data System (ADS)

Hurford, Anthony; Harou, Julien

2015-04-01

Climate change has challenged conventional methods of planning water resources infrastructure investment, relying on stationarity of time-series data. It is not clear how to best use projections of future climatic conditions. Many-objective simulation-optimisation and trade-off analysis using evolutionary algorithms has been proposed as an approach to addressing complex planning problems with multiple conflicting objectives. The search for promising assets and policies can be carried out across a range of climate projections, to identify the configurations of infrastructure investment shown by model simulation to be robust under diverse future conditions. Climate projections can be used in different ways within a simulation model to represent the range of possible future conditions and understand how optimal investments vary according to the different hydrological conditions. We compare two approaches, optimising over an ensemble of different 20-year flow and PET timeseries projections, and separately for individual future scenarios built synthetically from the original ensemble. Comparing trade-off curves and surfaces generated by the two approaches helps understand the limits and benefits of optimising under different sets of conditions. The comparison is made for the Tana Basin in Kenya, where climate change combined with multiple conflicting objectives of water management and infrastructure investment mean decision-making is particularly challenging.
Developing a test-bed for robust research governance of geoengineering: the contribution of ocean iron biogeochemistry

PubMed Central

Bressac, Matthieu

2016-01-01

Geoengineering to mitigate climate change has long been proposed, but remains nebulous. Exploration of the feasibility of geoengineering first requires the development of research governance to move beyond the conceptual towards scientifically designed pilot studies. Fortuitously, 12 mesoscale (approx. 1000 km2) iron enrichments, funded to investigate how ocean iron biogeochemistry altered Earth's carbon cycle in the geological past, provide proxies to better understand the benefits and drawbacks of geoengineering. The utility of these iron enrichments in the geoengineering debate is enhanced by the GEOTRACES global survey. Here, we outline how GEOTRACES surveys and process studies can provide invaluable insights into geoengineering. Surveys inform key unknowns including the regional influence and magnitude of modes of iron supply, and stimulate iron biogeochemical modelling. These advances will enable quantification of interannual variability of iron supply to assess whether any future purposeful multi-year iron-fertilization meets the principle of ‘additionality’ (sensu Kyoto protocol). Process studies address issues including upscaling of geoengineering, and how differing iron-enrichment strategies could stimulate wide-ranging biogeochemical outcomes. In summary, the availability of databases on both mesoscale iron-enrichment studies and the GEOTRACES survey, along with modelling, policy initiatives and legislation have positioned the iron-enrichment approach as a robust multifaceted test-bed to assess proposed research into climate intervention. This article is part of the themed issue ‘Biological and climatic impacts of ocean trace element chemistry’. PMID:29035263
Developing a test-bed for robust research governance of geoengineering: the contribution of ocean iron biogeochemistry.

PubMed

Boyd, Philip W; Bressac, Matthieu

2016-11-28

Geoengineering to mitigate climate change has long been proposed, but remains nebulous. Exploration of the feasibility of geoengineering first requires the development of research governance to move beyond the conceptual towards scientifically designed pilot studies. Fortuitously, 12 mesoscale (approx. 1000 km 2 ) iron enrichments, funded to investigate how ocean iron biogeochemistry altered Earth's carbon cycle in the geological past, provide proxies to better understand the benefits and drawbacks of geoengineering. The utility of these iron enrichments in the geoengineering debate is enhanced by the GEOTRACES global survey. Here, we outline how GEOTRACES surveys and process studies can provide invaluable insights into geoengineering. Surveys inform key unknowns including the regional influence and magnitude of modes of iron supply, and stimulate iron biogeochemical modelling. These advances will enable quantification of interannual variability of iron supply to assess whether any future purposeful multi-year iron-fertilization meets the principle of 'additionality' ( sensu Kyoto protocol). Process studies address issues including upscaling of geoengineering, and how differing iron-enrichment strategies could stimulate wide-ranging biogeochemical outcomes. In summary, the availability of databases on both mesoscale iron-enrichment studies and the GEOTRACES survey, along with modelling, policy initiatives and legislation have positioned the iron-enrichment approach as a robust multifaceted test-bed to assess proposed research into climate intervention.This article is part of the themed issue 'Biological and climatic impacts of ocean trace element chemistry'. © 2016 The Author(s).
Risk, Robustness and Water Resources Planning Under Uncertainty

NASA Astrophysics Data System (ADS)

Borgomeo, Edoardo; Mortazavi-Naeini, Mohammad; Hall, Jim W.; Guillod, Benoit P.

2018-03-01

Risk-based water resources planning is based on the premise that water managers should invest up to the point where the marginal benefit of risk reduction equals the marginal cost of achieving that benefit. However, this cost-benefit approach may not guarantee robustness under uncertain future conditions, for instance under climatic changes. In this paper, we expand risk-based decision analysis to explore possible ways of enhancing robustness in engineered water resources systems under different risk attitudes. Risk is measured as the expected annual cost of water use restrictions, while robustness is interpreted in the decision-theoretic sense as the ability of a water resource system to maintain performance—expressed as a tolerable risk of water use restrictions—under a wide range of possible future conditions. Linking risk attitudes with robustness allows stakeholders to explicitly trade-off incremental increases in robustness with investment costs for a given level of risk. We illustrate the framework through a case study of London's water supply system using state-of-the -art regional climate simulations to inform the estimation of risk and robustness.
Using Terrestrial GCMs to Understand Climate on Venus, Mars and Titan

NASA Astrophysics Data System (ADS)

Lebonnois, S.; Forget, F.; Hourdin, F.

2008-12-01

For many decades now, General Circulation Models have been developed for the Earth to model our climate. Using these tools, which complexity and efficiency increase with years and computers power, many features of the Earth's circulation may be analyzed and interpreted. But the Earth is a unique case, with rapid rotation rate, with seasons, with water oceans. The Earth GCMs have many parameters finely tuned to reproduce the many observations that are available. How robust are these models under evolving conditions ? To what point may we trust them when exploring Earth's future ? Earth, Venus, Mars and also Titan have dense atmospheres that present many differences, but also similarities. How mechanisms that are at work in the Earth's atmosphere do adapt under these different conditions ? Why are Venus and Titan's atmospheres in super-rotation ? Are the Martian storms produced by processes that also happen on the Earth ? Using the GCMs developed for the Earth for these different atmospheres is very appealing to study these questions. Though the amount of available data is much less for extraterrestrial atmospheres, adapting the terrestrial GCMs to these different environments is worth the effort. Even if the dynamical core may be used almost as it is, adapting the physical parameterizations is not straightforward, but based on the increasing amount of observational data, it may be done with more and more accuracy. These extraterrestrial GCMs may now be used to understand the different features of these climates, but also to compare the different behaviors of these atmospheres under different forcings. It explores the robustness of this kind of tools under widly different conditions, putting strength and confidence in our exploration of Earth's atmosphere future evolution. In this talk, we will present the adaptations that have been necessary to develop GCMs for Mars, Titan and Venus from the LMDZ Earth GCM. These models have then followed their own developments, with many successes in understanding these climates. We will also give exemples of mutual benefits, related to the common base for all these models.
Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as "field" or "global" significance. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Monthly temperature climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.
RECONSTRUCTING THE ORIGINS OF HIGH-ALPINE NICHES AND CUSHION LIFE FORM IN THE GENUS ANDROSACE S.L. (PRIMULACEAE)

PubMed Central

Boucher, Florian C.; Thuiller, Wilfried; Roquet, Cristina; Douzet, Rolland; Aubert, Serge; Alvarez, Nadir; Lavergne, Sébastien

2014-01-01

Relatively, few species have been able to colonize extremely cold alpine environments. We investigate the role played by the cushion life form in the evolution of climatic niches in the plant genus Androsace s.l., which spreads across the mountain ranges of the Northern Hemisphere. Using robust methods that account for phylogenetic uncertainty, intraspecific variability of climatic requirements and different life-history evolution scenarios, we show that climatic niches of Androsace s.l. exhibit low phylogenetic signal and that they evolved relatively recently and punctually. Models of niche evolution fitted onto phylogenies show that the cushion life form has been a key innovation providing the opportunity to occupy extremely cold environments, thus contributing to rapid climatic niche diversification in the genus Androsace s.l. We then propose a plausible scenario for the adaptation of plants to alpine habitats. PMID:22486702
Bidecadal North Atlantic ocean circulation variability controlled by timing of volcanic eruptions.

PubMed

Swingedouw, Didier; Ortega, Pablo; Mignot, Juliette; Guilyardi, Eric; Masson-Delmotte, Valérie; Butler, Paul G; Khodri, Myriam; Séférian, Roland

2015-03-30

While bidecadal climate variability has been evidenced in several North Atlantic paleoclimate records, its drivers remain poorly understood. Here we show that the subset of CMIP5 historical climate simulations that produce such bidecadal variability exhibits a robust synchronization, with a maximum in Atlantic Meridional Overturning Circulation (AMOC) 15 years after the 1963 Agung eruption. The mechanisms at play involve salinity advection from the Arctic and explain the timing of Great Salinity Anomalies observed in the 1970s and the 1990s. Simulations, as well as Greenland and Iceland paleoclimate records, indicate that coherent bidecadal cycles were excited following five Agung-like volcanic eruptions of the last millennium. Climate simulations and a conceptual model reveal that destructive interference caused by the Pinatubo 1991 eruption may have damped the observed decreasing trend of the AMOC in the 2000s. Our results imply a long-lasting climatic impact and predictability following the next Agung-like eruption.
Global Climate Change Adaptation Priorities for Biodiversity and Food Security

PubMed Central

Hannah, Lee; Ikegami, Makihiko; Hole, David G.; Seo, Changwan; Butchart, Stuart H. M.; Peterson, A. Townsend; Roehrdanz, Patrick R.

2013-01-01

International policy is placing increasing emphasis on adaptation to climate change, including the allocation of new funds to assist adaptation efforts. Climate change adaptation funding may be most effective where it meets integrated goals, but global geographic priorities based on multiple development and ecological criteria are not well characterized. Here we show that human and natural adaptation needs related to maintaining agricultural productivity and ecosystem integrity intersect in ten major areas globally, providing a coherent set of international priorities for adaptation funding. An additional seven regional areas are identified as worthy of additional study. The priority areas are locations where changes in crop suitability affecting impoverished farmers intersect with changes in ranges of restricted-range species. Agreement among multiple climate models and emissions scenarios suggests that these priorities are robust. Adaptation funding directed to these areas could simultaneously address multiple international policy goals, including poverty reduction, protecting agricultural production and safeguarding ecosystem services. PMID:23991125
Global climate change adaptation priorities for biodiversity and food security.

PubMed

Hannah, Lee; Ikegami, Makihiko; Hole, David G; Seo, Changwan; Butchart, Stuart H M; Peterson, A Townsend; Roehrdanz, Patrick R

2013-01-01

International policy is placing increasing emphasis on adaptation to climate change, including the allocation of new funds to assist adaptation efforts. Climate change adaptation funding may be most effective where it meets integrated goals, but global geographic priorities based on multiple development and ecological criteria are not well characterized. Here we show that human and natural adaptation needs related to maintaining agricultural productivity and ecosystem integrity intersect in ten major areas globally, providing a coherent set of international priorities for adaptation funding. An additional seven regional areas are identified as worthy of additional study. The priority areas are locations where changes in crop suitability affecting impoverished farmers intersect with changes in ranges of restricted-range species. Agreement among multiple climate models and emissions scenarios suggests that these priorities are robust. Adaptation funding directed to these areas could simultaneously address multiple international policy goals, including poverty reduction, protecting agricultural production and safeguarding ecosystem services.
More robust regional precipitation projection from selected CMIP5 models based on multiple-dimensional metrics

NASA Astrophysics Data System (ADS)

Qian, Y.; Wang, L.; Leung, L. R.; Lin, G.; Lu, J.; Gao, Y.; Zhang, Y.

2017-12-01

Projecting precipitation changes is challenging because of incomplete understanding of the climate system and biases and uncertainty in climate models. In East Asia where summer precipitation is dominantly influenced by the monsoon circulation and the global models from Coupled Model Intercomparison Project Phase 5 (CMIP5), however, give various projection of precipitation change for 21th century. It is critical for community to know which models' projection are more reliable in response to natural and anthropogenic forcings. In this study we defined multiple-dimensional metrics, measuring the model performance in simulating the present-day of large-scale circulation, regional precipitation and relationship between them. The large-scale circulation features examined in this study include the lower tropospheric southwesterly winds, the western North Pacific subtropical high, the South China Sea Subtropical High, and the East Asian westerly jet in the upper troposphere. Each of these circulation features transport moisture to East Asia, enhancing the moist static energy and strengthening the Meiyu moisture front that is the primary mechanism for precipitation generation in eastern China. Based on these metrics, 30 models in CMIP5 ensemble are classified into three groups. Models in the top performing group projected regional precipitation patterns that are more similar to each other than the bottom or middle performing group and consistently projected statistically significant increasing trends in two of the large-scale circulation indices and precipitation. In contrast, models in the bottom or middle performing group projected small drying or no trends in precipitation. We also find the models that only reasonably reproduce the observed precipitation climatology does not guarantee more reliable projection of future precipitation because good simulation skill could be achieved through compensating errors from multiple sources. Herein the potential for more robust projections of precipitation changes at regional scale is demonstrated through the use of discriminating metric to subsample the multi-model ensemble. The results from this study provides insights for how to select models from CMIP ensemble to project regional climate and hydrological cycle changes.
Regional warming of hot extremes accelerated by surface energy fluxes consistent with drying soils

NASA Astrophysics Data System (ADS)

Donat, M.; Pitman, A.; Seneviratne, S. I.

2017-12-01

Strong regional differences exist in how hot temperature extremes increase under global warming. Using an ensemble of coupled climate models, we examine the regional warming rates of hot extremes relative to annual average warming rates in the same regions. We identify hotspots of accelerated warming of model-simulated hot extremes in Europe, North America, South America and Southeast China. These hotspots indicate where the warm tail of a distribution of temperatures increases faster than the average and are robust across most CMIP5 models. Exploring the conditions on the specific day the hot extreme occurs demonstrates the hotspots are explained by changes in the surface energy fluxes consistent with drying soils. Furthermore, in these hotspot regions we find a relationship between the temperature - heat flux correlation under current climate conditions and the magnitude of future projected changes in hot extremes, pointing to a potential emergent constraint for simulations of future hot extremes. However, the model-simulated accelerated warming of hot extremes appears inconsistent with observations of the past 60 years, except over Europe. The simulated acceleration of hot extremes may therefore be unreliable, a result that necessitates a re-evaluation of how climate models resolve the relevant terrestrial processes.
Uncertainties in Past and Future Global Water Availability

NASA Astrophysics Data System (ADS)

Sheffield, J.; Kam, J.

2014-12-01

Understanding how water availability changes on inter-annual to decadal time scales and how it may change in the future under climate change are a key part of understanding future stresses on water and food security. Historic evaluations of water availability on regional to global scales are generally based on large-scale model simulations with their associated uncertainties, in particular for long-term changes. Uncertainties are due to model errors and missing processes, parameter uncertainty, and errors in meteorological forcing data. Recent multi-model inter-comparisons and impact studies have highlighted large differences for past reconstructions, due to different simplifying assumptions in the models or the inclusion of physical processes such as CO2 fertilization. Modeling of direct anthropogenic factors such as water and land management also carry large uncertainties in their physical representation and from lack of socio-economic data. Furthermore, there is little understanding of the impact of uncertainties in the meteorological forcings that underpin these historic simulations. Similarly, future changes in water availability are highly uncertain due to climate model diversity, natural variability and scenario uncertainty, each of which dominates at different time scales. In particular, natural climate variability is expected to dominate any externally forced signal over the next several decades. We present results from multi-land surface model simulations of the historic global availability of water in the context of natural variability (droughts) and long-term changes (drying). The simulations take into account the impact of uncertainties in the meteorological forcings and the incorporation of water management in the form of reservoirs and irrigation. The results indicate that model uncertainty is important for short-term drought events, and forcing uncertainty is particularly important for long-term changes, especially uncertainty in precipitation due to reduced gauge density in recent years. We also discuss uncertainties in future projections from these models as driven by bias-corrected and downscaled CMIP5 climate projections, in the context of the balance between climate model robustness and climate model diversity.
Rapid Late Holocene glacier fluctuations reconstructed from South Georgia lake sediments using novel analytical and numerical techniques

NASA Astrophysics Data System (ADS)

van der Bilt, Willem; Bakke, Jostein; Werner, Johannes; Paasche, Øyvind; Rosqvist, Gunhild

2016-04-01

The collapse of ice shelves, rapidly retreating glaciers and a dramatic recent temperature increase show that Southern Ocean climate is rapidly shifting. Also, instrumental and modelling data demonstrate transient interactions between oceanic and atmospheric forcings as well as climatic teleconnections with lower-latitude regions. Yet beyond the instrumental period, a lack of proxy climate timeseries impedes our understanding of Southern Ocean climate. Also, available records often lack the resolution and chronological control required to resolve rapid climate shifts like those observed at present. Alpine glaciers are found on most Southern Ocean islands and quickly respond to shifts in climate through changes in mass balance. Attendant changes in glacier size drive variations in the production of rock flour, the suspended product of glacial erosion. This climate response may be captured by downstream distal glacier-fed lakes, continuously recording glacier history. Sediment records from such lakes are considered prime sources for paleoclimate reconstructions. Here, we present the first reconstruction of Late Holocene glacier variability from the island of South Georgia. Using a toolbox of advanced physical, geochemical (XRF) and magnetic proxies, in combination with state-of-the-art numerical techniques, we fingerprinted a glacier signal from glacier-fed lake sediments. This lacustrine sediment signal was subsequently calibrated against mapped glacier extent with the help of geomorphological moraine evidence and remote sensing techniques. The outlined approach enabled us to robustly resolve variations of a complex glacier at sub-centennial timescales, while constraining the sedimentological imprint of other geomorphic catchment processes. From a paleoclimate perspective, our reconstruction reveals a dynamic Late Holocene climate, modulated by long-term shifts in regional circulation patterns. We also find evidence for rapid medieval glacier retreat as well as a synchronous bi-polar Little Ice Age (LIA). In conclusion, our work shows the potential of novel analytical and numerical tools to improve the robustness and resolution of lake sediment-based paleoclimate reconstructions beyond the current state-of-the-art.
The AgMIP Wheat Pilot: A multi-model approach for climate change impact assessments.

NASA Astrophysics Data System (ADS)

Asseng, S.

2012-12-01

Asseng S., F. Ewert, C. Rosenzweig, J.W. Jones, J.L. Hatfield, A. Ruane, K.J. Boote, P. Thorburn, R.P. Rötter, D. Cammarano, N. Brisson, B. Basso, P. Martre, D. Ripoche, P. Bertuzzi, P. Steduto, L. Heng, M.A. Semenov, P. Stratonovitch, C. Stockle, G. O'Leary, P.K. Aggarwal, S. Naresh Kumar, C. Izaurralde, J.W. White, L.A. Hunt, R. Grant, K.C. Kersebaum, T. Palosuo, J. Hooker, T. Osborne, J. Wolf, I. Supit, J.E. Olesen, J. Doltra, C. Nendel, S. Gayler, J. Ingwersen, E. Priesack, T. Streck, F. Tao, C. Müller, K. Waha, R. Goldberg, C. Angulo, I. Shcherbak, C. Biernath, D. Wallach, M. Travasso, A. Challinor. Abstract: Crop simulation models have been used to assess the impact of climate change on agriculture. These assessments are often carried out with a single model in a limited number of environments and without determining the uncertainty of simulated impacts. There is a need for a coordinated effort bringing together multiple modeling teams which has been recognized by the Agricultural Model Intercomparison and Improvement Project (AgMIP; www.agmip.org). AgMIP aims to provide more robust estimates of climate impacts on crop yields and agricultural trade, including estimates of associated uncertainties. Here, we present the AgMIP Wheat Pilot Study, the most comprehensive model intercomparison of the response of wheat crops to climate change to date, including 27 wheat models. Crop model uncertainties in assessing climate change impacts are explored and compared with field experimental and Global Circulation Model uncertainties. Causes of impact uncertainties and ways to reduce these are discussed.
Resolving Multi-Stakeholder Robustness Asymmetries in Coupled Agricultural and Urban Systems

NASA Astrophysics Data System (ADS)

Li, Yu; Giuliani, Matteo; Castelletti, Andrea; Reed, Patrick

2016-04-01

The evolving pressures from a changing climate and society are increasingly motivating decision support frameworks that consider the robustness of management actions across many possible futures. Focusing on robustness is helpful for investigating key vulnerabilities within current water systems and for identifying potential tradeoffs across candidate adaptation responses. To date, most robustness studies assume a social planner perspective by evaluating highly aggregated measures of system performance. This aggregate treatment of stakeholders does not explore the equity or intrinsic multi-stakeholder conflicts implicit to the system-wide measures of performance benefits and costs. The commonly present heterogeneity across complex management interests, however, may produce strong asymmetries for alternative adaptation options, designed to satisfy system-level targets. In this work, we advance traditional robustness decision frameworks by replacing the centralized social planner with a bottom-up, agent-based approach, where stakeholders are modeled as individuals, and represented as potentially self-interested agents. This agent-based model enables a more explicit exploration of the potential inequities and asymmetries in the distribution of the system-wide benefit. The approach is demonstrated by exploring the potential conflicts between urban flooding and agricultural production in the Lake Como system (Italy). Lake Como is a regulated lake that is operated to supply water to the downstream agricultural district (Muzza as the pilot study area in this work) composed of a set of farmers with heterogeneous characteristics in terms of water allocation, cropping patterns, and land properties. Supplying water to farmers increases the risk of floods along the lakeshore and therefore the system is operated based on the tradeoff between these two objectives. We generated an ensemble of co-varying climate and socio-economic conditions and evaluated the robustness of the current Lake Como system management as well as of possible adaptation options (e.g., improved irrigation efficiency or changes in the dam operating rules). Numerical results show that crops prices and costs are the main drivers of the simulated system failures when evaluated in terms of system-level expected profitability. Analysis conducted at the farmer-agent scale highlights alternatively that temperature and inflows are the critical drivers leading to failures. Finally, we show that the robustness of the considered adaptation options varies spatially, strongly influenced by stakeholders' context, the metrics used to define success, and the assumed preferences for reservoir operations in balancing urban flooding and agricultural productivity.
Using multiple climate projections for assessing hydrological response to climate change in the Thukela River Basin, South Africa

NASA Astrophysics Data System (ADS)

Graham, L. Phil; Andersson, Lotta; Horan, Mark; Kunz, Richard; Lumsden, Trevor; Schulze, Roland; Warburton, Michele; Wilk, Julie; Yang, Wei

This study used climate change projections from different regional approaches to assess hydrological effects on the Thukela River Basin in KwaZulu-Natal, South Africa. Projecting impacts of future climate change onto hydrological systems can be undertaken in different ways and a variety of effects can be expected. Although simulation results from global climate models (GCMs) are typically used to project future climate, different outcomes from these projections may be obtained depending on the GCMs themselves and how they are applied, including different ways of downscaling from global to regional scales. Projections of climate change from different downscaling methods, different global climate models and different future emissions scenarios were used as input to simulations in a hydrological model to assess climate change impacts on hydrology. A total of 10 hydrological change simulations were made, resulting in a matrix of hydrological response results. This matrix included results from dynamically downscaled climate change projections from the same regional climate model (RCM) using an ensemble of three GCMs and three global emissions scenarios, and from statistically downscaled projections using results from five GCMs with the same emissions scenario. Although the matrix of results does not provide complete and consistent coverage of potential uncertainties from the different methods, some robust results were identified. In some regards, the results were in agreement and consistent for the different simulations. For others, particularly rainfall, the simulations showed divergence. For example, all of the statistically downscaled simulations showed an annual increase in precipitation and corresponding increase in river runoff, while the RCM downscaled simulations showed both increases and decreases in runoff. According to the two projections that best represent runoff for the observed climate, increased runoff would generally be expected for this basin in the future. Dealing with such variability in results is not atypical for assessing climate change impacts in Africa and practitioners are faced with how to interpret them. This work highlights the need for additional, well-coordinated regional climate downscaling for the region to further define the range of uncertainties involved.
Seasonal climate change patterns due to cumulative CO 2 emissions

DOE PAGES

Partanen, Antti-Ilari; Leduc, Martin; Matthews, H. Damon

2017-06-28

Cumulative CO 2 emissions are near linearly related to both global and regional changes in annual-mean surface temperature. These relationships are known as the transient climate response to cumulative CO 2 emissions (TCRE) and the regional TCRE (RTCRE), and have been shown to remain approximately constant over a wide range of cumulative emissions. Here, we assessed how well this relationship holds for seasonal patterns of temperature change, as well as for annual-mean and seasonal precipitation patterns. We analyzed an idealized scenario with CO 2 concentration growing at an annual rate of 1% using data from 12 Earth system models frommore » the Coupled Model Intercomparison Project Phase 5 (CMIP5). Seasonal RTCRE values for temperature varied considerably, with the highest seasonal variation evident in the Arctic, where RTCRE was about 5.5 °C per Tt C for boreal winter and about 2.0 °C per Tt C for boreal summer. Also the precipitation response in the Arctic during boreal winter was stronger than during other seasons. We found that emission-normalized seasonal patterns of temperature change were relatively robust with respect to time, though they were sub-linear with respect to emissions particularly near the Arctic. Moreover, RTCRE patterns for precipitation could not be quantified robustly due to the large internal variability of precipitation. Here, our results suggest that cumulative CO 2 emissions are a useful metric to predict regional and seasonal changes in precipitation and temperature. This extension of the TCRE framework to seasonal and regional climate change is helpful for communicating the link between emissions and climate change to policy-makers and the general public, and is well-suited for impact studies that could make use of estimated regional-scale climate changes that are consistent with the carbon budgets associated with global temperature targets.« less
Seasonal climate change patterns due to cumulative CO 2 emissions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Partanen, Antti-Ilari; Leduc, Martin; Matthews, H. Damon

Cumulative CO 2 emissions are near linearly related to both global and regional changes in annual-mean surface temperature. These relationships are known as the transient climate response to cumulative CO 2 emissions (TCRE) and the regional TCRE (RTCRE), and have been shown to remain approximately constant over a wide range of cumulative emissions. Here, we assessed how well this relationship holds for seasonal patterns of temperature change, as well as for annual-mean and seasonal precipitation patterns. We analyzed an idealized scenario with CO 2 concentration growing at an annual rate of 1% using data from 12 Earth system models frommore » the Coupled Model Intercomparison Project Phase 5 (CMIP5). Seasonal RTCRE values for temperature varied considerably, with the highest seasonal variation evident in the Arctic, where RTCRE was about 5.5 °C per Tt C for boreal winter and about 2.0 °C per Tt C for boreal summer. Also the precipitation response in the Arctic during boreal winter was stronger than during other seasons. We found that emission-normalized seasonal patterns of temperature change were relatively robust with respect to time, though they were sub-linear with respect to emissions particularly near the Arctic. Moreover, RTCRE patterns for precipitation could not be quantified robustly due to the large internal variability of precipitation. Here, our results suggest that cumulative CO 2 emissions are a useful metric to predict regional and seasonal changes in precipitation and temperature. This extension of the TCRE framework to seasonal and regional climate change is helpful for communicating the link between emissions and climate change to policy-makers and the general public, and is well-suited for impact studies that could make use of estimated regional-scale climate changes that are consistent with the carbon budgets associated with global temperature targets.« less
Teaching Climate Social Science and Its Practices: A Two-Pronged Approach to Climate Literacy

NASA Astrophysics Data System (ADS)

Shwom, R.; Isenhour, C.; McCright, A.; Robinson, J.; Jordan, R.

2014-12-01

The Essential Principles of Climate Science Literacy states that a climate-literate individual can: "understand the essential principles of Earth's climate system, assess scientifically credible information about climate change, communicate about climate and climate change in a meaningful way, and make informed and responsible decisions with regard to actions that may affect climate." We argue that further integration of the social science dimensions of climate change will advance the climate literacy goals of communication and responsible actions. The underlying rationale for this argues: 1) teaching the habits of mind and scientific practices that have synergies across the social and natural sciences can strengthen students ability to understand and assess science in general and that 2) understanding the empirical research on the social, political, and economic processes (including climate science itself) that are part of the climate system is an important step for enabling effective action and communication. For example, while climate literacy has often identified the public's faulty mental models of climate processes as a partial explanation of complacency, emerging research suggests that the public's mental models of the social world are equally or more important in leading to informed and responsible climate decisions. Building student's ability to think across the social and natural sciences by understanding "how we know what we know" through the sciences and a scientific understanding of the social world allows us to achieve climate literacy goals more systematically and completely. To enable this integration we first identify the robust social science insights for the climate science literacy principles that involve social systems. We then briefly identify significant social science contributions to climate science literacy that do not clearly fit within the seven climate literacy principles but arguably could advance climate literacy goals. We conclude with suggestions on how the identified social science insights could be integrated into climate literacy efforts.

Using statistical models to explore ensemble uncertainty in climate impact studies: the example of air pollution in Europe

NASA Astrophysics Data System (ADS)

Lemaire, Vincent E. P.; Colette, Augustin; Menut, Laurent

2016-03-01

Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to climate change so that, in the future, a climate penalty could jeopardize the expected efficiency of air pollution mitigation measures. A common method to assess the impact of climate on air quality consists in implementing chemistry-transport models forced by climate projections. However, the computing cost of such methods requires optimizing ensemble exploration techniques. By using a training data set from a deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for eight regions in Europe and developed statistical models that could be used to predict air pollutant concentrations. The evolution of the key climate variables driving either particulate or gaseous pollution allows selecting the members of the EuroCordex ensemble of regional climate projections that should be used in priority for future air quality projections (CanESM2/RCA4; CNRM-CM5-LR/RCA4 and CSIRO-Mk3-6-0/RCA4 and MPI-ESM-LR/CCLM following the EuroCordex terminology). After having tested the validity of the statistical model in predictive mode, we can provide ranges of uncertainty attributed to the spread of the regional climate projection ensemble by the end of the century (2071-2100) for the RCP8.5. In the three regions where the statistical model of the impact of climate change on PM2.5 offers satisfactory performances, we find a climate benefit (a decrease of PM2.5 concentrations under future climate) of -1.08 (±0.21), -1.03 (±0.32), -0.83 (±0.14) µg m-3, for respectively Eastern Europe, Mid-Europe and Northern Italy. In the British-Irish Isles, Scandinavia, France, the Iberian Peninsula and the Mediterranean, the statistical model is not considered skillful enough to draw any conclusion for PM2.5. In Eastern Europe, France, the Iberian Peninsula, Mid-Europe and Northern Italy, the statistical model of the impact of climate change on ozone was considered satisfactory and it confirms the climate penalty bearing upon ozone of 10.51 (±3.06), 11.70 (±3.63), 11.53 (±1.55), 9.86 (±4.41), 4.82 (±1.79) µg m-3, respectively. In the British-Irish Isles, Scandinavia and the Mediterranean, the skill of the statistical model was not considered robust enough to draw any conclusion for ozone pollution.
A robust impact assessment that informs actionable climate change adaptation: future sunburn browning risk in apple

NASA Astrophysics Data System (ADS)

Webb, Leanne; Darbyshire, Rebecca; Erwin, Tim; Goodwin, Ian

2017-05-01

Climate change impact assessments are predominantly undertaken for the purpose of informing future adaptation decisions. Often, the complexity of the methodology hinders the actionable outcomes. The approach used here illustrates the importance of considering uncertainty in future climate projections, at the same time providing robust and simple to interpret information for decision-makers. By quantifying current and future exposure of Royal Gala apple to damaging temperature extremes across ten important pome fruit-growing locations in Australia, differences in impact to ripening fruit are highlighted, with, by the end of the twenty-first century, some locations maintaining no sunburn browning risk, while others potentially experiencing the risk for the majority of the January ripening period. Installation of over-tree netting can reduce the impact of sunburn browning. The benefits from employing this management option varied across the ten study locations. The two approaches explored to assist decision-makers assess this information (a) using sunburn browning risk analogues and (b) through identifying hypothetical sunburn browning risk thresholds, resulted in varying recommendations for introducing over-tree netting. These recommendations were location and future time period dependent with some sites showing no benefit for sunburn protection from nets even by the end of the twenty-first century and others already deriving benefits from employing this adaptation option. Potential best and worst cases of sunburn browning risk and its potential reduction through introduction of over-tree nets were explored. The range of results presented highlights the importance of addressing uncertainty in climate projections that result from different global climate models and possible future emission pathways.
A robust impact assessment that informs actionable climate change adaptation: future sunburn browning risk in apple.

PubMed

Webb, Leanne; Darbyshire, Rebecca; Erwin, Tim; Goodwin, Ian

2017-05-01

Climate change impact assessments are predominantly undertaken for the purpose of informing future adaptation decisions. Often, the complexity of the methodology hinders the actionable outcomes. The approach used here illustrates the importance of considering uncertainty in future climate projections, at the same time providing robust and simple to interpret information for decision-makers. By quantifying current and future exposure of Royal Gala apple to damaging temperature extremes across ten important pome fruit-growing locations in Australia, differences in impact to ripening fruit are highlighted, with, by the end of the twenty-first century, some locations maintaining no sunburn browning risk, while others potentially experiencing the risk for the majority of the January ripening period. Installation of over-tree netting can reduce the impact of sunburn browning. The benefits from employing this management option varied across the ten study locations. The two approaches explored to assist decision-makers assess this information (a) using sunburn browning risk analogues and (b) through identifying hypothetical sunburn browning risk thresholds, resulted in varying recommendations for introducing over-tree netting. These recommendations were location and future time period dependent with some sites showing no benefit for sunburn protection from nets even by the end of the twenty-first century and others already deriving benefits from employing this adaptation option. Potential best and worst cases of sunburn browning risk and its potential reduction through introduction of over-tree nets were explored. The range of results presented highlights the importance of addressing uncertainty in climate projections that result from different global climate models and possible future emission pathways.
Development of ALARO-Climate regional climate model for a very high resolution

NASA Astrophysics Data System (ADS)

Skalak, Petr; Farda, Ales; Brozkova, Radmila; Masek, Jan

2014-05-01

ALARO-Climate is a new regional climate model (RCM) derived from the ALADIN LAM model family. It is based on the numerical weather prediction model ALARO and developed at the Czech Hydrometeorological Institute. The model is expected to able to work in the so called "grey zone" physics (horizontal resolution of 4 - 7 km) and at the same time retain its ability to be operated in resolutions in between 20 and 50 km, which are typical for contemporary generation of regional climate models. Here we present the main results of the RCM ALARO-Climate model simulations in 25 and 6.25 km resolutions on the longer time-scale (1961-1990). The model was driven by the ERA-40 re-analyses and run on the integration domain of ~ 2500 x 2500 km size covering the central Europe. The simulated model climate was compared with the gridded observation of air temperature (mean, maximum, minimum) and precipitation from the E-OBS version dataset 8. Other simulated parameters (e.g., cloudiness, radiation or components of water cycle) were compared to the ERA-40 re-analyses. The validation of the first ERA-40 simulation in both, 25 km and 6.25 km resolutions, revealed significant cold biases in all seasons and overestimation of precipitation in the selected Central Europe target area (0° - 30° eastern longitude ; 40° - 60° northern latitude). The differences between these simulations were small and thus revealed a robustness of the model's physical parameterization on the resolution change. The series of 25 km resolution simulations with several model adaptations was carried out to study their effect on the simulated properties of climate variables and thus possibly identify a source of major errors in the simulated climate. The current investigation suggests the main reason for biases is related to the model physic. Acknowledgements: This study was performed within the frame of projects ALARO (project P209/11/2405 sponsored by the Czech Science Foundation) and CzechGlobe Centre (CZ.1.05/1.1.00/02.0073). The partial support was also provided under the projects P209-11-0956 of the Czech Science Foundation and CZ.1.07/2.4.00/31.0056 (Operational Programme of Education for Competitiveness of Ministry of Education, Youth and Sports of the Czech Republic).
Science of Global Climate Modeling: Confirmation from Discoveries on Mars

NASA Astrophysics Data System (ADS)

Hartmann, William K.

2012-10-01

As early as 1993, analysis of obliquity changes on Mars revealed irregular cycles of high excursion, over 45°1. Further obliquity analyses indicated that insolation and climatic conditions vary with time, with the four most recent episodes of obliquity >45° occurring about 5.5, 8, 9, and 15 My.2 Various researchers applied global climate models, using Martian parameters and obliquity changes. The models (independent of Martian geomorphological observations) indicate exceptional climate conditions during the high-obliquity episodes at >45°3,4, with localized massive ice deposition effects east of Hellas and on the west slopes of Tharsis.5 At last year’s DPS my co-authors and I detailed evidence of unusual active glaciation in Greg crater, near the center of the predicted area of ice accumulation during high obliquity.6 We found that the timescale of glacial surface layer activity matches the general 5-15 My timescale of the last episodes of high obliquity and ice deposition. Radar results confirm ice deposits in debris aprons concentrated in the same area.7 Less direct evidence has also been found for glacial ice deposits in the west Tharsis region.8 Here I emphasize that if the models can be adjusted to Mars and then successfully indicate unusual, specific features that we see there, it is an argument for the robustness of climate modeling in general. In recent years we have see various public figures casting doubt on the validity of terrestrial global modeling. The successful match of Martian climate modeling with direct Martian geological and chronometric observations provides an interesting and teachable refutation of the attacks on climate science. References: 1. Science 259:1294-1297; 2. LPSC XXXV, Abs. 1600; 3. Nature 412:411-413; 4. Science 295:110-113; 5. Science 311:368-371; 6. EPSC-DPS Abs. 1394; 7. Science 322:1235-1238; 8. Nature 434:346-351.
Vulnerability-based evaluation of water supply design under climate change

NASA Astrophysics Data System (ADS)

Umit Taner, Mehmet; Ray, Patrick; Brown, Casey

2015-04-01

Long-lived water supply infrastructures are strategic investments in the developing world, serving the purpose of balancing water deficits compounded by both population growth and socio-economic development. Robust infrastructure design under climate change is compelling, and often addressed by focusing on the outcomes of climate model projections ('scenario-led' planning), or by identifying design options that are less vulnerable to a wide range of plausible futures ('vulnerability-based' planning). Decision-Scaling framework combines these two approaches by first applying a climate stress test on the system to explore vulnerabilities across many traces of the future, and then employing climate projections to inform the decision-making process. In this work, we develop decision scaling's nascent risk management concepts further, directing actions on vulnerabilities identified during the climate stress test. In the process, we present a new way to inform climate vulnerability space using climate projections, and demonstrate the use of multiple decision criteria to guide to a final design recommendation. The concepts are demonstrated for a water supply project in the Mombasa Province of Kenya, planned to provide domestic and irrigation supply. Six storage design capacities (from 40 to 140 million cubic meters) are explored through a stress test, under a large number climate traces representing both natural climate variability and plausible climate changes. Design outcomes are simulated over a 40-year planning period with a coupled hydrologic-water resources systems model and using standard reservoir operation rules. Resulting performance is expressed in terms of water supply reliability and economic efficiency. Ensemble climate projections are used for assigning conditional likelihoods to the climate traces using a statistical distance measure. The final design recommendations are presented and discussed for the decision criteria of expected regret, satisficing, and conditional value-at-risk (CVaR).
Herding cats? A multi-model perspective on tropospheric ozone

NASA Astrophysics Data System (ADS)

Young, P. J.

2015-12-01

Various global multi-model studies have investigated tropospheric ozone changes over multi-decadal timescales. Several robust features emerge, which - for instance - allows the IPCC to associate high confidence in the radiative forcing associated with ozone increases between 1750 and the present day. However, such quantities hide the spread in results between different models, particularly when looking at seasonal and regional scales, and including for comparisons with observations. What can we learn about our scientific understanding from the model spread? What can we learn about models from the model spread? And can we make recommendations for deficient or missing processes if we wish to use our models for environmental prediction? Of course, these questions also have to be asked in the context of what we want the model(s) to do (air quality, climate, stratospheric ozone depletion etc.). This poster will report ongoing work in my group which draws on results from multi-model experiments conducted in support of the most recent IPCC report (CMIP5 and ACCMIP), with an eye to the expected outcomes from the ongoing Chemistry-Climate Model Initiative (CCMI) model simulations.
The Earth System Grid Federation (ESGF): Climate Science Infrastructure for Large-scale Data Management and Dissemination

NASA Astrophysics Data System (ADS)

Williams, D. N.

2015-12-01

Progress in understanding and predicting climate change requires advanced tools to securely store, manage, access, process, analyze, and visualize enormous and distributed data sets. Only then can climate researchers understand the effects of climate change across all scales and use this information to inform policy decisions. With the advent of major international climate modeling intercomparisons, a need emerged within the climate-change research community to develop efficient, community-based tools to obtain relevant meteorological and other observational data, develop custom computational models, and export analysis tools for climate-change simulations. While many nascent efforts to fill these gaps appeared, they were not integrated and therefore did not benefit from collaborative development. Sharing huge data sets was difficult, and the lack of data standards prevented the merger of output data from different modeling groups. Thus began one of the largest-ever collaborative data efforts in climate science, resulting in the Earth System Grid Federation (ESGF), which is now used to disseminate model, observational, and reanalysis data for research assessed by the Intergovernmental Panel on Climate Change (IPCC). Today, ESGF is an open-source petabyte-level data storage and dissemination operational code-base that manages secure resources essential for climate change study. It is designed to remain robust even as data volumes grow exponentially. The internationally distributed, peer-to-peer ESGF "data cloud" archive represents the culmination of an effort that began in the late 1990s. ESGF portals are gateways to scientific data collections hosted at sites around the globe that allow the user to register and potentially access the entire ESGF network of data and services. The growing international interest in ESGF development efforts has attracted many others who want to make their data more widely available and easy to use. For example, the World Climate Research Program, which provides governance for CMIP, has now endorsed the ESGF software foundation to be used for ~70 other model intercomparison projects (MIPs), such as obs4MIPs, TAMIP, CFMIP, and GeoMIP. At present, more than 40 projects disseminate their data via ESGF.
Baseline for Climate Change: Modeling Watershed Aquatic Biodiversity Relative to Environmental and Anthropogenic Factors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maurakis, Eugene G

Objectives of the two-year study were to (1) establish baselines for fish and macroinvertebrate community structures in two mid-Atlantic lower Piedmont watersheds (Quantico Creek, a pristine forest watershed; and Cameron Run, an urban watershed, Virginia) that can be used to monitor changes relative to the impacts related to climate change in the future; (2) create mathematical expressions to model fish species richness and diversity, and macroinvertebrate taxa and macroinvertebrate functional feeding group taxa richness and diversity that can serve as a baseline for future comparisons in these and other watersheds in the mid-Atlantic region; and (3) heighten people’s awareness, knowledgemore » and understanding of climate change and impacts on watersheds in a laboratory experience and interactive exhibits, through internship opportunities for undergraduate and graduate students, a week-long teacher workshop, and a website about climate change and watersheds. Mathematical expressions modeled fish and macroinvertebrate richness and diversity accurately well during most of the six thermal seasons where sample sizes were robust. Additionally, hydrologic models provide the basis for estimating flows under varying meteorological conditions and landscape changes. Continuations of long-term studies are requisite for accurately teasing local human influences (e.g. urbanization and watershed alteration) from global anthropogenic impacts (e.g. climate change) on watersheds. Effective and skillful translations (e.g. annual potential exposure of 750,000 people to our inquiry-based laboratory activities and interactive exhibits in Virginia) of results of scientific investigations are valuable ways of communicating information to the general public to enhance their understanding of climate change and its effects in watersheds.« less
An Observationally-Centred Method to Quantify the Changing Shape of Local Temperature Distributions

NASA Astrophysics Data System (ADS)

Chapman, S. C.; Stainforth, D. A.; Watkins, N. W.

2014-12-01

For climate sensitive decisions and adaptation planning, guidance on how local climate is changing is needed at the specific thresholds relevant to particular impacts or policy endeavours. This requires the quantification of how the distributions of variables, such as daily temperature, are changing at specific quantiles. These temperature distributions are non-normal and vary both geographically and in time. We present a method[1,2] for analysing local climatic time series data to assess which quantiles of the local climatic distribution show the greatest and most robust changes. We have demonstrated this approach using the E-OBS gridded dataset[3] which consists of time series of local daily temperature across Europe over the last 60 years. Our method extracts the changing cumulative distribution function over time and uses a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. The change in temperature can be tracked at a temperature threshold, at a likelihood, or at a given return time, independently for each geographical location. Geographical correlations are thus an output of our method and reflect both climatic properties (local and synoptic), and spatial correlations inherent in the observation methodology. We find as an output many regionally consistent patterns of response of potential value in adaptation planning. For instance, in a band from Northern France to Denmark the hottest days in the summer temperature distribution have seen changes of at least 2°C over a 43 year period; over four times the global mean change over the same period. We discuss methods to quantify the robustness of these observed sensitivities and their statistical likelihood. This approach also quantifies the level of detail at which one might wish to see agreement between climate models and observations if such models are to be used directly as tools to assess climate change impacts at local scales. [1] S C Chapman, D A Stainforth, N W Watkins, 2013, Phil. Trans. R. Soc. A, 371 20120287. [2] D A Stainforth, S C Chapman, N W Watkins, 2013, Environ. Res. Lett. 8, 034031 [3] Haylock, M.R. et al., 2008, J. Geophys. Res (Atmospheres), 113, D20119
Climate change and species interactions: ways forward.

PubMed

Angert, Amy L; LaDeau, Shannon L; Ostfeld, Richard S

2013-09-01

With ongoing and rapid climate change, ecologists are being challenged to predict how individual species will change in abundance and distribution, how biotic communities will change in structure and function, and the consequences of these climate-induced changes for ecosystem functioning. It is now well documented that indirect effects of climate change on species abundances and distributions, via climatic effects on interspecific interactions, can outweigh and even reverse the direct effects of climate. However, a clear framework for incorporating species interactions into projections of biological change remains elusive. To move forward, we suggest three priorities for the research community: (1) utilize tractable study systems as case studies to illustrate possible outcomes, test processes highlighted by theory, and feed back into modeling efforts; (2) develop a robust analytical framework that allows for better cross-scale linkages; and (3) determine over what time scales and for which systems prediction of biological responses to climate change is a useful and feasible goal. We end with a list of research questions that can guide future research to help understand, and hopefully mitigate, the negative effects of climate change on biota and the ecosystem services they provide. © 2013 New York Academy of Sciences.
Changes in the Global Hydrological Cycle: Lessons from Modeling Lake Levels at the Last Glacial Maximum

NASA Astrophysics Data System (ADS)

Lowry, D. P.; Morrill, C.

2011-12-01

Geologic evidence shows that lake levels in currently arid regions were higher and lakes in currently wet regions were lower during the Last Glacial Maximum (LGM). Current hypotheses used to explain these lake level changes include the thermodynamic hypothesis, in which decreased tropospheric water vapor coupled with patterns of convergence and divergence caused dry areas to become more wet and vice versa, the dynamic hypothesis, in which shifts in the jet stream and Inter-Tropical Convergence Zone (ITCZ) altered precipitation patterns, and the evaporation hypothesis, in which lake expansions are attributed to reduced evaporation in a colder climate. This modeling study uses the output of four climate models participating in phase 2 of the Paleoclimate Modeling Intercomparison Project (PMIP2) as input into a lake energy-balance model, in order to test the accuracy of the models and understand the causes of lake level changes. We model five lakes which include the Great Basin lakes, USA; Lake Petén Itzá, Guatemala; Lake Caçó, northern Brazil; Lake Tauca (Titicaca), Bolivia and Peru; and Lake Cari-Laufquen, Argentina. These lakes create a transect through the drylands of North America through the tropics and to the drylands of South America. The models accurately recreate LGM conditions in 14 out of 20 simulations, with the Great Basin lakes being the most robust and Lake Caçó being the least robust, due to model biases in portraying the ITCZ over South America. An analysis of the atmospheric moisture budget from one of the climate models shows that thermodynamic processes contribute most significantly to precipitation changes over the Great Basin, while dynamic processes are most significant for the other lakes. Lake Cari-Laufquen shows a lake expansion that is most likely attributed to reduced evaporation rather than changes in regional precipitation, suggesting that lake levels alone may not be the best indicator of how much precipitation this region receives. Our results indicate that the causes of hydrologic fluctuations are spatially diverse and that future projections will need to consider more than just thermodynamic changes for accurate regional predictions.
Effects of the Bering Strait closure on AMOC and global climate under different background climates

NASA Astrophysics Data System (ADS)

Hu, Aixue; Meehl, Gerald A.; Han, Weiqing; Otto-Bliestner, Bette; Abe-Ouchi, Ayako; Rosenbloom, Nan

2015-03-01

Previous studies have suggested that the status of the Bering Strait may have a significant influence on global climate variability on centennial, millennial, and even longer time scales. Here we use multiple versions of the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, versions 2 and 3) to investigate the influence of the Bering Strait closure/opening on the Atlantic Meridional Overturning Circulation (AMOC) and global mean climate under present-day, 15 thousand-year before present (kyr BP), and 112 kyr BP climate boundary conditions. Our results show that regardless of the version of the model used or the widely different background climates, the Bering Strait's closure produces a robust result of a strengthening of the AMOC, and an increase in the northward meridional heat transport in the Atlantic. As a consequence, the climate becomes warmer in the North Atlantic and the surrounding regions, but cooler in the North Pacific, leading to a seesaw-like climate change between these two basins. For the first time it is noted that the absence of the Bering Strait throughflow causes a slower motion of Arctic sea ice, a reduced upper ocean water exchange between the Arctic and North Atlantic, reduced sea ice export and less fresh water in the North Atlantic. These changes contribute positively to the increased upper ocean density there, thus strengthening the AMOC. Potentially these changes in the North Atlantic could have a significant effect on the ice sheets both upstream and downstream in ice age climate, and further influence global sea level changes.
Constraints on Oceanic Meridional Transport of Heat and Carbon from Combined Oceanic and Atmospheric Measurements.

NASA Astrophysics Data System (ADS)

Resplandy, L.; Keeling, R. F.; Stephens, B. B.; Bent, J. D.; Jacobson, A. R.; Rödenbeck, C.; Khatiwala, S.

2016-02-01

The global ocean transports heat northward. The magnitude of this asymmetry between the two hemispheres is a key factor of the climate system through the displacement of tropical precipitation north of the equator and its influence on Arctic temperature and sea-ice extent. These asymmetric influences on heat are however not well constrained by observations or models. We identify a robust link between the ocean heat asymmetry and the large-scale distribution in atmospheric oxygen, using both atmospheric and oceanic observations and a suite of models (oceanic, climate and inverse). Novel aircraft observations from the pole-to-pole HIPPO campaign reveal that the ocean northward heat transport necessary to explain the atmospheric oxygen distribution is in the upper range of previous estimates from hydrographic sections and atmospheric reanalyses. Finally, we evidence a strong link between the oceanic transports of heat and natural carbon. This supports the existence of a strong southward transport of natural carbon at the global scale, a feature present at pre-industrial times and still underlying the anthropogenic signal today. We find that current climate models systematically underestimate these natural large-scale ocean meridional transports of heat and carbon, which bears on future climate projections, in particular concerning Arctic climate, possible shifts in rainfall and carbon sinks partition between the land and the ocean.
Modeling Hawaiian ecosystem degradation due to invasive plants under current and future climates

USGS Publications Warehouse

Vorsino, Adam E.; Fortini, Lucas B.; Amidon, Fred A.; Miller, Stephen E.; Jacobi, James D.; Price, Jonathan P.; `Ohukani`ohi`a Gon, Sam; Koob, Gregory A.

2014-01-01

Occupation of native ecosystems by invasive plant species alters their structure and/or function. In Hawaii, a subset of introduced plants is regarded as extremely harmful due to competitive ability, ecosystem modification, and biogeochemical habitat degradation. By controlling this subset of highly invasive ecosystem modifiers, conservation managers could significantly reduce native ecosystem degradation. To assess the invasibility of vulnerable native ecosystems, we selected a proxy subset of these invasive plants and developed robust ensemble species distribution models to define their respective potential distributions. The combinations of all species models using both binary and continuous habitat suitability projections resulted in estimates of species richness and diversity that were subsequently used to define an invasibility metric. The invasibility metric was defined from species distribution models with 0.8; True Skill Statistic >0.75) as evaluated per species. Invasibility was further projected onto a 2100 Hawaii regional climate change scenario to assess the change in potential habitat degradation. The distribution defined by the invasibility metric delineates areas of known and potential invasibility under current climate conditions and, when projected into the future, estimates potential reductions in native ecosystem extent due to climate-driven invasive incursion. We have provided the code used to develop these metrics to facilitate their wider use (Code S1). This work will help determine the vulnerability of native-dominated ecosystems to the combined threats of climate change and invasive species, and thus help prioritize ecosystem and species management actions.
Climate Cycles and Forecasts of Cutaneous Leishmaniasis, a Nonstationary Vector-Borne Disease

PubMed Central

Chaves, Luis Fernando; Pascual, Mercedes

2006-01-01

Background Cutaneous leishmaniasis (CL) is one of the main emergent diseases in the Americas. As in other vector-transmitted diseases, its transmission is sensitive to the physical environment, but no study has addressed the nonstationary nature of such relationships or the interannual patterns of cycling of the disease. Methods and Findings We studied monthly data, spanning from 1991 to 2001, of CL incidence in Costa Rica using several approaches for nonstationary time series analysis in order to ensure robustness in the description of CL's cycles. Interannual cycles of the disease and the association of these cycles to climate variables were described using frequency and time-frequency techniques for time series analysis. We fitted linear models to the data using climatic predictors, and tested forecasting accuracy for several intervals of time. Forecasts were evaluated using “out of fit” data (i.e., data not used to fit the models). We showed that CL has cycles of approximately 3 y that are coherent with those of temperature and El Niño Southern Oscillation indices (Sea Surface Temperature 4 and Multivariate ENSO Index). Conclusions Linear models using temperature and MEI can predict satisfactorily CL incidence dynamics up to 12 mo ahead, with an accuracy that varies from 72% to 77% depending on prediction time. They clearly outperform simpler models with no climate predictors, a finding that further supports a dynamical link between the disease and climate. PMID:16903778
Mapping the changing pattern of local climate as an observed distribution

NASA Astrophysics Data System (ADS)

Chapman, Sandra; Stainforth, David; Watkins, Nicholas

2013-04-01

It is at local scales that the impacts of climate change will be felt directly and at which adaptation planning decisions must be made. This requires quantifying the geographical patterns in trends at specific quantiles in distributions of variables such as daily temperature or precipitation. Here we focus on these local changes and on the way observational data can be analysed to inform us about the pattern of local climate change. We present a method[1] for analysing local climatic timeseries data to assess which quantiles of the local climatic distribution show the greatest and most robust trends. We demonstrate this approach using E-OBS gridded data[2] timeseries of local daily temperature from specific locations across Europe over the last 60 years. Our method extracts the changing cumulative distribution function over time and uses a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. This deconstruction facilitates an assessment of the sensitivity of different quantiles of the distributions to changing climate. Geographical location and temperature are treated as independent variables, we thus obtain as outputs the pattern of variation in sensitivity with temperature (or occurrence likelihood), and with geographical location. We find as an output many regionally consistent patterns of response of potential value in adaptation planning. We discuss methods to quantify and map the robustness of these observed sensitivities and their statistical likelihood. This also quantifies the level of detail needed from climate models if they are to be used as tools to assess climate change impact. [1] S C Chapman, D A Stainforth, N W Watkins, 2013, On Estimating Local Long Term Climate Trends, Phil. Trans. R. Soc. A, in press [2] Haylock, M.R., N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones and M. New. 2008: A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res (Atmospheres), 113, D20119, doi:10.1029/2008JD10201
Underlying mechanisms leading to El Niño-to-La Niña transition are unchanged under global warming

NASA Astrophysics Data System (ADS)

Yun, Kyung-Sook; Yeh, Sang-Wook; Ha, Kyung-Ja

2018-05-01

El Niño's transitions play critical roles in modulating severe weather and climate events. Therefore, understanding the dynamic factors leading to El Niño's transitions and its future projection is a great challenge in predicting the diverse socioeconomic influences of El Niño over the globe. This study focuses on two dynamic factors controlling the El Niño-to-La Niña transition from the present climate and to future climate, using the observation, the historical and the RCP8.5 simulations of Coupled Model Intercomparison phase 5 climate models. The first is the inter-basin coupling between the Indian Ocean and the western North Pacific through the subtropical high variability. The second is the enhanced sensitivity between sea surface temperature and a deep tropical convection in the central tropical Pacific during the El Niño's developing phase. We show that the dynamic factors leading to El Niño-to-La Niña transition in the present climate are unchanged in spite of the increase of greenhouse gas concentrations. We argue that the two dynamic factors are strongly constrained by the climatological precipitation distribution over the central tropical Pacific and western North Pacific as little changed from the present climate to future climate. This implies that two dynamical processes leading to El Niño-to-La Niña transitions in the present climate will also play a robust role in global warming.
An integrated approach to modeling changes in land use, land cover, and disturbance and their impact on ecosystem carbon dynamics: a case study in the Sierra Nevada Mountains of California

USGS Publications Warehouse

Sleeter, Benjamin M.; Liu, Jinxun; Daniel, Colin; Frid, Leonardo; Zhu, Zhiliang

2015-01-01

Increased land-use intensity (e.g. clearing of forests for cultivation, urbanization), often results in the loss of ecosystem carbon storage, while changes in productivity resulting from climate change may either help offset or exacerbate losses. However, there are large uncertainties in how land and climate systems will evolve and interact to shape future ecosystem carbon dynamics. To address this we developed the Land Use and Carbon Scenario Simulator (LUCAS) to track changes in land use, land cover, land management, and disturbance, and their impact on ecosystem carbon storage and flux within a scenario-based framework. We have combined a state-and-transition simulation model (STSM) of land change with a stock and flow model of carbon dynamics. Land-change projections downscaled from the Intergovernmental Panel on Climate Change’s (IPCC) Special Report on Emission Scenarios (SRES) were used to drive changes within the STSM, while the Integrated Biosphere Simulator (IBIS) ecosystem model was used to derive input parameters for the carbon stock and flow model. The model was applied to the Sierra Nevada Mountains ecoregion in California, USA, a region prone to large wildfires and a forestry sector projected to intensify over the next century. Three scenario simulations were conducted, including a calibration scenario, a climate-change scenario, and an integrated climate- and land-change scenario. Based on results from the calibration scenario, the LUCAS age-structured carbon accounting model was able to accurately reproduce results obtained from the process-based biogeochemical model. Under the climate-only scenario, the ecoregion was projected to be a reliable net sink of carbon, however, when land use and disturbance were introduced, the ecoregion switched to become a net source. This research demonstrates how an integrated approach to carbon accounting can be used to evaluate various drivers of ecosystem carbon change in a robust, yet transparent modeling environment.
A Scaling Model for the Anthropocene Climate Variability with Projections to 2100

NASA Astrophysics Data System (ADS)

Hébert, Raphael; Lovejoy, Shaun

2017-04-01

The determination of the climate sensitivity to radiative forcing is a fundamental climate science problem with important policy implications. We use a scaling model, with a limited set of parameters, which can directly calculate the forced globally-average surface air temperature response to anthropogenic and natural forcings. At timescales larger than an inner scale τ, which we determine as the ocean-atmosphere coupling scale at around 2 years, the global system responds, approximately, linearly, so that the variability may be decomposed into additive forced and internal components. The Ruelle response theory extends the classical linear response theory for small perturbations to systems far from equilibrium. Our model thus relates radiative forcings to a forced temperature response by convolution with a suitable Green's function, or climate response function. Motivated by scaling symmetries which allow for long range dependence, we assume a general scaling form, a scaling climate response function (SCRF) which is able to produce a wide range of responses: a power-law truncated at τ. This allows us to analytically calculate the climate sensitivity at different time scales, yielding a one-to-one relation from the transient climate response to the equilibrium climate sensitivity which are estimated, respectively, as 1.6+0.3-0.2K and 2.4+1.3-0.6K at the 90 % confidence level. The model parameters are estimated within a Bayesian framework, with a fractional Gaussian noise error model as the internal variability, from forcing series, instrumental surface temperature datasets and CMIP5 GCMs Representative Concentration Pathways (RCP) scenario runs. This observation based model is robust and projections for the coming century are made following the RCP scenario 2.6, 4.5 and 8.5, yielding in the year 2100, respectively : 1.5 +0.3)_{-0.2K, 2.3 ± 0.4 K and 4.0 ± 0.6 K at the 90 % confidence level. For comparison, the associated projections from a CMIP5 multi-model ensemble(MME) (32 models) are: 1.7 ± 0.8 K, 2.6 ± 0.8 K and 4.8 ± 1.3 K. Therefore, our projection uncertainty is less than half the structural uncertainty of this CMIP5 MME.

Modeling and Understanding the Mass Balance of Himalayan Glaciers

NASA Astrophysics Data System (ADS)

Rengaraju, S.; Achutarao, K. M.

2017-12-01

Changes in glaciers are among the most visible manifestations of a changing climate. Retreating glaciers have significant impacts on global sea-level rise and stream flow of rivers. Modeling the response of glaciers to climate change is important for many reasons including predicting changes in global sea level and water resources. The mass balance of a glacier provides a robust way of ascertaining whether there has been a net loss or gain of ice from the glacier. The mass balance reflects all of the meteorological forcing of the glacier - from the accumulation of snow and the combined losses from ablation and sublimation. The glaciers in the Himalayan region are considered sensitive to climate change and their fate under climate change is critical to the billions of humans that rely on rivers originating from these glaciers. Owing to complex terrain and harsh climate, Himalayan glaciers have historically been poorly monitored and this makes it harder to understand and predict their fate.In this study we model the observed mass balance of Himalayan glaciers using the methods of Radic and Hock (2011) and analyze the response to future changes in climate based on the model projections from the Coupled Model Intercomparison Project Phase-5 (CMIP5; Taylor et al., 2012). We make use of available observations of mass balance from various sources for 14 glaciers across the Himalayas. These glaciers are located across distinct climatic conditions - from the Karakoram and Hindu Kush in the West that are fed by winter precipitation caused by westerly disturbances to the Eastern Himalayas where the summer monsoon provides the bulk of the precipitation. For the historical observed period, we use the ECMWF Re-Analysis (ERA-40) for temperature and VASClimO (GPCC) data at 2.5°x2.5° resolution to calibrate the mass balance model. We evaluate the CMIP5 model simulations for their fidelity in capturing the distinct climatic conditions across the Himalayas in order to select suitable models to study future response of the glaciers in this region. We also quantify the range and sources of uncertainty in projected changes.
Regionalisation of statistical model outputs creating gridded data sets for Germany

NASA Astrophysics Data System (ADS)

Höpp, Simona Andrea; Rauthe, Monika; Deutschländer, Thomas

2016-04-01

The goal of the German research program ReKliEs-De (regional climate projection ensembles for Germany, http://.reklies.hlug.de) is to distribute robust information about the range and the extremes of future climate for Germany and its neighbouring river catchment areas. This joint research project is supported by the German Federal Ministry of Education and Research (BMBF) and was initiated by the German Federal States. The Project results are meant to support the development of adaptation strategies to mitigate the impacts of future climate change. The aim of our part of the project is to adapt and transfer the regionalisation methods of the gridded hydrological data set (HYRAS) from daily station data to the station based statistical regional climate model output of WETTREG (regionalisation method based on weather patterns). The WETTREG model output covers the period of 1951 to 2100 with a daily temporal resolution. For this, we generate a gridded data set of the WETTREG output for precipitation, air temperature and relative humidity with a spatial resolution of 12.5 km x 12.5 km, which is common for regional climate models. Thus, this regionalisation allows comparing statistical to dynamical climate model outputs. The HYRAS data set was developed by the German Meteorological Service within the German research program KLIWAS (www.kliwas.de) and consists of daily gridded data for Germany and its neighbouring river catchment areas. It has a spatial resolution of 5 km x 5 km for the entire domain for the hydro-meteorological elements precipitation, air temperature and relative humidity and covers the period of 1951 to 2006. After conservative remapping the HYRAS data set is also convenient for the validation of climate models. The presentation will consist of two parts to present the actual state of the adaptation of the HYRAS regionalisation methods to the statistical regional climate model WETTREG: First, an overview of the HYRAS data set and the regionalisation methods for precipitation (REGNIE method based on a combination of multiple linear regression with 5 predictors and inverse distance weighting), air temperature and relative humidity (optimal interpolation) will be given. Finally, results of the regionalisation of WETTREG model output will be shown.
Impacts of boundary condition changes on regional climate projections over West Africa

NASA Astrophysics Data System (ADS)

Kim, Jee Hee; Kim, Yeonjoo; Wang, Guiling

2017-06-01

Future projections using regional climate models (RCMs) are driven with boundary conditions (BCs) typically derived from global climate models. Understanding the impact of the various BCs on regional climate projections is critical for characterizing their robustness and uncertainties. In this study, the International Center for Theoretical Physics Regional Climate Model Version 4 (RegCM4) is used to investigate the impact of different aspects of boundary conditions, including lateral BCs and sea surface temperature (SST), on projected future changes of regional climate in West Africa, and BCs from the coupled European Community-Hamburg Atmospheric Model 5/Max Planck Institute Ocean Model are used as an example. Historical, future, and several sensitivity experiments are conducted with various combinations of BCs and CO2 concentration, and differences among the experiments are compared to identify the most important drivers for RCMs. When driven by changes in all factors, the RegCM4-produced future climate changes include significantly drier conditions in Sahel and wetter conditions along the Guinean coast. Changes in CO2 concentration within the RCM domain alone or changes in wind vectors at the domain boundaries alone have minor impact on projected future climate changes. Changes in the atmospheric humidity alone at the domain boundaries lead to a wetter Sahel due to the northward migration of rain belts during summer. This impact, although significant, is offset and dominated by changes of other BC factors (primarily temperature) that cause a drying signal. Future changes of atmospheric temperature at the domain boundaries combined with SST changes over oceans are sufficient to cause a future climate that closely resembles the projection that accounts for all factors combined. Therefore, climate variability and changes simulated by RCMs depend primarily on the variability and change of temperature aspects of the RCM BCs. Moreover, it is found that the response of the RCM climate to different climate change factors is roughly linear in that the projected changes driven by combined factors are close to the sum of projected changes due to each individual factor alone at least for long-term averages. Findings from this study are important for understanding the source(s) of uncertainties in regional climate projections and for designing innovative approaches to climate downscaling and impact assessment.
Asian Summer Monsoon Pollutes the Northern Hemispheric Stratosphere

NASA Astrophysics Data System (ADS)

Yu, P.; Gao, R. S.; Rosenlof, K. H.; Telg, H.; Liu, S.; Zhixuan, B.; Bian, J.

2016-12-01

An enhanced aerosol layer near the tropopause over Asia during the period of the Asian summer monsoon (ASM) was recently identified by satellites. Previous modeling studies suggest the layer is largely composed of organics and sulfate. However its source, detailed distribution, and climate implications are presently not well understood. To address this issue, in-situ measurements of aerosol size distribution during the 2015 ASM were made from Kunming, China. These showed a robust aerosol enhancement up to 2 km above the tropopause. We use a global climate model coupled with the Community Aerosol and Radiation Model for Atmospheres (CARMA) to show that these aerosol particles are transported to the entire 10°N - 50°N latitude band in the lower stratosphere. These transported particles, originally formed in the region of the ASM anticyclone, account for a significant fraction of total aerosol mass in the 10°N - 50°N latitude band between 16 to 20 km.
Divergent surface and total soil moisture projections under global warming

USGS Publications Warehouse

Berg, Alexis; Sheffield, Justin; Milly, Paul C.D.

2017-01-01

Land aridity has been projected to increase with global warming. Such projections are mostly based on off-line aridity and drought metrics applied to climate model outputs but also are supported by climate-model projections of decreased surface soil moisture. Here we comprehensively analyze soil moisture projections from the Coupled Model Intercomparison Project phase 5, including surface, total, and layer-by-layer soil moisture. We identify a robust vertical gradient of projected mean soil moisture changes, with more negative changes near the surface. Some regions of the northern middle to high latitudes exhibit negative annual surface changes but positive total changes. We interpret this behavior in the context of seasonal changes in the surface water budget. This vertical pattern implies that the extensive drying predicted by off-line drought metrics, while consistent with the projected decline in surface soil moisture, will tend to overestimate (negatively) changes in total soil water availability.
Robust assessment of the expansion and retreat of Mediterranean climate in the 21st century

PubMed Central

Alessandri, Andrea; De Felice, Matteo; Zeng, Ning; Mariotti, Annarita; Pan, Yutong; Cherchi, Annalisa; Lee, June-Yi; Wang, Bin; Ha, Kyung-Ja; Ruti, Paolo; Artale, Vincenzo

2014-01-01

The warm-temperate regions of the globe characterized by dry summers and wet winters (Mediterranean climate; MED) are especially vulnerable to climate change. The potential impact on water resources, ecosystems and human livelihood requires a detailed picture of the future changes in this unique climate zone. Here we apply a probabilistic approach to quantitatively address how and why the geographic distribution of MED will change based on the latest-available climate projections for the 21st century. Our analysis provides, for the first time, a robust assessment of significant northward and eastward future expansions of MED over both the Euro-Mediterranean and western North America. Concurrently, we show a significant 21st century replacement of the equatorward MED margins by the arid climate type. Moreover, future winters will become wetter and summers drier in both the old and newly established MED zones. Should these projections be realized, living conditions in some of the most densely populated regions in the world will be seriously jeopardized. PMID:25448867
European drought under climate change and an assessment of the uncertainties in projections

NASA Astrophysics Data System (ADS)

Yu, R. M. S.; Osborn, T.; Conway, D.; Warren, R.; Hankin, R.

2012-04-01

Extreme weather/climate events have significant environmental and societal impacts, and anthropogenic climate change has and will continue to alter their characteristics (IPCC, 2011). Drought is one of the most damaging natural hazards through its effects on agricultural, hydrological, ecological and socio-economic systems. Climate change is stimulating demand, from public and private sector decision-makers and also other stakeholders, for better understanding of potential future drought patterns which could facilitate disaster risk management. There remain considerable levels of uncertainty in climate change projections, particularly in relation to extreme events. Our incomplete understanding of the behaviour of the climate system has led to the development of various emission scenarios, carbon cycle models and global climate models (GCMs). Uncertainties arise also from the different types and definitions of drought. This study examines climate change-induced changes in European drought characteristics, and illustrates the robustness of these projections by quantifying the effects of using different emission scenarios, carbon cycle models and GCMs. This is achieved by using the multi-institutional modular "Community Integrated Assessment System (CIAS)" (Warren et al., 2008), a flexible integrated assessment system for modelling climate change. Simulations generated by the simple climate model MAGICC6.0 are assessed. These include ten C4MIP carbon cycle models and eighteen CMIP3 GCMs under five IPCC SRES emission scenarios, four Representative Concentration Pathway (RCP) scenarios, and three mitigation scenarios with CO2-equivalent levels stabilising at 550 ppm, 500 ppm and 450 ppm. Using an ensemble of 2160 future precipitation scenarios, we present an analysis on both short (3-month) and long (12-month) meteorological droughts based on the Standardised Precipitation Index (SPI) for the baseline period (1951-2000) and two future periods of 2001-2050 and 2051-2100. Results indicate, with the exception of high latitude regions, a marked increase in drought condition across Europe especially in the second half of 21st century. Patterns, however, vary substantially depending on the model, emission scenario, region and season. While the variance introduced by choice of carbon cycle model is of minor importance, contribution of emission scenario becomes more important in the second half of the century; nevertheless, GCM uncertainty remains the dominant source throughout the 21st century and across all regions.
El Niño-like teleconnection increases California precipitation in response to warming

PubMed Central

Allen, Robert J.; Luptowitz, Rainer

2017-01-01

Future California (CA) precipitation projections, including those from the most recent Climate Model Intercomparison Project (CMIP5), remain uncertain. This uncertainty is related to several factors, including relatively large internal climate variability, model shortcomings, and because CA lies within a transition zone, where mid-latitude regions are expected to become wetter and subtropical regions drier. Here, we use a multitude of models to show CA may receive more precipitation in the future under a business-as-usual scenario. The boreal winter season-when most of the CA precipitation increase occurs-is associated with robust changes in the mean circulation reminiscent of an El Niño teleconnection. Using idealized simulations with two different models, we further show that warming of tropical Pacific sea surface temperatures accounts for these changes. Models that better simulate the observed El Niño-CA precipitation teleconnection yield larger, and more consistent increases in CA precipitation through the twenty-first century. PMID:28681837
El Niño-like teleconnection increases California precipitation in response to warming

NASA Astrophysics Data System (ADS)

Allen, Robert J.; Luptowitz, Rainer

2017-07-01

Future California (CA) precipitation projections, including those from the most recent Climate Model Intercomparison Project (CMIP5), remain uncertain. This uncertainty is related to several factors, including relatively large internal climate variability, model shortcomings, and because CA lies within a transition zone, where mid-latitude regions are expected to become wetter and subtropical regions drier. Here, we use a multitude of models to show CA may receive more precipitation in the future under a business-as-usual scenario. The boreal winter season-when most of the CA precipitation increase occurs-is associated with robust changes in the mean circulation reminiscent of an El Niño teleconnection. Using idealized simulations with two different models, we further show that warming of tropical Pacific sea surface temperatures accounts for these changes. Models that better simulate the observed El Niño-CA precipitation teleconnection yield larger, and more consistent increases in CA precipitation through the twenty-first century.
Landscape fires dominate terrestrial natural aerosol - climate feedbacks

NASA Astrophysics Data System (ADS)

Scott, C.; Arnold, S.; Monks, S. A.; Asmi, A.; Paasonen, P.; Spracklen, D. V.

2017-12-01

The terrestrial biosphere is an important source of natural aerosol including landscape fire emissions and secondary organic aerosol (SOA) formed from biogenic volatile organic compounds (BVOCs). Atmospheric aerosol alters the Earth's climate by absorbing and scattering radiation (direct radiative effect; DRE) and by perturbing the properties of clouds (aerosol indirect effect; AIE). Natural aerosol sources are strongly controlled by, and can influence, climate; giving rise to potential natural aerosol-climate feedbacks. Earth System Models (ESMs) include a description of some of these natural aerosol-climate feedbacks, predicting substantial changes in natural aerosol over the coming century with associated radiative perturbations. Despite this, the sensitivity of natural aerosols simulated by ESMs to changes in climate or emissions has not been robustly tested against observations. Here we combine long-term observations of aerosol number and a global aerosol microphysics model to assess terrestrial natural aerosol-climate feedbacks. We find a strong positive relationship between the summertime anomaly in observed concentration of particles greater than 100 nm diameter and the anomaly in local air temperature. This relationship is reproduced by the model and driven by variability in dynamics and meteorology, as well as natural sources of aerosol. We use an offline radiative transfer model to determine radiative effects due to changes in two natural aerosol sources: landscape fire and biogenic SOA. We find that interannual variability in the simulated global natural aerosol radiative effect (RE) is negatively related to the global temperature anomaly. The magnitude of global aerosol-climate feedback (sum of DRE and AIE) is estimated to be -0.15 Wm-2 K-1 for landscape fire aerosol and -0.06 Wm-2 K-1 for biogenic SOA. These feedbacks are comparable in magnitude, but opposite in sign to the snow albedo feedback, highlighting the need for natural aerosol feedbacks to be included in climate simulations.
Local Colonization-Extinction Dynamics Generate Lags in the Response to Climate Change in Eastern North American Forests

NASA Astrophysics Data System (ADS)

Talluto, M. V.; Boulangeat, I.; Vissault, S.; Gravel, D.

2015-12-01

Climate change is likely to push many species to the limits of their ecological niches and lead to mismatches between species ranges and local environmental conditions. Forested ecosystems in particular may have difficulty tracking climate change due to slow growth and dispersal rates. Correlative species distribution models (SDMs), commonly used to predict the response of species distributions to climate change, relate species occurrences to climate to describe the present niche; however they often project into the future without accounting for slow processes that might produce lags in the response to climate change. An alternative type of model that analyzes patch-scale colonization and extinction (C-E) rates along an environmental gradient has been successful in describing species range limits in theoretical studies. Because the model is stochastic and dynamic, it is more robust to changes in the environmental gradient than static SDMs. We applied such a model to 40 of the most abundant trees in eastern North American forests, using repeated observations across multiple decades to parameterize the C-E rates. We show that C-E rates for many species respond to climate in a manner that generates predicted range limits when the species is at equilibrium with the environment. Moreover, current distributions of many species are significantly out of equilibrium with the present climate, with predicted range limits shifted 10s to 100s of km northward from the present distribution. These results suggest that present warming has already exceeded the thermal tolerance at the southern range limits for the dominant trees of eastern North American forests, producing millions of ha of newly suitable areas north of the present distribution of these species that have not yet been colonized, as well as large southern regions where species are present but expected to be lost in the long-term as dead trees are not replaced, even if no further climate warming occurs.
A Simple Approach to Account for Climate Model Interdependence in Multi-Model Ensembles

NASA Astrophysics Data System (ADS)

Herger, N.; Abramowitz, G.; Angelil, O. M.; Knutti, R.; Sanderson, B.

2016-12-01

Multi-model ensembles are an indispensable tool for future climate projection and its uncertainty quantification. Ensembles containing multiple climate models generally have increased skill, consistency and reliability. Due to the lack of agreed-on alternatives, most scientists use the equally-weighted multi-model mean as they subscribe to model democracy ("one model, one vote").Different research groups are known to share sections of code, parameterizations in their model, literature, or even whole model components. Therefore, individual model runs do not represent truly independent estimates. Ignoring this dependence structure might lead to a false model consensus, wrong estimation of uncertainty and effective number of independent models.Here, we present a way to partially address this problem by selecting a subset of CMIP5 model runs so that its climatological mean minimizes the RMSE compared to a given observation product. Due to the cancelling out of errors, regional biases in the ensemble mean are reduced significantly.Using a model-as-truth experiment we demonstrate that those regional biases persist into the future and we are not fitting noise, thus providing improved observationally-constrained projections of the 21st century. The optimally selected ensemble shows significantly higher global mean surface temperature projections than the original ensemble, where all the model runs are considered. Moreover, the spread is decreased well beyond that expected from the decreased ensemble size.Several previous studies have recommended an ensemble selection approach based on performance ranking of the model runs. Here, we show that this approach can perform even worse than randomly selecting ensemble members and can thus be harmful. We suggest that accounting for interdependence in the ensemble selection process is a necessary step for robust projections for use in impact assessments, adaptation and mitigation of climate change.
Extreme Events in China under Climate Change: Uncertainty and related impacts (CSSP-FOREX)

NASA Astrophysics Data System (ADS)

Leckebusch, Gregor C.; Befort, Daniel J.; Hodges, Kevin I.

2016-04-01

Suitable adaptation strategies or the timely initiation of related mitigation efforts in East Asia will strongly depend on robust and comprehensive information about future near-term as well as long-term potential changes in the climate system. Therefore, understanding the driving mechanisms associated with the East Asian climate is of major importance. The FOREX project (Fostering Regional Decision Making by the Assessment of Uncertainties of Future Regional Extremes and their Linkage to Global Climate System Variability for China and East Asia) focuses on the investigation of extreme wind and rainfall related events over Eastern Asia and their possible future changes. Here, analyses focus on the link between local extreme events and their driving weather systems. This includes the coupling between local rainfall extremes and tropical cyclones, the Meiyu frontal system, extra-tropical teleconnections and monsoonal activity. Furthermore, the relation between these driving weather systems and large-scale variability modes, e.g. NAO, PDO, ENSO is analysed. Thus, beside analysing future changes of local extreme events, the temporal variability of their driving weather systems and related large-scale variability modes will be assessed in current CMIP5 global model simulations to obtain more robust results. Beyond an overview of FOREX itself, first results regarding the link between local extremes and their steering weather systems based on observational and reanalysis data are shown. Special focus is laid on the contribution of monsoonal activity, tropical cyclones and the Meiyu frontal system on the inter-annual variability of the East Asian summer rainfall.
Assessment of urban pluvial flood risk and efficiency of adaptation options through simulations - A new generation of urban planning tools

NASA Astrophysics Data System (ADS)

Löwe, Roland; Urich, Christian; Sto. Domingo, Nina; Mark, Ole; Deletic, Ana; Arnbjerg-Nielsen, Karsten

2017-07-01

We present a new framework for flexible testing of flood risk adaptation strategies in a variety of urban development and climate scenarios. This framework couples the 1D-2D hydrodynamic simulation package MIKE FLOOD with the agent-based urban development model DAnCE4Water and provides the possibility to systematically test various flood risk adaptation measures ranging from large infrastructure changes over decentralised water management to urban planning policies. We have tested the framework in a case study in Melbourne, Australia considering 9 scenarios for urban development and climate and 32 potential combinations of flood adaptation measures. We found that the performance of adaptation measures strongly depended on the considered climate and urban development scenario and the other implementation measures implemented, suggesting that adaptive strategies are preferable over one-off investments. Urban planning policies proved to be an efficient means for the reduction of flood risk, while implementing property buyback and pipe increases in a guideline-oriented manner was too costly. Random variations in location and time point of urban development could have significant impact on flood risk and would in some cases outweigh the benefits of less efficient adaptation strategies. The results of our setup can serve as an input for robust decision making frameworks and thus support the identification of flood risk adaptation measures that are economically efficient and robust to variations of climate and urban layout.
Some conservation issues for the dynamical cores of NWP and climate models

NASA Astrophysics Data System (ADS)

Thuburn, J.

2008-03-01

The rationale for designing atmospheric numerical model dynamical cores with certain conservation properties is reviewed. The conceptual difficulties associated with the multiscale nature of realistic atmospheric flow, and its lack of time-reversibility, are highlighted. A distinction is made between robust invariants, which are conserved or nearly conserved in the adiabatic and frictionless limit, and non-robust invariants, which are not conserved in the limit even though they are conserved by exactly adiabatic frictionless flow. For non-robust invariants, a further distinction is made between processes that directly transfer some quantity from large to small scales, and processes involving a cascade through a continuous range of scales; such cascades may either be explicitly parameterized, or handled implicitly by the dynamical core numerics, accepting the implied non-conservation. An attempt is made to estimate the relative importance of different conservation laws. It is argued that satisfactory model performance requires spurious sources of a conservable quantity to be much smaller than any true physical sources; for several conservable quantities the magnitudes of the physical sources are estimated in order to provide benchmarks against which any spurious sources may be measured.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less
Impact of climate change on water resources status: A case study for Crete Island, Greece

NASA Astrophysics Data System (ADS)

Koutroulis, Aristeidis G.; Tsanis, Ioannis K.; Daliakopoulos, Ioannis N.; Jacob, Daniela

2013-02-01

SummaryAn assessment of the impact of global climate change on the water resources status of the island of Crete, for a range of 24 different scenarios of projected hydro-climatological regime is presented. Three "state of the art" Global Climate Models (GCMs) and an ensemble of Regional Climate Models (RCMs) under emission scenarios B1, A2 and A1B provide future precipitation (P) and temperature (T) estimates that are bias adjusted against observations. The ensemble of RCMs for the A1B scenario project a higher P reduction compared to GCMs projections under A2 and B1 scenarios. Among GCMs model results, the ECHAM model projects a higher P reduction compared to IPSL and CNCM. Water availability for the whole island at basin scale until 2100 is estimated using the SAC-SMA rainfall-runoff model And a set of demand and infrastructure scenarios are adopted to simulate potential water use. While predicted reduction of water availability under the B1 emission scenario can be handled with water demand stabilized at present values and full implementation of planned infrastructure, other scenarios require additional measures and a robust signal of water insufficiency is projected. Despite inherent uncertainties, the quantitative impact of the projected changes on water availability indicates that climate change plays an important role to water use and management in controlling future water status in a Mediterranean island like Crete. The results of the study reinforce the necessity to improve and update local water management planning and adaptation strategies in order to attain future water security.
A parametric approach for simultaneous bias correction and high-resolution downscaling of climate model rainfall

NASA Astrophysics Data System (ADS)

Mamalakis, Antonios; Langousis, Andreas; Deidda, Roberto; Marrocu, Marino

2017-03-01

Distribution mapping has been identified as the most efficient approach to bias-correct climate model rainfall, while reproducing its statistics at spatial and temporal resolutions suitable to run hydrologic models. Yet its implementation based on empirical distributions derived from control samples (referred to as nonparametric distribution mapping) makes the method's performance sensitive to sample length variations, the presence of outliers, the spatial resolution of climate model results, and may lead to biases, especially in extreme rainfall estimation. To address these shortcomings, we propose a methodology for simultaneous bias correction and high-resolution downscaling of climate model rainfall products that uses: (a) a two-component theoretical distribution model (i.e., a generalized Pareto (GP) model for rainfall intensities above a specified threshold u*, and an exponential model for lower rainrates), and (b) proper interpolation of the corresponding distribution parameters on a user-defined high-resolution grid, using kriging for uncertain data. We assess the performance of the suggested parametric approach relative to the nonparametric one, using daily raingauge measurements from a dense network in the island of Sardinia (Italy), and rainfall data from four GCM/RCM model chains of the ENSEMBLES project. The obtained results shed light on the competitive advantages of the parametric approach, which is proved more accurate and considerably less sensitive to the characteristics of the calibration period, independent of the GCM/RCM combination used. This is especially the case for extreme rainfall estimation, where the GP assumption allows for more accurate and robust estimates, also beyond the range of the available data.
Greater future global warming inferred from Earth’s recent energy budget

NASA Astrophysics Data System (ADS)

Brown, Patrick T.; Caldeira, Ken

2017-12-01

Climate models provide the principal means of projecting global warming over the remainder of the twenty-first century but modelled estimates of warming vary by a factor of approximately two even under the same radiative forcing scenarios. Across-model relationships between currently observable attributes of the climate system and the simulated magnitude of future warming have the potential to inform projections. Here we show that robust across-model relationships exist between the global spatial patterns of several fundamental attributes of Earth’s top-of-atmosphere energy budget and the magnitude of projected global warming. When we constrain the model projections with observations, we obtain greater means and narrower ranges of future global warming across the major radiative forcing scenarios, in general. In particular, we find that the observationally informed warming projection for the end of the twenty-first century for the steepest radiative forcing scenario is about 15 per cent warmer (+0.5 degrees Celsius) with a reduction of about a third in the two-standard-deviation spread (-1.2 degrees Celsius) relative to the raw model projections reported by the Intergovernmental Panel on Climate Change. Our results suggest that achieving any given global temperature stabilization target will require steeper greenhouse gas emissions reductions than previously calculated.
Greater future global warming inferred from Earth's recent energy budget.

PubMed

Brown, Patrick T; Caldeira, Ken

2017-12-06

Climate models provide the principal means of projecting global warming over the remainder of the twenty-first century but modelled estimates of warming vary by a factor of approximately two even under the same radiative forcing scenarios. Across-model relationships between currently observable attributes of the climate system and the simulated magnitude of future warming have the potential to inform projections. Here we show that robust across-model relationships exist between the global spatial patterns of several fundamental attributes of Earth's top-of-atmosphere energy budget and the magnitude of projected global warming. When we constrain the model projections with observations, we obtain greater means and narrower ranges of future global warming across the major radiative forcing scenarios, in general. In particular, we find that the observationally informed warming projection for the end of the twenty-first century for the steepest radiative forcing scenario is about 15 per cent warmer (+0.5 degrees Celsius) with a reduction of about a third in the two-standard-deviation spread (-1.2 degrees Celsius) relative to the raw model projections reported by the Intergovernmental Panel on Climate Change. Our results suggest that achieving any given global temperature stabilization target will require steeper greenhouse gas emissions reductions than previously calculated.

Detailed climate-change projections for urban land-use change and green-house gas increases for Belgium with COSMO-CLM coupled to TERRA_URB

NASA Astrophysics Data System (ADS)

Wouters, Hendrik; Vanden Broucke, Sam; van Lipzig, Nicole; Demuzere, Matthias

2016-04-01

Recent research clearly show that climate modelling at high resolution - which resolve the deep convection, the detailed orography and land-use including urbanization - leads to better modelling performance with respect to temperatures, the boundary-layer, clouds and precipitation. The increasing computational power enables the climate research community to address climate-change projections with higher accuracy and much more detail. In the framework of the CORDEX.be project aiming for coherent high-resolution micro-ensemble projections for Belgium employing different GCMs and RCMs, the KU Leuven contributes by means of the downscaling of EC-EARTH global climate model projections (provided by the Royal Meteorological Institute of the Netherlands) to the Belgian domain. The downscaling is obtained with regional climate simulations at 12.5km resolution over Europe (CORDEX-EU domain) and at 2.8km resolution over Belgium (CORDEX.be domain) using COSMO-CLM coupled to urban land-surface parametrization TERRA_URB. This is done for the present-day (1975-2005) and future (2040 → 2070 and 2070 → 2100). In these high-resolution runs, both GHG changes (in accordance to RCP8.5) and urban land-use changes (in accordance to a business-as-usual urban expansion scenario) are taken into account. Based on these simulations, it is shown how climate-change statistics are modified when going from coarse resolution modelling to high-resolution modelling. The climate-change statistics of particular interest are the changes in number of extreme precipitation events and extreme heat waves in cities. Hereby, it is futher investigated for the robustness of the signal change between the course and high-resolution and whether a (statistical) translation is possible. The different simulations also allow to address the relative impact and synergy between the urban expansion and increased GHG on the climate-change statistics. Hereby, it is investigated for which climate-change statistics the urban heat island and urban expansion is relevant, and to what extent the urban expansion can be included in the coarse-to-high resolution translation.
Climate change indices for Greenland applied directly for other arctic regions - Enhanced and utilized climate information from one high resolution RCM downscaling for Greenland evaluated through pattern scaling and CMIP5

NASA Astrophysics Data System (ADS)

Olesen, M.; Christensen, J. H.; Boberg, F.

2016-12-01

Climate change indices for Greenland applied directly for other arctic regions - Enhanced and utilized climate information from one high resolution RCM downscaling for Greenland evaluated through pattern scaling and CMIP5Climate change affects the Greenlandic society both advantageously and disadvantageously. Changes in temperature and precipitation patterns may result in changes in a number of derived society related climate indices, such as the length of growing season or the number of annual dry days or a combination of the two - indices of substantial importance to society in a climate adaptation context.Detailed climate indices require high resolution downscaling. We have carried out a very high resolution (5 km) simulation with the regional climate model HIRHAM5, forced by the global model EC-Earth. Evaluation of RCM output is usually done with an ensemble of downscaled output with multiple RCM's and GCM's. Here we have introduced and tested a new technique; a translation of the robustness of an ensemble of GCM models from CMIP5 into the specific index from the HIRHAM5 downscaling through a correlation between absolute temperatures and its corresponding index values from the HIRHAM5 output.The procedure is basically conducted in two steps: First, the correlation between temperature and a given index for the HIRHAM5 simulation by a best fit to a second order polynomial is identified. Second, the standard deviation from the CMIP5 simulations is introduced to show the corresponding standard deviation of the index from the HIRHAM5 run. The change of specific climate indices due to global warming will then be possible to evaluate elsewhere corresponding to the change in absolute temperature.Results based on selected indices with focus on the future climate in Greenland calculated for the rcp4.5 and rcp8.5 scenarios will be presented.
Modeling precipitation δ 18O variability in East Asia since the Last Glacial Maximum: temperature and amount effects across different timescales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wen, Xinyu; Liu, Zhengyu; Chen, Zhongxiao

Water isotopes in precipitation have played a key role in the reconstruction of past climate on millennial timescales and longer. But, for midlatitude regions like East Asia with complex terrain, the reliability behind the basic assumptions of the temperature effect and amount effect is based on modern observational data and still remains unclear for past climate. In the present work, we reexamine the two basic effects on seasonal, interannual, and millennial timescales in a set of time slice experiments for the period 22–0 ka using an isotope-enabled atmospheric general circulation model (AGCM). Our study confirms the robustness of the temperaturemore » and amount effects on the seasonal cycle over China in the present climatic conditions, with the temperature effect dominating in northern China and the amount effect dominating in the far south of China but no distinct effect in the transition region of central China. However, our analysis shows that neither temperature nor amount effect is significantly dominant over China on millennial and interannual timescales, which is a challenge to those classic assumptions in past climate reconstruction. This work helps shed light on the interpretation of the proxy record of δ 18O from a modeling point of view.« less
Modeling precipitation δ 18O variability in East Asia since the Last Glacial Maximum: temperature and amount effects across different timescales

DOE PAGES

Wen, Xinyu; Liu, Zhengyu; Chen, Zhongxiao; ...

2016-11-06

Water isotopes in precipitation have played a key role in the reconstruction of past climate on millennial timescales and longer. But, for midlatitude regions like East Asia with complex terrain, the reliability behind the basic assumptions of the temperature effect and amount effect is based on modern observational data and still remains unclear for past climate. In the present work, we reexamine the two basic effects on seasonal, interannual, and millennial timescales in a set of time slice experiments for the period 22–0 ka using an isotope-enabled atmospheric general circulation model (AGCM). Our study confirms the robustness of the temperaturemore » and amount effects on the seasonal cycle over China in the present climatic conditions, with the temperature effect dominating in northern China and the amount effect dominating in the far south of China but no distinct effect in the transition region of central China. However, our analysis shows that neither temperature nor amount effect is significantly dominant over China on millennial and interannual timescales, which is a challenge to those classic assumptions in past climate reconstruction. This work helps shed light on the interpretation of the proxy record of δ 18O from a modeling point of view.« less
The southern African climate under 1.5 °C and 2 °C of global warming as simulated by CORDEX regional climate models

NASA Astrophysics Data System (ADS)

Maúre, G.; Pinto, I.; Ndebele-Murisa, M.; Muthige, M.; Lennard, C.; Nikulin, G.; Dosio, A.; Meque, A.

2018-06-01

Results from an 25 regional climate model simulations from the Coordinated Regional Downscaling Experiment Africa initiative are used to assess the projected changes in temperature and precipitation over southern Africa at two global warming levels (GWLs), namely 1.5 °C and 2.0 °C, relative to pre-industrial values, under the Representative Concentration Pathway 8.5. The results show a robust increase in temperature compared to the control period (1971–2000) ranging from 0.5 °C–1.5 °C for the 1.5 °C GWL and from 1.5 °C–2.5 °C, for the 2.0 °C GWL. Areas in the south-western region of the subcontinent, covering South Africa and parts of Namibia and Botswana are projected to experience the largest increase in temperature, which are greater than the global mean warming, particularly during the September–October–November season. On the other hand, under 1.5 °C GWL, models exhibit a robust reduction in precipitation of up to 0.4 mm day‑1 (roughly 20% of the climatological values) over the Limpopo Basin and smaller areas of the Zambezi Basin in Zambia, and also parts of Western Cape, South Africa. Models project precipitation increase of up to 0.1 mm day‑1 over central and western South Africa and in southern Namibia. Under 2.0 °C GWL, a larger fraction of land is projected to face robust decreases between 0.2 and 0.4 mm day‑1 (around 10%–20% of the climatological values) over most of the central subcontinent and parts of western South Africa and northern Mozambique. Decreases in precipitation are accompanied by increases in the number of consecutive dry days and decreases in consecutive wet days over the region. The importance of achieving the Paris Agreement is imperative for southern Africa as the projected changes under both the 1.5 °C, and more so, 2.0 °C GWL imply significant potential risks to agricultural and economic productivity, human and ecological systems health and water resources with implied increase in regional water stresses.
Identifying traits for genotypic adaptation using crop models.

PubMed

Ramirez-Villegas, Julian; Watson, James; Challinor, Andrew J

2015-06-01

Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.
C4MIP - The Coupled Climate-Carbon Cycle Model Intercomparison Project: experimental protocol for CMIP6

NASA Astrophysics Data System (ADS)

Jones, Chris D.; Arora, Vivek; Friedlingstein, Pierre; Bopp, Laurent; Brovkin, Victor; Dunne, John; Graven, Heather; Hoffman, Forrest; Ilyina, Tatiana; John, Jasmin G.; Jung, Martin; Kawamiya, Michio; Koven, Charlie; Pongratz, Julia; Raddatz, Thomas; Randerson, James T.; Zaehle, Sönke

2016-08-01

Coordinated experimental design and implementation has become a cornerstone of global climate modelling. Model Intercomparison Projects (MIPs) enable systematic and robust analysis of results across many models, by reducing the influence of ad hoc differences in model set-up or experimental boundary conditions. As it enters its 6th phase, the Coupled Model Intercomparison Project (CMIP6) has grown significantly in scope with the design and documentation of individual simulations delegated to individual climate science communities. The Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP) takes responsibility for design, documentation, and analysis of carbon cycle feedbacks and interactions in climate simulations. These feedbacks are potentially large and play a leading-order contribution in determining the atmospheric composition in response to human emissions of CO2 and in the setting of emissions targets to stabilize climate or avoid dangerous climate change. For over a decade, C4MIP has coordinated coupled climate-carbon cycle simulations, and in this paper we describe the C4MIP simulations that will be formally part of CMIP6. While the climate-carbon cycle community has created this experimental design, the simulations also fit within the wider CMIP activity, conform to some common standards including documentation and diagnostic requests, and are designed to complement the CMIP core experiments known as the Diagnostic, Evaluation and Characterization of Klima (DECK). C4MIP has three key strands of scientific motivation and the requested simulations are designed to satisfy their needs: (1) pre-industrial and historical simulations (formally part of the common set of CMIP6 experiments) to enable model evaluation, (2) idealized coupled and partially coupled simulations with 1 % per year increases in CO2 to enable diagnosis of feedback strength and its components, (3) future scenario simulations to project how the Earth system will respond to anthropogenic activity over the 21st century and beyond. This paper documents in detail these simulations, explains their rationale and planned analysis, and describes how to set up and run the simulations. Particular attention is paid to boundary conditions, input data, and requested output diagnostics. It is important that modelling groups participating in C4MIP adhere as closely as possible to this experimental design.
A robust definition of South Asian monsoon onset and retreat

NASA Astrophysics Data System (ADS)

Walker, J. M.; Bordoni, S.

2017-12-01

In this study, we revisit one of the major outstanding problems in the monsoon literature: defining the onset and retreat of the South Asian summer monsoon (SASM). The SASM rainy season, which provides essential water resources to densely populated and rapidly growing countries in South Asia, begins with a dramatic increase in rainfall and an abrupt reversal in near-surface winds, and concludes with a more gradual transition at season's end. Many different measures of SASM onset and retreat have been developed for specific applications, but there is no widely accepted and broadly applicable objective definition. Existing definitions generally rely upon thresholds, posing challenges such as sensitivity to threshold selection and susceptibility to false onsets due to transient weather conditions. In this study, we use the large-scale atmospheric moisture budget to define an SASM onset and retreat index that captures the seasonal transitions in both precipitation and circulation. Our use of change point detection eliminates the need for thresholds, provides a precise characterization of the timescales and stages of the SASM, and allows straightforward comparison across different datasets and climate models. This robust and flexible methodology is ideal for studying variability and trends in monsoon timing, as well as comparing model performance and assessing future SASM changes in climate simulations.
A conditional approach to determining the effect of anthropogenic climate change on very rare events.

NASA Astrophysics Data System (ADS)

Wehner, Michael; Pall, Pardeep; Zarzycki, Colin; Stone, Daithi

2016-04-01

Probabilistic extreme event attribution is especially difficult for weather events that are caused by extremely rare large-scale meteorological patterns. Traditional modeling techniques have involved using ensembles of climate models, either fully coupled or with prescribed ocean and sea ice. Ensemble sizes for the latter case ranges from several 100 to tens of thousand. However, even if the simulations are constrained by the observed ocean state, the requisite large-scale meteorological pattern may not occur frequently enough or even at all in free running climate model simulations. We present a method to ensure that simulated events similar to the observed event are modeled with enough fidelity that robust statistics can be determined given the large scale meteorological conditions. By initializing suitably constrained short term ensemble hindcasts of both the actual weather system and a counterfactual weather system where the human interference in the climate system is removed, the human contribution to the magnitude of the event can be determined. However, the change (if any) in the probability of an event of the observed magnitude is conditional not only on the state of the ocean/sea ice system but also on the prescribed initial conditions determined by the causal large scale meteorological pattern. We will discuss the implications of this technique through two examples; the 2013 Colorado flood and the 2014 Typhoon Haiyan.
Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail.

PubMed

Woodin, Sarah A; Hilbish, Thomas J; Helmuth, Brian; Jones, Sierra J; Wethey, David S

2013-09-01

Modeling the biogeographic consequences of climate change requires confidence in model predictions under novel conditions. However, models often fail when extended to new locales, and such instances have been used as evidence of a change in physiological tolerance, that is, a fundamental niche shift. We explore an alternative explanation and propose a method for predicting the likelihood of failure based on physiological performance curves and environmental variance in the original and new environments. We define the transient event margin (TEM) as the gap between energetic performance failure, defined as CTmax, and the upper lethal limit, defined as LTmax. If TEM is large relative to environmental fluctuations, models will likely fail in new locales. If TEM is small relative to environmental fluctuations, models are likely to be robust for new locales, even when mechanism is unknown. Using temperature, we predict when biogeographic models are likely to fail and illustrate this with a case study. We suggest that failure is predictable from an understanding of how climate drives nonlethal physiological responses, but for many species such data have not been collected. Successful biogeographic forecasting thus depends on understanding when the mechanisms limiting distribution of a species will differ among geographic regions, or at different times, resulting in realized niche shifts. TEM allows prediction of the likelihood of such model failure.
Use of Machine Learning Techniques for Iidentification of Robust Teleconnections to East African Rainfall Variability in Observations and Models

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Funk, Chris

2014-01-01

Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Niño, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations.
Decadal climate prediction in the large ensemble limit

NASA Astrophysics Data System (ADS)

Yeager, S. G.; Rosenbloom, N. A.; Strand, G.; Lindsay, K. T.; Danabasoglu, G.; Karspeck, A. R.; Bates, S. C.; Meehl, G. A.

2017-12-01

In order to quantify the benefits of initialization for climate prediction on decadal timescales, two parallel sets of historical simulations are required: one "initialized" ensemble that incorporates observations of past climate states and one "uninitialized" ensemble whose internal climate variations evolve freely and without synchronicity. In the large ensemble limit, ensemble averaging isolates potentially predictable forced and internal variance components in the "initialized" set, but only the forced variance remains after averaging the "uninitialized" set. The ensemble size needed to achieve this variance decomposition, and to robustly distinguish initialized from uninitialized decadal predictions, remains poorly constrained. We examine a large ensemble (LE) of initialized decadal prediction (DP) experiments carried out using the Community Earth System Model (CESM). This 40-member CESM-DP-LE set of experiments represents the "initialized" complement to the CESM large ensemble of 20th century runs (CESM-LE) documented in Kay et al. (2015). Both simulation sets share the same model configuration, historical radiative forcings, and large ensemble sizes. The twin experiments afford an unprecedented opportunity to explore the sensitivity of DP skill assessment, and in particular the skill enhancement associated with initialization, to ensemble size. This talk will highlight the benefits of a large ensemble size for initialized predictions of seasonal climate over land in the Atlantic sector as well as predictions of shifts in the likelihood of climate extremes that have large societal impact.
Preindustrial nitrous oxide emissions from the land biosphere estimated by using a global biogeochemistry model

NASA Astrophysics Data System (ADS)

Xu, Rongting; Tian, Hanqin; Lu, Chaoqun; Pan, Shufen; Chen, Jian; Yang, Jia; Zhang, Bowen

2017-07-01

To accurately assess how increased global nitrous oxide (N2O) emission has affected the climate system requires a robust estimation of the preindustrial N2O emissions since only the difference between current and preindustrial emissions represents net drivers of anthropogenic climate change. However, large uncertainty exists in previous estimates of preindustrial N2O emissions from the land biosphere, while preindustrial N2O emissions on the finer scales, such as regional, biome, or sector scales, have not been well quantified yet. In this study, we applied a process-based Dynamic Land Ecosystem Model (DLEM) to estimate the magnitude and spatial patterns of preindustrial N2O fluxes at the biome, continental, and global level as driven by multiple environmental factors. Uncertainties associated with key parameters were also evaluated. Our study indicates that the mean of the preindustrial N2O emission was approximately 6.20 Tg N yr-1, with an uncertainty range of 4.76 to 8.13 Tg N yr-1. The estimated N2O emission varied significantly at spatial and biome levels. South America, Africa, and Southern Asia accounted for 34.12, 23.85, and 18.93 %, respectively, together contributing 76.90 % of global total emission. The tropics were identified as the major source of N2O released into the atmosphere, accounting for 64.66 % of the total emission. Our multi-scale estimates provide a robust reference for assessing the climate forcing of anthropogenic N2O emission from the land biosphere
The effects of 1.5 and 2 degrees of global warming on Africa in the CORDEX ensemble

NASA Astrophysics Data System (ADS)

Nikulin, Grigory; Lennard, Chris; Dosio, Alessandro; Kjellström, Erik; Chen, Youmin; Hänsler, Andreas; Kupiainen, Marco; Laprise, René; Mariotti, Laura; Fox Maule, Cathrine; van Meijgaard, Erik; Panitz, Hans-Jürgen; Scinocca, John F.; Somot, Samuel

2018-06-01

There is a general lack of information about the potential effects of 1.5, 2 or more degrees of global warming on the regional climates within Africa, and most studies that address this use data from coarse resolution global models. Using a large ensemble of CORDEX Africa simulations, we present a pan-African overview of the effects of 1.5 and 2 °C global warming levels (GWLs) on the African climate. The CORDEX simulations, consistent with their driving global models, show a robust regional warming exceeding the mean global one over most of Africa. The highest increase in annual mean temperature is found over the subtropics and the smallest one over many coastal regions. Projected changes in annual mean precipitation have a tendency to wetter conditions in some parts of Africa (e.g. central/eastern Sahel and eastern Africa) at both GWLs, but models’ agreement on the sign of change is low. In contrast to mean precipitation, there is a consistent increase in daily precipitation intensity of wet days over a large fraction of tropical Africa emerging already at 1.5 °C GWL and strengthening at 2 °C. A consistent difference between 2 °C and 1.5 °C warmings is also found for projected changes in annual mean temperature and daily precipitation intensity. Our study indicates that a 0.5 °C further warming (from 1.5 °C–2 °C) can indeed produce a robust change in some aspects of the African climate and its extremes.
Making Scientific Data Available to Adaptation Practitioners - the Wallace Initiative

NASA Astrophysics Data System (ADS)

Price, J. T.; Warren, R. F.; Vanderwal, J.; Shoo, L.; Ramirez, J.; Jarvis, A.; Goswami, S.

2010-12-01

Conservation strategies have largely been developed under an assumption of a stationary climate. These strategies may fail with changing climates, especially when acting with existing anthropogenic pressures. The Wallace Initiative is a global effort to rapidly assess the potential impacts of climate change on nearly 50,000 plant and animal species. Climate change data from the Community Integrated Assessment System (CIAS) is then used to look at different future climate change scenarios. Governments and conservation organizations have dedicated extensive resources to protect biodiversity. These investments are at risk and efforts will need to take into account a dynamic climate. We used the species models to calculate projected changes in percent species richness - looking at areas likely to be refugia and areas likely to undergo the greatest loss. This information can provide guidance to natural resource managers on how they may need to adapt to climate change to avoid biodiversity loss. Managers will also need to take into account issues with spatial scale. While these models might project a species being “lost” in a 0.5° x 0.5° grid, thermal buffering (e.g., taking into account elevation, slope, aspect, distance to stream and canopy cover) provides guidance on areas that may allow a species to persist at more local scales (1-5 km). This approach may help alleviate the issues of downscaling climate and climate change data in data-poor areas. Understanding the vulnerability of biodiversity requires an understanding of the climate and projected climate changes. Thus, developing long term adaptation options requires robust vulnerability analyses at appropriate scales. These assessments are often hindered by data quality and availability, capacity and an understanding of appropriate scales, methods and tools. The program ClimaScope has been designed to help provide better access to climate data for modelers and practitioners, data that has also been linked to impacts. ClimaScope is a data visualization engine providing easy access to data without running the models. For a selected GCM pattern and emissions scenario, ClimaScope provides maps, charts and data on the projected climate changes with some indication of uncertainty range. Variables available include terrestrial temperature change (maximum, minimum and average), total precipitation, wet-day frequency, and sea-surface temperature. This data can be presented both as observed climate and/or projected climate for a user defined time period. Some of these data have been used in the Wallace Initiative and data from the Wallace Initiative are also available to users. So, practitioners can select family, genus, species, dispersal model, climate model, emission model, and time slice and get maps showing projected range of the species over time. These tools have been designed to help make scientific data more readily available to adaptation practitioners around the world, especially in developing countries.
Separating out the influence of climatic trend, fluctuations, and extreme events on crop yield: a case study in Hunan Province, China

NASA Astrophysics Data System (ADS)

Wang, Zhu; Shi, Peijun; Zhang, Zhao; Meng, Yongchang; Luan, Yibo; Wang, Jiwei

2017-09-01

Separating out the influence of climatic trend, fluctuations and extreme events on crop yield is of paramount importance to climate change adaptation, resilience, and mitigation. Previous studies lack systematic and explicit assessment of these three fundamental aspects of climate change on crop yield. This research attempts to separate out the impacts on rice yields of climatic trend (linear trend change related to mean value), fluctuations (variability surpassing the "fluctuation threshold" which defined as one standard deviation (1 SD) of the residual between the original data series and the linear trend value for each climatic variable), and extreme events (identified by absolute criterion for each kind of extreme events related to crop yield). The main idea of the research method was to construct climate scenarios combined with crop system simulation model. Comparable climate scenarios were designed to express the impact of each climate change component and, were input to the crop system model (CERES-Rice), which calculated the related simulated yield gap to quantify the percentage impacts of climatic trend, fluctuations, and extreme events. Six Agro-Meteorological Stations (AMS) in Hunan province were selected to study the quantitatively impact of climatic trend, fluctuations and extreme events involving climatic variables (air temperature, precipitation, and sunshine duration) on early rice yield during 1981-2012. The results showed that extreme events were found to have the greatest impact on early rice yield (-2.59 to -15.89%). Followed by climatic fluctuations with a range of -2.60 to -4.46%, and then the climatic trend (4.91-2.12%). Furthermore, the influence of climatic trend on early rice yield presented "trade-offs" among various climate variables and AMS. Climatic trend and extreme events associated with air temperature showed larger effects on early rice yield than other climatic variables, particularly for high-temperature events (-2.11 to -12.99%). Finally, the methodology use to separate out the influences of the climatic trend, fluctuations, and extreme events on crop yield was proved to be feasible and robust. Designing different climate scenarios and feeding them into a crop system model is a potential way to evaluate the quantitative impact of each climate variable.
Agricultural Impacts: Robust uncertainty

NASA Astrophysics Data System (ADS)

Rötter, Reimund P.

2014-04-01

An up-to-date synthesis of climate change impacts on crop yields shows that the bounds of uncertainty are increasing. So why do estimates of the effect of climate change on crop productivity differ so much?
A New Perspective on Increasing Activity of Extratropical Disturbances: Spatial and Temporal Trends of Wave Activity

NASA Astrophysics Data System (ADS)

Hsu, P. C.; Hsu, H. H.

2016-12-01

Changes in extratropical disturbance behavior could play an important role in climate dynamics and be responsible for a part of climate-related damage. However, robust observational evidence for long-term trends in the activity is still lacking, and understanding of how it is linked with climate phenomena is limited. In this study, we define an accumulated perturbation index (API) to quantify the variation in some scalar quantities of atmospheric disturbances. API measures the areas (e.g., % of total surface area of Earth) where a certain perturbation quantity exceeds the long-term mean value plus 0.5 standard deviations. This index reflects more realistically the ensemble impacts of a climate perturbation and/or trend (such as global warming and ENSO) on the extratropical disturbances, even though its impact on different regions might vary from year to year due to stochastic processes. API represents an integrated activity of extratropical disturbances at a given time relative to a long time span. API is calculated for the 5-day running mean and 10-30-day stream function fluctuations during DJF and JJA. The analysis reveals an increasing trend in API and variance of stream function, especially in the Southern Hemisphere. The findings suggest that atmospheric extratropical disturbances have strengthened in widening areas during the past six decades, even though there might not be robust trends in wave activity at regional scales. Whether the observed trends in API are associated with certain climate patterns is under investigation. Impact of global warming is likely one of the major sources for the increasing activity. The future change in API under global warming scenarios will be further studied by analyzing the projection of the CMIP5 models.
Climate-driven disturbances in the San Juan River sub-basin of the Colorado River

NASA Astrophysics Data System (ADS)

Bennett, Katrina E.; Bohn, Theodore J.; Solander, Kurt; McDowell, Nathan G.; Xu, Chonggang; Vivoni, Enrique; Middleton, Richard S.

2018-01-01

Accelerated climate change and associated forest disturbances in the southwestern USA are anticipated to have substantial impacts on regional water resources. Few studies have quantified the impact of both climate change and land cover disturbances on water balances on the basin scale, and none on the regional scale. In this work, we evaluate the impacts of forest disturbances and climate change on a headwater basin to the Colorado River, the San Juan River watershed, using a robustly calibrated (Nash-Sutcliffe efficiency 0.76) hydrologic model run with updated formulations that improve estimates of evapotranspiration for semi-arid regions. Our results show that future disturbances will have a substantial impact on streamflow with implications for water resource management. Our findings are in contradiction with conventional thinking that forest disturbances reduce evapotranspiration and increase streamflow. In this study, annual average regional streamflow under the coupled climate-disturbance scenarios is at least 6-11 % lower than those scenarios accounting for climate change alone; for forested zones of the San Juan River basin, streamflow is 15-21 % lower. The monthly signals of altered streamflow point to an emergent streamflow pattern related to changes in forests of the disturbed systems. Exacerbated reductions of mean and low flows under disturbance scenarios indicate a high risk of low water availability for forested headwater systems of the Colorado River basin. These findings also indicate that explicit representation of land cover disturbances is required in modeling efforts that consider the impact of climate change on water resources.
Climate Change Impacts on the Cryosphere of Mountain Regions: Validation of a Novel Model Using the Alaska Range

NASA Astrophysics Data System (ADS)

Mosier, T. M.; Hill, D. F.; Sharp, K. V.

2015-12-01

Mountain regions are natural water towers, storing water seasonally as snowpack and for much longer as glaciers. Understanding the response of these systems to climate change is necessary in order to make informed decisions about prevention or mitigation measures. Yet, mountain regions are often data sparse, leading many researchers to implement simple or enhanced temperature index (ETI) models to simulate cryosphere processes. These model structures do not account for the thermal inertia of snowpack and glaciers and do not robustly capture differences in system response to climate regimes that differ from those the model was calibrated for. For instance, a temperature index calibration parameter will differ substantially in cold-dry conditions versus warm-wet ones. To overcome these issues, we have developed a cryosphere hydrology model, called the Significantly Enhanced Temperature Index (SETI), which uses an energy balance structure but parameterizes energy balance components in terms of minimum, maximum and mean temperature, precipitation, and geometric inputs using established relationships. Additionally, the SETI model includes a glacier sliding model and can therefore be used to estimate long-term glacier response to climate change. Sensitivity of the SETI model to changing climate is compared with an ETI and a simple temperature index model for several partially-glaciated watersheds within Alaska, including Wolverine glacier where multi-decadal glacier stake measurements are available, to highlight the additional fidelity attributed to the increased complexity of the SETI structure. The SETI model is then applied to the entire Alaska Range region for an ensemble of global climate models (GCMs), using representative concentration pathways 4.5 and 8.5. Comparing model runs based on ensembles of GCM projections to historic conditions, total annual snowfall within the Alaska region is not expected to change appreciably, but the spatial distribution of snow shifts towards higher elevations and for a large portion of the region the duration of snow cover decreases. The changes in temperature and snow distribution also lead to spatially heterogeneous responses by glaciers within the region. The SETI model is designed to be easy to apply for any mountain region where cryospheric processes dominate.

Evaluating the ClimEx Single Model large ensemble in comparison with EURO-CORDEX results of heatwave and drought indicators

NASA Astrophysics Data System (ADS)

von Trentini, F.; Schmid, F. J.; Braun, M.; Frigon, A.; Leduc, M.; Martel, J. L.; Willkofer, F.; Wood, R. R.; Ludwig, R.

2017-12-01

Meteorological extreme events seem to become more frequent in the present and future, and a seperation of natural climate variability and a clear climate change effect on these extreme events gains more and more interest. Since there is only one realisation of historical events, natural variability in terms of very long timeseries for a robust statistical analysis is not possible with observation data. A new single model large ensemble (SMLE), developed for the ClimEx project (Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec) is supposed to overcome this lack of data by downscaling 50 members of the CanESM2 (RCP 8.5) with the Canadian CRCM5 regional model (using the EURO-CORDEX grid specifications) for timeseries of 1950-2099 each, resulting in 7500 years of simulated climate. This allows for a better probabilistic analysis of rare and extreme events than any preceding dataset. Besides seasonal sums, several indicators concerning heatwave frequency, duration and mean temperature a well as number and maximum length of dry periods (cons. days <1mm) are calculated for the ClimEx ensemble and several EURO-CORDEX runs. This enables us to investigate the interaction between natural variability (as it appears in the CanESM2-CRCM5 members) and a climate change signal of those members for past, present and future conditions. Adding the EURO-CORDEX results to this, we can also assess the role of internal model variability (or natural variability) in climate change simulations. A first comparison shows similar magnitudes of variability of climate change signals between the ClimEx large ensemble and the CORDEX runs for some indicators, while for most indicators the spread of the SMLE is smaller than the spread of different CORDEX models.
The UPSCALE project: a large simulation campaign

NASA Astrophysics Data System (ADS)

Mizielinski, Matthew; Roberts, Malcolm; Vidale, Pier Luigi; Schiemann, Reinhard; Demory, Marie-Estelle; Strachan, Jane

2014-05-01

The development of a traceable hierarchy of HadGEM3 global climate models, based upon the Met Office Unified Model, at resolutions from 135 km to 25 km, now allows the impact of resolution on the mean state, variability and extremes of climate to be studied in a robust fashion. In 2011 we successfully obtained a single-year grant of 144 million core hours of supercomputing time from the PRACE organization to run ensembles of 27 year atmosphere-only (HadGEM3-A GA3.0) climate simulations at 25km resolution, as used in present global weather forecasting, on HERMIT at HLRS. Through 2012 the UPSCALE project (UK on PRACE: weather-resolving Simulations of Climate for globAL Environmental risk) ran over 650 years of simulation at resolutions of 25 km (N512), 60 km (N216) and 135 km (N96) to look at the value of high resolution climate models in the study of both present climate and a potential future climate scenario based on RCP8.5. Over 400 TB of data was produced using HERMIT, with additional simulations run on HECToR (UK supercomputer) and MONSooN (Met Office NERC Supercomputing Node). The data generated was transferred to the JASMIN super-data cluster, hosted by STFC CEDA in the UK, where analysis facilities are allowing rapid scientific exploitation of the data set. Many groups across the UK and Europe are already taking advantage of these facilities and we welcome approaches from other interested scientists. This presentation will briefly cover the following points; Purpose and requirements of the UPSCALE project and facilities used. Technical implementation and hurdles (model porting and optimisation, automation, numerical failures, data transfer). Ensemble specification. Current analysis projects and access to the data set. A full description of UPSCALE and the data set generated has been submitted to Geoscientific Model development, with overview information available from http://proj.badc.rl.ac.uk/upscale .
Flood projections within the Niger River Basin under future land use and climate change.

PubMed

Aich, Valentin; Liersch, Stefan; Vetter, Tobias; Fournet, Samuel; Andersson, Jafet C M; Calmanti, Sandro; van Weert, Frank H A; Hattermann, Fred F; Paton, Eva N

2016-08-15

This study assesses future flood risk in the Niger River Basin (NRB), for the first time considering the simultaneous effects of both projected climate change and land use changes. For this purpose, an ecohydrological process-based model (SWIM) was set up and validated for past climate and land use dynamics of the entire NRB. Model runs for future flood risks were conducted with an ensemble of 18 climate models, 13 of them dynamically downscaled from the CORDEX Africa project and five statistically downscaled Earth System Models. Two climate and two land use change scenarios were used to cover a broad range of potential developments in the region. Two flood indicators (annual 90th percentile and the 20-year return flood) were used to assess the future flood risk for the Upper, Middle and Lower Niger as well as the Benue. The modeling results generally show increases of flood magnitudes when comparing a scenario period in the near future (2021-2050) with a base period (1976-2005). Land use effects are more uncertain, but trends and relative changes for the different catchments of the NRB seem robust. The dry areas of the Sahelian and Sudanian regions of the basin show a particularly high sensitivity to climatic and land use changes, with an alarming increase of flood magnitudes in parts. A scenario with continuing transformation of natural vegetation into agricultural land and urbanization intensifies the flood risk in all parts of the NRB, while a "regreening" scenario can reduce flood magnitudes to some extent. Yet, land use change effects were smaller when compared to the effects of climate change. In the face of an already existing adaptation deficit to catastrophic flooding in the region, the authors argue for a mix of adaptation and mitigation efforts in order to reduce the flood risk in the NRB. Copyright © 2016 Elsevier B.V. All rights reserved.
Future Evolution of Marine Heat Waves in the Mediterranean: Coupled Regional Climate Projections

NASA Astrophysics Data System (ADS)

Darmaraki, Sofia; Somot, Samuel; Sevault, Florence; Nabat, Pierre; Cavicchia, Leone; Djurdjevic, Vladimir; Cabos, William; Sein, Dmitry

2017-04-01

FUTURE EVOLUTION OF MARINE HEAT WAVES IN THE MEDITERRANEAN : COUPLED REGIONAL CLIMATE PROJECTIONS The Mediterranean area is identified as a « Hot Spot » region, vulnerable to future climate change with potentially strong impacts over the sea. By 2100, climate models predict increased warming over the sea surface, with possible implications on the Mediterranean thermohaline and surface circulation,associated also with severe impacts on the ecosystems (e.g. fish habitat loss, species extinction and migration, invasive species). However, a robust assesment of the future evolution of the extreme marine temperatures remains still an open issue of primary importance, under the anthropogenic pressure. In this context, we study here the probability and characteristics of marine heat wave (MHW) occurrence in the Mediterranean Sea in future climate projections. To this end, we use an ensemble of fully coupled regional climate system models (RCSM) from the Med- CORDEX initiative. This multi-model approach includes a high-resolution representation of the atmospheric, land and ocean component, with a free air-sea interface.Specifically, dedicated simulations for the 20th and the 21st century are carried out with respect to the different IPCC-AR5 socioeconomic scenarios (1950-2100, RCP8.5, RCP4.5, RCP2.6). Model evaluation for the historical period is performed using satellite and in situ data. Then, the variety of factors that can cause the MHW (e.g. direct radiative forcing, ocean advection, stratification change) are examined to disentangle the dominant driving force. Finally, the spatial variability and temporal evolution of MHW are analyzed on an annual basis, along with additional integrated indicators, useful for marine ecosystems.
An ecophysiological perspective on likely giant panda habitat responses to climate change.

PubMed

Zhang, Yuke; Mathewson, Paul D; Zhang, Qiongyue; Porter, Warren P; Ran, Jianghong

2018-04-01

Threatened and endangered species are more vulnerable to climate change due to small population and specific geographical distribution. Therefore, identifying and incorporating the biological processes underlying a species' adaptation to its environment are important for determining whether they can persist in situ. Correlative models are widely used to predict species' distribution changes, but generally fail to capture the buffering capacity of organisms. Giant pandas (Ailuropoda melanoleuca) live in topographically complex mountains and are known to avoid heat stress. Although many studies have found that climate change will lead to severe habitat loss and threaten previous conservation efforts, the mechanisms underlying panda's responses to climate change have not been explored. Here, we present a case study in Daxiangling Mountains, one of the six Mountain Systems that giant panda distributes. We used a mechanistic model, Niche Mapper, to explore what are likely panda habitat response to climate change taking physiological, behavioral and ecological responses into account, through which we map panda's climatic suitable activity area (SAA) for the first time. We combined SAA with bamboo forest distribution to yield highly suitable habitat (HSH) and seasonal suitable habitat (SSH), and their temporal dynamics under climate change were predicted. In general, SAA in the hottest month (July) would reduce 11.7%-52.2% by 2070, which is more moderate than predicted bamboo habitat loss (45.6%-86.9%). Limited by the availability of bamboo and forest, panda's suitable habitat loss increases, and only 15.5%-68.8% of current HSH would remain in 2070. Our method of mechanistic modeling can help to distinguish whether habitat loss is caused by thermal environmental deterioration or food loss under climate change. Furthermore, mechanistic models can produce robust predictions by incorporating ecophysiological feedbacks and minimizing extrapolation into novel environments. We suggest that a mechanistic approach should be incorporated into distribution predictions and conservation planning. © 2017 John Wiley & Sons Ltd.
The BRIDGE HadCM3 family of climate models: HadCM3@Bristol v1.0

NASA Astrophysics Data System (ADS)

Valdes, Paul J.; Armstrong, Edward; Badger, Marcus P. S.; Bradshaw, Catherine D.; Bragg, Fran; Crucifix, Michel; Davies-Barnard, Taraka; Day, Jonathan J.; Farnsworth, Alex; Gordon, Chris; Hopcroft, Peter O.; Kennedy, Alan T.; Lord, Natalie S.; Lunt, Dan J.; Marzocchi, Alice; Parry, Louise M.; Pope, Vicky; Roberts, William H. G.; Stone, Emma J.; Tourte, Gregory J. L.; Williams, Jonny H. T.

2017-10-01

Understanding natural and anthropogenic climate change processes involves using computational models that represent the main components of the Earth system: the atmosphere, ocean, sea ice, and land surface. These models have become increasingly computationally expensive as resolution is increased and more complex process representations are included. However, to gain robust insight into how climate may respond to a given forcing, and to meaningfully quantify the associated uncertainty, it is often required to use either or both ensemble approaches and very long integrations. For this reason, more computationally efficient models can be very valuable tools. Here we provide a comprehensive overview of the suite of climate models based around the HadCM3 coupled general circulation model. This model was developed at the UK Met Office and has been heavily used during the last 15 years for a range of future (and past) climate change studies, but has now been largely superseded for many scientific studies by more recently developed models. However, it continues to be extensively used by various institutions, including the BRIDGE (Bristol Research Initiative for the Dynamic Global Environment) research group at the University of Bristol, who have made modest adaptations to the base HadCM3 model over time. These adaptations mean that the original documentation is not entirely representative, and several other relatively undocumented configurations are in use. We therefore describe the key features of a number of configurations of the HadCM3 climate model family, which together make up HadCM3@Bristol version 1.0. In order to differentiate variants that have undergone development at BRIDGE, we have introduced the letter B into the model nomenclature. We include descriptions of the atmosphere-only model (HadAM3B), the coupled model with a low-resolution ocean (HadCM3BL), the high-resolution atmosphere-only model (HadAM3BH), and the regional model (HadRM3B). These also include three versions of the land surface scheme. By comparing with observational datasets, we show that these models produce a good representation of many aspects of the climate system, including the land and sea surface temperatures, precipitation, ocean circulation, and vegetation. This evaluation, combined with the relatively fast computational speed (up to 1000 times faster than some CMIP6 models), motivates continued development and scientific use of the HadCM3B family of coupled climate models, predominantly for quantifying uncertainty and for long multi-millennial-scale simulations.
Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output

DOE PAGES

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

2017-09-10

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less
A hierarchy of models for ENSO flavors in past climates.

NASA Astrophysics Data System (ADS)

Karamperidou, C.; Xie, R.; Di Nezio, P. N.

2017-12-01

The existence of two distinct ENSO flavors versus an ENSO continuum remains an open question. Investigating the response of ENSO diversity to past climate forcings provides a framework to approach this question. Previous work using GCMs has shown that ENSO flavors may respond differentially to mid-Holocene orbital forcing, with a significant suppression of Eastern Pacific ENSO as opposed to insensitivity of Central Pacific ENSO. Here, we employ a hierarchy of models to explore the robustness of ENSO-flavor response to orbital forcing. First, we use a modified version of the Zebiak-Cane model which simulates two ENSO modes reminiscent of ENSO flavors. We find a quasi-linear response of these two modes to orbital forcing corresponding to 6ka, 111ka, and 121ka BP in terms of growth rates, frequency and spatial pattern of SST anomalies. We then employ an Earth System Model subject only to orbital forcing to show the corresponding response in the three past climates. This investigation indicates that no extratropical influences may be required to produce such quasi-linear ENSO-flavor response to orbital forcing. Aided by paleoclimate proxies, the hierarchy of models employed here presents a paleoclimate perspective to the fundamental and elusive question of the nature and origins of ENSO diversity.
Projected wetland densities under climate change: Habitat loss but little geographic shift in conservation strategy

USGS Publications Warehouse

Sofaer, Helen R.; Skagen, Susan K.; Barsugli, Joseph J.; Rashford, Benjamin S.; Reese, Gordon C.; Hoeting, Jennifer A.; Wood, Andrew W.; Noon, Barry R.

2016-01-01

Climate change poses major challenges for conservation and management because it alters the area, quality, and spatial distribution of habitat for natural populations. To assess species’ vulnerability to climate change and target ongoing conservation investments, researchers and managers often consider the effects of projected changes in climate and land use on future habitat availability and quality and the uncertainty associated with these projections. Here, we draw on tools from hydrology and climate science to project the impact of climate change on the density of wetlands in the Prairie Pothole Region of the USA, a critical area for breeding waterfowl and other wetland-dependent species. We evaluate the potential for a trade-off in the value of conservation investments under current and future climatic conditions and consider the joint effects of climate and land use. We use an integrated set of hydrological and climatological projections that provide physically based measures of water balance under historical and projected future climatic conditions. In addition, we use historical projections derived from ten general circulation models (GCMs) as a baseline from which to assess climate change impacts, rather than historical climate data. This method isolates the impact of greenhouse gas emissions and ensures that modeling errors are incorporated into the baseline rather than attributed to climate change. Our work shows that, on average, densities of wetlands (here defined as wetland basins holding water) are projected to decline across the U.S. Prairie Pothole Region, but that GCMs differ in both the magnitude and the direction of projected impacts. However, we found little evidence for a shift in the locations expected to provide the highest wetland densities under current vs. projected climatic conditions. This result was robust to the inclusion of projected changes in land use under climate change. We suggest that targeting conservation towards wetland complexes containing both small and relatively large wetland basins, which is an ongoing conservation strategy, may also act to hedge against uncertainty in the effects of climate change.
Lilac and honeysuckle phenology data 1956-2014.

PubMed

Rosemartin, Alyssa H; Denny, Ellen G; Weltzin, Jake F; Lee Marsh, R; Wilson, Bruce E; Mehdipoor, Hamed; Zurita-Milla, Raul; Schwartz, Mark D

2015-01-01

The dataset is comprised of leafing and flowering data collected across the continental United States from 1956 to 2014 for purple common lilac (Syringa vulgaris), a cloned lilac cultivar (S. x chinensis 'Red Rothomagensis') and two cloned honeysuckle cultivars (Lonicera tatarica 'Arnold Red' and L. korolkowii 'Zabeli'). Applications of this observational dataset range from detecting regional weather patterns to understanding the impacts of global climate change on the onset of spring at the national scale. While minor changes in methods have occurred over time, and some documentation is lacking, outlier analyses identified fewer than 3% of records as unusually early or late. Lilac and honeysuckle phenology data have proven robust in both model development and climatic research.
Implementation and calibration of a stochastic multicloud convective parameterization in the NCEP Climate Forecast System (CFSv2)

NASA Astrophysics Data System (ADS)

Goswami, B. B.; Khouider, B.; Phani, R.; Mukhopadhyay, P.; Majda, A. J.

2017-07-01

A comparative analysis of fourteen 5 year long climate simulations produced by the National Centers for Environmental Predictions (NCEP) Climate Forecast System version 2 (CFSv2), in which a stochastic multicloud (SMCM) cumulus parameterization is implemented, is presented here. These 5 year runs are made with different sets of parameters in order to figure out the best model configuration based on a suite of state-of-the-art metrics. This analysis is also a systematic attempt to understand the model sensitivity to the SMCM parameters. The model is found to be resilient to minor changes in the parameters used implying robustness of the SMCM formulation. The model is found to be most sensitive to the midtropospheric dryness parameter (MTD) and to the stratiform cloud decay timescale (τ30). MTD is more effective in controlling the global mean precipitation and its distribution while τ30 has more effect on the organization of convection as noticed in the simulation of the Madden-Julian oscillation (MJO). This is consistent with the fact that in the SMCM formulation, midtropospheric humidity controls the deepening of convection and stratiform clouds control the backward tilt of tropospheric heating and the strength of unsaturated downdrafts which cool and dry the boundary layer and trigger the propagation of organized convection. Many other studies have also found midtropospheric humidity to be a key factor in the capacity of a global climate model to simulate organized convection on the synoptic and intraseasonal scales.
Increasing potential for intense tropical and subtropical thunderstorms under global warming.

PubMed

Singh, Martin S; Kuang, Zhiming; Maloney, Eric D; Hannah, Walter M; Wolding, Brandon O

2017-10-31

Intense thunderstorms produce rapid cloud updrafts and may be associated with a range of destructive weather events. An important ingredient in measures of the potential for intense thunderstorms is the convective available potential energy (CAPE). Climate models project increases in summertime mean CAPE in the tropics and subtropics in response to global warming, but the physical mechanisms responsible for such increases and the implications for future thunderstorm activity remain uncertain. Here, we show that high percentiles of the CAPE distribution (CAPE extremes) also increase robustly with warming across the tropics and subtropics in an ensemble of state-of-the-art climate models, implying strong increases in the frequency of occurrence of environments conducive to intense thunderstorms in future climate projections. The increase in CAPE extremes is consistent with a recently proposed theoretical model in which CAPE depends on the influence of convective entrainment on the tropospheric lapse rate, and we demonstrate the importance of this influence for simulated CAPE extremes using a climate model in which the convective entrainment rate is varied. We further show that the theoretical model is able to account for the climatological relationship between CAPE and a measure of lower-tropospheric humidity in simulations and in observations. Our results provide a physical basis on which to understand projected future increases in intense thunderstorm potential, and they suggest that an important mechanism that contributes to such increases may be present in Earth's atmosphere. Published under the PNAS license.
Increasing potential for intense tropical and subtropical thunderstorms under global warming

PubMed Central

Kuang, Zhiming; Maloney, Eric D.; Hannah, Walter M.; Wolding, Brandon O.

2017-01-01

Intense thunderstorms produce rapid cloud updrafts and may be associated with a range of destructive weather events. An important ingredient in measures of the potential for intense thunderstorms is the convective available potential energy (CAPE). Climate models project increases in summertime mean CAPE in the tropics and subtropics in response to global warming, but the physical mechanisms responsible for such increases and the implications for future thunderstorm activity remain uncertain. Here, we show that high percentiles of the CAPE distribution (CAPE extremes) also increase robustly with warming across the tropics and subtropics in an ensemble of state-of-the-art climate models, implying strong increases in the frequency of occurrence of environments conducive to intense thunderstorms in future climate projections. The increase in CAPE extremes is consistent with a recently proposed theoretical model in which CAPE depends on the influence of convective entrainment on the tropospheric lapse rate, and we demonstrate the importance of this influence for simulated CAPE extremes using a climate model in which the convective entrainment rate is varied. We further show that the theoretical model is able to account for the climatological relationship between CAPE and a measure of lower-tropospheric humidity in simulations and in observations. Our results provide a physical basis on which to understand projected future increases in intense thunderstorm potential, and they suggest that an important mechanism that contributes to such increases may be present in Earth’s atmosphere. PMID:29078312
Climate Risk Informed Decision Analysis: A Hypothetical Application to the Waas Region

NASA Astrophysics Data System (ADS)

Gilroy, Kristin; Mens, Marjolein; Haasnoot, Marjolijn; Jeuken, Ad

2016-04-01

More frequent and intense hydrologic events under climate change are expected to enhance water security and flood risk management challenges worldwide. Traditional planning approaches must be adapted to address climate change and develop solutions with an appropriate level of robustness and flexibility. The Climate Risk Informed Decision Analysis (CRIDA) method is a novel planning approach embodying a suite of complementary methods, including decision scaling and adaptation pathways. Decision scaling offers a bottom-up approach to assess risk and tailors the complexity of the analysis to the problem at hand and the available capacity. Through adaptation pathway,s an array of future strategies towards climate robustness are developed, ranging in flexibility and immediacy of investments. Flexible pathways include transfer points to other strategies to ensure that the system can be adapted if future conditions vary from those expected. CRIDA combines these two approaches in a stakeholder driven process which guides decision makers through the planning and decision process, taking into account how the confidence in the available science, the consequences in the system, and the capacity of institutions should influence strategy selection. In this presentation, we will explain the CRIDA method and compare it to existing planning processes, such as the US Army Corps of Engineers Principles and Guidelines as well as Integrated Water Resources Management Planning. Then, we will apply the approach to a hypothetical case study for the Waas Region, a large downstream river basin facing rapid development threatened by increased flood risks. Through the case study, we will demonstrate how a stakeholder driven process can be used to evaluate system robustness to climate change; develop adaptation pathways for multiple objectives and criteria; and illustrate how varying levels of confidence, consequences, and capacity would play a role in the decision making process, specifically in regards to the level of robustness and flexibility in the selected strategy. This work will equip practitioners and decision makers with an example of a structured process for decision making under climate uncertainty that can be scaled as needed to the problem at hand. This presentation builds further on another submitted abstract "Climate Risk Informed Decision Analysis (CRIDA): A novel practical guidance for Climate Resilient Investments and Planning" by Jeuken et al.
The influence of climate change on Tanzania's hydropower sustainability

NASA Astrophysics Data System (ADS)

Sperna Weiland, Frederiek; Boehlert, Brent; Meijer, Karen; Schellekens, Jaap; Magnell, Jan-Petter; Helbrink, Jakob; Kassana, Leonard; Liden, Rikard

2015-04-01

Economic costs induced by current climate variability are large for Tanzania and may further increase due to future climate change. The Tanzanian National Climate Change Strategy addressed the need for stabilization of hydropower generation and strengthening of water resources management. Increased hydropower generation can contribute to sustainable use of energy resources and stabilization of the national electricity grid. To support Tanzania the World Bank financed this study in which the impact of climate change on the water resources and related hydropower generation capacity of Tanzania is assessed. To this end an ensemble of 78 GCM projections from both the CMIP3 and CMIP5 datasets was bias-corrected and down-scaled to 0.5 degrees resolution following the BCSD technique using the Princeton Global Meteorological Forcing Dataset as a reference. To quantify the hydrological impacts of climate change by 2035 the global hydrological model PCR-GLOBWB was set-up for Tanzania at a resolution of 3 minutes and run with all 78 GCM datasets. From the full set of projections a probable (median) and worst case scenario (95th percentile) were selected based upon (1) the country average Climate Moisture Index and (2) discharge statistics of relevance to hydropower generation. Although precipitation from the Princeton dataset shows deviations from local station measurements and the global hydrological model does not perfectly reproduce local scale hydrographs, the main discharge characteristics and precipitation patterns are represented well. The modeled natural river flows were adjusted for water demand and irrigation within the water resources model RIBASIM (both historical values and future scenarios). Potential hydropower capacity was assessed with the power market simulation model PoMo-C that considers both reservoir inflows obtained from RIBASIM and overall electricity generation costs. Results of the study show that climate change is unlikely to negatively affect the average potential of future hydropower production; it will likely make hydropower more profitable. Yet, the uncertainty in climate change projections remains large and risks are significant, adaptation strategies should ideally consider a worst case scenario to ensure robust power generation. Overall a diversified power generation portfolio, anchored in hydropower and supported by other renewables and fossil fuel-based energy sources, is the best solution for Tanzania
Assessing climate change impact by integrated hydrological modelling

NASA Astrophysics Data System (ADS)

Lajer Hojberg, Anker; Jørgen Henriksen, Hans; Olsen, Martin; der Keur Peter, van; Seaby, Lauren Paige; Troldborg, Lars; Sonnenborg, Torben; Refsgaard, Jens Christian

2013-04-01

Future climate may have a profound effect on the freshwater cycle, which must be taken into consideration by water management for future planning. Developments in the future climate are nevertheless uncertain, thus adding to the challenge of managing an uncertain system. To support the water managers at various levels in Denmark, the national water resources model (DK-model) (Højberg et al., 2012; Stisen et al., 2012) was used to propagate future climate to hydrological response under considerations of the main sources of uncertainty. The DK-model is a physically based and fully distributed model constructed on the basis of the MIKE SHE/MIKE11 model system describing groundwater and surface water systems and the interaction between the domains. The model has been constructed for the entire 43.000 km2 land area of Denmark only excluding minor islands. Future climate from General Circulation Models (GCM) was downscaled by Regional Climate Models (RCM) by a distribution-based scaling method (Seaby et al., 2012). The same dataset was used to train all combinations of GCM-RCMs and they were found to represent the mean and variance at the seasonal basis equally well. Changes in hydrological response were computed by comparing the short term development from the period 1990 - 2010 to 2021 - 2050, which is the time span relevant for water management. To account for uncertainty in future climate predictions, hydrological response from the DK-model using nine combinations of GCMs and RCMs was analysed for two catchments representing the various hydrogeological conditions in Denmark. Three GCM-RCM combinations displaying high, mean and low future impacts were selected as representative climate models for which climate impact studies were carried out for the entire country. Parameter uncertainty was addressed by sensitivity analysis and was generally found to be of less importance compared to the uncertainty spanned by the GCM-RCM combinations. Analysis of the simulations showed some unexpected results, where climate models predicting the largest increase in net precipitation did not result in the largest increase in groundwater heads. This was found to be the result of different initial conditions (1990 - 2010) for the various climate models. In some areas a combination of a high initial groundwater head and an increase in precipitation towards 2021 - 2050 resulted in a groundwater head raise that reached the drainage or the surface water system. This will increase the exchange from the groundwater to the surface water system, but reduce the raise in groundwater heads. An alternative climate model, with a lower initial head can thus predict a higher increase in the groundwater head, although the increase in precipitation is lower. This illustrates an extra dimension in the uncertainty assessment, namely the climate models capability of simulating the current climatic conditions in a way that can reproduce the observed hydrological response. Højberg, AL, Troldborg, L, Stisen, S, et al. (2012) Stakeholder driven update and improvement of a national water resources model - http://www.sciencedirect.com/science/article/pii/S1364815212002423 Seaby, LP, Refsgaard, JC, Sonnenborg, TO, et al. (2012) Assessment of robustness and significance of climate change signals for an ensemble of distribution-based scaled climate projections (submitted) Journal of Hydrology Stisen, S, Højberg, AL, Troldborg, L et al., (2012): On the importance of appropriate rain-gauge catch correction for hydrological modelling at mid to high latitudes - http://www.hydrol-earth-syst-sci.net/16/4157/2012/
Ecological feedbacks. Termite mounds can increase the robustness of dryland ecosystems to climatic change.

PubMed

Bonachela, Juan A; Pringle, Robert M; Sheffer, Efrat; Coverdale, Tyler C; Guyton, Jennifer A; Caylor, Kelly K; Levin, Simon A; Tarnita, Corina E

2015-02-06

Self-organized spatial vegetation patterning is widespread and has been described using models of scale-dependent feedback between plants and water on homogeneous substrates. As rainfall decreases, these models yield a characteristic sequence of patterns with increasingly sparse vegetation, followed by sudden collapse to desert. Thus, the final, spot-like pattern may provide early warning for such catastrophic shifts. In many arid ecosystems, however, termite nests impart substrate heterogeneity by altering soil properties, thereby enhancing plant growth. We show that termite-induced heterogeneity interacts with scale-dependent feedbacks to produce vegetation patterns at different spatial grains. Although the coarse-grained patterning resembles that created by scale-dependent feedback alone, it does not indicate imminent desertification. Rather, mound-field landscapes are more robust to aridity, suggesting that termites may help stabilize ecosystems under global change. Copyright © 2015, American Association for the Advancement of Science.
On the Representation of Ice Nucleation in Global Climate Models, and its Importance for Simulations of Climate Forcings and Feedbacks

NASA Astrophysics Data System (ADS)

Storelvmo, T.

2015-12-01

Substantial improvements have been made to the cloud microphysical schemes used in the latest generation of global climate models (GCMs), however, an outstanding weakness of these schemes lies in the arbitrariness of their tuning parameters. Despite the growing effort in improving the cloud microphysical schemes in GCMs, most of this effort has not focused on improving the ability of GCMs to accurately simulate phase partitioning in mixed-phase clouds. Getting the relative proportion of liquid droplets and ice crystals in clouds right in GCMs is critical for the representation of cloud radiative forcings and cloud-climate feedbacks. Here, we first present satellite observations of cloud phase obtained by NASA's CALIOP instrument, and report on robust statistical relationships between cloud phase and several aerosols species that have been demonstrated to act as ice nuclei (IN) in laboratory studies. We then report on results from model intercomparison projects that reveal that GCMs generally underestimate the amount of supercooled liquid in clouds. For a selected GCM (NCAR 's CAM5), we thereafter show that the underestimate can be attributed to two main factors: i) the presence of IN in the mixed-phase temperature range, and ii) the Wegener-Bergeron-Findeisen process, which converts liquid to ice once ice crystals have formed. Finally, we show that adjusting these two processes such that the GCM's cloud phase is in agreement with the observed has a substantial impact on the simulated radiative forcing due to IN perturbations, as well as on the cloud-climate feedbacks and ultimately climate sensitivity simulated by the GCM.
Robust, Optimal Water Infrastructure Planning Under Deep Uncertainty Using Metamodels

NASA Astrophysics Data System (ADS)

Maier, H. R.; Beh, E. H. Y.; Zheng, F.; Dandy, G. C.; Kapelan, Z.

2015-12-01

Optimal long-term planning plays an important role in many water infrastructure problems. However, this task is complicated by deep uncertainty about future conditions, such as the impact of population dynamics and climate change. One way to deal with this uncertainty is by means of robustness, which aims to ensure that water infrastructure performs adequately under a range of plausible future conditions. However, as robustness calculations require computationally expensive system models to be run for a large number of scenarios, it is generally computationally intractable to include robustness as an objective in the development of optimal long-term infrastructure plans. In order to overcome this shortcoming, an approach is developed that uses metamodels instead of computationally expensive simulation models in robustness calculations. The approach is demonstrated for the optimal sequencing of water supply augmentation options for the southern portion of the water supply for Adelaide, South Australia. A 100-year planning horizon is subdivided into ten equal decision stages for the purpose of sequencing various water supply augmentation options, including desalination, stormwater harvesting and household rainwater tanks. The objectives include the minimization of average present value of supply augmentation costs, the minimization of average present value of greenhouse gas emissions and the maximization of supply robustness. The uncertain variables are rainfall, per capita water consumption and population. Decision variables are the implementation stages of the different water supply augmentation options. Artificial neural networks are used as metamodels to enable all objectives to be calculated in a computationally efficient manner at each of the decision stages. The results illustrate the importance of identifying optimal staged solutions to ensure robustness and sustainability of water supply into an uncertain long-term future.
A dataset mapping the potential biophysical effects of vegetation cover change

NASA Astrophysics Data System (ADS)

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-02-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes.

A dataset mapping the potential biophysical effects of vegetation cover change

PubMed Central

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-01-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes. PMID:29461538
1,500 year quantitative reconstruction of winter precipitation in the Pacific Northwest

PubMed Central

Steinman, Byron A.; Abbott, Mark B.; Mann, Michael E.; Stansell, Nathan D.; Finney, Bruce P.

2012-01-01

Multiple paleoclimate proxies are required for robust assessment of past hydroclimatic conditions. Currently, estimates of drought variability over the past several thousand years are based largely on tree-ring records. We produced a 1,500-y record of winter precipitation in the Pacific Northwest using a physical model-based analysis of lake sediment oxygen isotope data. Our results indicate that during the Medieval Climate Anomaly (MCA) (900–1300 AD) the Pacific Northwest experienced exceptional wetness in winter and that during the Little Ice Age (LIA) (1450–1850 AD) conditions were drier, contrasting with hydroclimatic anomalies in the desert Southwest and consistent with climate dynamics related to the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). These findings are somewhat discordant with drought records from tree rings, suggesting that differences in seasonal sensitivity between the two proxies allow a more compete understanding of the climate system and likely explain disparities in inferred climate trends over centennial timescales. PMID:22753510
Modeling Hawaiian Ecosystem Degradation due to Invasive Plants under Current and Future Climates

PubMed Central

Vorsino, Adam E.; Fortini, Lucas B.; Amidon, Fred A.; Miller, Stephen E.; Jacobi, James D.; Price, Jonathan P.; Gon, Sam 'Ohukani'ohi'a; Koob, Gregory A.

2014-01-01

Occupation of native ecosystems by invasive plant species alters their structure and/or function. In Hawaii, a subset of introduced plants is regarded as extremely harmful due to competitive ability, ecosystem modification, and biogeochemical habitat degradation. By controlling this subset of highly invasive ecosystem modifiers, conservation managers could significantly reduce native ecosystem degradation. To assess the invasibility of vulnerable native ecosystems, we selected a proxy subset of these invasive plants and developed robust ensemble species distribution models to define their respective potential distributions. The combinations of all species models using both binary and continuous habitat suitability projections resulted in estimates of species richness and diversity that were subsequently used to define an invasibility metric. The invasibility metric was defined from species distribution models with <0.7 niche overlap (Warrens I) and relatively discriminative distributions (Area Under the Curve >0.8; True Skill Statistic >0.75) as evaluated per species. Invasibility was further projected onto a 2100 Hawaii regional climate change scenario to assess the change in potential habitat degradation. The distribution defined by the invasibility metric delineates areas of known and potential invasibility under current climate conditions and, when projected into the future, estimates potential reductions in native ecosystem extent due to climate-driven invasive incursion. We have provided the code used to develop these metrics to facilitate their wider use (Code S1). This work will help determine the vulnerability of native-dominated ecosystems to the combined threats of climate change and invasive species, and thus help prioritize ecosystem and species management actions. PMID:24805254
Modeling Hawaiian ecosystem degradation due to invasive plants under current and future climates.

PubMed

Vorsino, Adam E; Fortini, Lucas B; Amidon, Fred A; Miller, Stephen E; Jacobi, James D; Price, Jonathan P; Gon, Sam 'ohukani'ohi'a; Koob, Gregory A

2014-01-01

Occupation of native ecosystems by invasive plant species alters their structure and/or function. In Hawaii, a subset of introduced plants is regarded as extremely harmful due to competitive ability, ecosystem modification, and biogeochemical habitat degradation. By controlling this subset of highly invasive ecosystem modifiers, conservation managers could significantly reduce native ecosystem degradation. To assess the invasibility of vulnerable native ecosystems, we selected a proxy subset of these invasive plants and developed robust ensemble species distribution models to define their respective potential distributions. The combinations of all species models using both binary and continuous habitat suitability projections resulted in estimates of species richness and diversity that were subsequently used to define an invasibility metric. The invasibility metric was defined from species distribution models with <0.7 niche overlap (Warrens I) and relatively discriminative distributions (Area Under the Curve >0.8; True Skill Statistic >0.75) as evaluated per species. Invasibility was further projected onto a 2100 Hawaii regional climate change scenario to assess the change in potential habitat degradation. The distribution defined by the invasibility metric delineates areas of known and potential invasibility under current climate conditions and, when projected into the future, estimates potential reductions in native ecosystem extent due to climate-driven invasive incursion. We have provided the code used to develop these metrics to facilitate their wider use (Code S1). This work will help determine the vulnerability of native-dominated ecosystems to the combined threats of climate change and invasive species, and thus help prioritize ecosystem and species management actions.
Commensurate comparisons of models with energy budget observations reveal consistent climate sensitivities

NASA Astrophysics Data System (ADS)

Armour, K.

2017-12-01

Global energy budget observations have been widely used to constrain the effective, or instantaneous climate sensitivity (ICS), producing median estimates around 2°C (Otto et al. 2013; Lewis & Curry 2015). A key question is whether the comprehensive climate models used to project future warming are consistent with these energy budget estimates of ICS. Yet, performing such comparisons has proven challenging. Within models, values of ICS robustly vary over time, as surface temperature patterns evolve with transient warming, and are generally smaller than the values of equilibrium climate sensitivity (ECS). Naively comparing values of ECS in CMIP5 models (median of about 3.4°C) to observation-based values of ICS has led to the suggestion that models are overly sensitive. This apparent discrepancy can partially be resolved by (i) comparing observation-based values of ICS to model values of ICS relevant for historical warming (Armour 2017; Proistosescu & Huybers 2017); (ii) taking into account the "efficacies" of non-CO2 radiative forcing agents (Marvel et al. 2015); and (iii) accounting for the sparseness of historical temperature observations and differences in sea-surface temperature and near-surface air temperature over the oceans (Richardson et al. 2016). Another potential source of discrepancy is a mismatch between observed and simulated surface temperature patterns over recent decades, due to either natural variability or model deficiencies in simulating historical warming patterns. The nature of the mismatch is such that simulated patterns can lead to more positive radiative feedbacks (higher ICS) relative to those engendered by observed patterns. The magnitude of this effect has not yet been addressed. Here we outline an approach to perform fully commensurate comparisons of climate models with global energy budget observations that take all of the above effects into account. We find that when apples-to-apples comparisons are made, values of ICS in models are consistently in good agreement with values of ICS inferred from global energy budget constraints. This suggests that the current generation of coupled climate models are not overly sensitive. However, since global energy budget observations do not constrain ECS, it is less certain whether model ECS values are realistic.
Overview of experiment design and comparison of models participating in phase 1 of the SPARC Quasi-Biennial Oscillation initiative (QBOi)

NASA Astrophysics Data System (ADS)

Butchart, Neal; Anstey, James A.; Hamilton, Kevin; Osprey, Scott; McLandress, Charles; Bushell, Andrew C.; Kawatani, Yoshio; Kim, Young-Ha; Lott, Francois; Scinocca, John; Stockdale, Timothy N.; Andrews, Martin; Bellprat, Omar; Braesicke, Peter; Cagnazzo, Chiara; Chen, Chih-Chieh; Chun, Hye-Yeong; Dobrynin, Mikhail; Garcia, Rolando R.; Garcia-Serrano, Javier; Gray, Lesley J.; Holt, Laura; Kerzenmacher, Tobias; Naoe, Hiroaki; Pohlmann, Holger; Richter, Jadwiga H.; Scaife, Adam A.; Schenzinger, Verena; Serva, Federico; Versick, Stefan; Watanabe, Shingo; Yoshida, Kohei; Yukimoto, Seiji

2018-03-01

The Stratosphere-troposphere Processes And their Role in Climate (SPARC) Quasi-Biennial Oscillation initiative (QBOi) aims to improve the fidelity of tropical stratospheric variability in general circulation and Earth system models by conducting coordinated numerical experiments and analysis. In the equatorial stratosphere, the QBO is the most conspicuous mode of variability. Five coordinated experiments have therefore been designed to (i) evaluate and compare the verisimilitude of modelled QBOs under present-day conditions, (ii) identify robustness (or alternatively the spread and uncertainty) in the simulated QBO response to commonly imposed changes in model climate forcings (e.g. a doubling of CO2 amounts), and (iii) examine model dependence of QBO predictability. This paper documents these experiments and the recommended output diagnostics. The rationale behind the experimental design and choice of diagnostics is presented. To facilitate scientific interpretation of the results in other planned QBOi studies, consistent descriptions of the models performing each experiment set are given, with those aspects particularly relevant for simulating the QBO tabulated for easy comparison.
Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO2 concentration on crop productivity

PubMed Central

Yin, Xinyou

2013-01-01

Background Process-based ecophysiological crop models are pivotal in assessing responses of crop productivity and designing strategies of adaptation to climate change. Most existing crop models generally over-estimate the effect of elevated atmospheric [CO2], despite decades of experimental research on crop growth response to [CO2]. Analysis A review of the literature indicates that the quantitative relationships for a number of traits, once expressed as a function of internal plant nitrogen status, are altered little by the elevated [CO2]. A model incorporating these nitrogen-based functional relationships and mechanisms simulated photosynthetic acclimation to elevated [CO2], thereby reducing the chance of over-estimating crop response to [CO2]. Robust crop models to have small parameterization requirements and yet generate phenotypic plasticity under changing environmental conditions need to capture the carbon–nitrogen interactions during crop growth. Conclusions The performance of the improved models depends little on the type of the experimental facilities used to obtain data for parameterization, and allows accurate projections of the impact of elevated [CO2] and other climatic variables on crop productivity. PMID:23388883
Final Report for High Latitude Climate Modeling: ARM Takes Us Beyond Case Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Russell, Lynn M; Lubin, Dan

2013-06-18

The main thrust of this project was to devise a method by which the majority of North Slope of Alaska (NSA) meteorological and radiometric data, collected on a daily basis, could be used to evaluate and improve global climate model (GCM) simulations and their parameterizations, particularly for cloud microphysics. Although the standard ARM Program sensors for a less complete suite of instruments for cloud and aerosol studies than the instruments on an intensive field program such as the 2008 Indirect and Semi-Direct Aerosol Campaign (ISDAC), the advantage they offer lies in the long time base and large volume of datamore » that covers a wide range of meteorological and climatological conditions. The challenge has been devising a method to interpret the NSA data in a practical way, so that a wide variety of meteorological conditions in all seasons can be examined with climate models. If successful, climate modelers would have a robust alternative to the usual “case study” approach (i.e., from intensive field programs only) for testing and evaluating their parameterizations’ performance. Understanding climate change on regional scales requires a broad scientific consideration of anthropogenic influences that goes beyond greenhouse gas emissions to also include aerosol-induced changes in cloud properties. For instance, it is now clear that on small scales, human-induced aerosol plumes can exert microclimatic radiative and hydrologic forcing that rivals that of greenhouse gas–forced warming. This project has made significant scientific progress by investigating what causes successive versions of climate models continue to exhibit errors in cloud amount, cloud microphysical and radiative properties, precipitation, and radiation balance, as compared with observations and, in particular, in Arctic regions. To find out what is going wrong, we have tested the models' cloud representation over the full range of meteorological conditions found in the Arctic using the ARM North Slope of Alaska (NSA) data.« less
Pollen-inferred quantitative reconstructions of Holocene land-cover in NW Europe for the evaluation of past climate-vegetation feedbacks - The Swedish LANDCLIM project and the NordForsk LANDCLIM network

NASA Astrophysics Data System (ADS)

Gaillard, Marie-Jose; Sugita, Shinya; Rundgren, Mats; Smith, Benjamin; Mazier, Florence; Trondman, Anna-Kari; Fyfe, Ralph; Kokfelt, Ulla; Nielsen, Anne-Birgitte; Strandberg, Gustav

2010-05-01

Reliable predictive models are needed to describe potential future climate changes and their impacts. Land surface-atmosphere feedbacks and their impacts on climate are a current priority in the climate modelling community, but reliable records of long-term land use and vegetation change required for model evaluation are limited. Palaeoecological and palaeo-climatic data provide a unique record of the past changes in vegetation, land use and climate on time scales relevant to vegetation processes and global change projections. The application of a new technique (the REVEALS model (Sugita 2007) to landscape reconstruction using fossil pollen data makes robust comparisons with vegetation model output possible . The model corrects for biases caused by e.g. inter-taxonomic differences in pollen productivity and dispersal. Our results show that pollen percentages, a traditional indicator of land cover changes, generally underestimate the unforested areas and certain broad-leaved trees such as Corylus and Tilia, while they often overestimate Betula and Pinus (see Cui et al. BG 6.2). Climate models use simplified land-surface classifications (plant functional types (PFTs)), such as grass (i.e. open land), deciduous trees, and conifers. Therefore, the observed large discrepancies in past land cover between the REVEALS estimates and pollen percentages are expected to influence model outcomes of the Holocene regional climate in NW Europe. The LANDCLIM project and research network (sponsored by the Swedish [VR] and Nordic [NordForsk] Research Councils) aim to quantify human-induced changes in regional vegetation/land-cover in NW Europe during the Holocene, and to evaluate the effects of these changes on the regional climate through altered feedbacks. We use the REVEALS model, theoretically derived and empirically tested, to estimate the percentage cover of taxa and groups of taxa (PFTs) from fossil pollen data for selected time windows of the Holocene, at a spatial resolution of ca. 1o x 1o. The REVEALS estimates of the past cover of PFTs will be 1) compared with the outputs of the LPJ-GUESS (10 PFTs), a widely-used dynamic vegetation model and 2) used as an alternative to the LPJ-GUESS-simulated vegetation (3 PFTs) to run for the past the regional climate model RCA3 developed at the Rossby Centre, Norrköping, Sweden. The study will evaluate and further refine these models (RCA3 and LPJ-GUESS) using a data-model comparison approach that incorporates new syntheses of palaeoclimatic data as well. It will lead to new assessments of the possible effect of various factors on climate, such as deforestations and afforestations, and changes in vegetation composition and spatial patterns of land cover/land use. Refined climate models and empirical land-cover reconstructions will shed new light on controversial hypotheses of past climate change and human impacts, such as the "Ruddiman hypothesis". First maps of REVEALS estimates of plant functional types (PFTs) are now available for Sweden, Norway, Finland, Denmark, Estonia, Poland, Germany, The Czech Republic, Switzerland and Britain (see Mazier et al. C1.21 and Trondman et al. C1.22). Correlation tests show that the REVEALS estimates are robust in terms of ranking of the PFTs' abundance (see Mazier et al, C1.21). The LANDCLIM project and network are a contribution to the IGBP-PAGES-Focus 4 PHAROS programme on human impact on environmental changes in the past. The following LANDCLIM members are acknowledged for providing pollen records, for help with pollen databases, and for providing results to the project: Mihkel Kangur and Tiiu Koff (Univ. Tallinn, Tallinn); Erik Kjellström (SMHI, Norrköping), Anna Broström, Lena Barnekow and Thomas Persson (GeoBiosphere Science Centre, Lund University); Anneli Poska (Physical Geography and Ecosystems Analysis, Lund University); Thomas Giesecke (Albrecht-von-Haller-Institute for Plant Sciences, University of Göttingen), Anne Bjune and John Birks (Dept. of Biology, University of Bergen); Pim van der Knaap (Institute of Plant Sciences, University of Bern); Malgorzata Latalowa (University of Gdansk); Michelle Leydet (IMEP CNRS 6116, University of Marseille III); Teija Alenius (Finnish Geological Survey, Espoo), Heather Almquist-Jacobson (Univ. Montana, USA), Jonas Bergman (Univ. Stockholm), Rixt de Jong (Univ. Bern), Jutta Lechterbeck (Hemmenhofen, Germany), Ann-Marie Robertsson (Univ. Stockholm), Ulf Segerström and Henrik von Stedingk (Univ. Umeå), Heikki Seppä (Univ. Helsinki). Sugita 2007. The Holocene, 17, 229-241.
Improved Uncertainty Quantification in Groundwater Flux Estimation Using GRACE

NASA Astrophysics Data System (ADS)

Reager, J. T., II; Rao, P.; Famiglietti, J. S.; Turmon, M.

2015-12-01

Groundwater change is difficult to monitor over large scales. One of the most successful approaches is in the remote sensing of time-variable gravity using NASA Gravity Recovery and Climate Experiment (GRACE) mission data, and successful case studies have created the opportunity to move towards a global groundwater monitoring framework for the world's largest aquifers. To achieve these estimates, several approximations are applied, including those in GRACE processing corrections, the formulation of the formal GRACE errors, destriping and signal recovery, and the numerical model estimation of snow water, surface water and soil moisture storage states used to isolate a groundwater component. A major weakness in these approaches is inconsistency: different studies have used different sources of primary and ancillary data, and may achieve different results based on alternative choices in these approximations. In this study, we present two cases of groundwater change estimation in California and the Colorado River basin, selected for their good data availability and varied climates. We achieve a robust numerical estimate of post-processing uncertainties resulting from land-surface model structural shortcomings and model resolution errors. Groundwater variations should demonstrate less variability than the overlying soil moisture state does, as groundwater has a longer memory of past events due to buffering by infiltration and drainage rate limits. We apply a model ensemble approach in a Bayesian framework constrained by the assumption of decreasing signal variability with depth in the soil column. We also discuss time variable errors vs. time constant errors, across-scale errors v. across-model errors, and error spectral content (across scales and across model). More robust uncertainty quantification for GRACE-based groundwater estimates would take all of these issues into account, allowing for more fair use in management applications and for better integration of GRACE-based measurements with observations from other sources.
Quantifying the impact of Teleconnections on Hydrologic Regimes in Texas

NASA Astrophysics Data System (ADS)

Bhatia, N.; Singh, V. P.; Srivastav, R. K.

2016-12-01

Climate change is being alleged to have led to the increased frequency of extreme flooding events and the resulting damages are severe, especially where the flood-plain population densities are higher. Much research in the field of hydroclimatology is focusing on improving real-time flood forecasting models. Recent studies show that, in the state of Texas, extreme regional floods are actually triggered by abruptly higher precipitation intensities. Such intensities are further driven by sea-surface temperature and pressure anomalies, defined by certain patterns of teleconnections. In this study, climate variability is defined on the basis of five major Atlantic and Pacific Ocean related teleconnections: (i) Atlantic Multidecadal Oscillation (AMO), (ii) North Atlantic Oscillation (NAO), (iii) Pacific Decadal Oscillation (PDO), (iv) Pacific North American Pattern (PNA), and (v) Southern Oscillation Index (SOI). Hydrologic extremes will be modeled using probabilistic distributions. Leave-One-Out-Test (LOOT) will be employed to address the outliers in the extremes, and to eventually obtain the robust correlation coefficient. The variation in the effect of most correlated teleconnection with respect to hydrologic attributes will be investigated for the entire state. This study will attempt to identify potential teleconnection inputs for data-driven hydrologic models under varying climatic conditions.
Statistical prediction of September Arctic Sea Ice minimum based on stable teleconnections with global climate and oceanic patterns

NASA Astrophysics Data System (ADS)

Ionita, M.; Grosfeld, K.; Scholz, P.; Lohmann, G.

2016-12-01

Sea ice in both Polar Regions is an important indicator for the expression of global climate change and its polar amplification. Consequently, a broad information interest exists on sea ice, its coverage, variability and long term change. Knowledge on sea ice requires high quality data on ice extent, thickness and its dynamics. However, its predictability depends on various climate parameters and conditions. In order to provide insights into the potential development of a monthly/seasonal signal, we developed a robust statistical model based on ocean heat content, sea surface temperature and atmospheric variables to calculate an estimate of the September minimum sea ice extent for every year. Although previous statistical attempts at monthly/seasonal forecasts of September sea ice minimum show a relatively reduced skill, here it is shown that more than 97% (r = 0.98) of the September sea ice extent can predicted three months in advance by using previous months conditions via a multiple linear regression model based on global sea surface temperature (SST), mean sea level pressure (SLP), air temperature at 850hPa (TT850), surface winds and sea ice extent persistence. The statistical model is based on the identification of regions with stable teleconnections between the predictors (climatological parameters) and the predictand (here sea ice extent). The results based on our statistical model contribute to the sea ice prediction network for the sea ice outlook report (https://www.arcus.org/sipn) and could provide a tool for identifying relevant regions and climate parameters that are important for the sea ice development in the Arctic and for detecting sensitive and critical regions in global coupled climate models with focus on sea ice formation.
Understanding the Influence of Climate Forecasts on Farmer Decisions as Planned Behavior

NASA Astrophysics Data System (ADS)

Artikov, Ikrom; Hoffman, Stacey J.; Lynne, Gary D.; Pytlik Zillig, Lisa M.; Hu, Qi; Tomkins, Alan J.; Hubbard, Kenneth G.; Hayes, Michael J.; Waltman, William

2006-09-01

Results of a set of four regression models applied to recent survey data of farmers in eastern Nebraska suggest the causes that drive farmer intentions of using weather and climate information and forecasts in farming decisions. The model results quantify the relative importance of attitude, social norm, perceived behavioral control, and financial capability in explaining the influence of climate-conditions information and short-term and long-term forecasts on agronomic, crop insurance, and crop marketing decisions. Attitude, serving as a proxy for the utility gained from the use of such information, had the most profound positive influence on the outcome of all the decisions, followed by norms. The norms in the community, as a proxy for the utility gained from allowing oneself to be influenced by others, played a larger role in agronomic decisions than in insurance or marketing decisions. In addition, the interaction of controllability (accuracy, availability, reliability, timeliness of weather and climate information), self-efficacy (farmer ability and understanding), and general preference for control was shown to be a substantive cause. Yet control variables also have an economic side: The farm-sales variable as a measure of financial ability and motivation intensified and clarified the role of control while also enhancing the statistical robustness of the attitude and norms variables in better clarifying how they drive the influence. Overall, the integrated model of planned behavior from social psychology and derived demand from economics, that is, the “planned demand model,” is more powerful than models based on either of these approaches alone. Taken together, these results suggest that the “human dimension” needs to be better recognized so as to improve effective use of climate and weather forecasts and information for farming decision making.
Assessing Extratropical Influence on Tropical Climatology and Variability with Regional Coupled Data Assimilation

NASA Astrophysics Data System (ADS)

Lu, F.; Liu, Z.; Liu, Y.; Zhang, S.; Jacob, R. L.

2017-12-01

The Regional Coupled Data Assimilation (RCDA) method is introduced as a tool to study coupled climate dynamics and teleconnections. The RCDA method is built on an ensemble-based coupled data assimilation (CDA) system in a coupled general circulation model (CGCM). The RCDA method limits the data assimilation to the desired model components (e.g. atmosphere) and regions (e.g. the extratropics), and studies the ensemble-mean model response (e.g. tropical response to "observed" extratropical atmospheric variability). When applied to the extratropical influence on tropical climate, the RCDA method has shown some unique advantages, namely the combination of a fully coupled model, real-world observations and an ensemble approach. Tropical variability (e.g. El Niño-Southern Oscillation or ENSO) and climatology (e.g. asymmetric Inter-Tropical Convergence Zone or ITCZ) were initially thought to be determined mostly by local forcing and ocean-atmosphere interaction in the tropics. Since late 20th century, numerous studies have showed that extratropical forcing could affect, or even largely determine some aspects of the tropical climate. Due to the coupled nature of the climate system, however, the challenge of determining and further quantifying the causality of extratropical forcing on the tropical climate remains. Using the RCDA method, we have demonstrated significant control of extratropical atmospheric forcing on ENSO variability in a CGCM, both with model-generated and real-world observation datasets. The RCDA method has also shown robust extratropical impact on the tropical double-ITCZ bias in a CGCM. The RCDA method has provided the first systematic and quantitative assessment of extratropical influence on tropical climatology and variability by incorporating real world observations in a CGCM.
September Arctic Sea Ice minimum prediction - a new skillful statistical approach

NASA Astrophysics Data System (ADS)

Ionita-Scholz, Monica; Grosfeld, Klaus; Scholz, Patrick; Treffeisen, Renate; Lohmann, Gerrit

2017-04-01

Sea ice in both Polar Regions is an important indicator for the expression of global climate change and its polar amplification. Consequently, a broad interest exists on sea ice, its coverage, variability and long term change. Knowledge on sea ice requires high quality data on ice extent, thickness and its dynamics. However, its predictability is complex and it depends on various climate and oceanic parameters and conditions. In order to provide insights into the potential development of a monthly/seasonal signal of sea ice evolution, we developed a robust statistical model based on ocean heat content, sea surface temperature and different atmospheric variables to calculate an estimate of the September Sea ice extent (SSIE) on monthly time scale. Although previous statistical attempts at monthly/seasonal forecasts of SSIE show a relatively reduced skill, we show here that more than 92% (r = 0.96) of the September sea ice extent can be predicted at the end of May by using previous months' climate and oceanic conditions. The skill of the model increases with a decrease in the time lag used for the forecast. At the end of August, our predictions are even able to explain 99% of the SSIE. Our statistical model captures both the general trend as well as the interannual variability of the SSIE. Moreover, it is able to properly forecast the years with extreme high/low SSIE (e.g. 1996/ 2007, 2012, 2013). Besides its forecast skill for SSIE, the model could provide a valuable tool for identifying relevant regions and climate parameters that are important for the sea ice development in the Arctic and for detecting sensitive and critical regions in global coupled climate models with focus on sea ice formation.
Quantifying the increasing sensitivity of power systems to climate variability

NASA Astrophysics Data System (ADS)

Bloomfield, H. C.; Brayshaw, D. J.; Shaffrey, L. C.; Coker, P. J.; Thornton, H. E.

2016-12-01

Large quantities of weather-dependent renewable energy generation are expected in power systems under climate change mitigation policies, yet little attention has been given to the impact of long term climate variability. By combining state-of-the-art multi-decadal meteorological records with a parsimonious representation of a power system, this study characterises the impact of year-to-year climate variability on multiple aspects of the power system of Great Britain (including coal, gas and nuclear generation), demonstrating why multi-decadal approaches are necessary. All aspects of the example system are impacted by inter-annual climate variability, with the impacts being most pronounced for baseload generation. The impacts of inter-annual climate variability increase in a 2025 wind-power scenario, with a 4-fold increase in the inter-annual range of operating hours for baseload such as nuclear. The impacts on peak load and peaking-plant are comparably small. Less than 10 years of power supply and demand data are shown to be insufficient for providing robust power system planning guidance. This suggests renewable integration studies—widely used in policy, investment and system design—should adopt a more robust approach to climate characterisation.
Climate variability and vulnerability to climate change: a review

PubMed Central

Thornton, Philip K; Ericksen, Polly J; Herrero, Mario; Challinor, Andrew J

2014-01-01

The focus of the great majority of climate change impact studies is on changes in mean climate. In terms of climate model output, these changes are more robust than changes in climate variability. By concentrating on changes in climate means, the full impacts of climate change on biological and human systems are probably being seriously underestimated. Here, we briefly review the possible impacts of changes in climate variability and the frequency of extreme events on biological and food systems, with a focus on the developing world. We present new analysis that tentatively links increases in climate variability with increasing food insecurity in the future. We consider the ways in which people deal with climate variability and extremes and how they may adapt in the future. Key knowledge and data gaps are highlighted. These include the timing and interactions of different climatic stresses on plant growth and development, particularly at higher temperatures, and the impacts on crops, livestock and farming systems of changes in climate variability and extreme events on pest-weed-disease complexes. We highlight the need to reframe research questions in such a way that they can provide decision makers throughout the food system with actionable answers, and the need for investment in climate and environmental monitoring. Improved understanding of the full range of impacts of climate change on biological and food systems is a critical step in being able to address effectively the effects of climate variability and extreme events on human vulnerability and food security, particularly in agriculturally based developing countries facing the challenge of having to feed rapidly growing populations in the coming decades. PMID:24668802
Forecasted Impact of Climate Change on Infectious Disease and Health Security in Hawaii by 2050.

PubMed

Canyon, Deon V; Speare, Rick; Burkle, Frederick M

2016-12-01

Climate change is expected to cause extensive shifts in the epidemiology of infectious and vector-borne diseases. Scenarios on the effects of climate change typically attribute altered distribution of communicable diseases to a rise in average temperature and altered incidence of infectious diseases to weather extremes. Recent evaluations of the effects of climate change on Hawaii have not explored this link. It may be expected that Hawaii's natural geography and robust water, sanitation, and health care infrastructure renders residents less vulnerable to many threats that are the focus on smaller, lesser developed, and more vulnerable Pacific islands. In addition, Hawaii's communicable disease surveillance and response system can act rapidly to counter increases in any disease above baseline and to redirect resources to deal with changes, particularly outbreaks due to exotic pathogens. The evidence base examined in this article consistently revealed very low climate sensitivity with respect to infectious and mosquito-borne diseases. A community resilience model is recommended to increase adaptive capacity for all possible climate change impacts rather an approach that focuses specifically on communicable diseases. (Disaster Med Public Health Preparedness. 2016;10:797-804).
A framework for the cross-sectoral integration of multi-model impact projections: land use decisions under climate impacts uncertainties

NASA Astrophysics Data System (ADS)

Frieler, K.; Levermann, A.; Elliott, J.; Heinke, J.; Arneth, A.; Bierkens, M. F. P.; Ciais, P.; Clark, D. B.; Deryng, D.; Döll, P.; Falloon, P.; Fekete, B.; Folberth, C.; Friend, A. D.; Gellhorn, C.; Gosling, S. N.; Haddeland, I.; Khabarov, N.; Lomas, M.; Masaki, Y.; Nishina, K.; Neumann, K.; Oki, T.; Pavlick, R.; Ruane, A. C.; Schmid, E.; Schmitz, C.; Stacke, T.; Stehfest, E.; Tang, Q.; Wisser, D.; Huber, V.; Piontek, F.; Warszawski, L.; Schewe, J.; Lotze-Campen, H.; Schellnhuber, H. J.

2015-07-01

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.
Hadley circulation extent and strength in a wide range of simulated climates

NASA Astrophysics Data System (ADS)

D'Agostino, Roberta; Adam, Ori; Lionello, Piero; Schneider, Tapio

2017-04-01

Understanding the Hadley circulation (HC) dynamics is crucial because its changes affect the seasonal migration of the ITCZ, the extent of subtropical arid regions and the strength of the monsoons. Despite decades of study, the factors controlling its strength and extent have remained unclear. Here we analyse how HC strength and extent change over a wide range of climate conditions from the Last Glacial Maximum to future projections. The large climate change between paleoclimate simulations and future scenarios offers the chance to analyse robust HC changes and their link to large-scale factors. The HC shrinks and strengthens in the coldest simulation relative to the warmest. A progressive poleward shift of its edges is evident as the climate warms (at a rate of 0.35°lat./K in each hemisphere). The HC extent and strength both depend on the isentropic slope, which in turn is related to the meridional temperature gradient, subtropical static stability and tropopause height. In multiple robust regression analysis using these as predictors, we find that the tropical tropopause height does not add relevant information to the model beyond surface temperature. Therefore, primarily the static stability and secondarily the meridional temperature contrast together account for the bulk of the almost the total HC variance. However, the regressions leave some of the northern HC edge and southern HC strength variance unexplained. The effectiveness of this analysis is limited by the correlation among the predictors and their relationship with mean temperature. In fact, for all simulations, the tropical temperature explains well the variations of HC except its southern hemisphere intensity. Hence, it can be used as the sole predictor to diagnose the HC response to greenhouse-induced global warming. How to account for the evolution of the southern HC strength remains unclear, because of the large inter-model spread in this quantity.

New mechanism under International Flood Initiative toward robustness for flood management in the Asia Pacific region

NASA Astrophysics Data System (ADS)

Murase, M.; Yoshitani, J.; Takeuchi, K.; Koike, T.

2015-12-01

Climate change is likely to result in increases in the frequency or intensity of extreme weather events. It is imperative that a good understanding is developed of how climate change affects the events that are reflected in hydrological extremes such as floods and how practitioners in water resources management deal with them. Since there is still major uncertainty as to how the impact of climate change affect actual water resources management, it is important to build robustness into management schemes and communities. Flood management under such variety of uncertainty favors the flexible and adaptive implementation both in top-down and bottom-up approaches. The former uses projections of global or spatially downscaled models to drive resource models and project resource impacts. The latter utilizes policy or planning tools to identify what changes in climate would be most threatening to their long-range operations. Especially for the bottom-up approaches, it is essential to identify the gap between what should be done and what has not been achieved for disaster risks. Indicators or index are appropriate tools to measure such gaps, but they are still in progress to cover the whole world. The International Flood Initiative (IFI), initiated in January 2005 by UNESCO and WMO in close cooperation with UNU and ISDR, IAHS and IAHR, has promoted an integrated approach to flood management to take advantage of floods and use of flood plains while reducing the social, environmental and economic risks. Its secretariat is located in ICHARM. The initiative objective is to support national platforms to practice evidence-based disaster risk reduction through mobilizing scientific and research networks at national, regional and international levels. The initiative is now preparing for a new mechanism to facilitate the integrated approach for flood management on the ground regionally in the Asia Pacific (IFI-AP) through monitoring, assessment and capacity building.
Forecasting wildlife response to rapid warming in the Alaskan Arctic

USGS Publications Warehouse

Van Hemert, Caroline R.; Flint, Paul L.; Udevitz, Mark S.; Koch, Joshua C.; Atwood, Todd C.; Oakley, Karen L.; Pearce, John M.

2015-01-01

Arctic wildlife species face a dynamic and increasingly novel environment because of climate warming and the associated increase in human activity. Both marine and terrestrial environments are undergoing rapid environmental shifts, including loss of sea ice, permafrost degradation, and altered biogeochemical fluxes. Forecasting wildlife responses to climate change can facilitate proactive decisions that balance stewardship with resource development. In this article, we discuss the primary and secondary responses to physical climate-related drivers in the Arctic, associated wildlife responses, and additional sources of complexity in forecasting wildlife population outcomes. Although the effects of warming on wildlife populations are becoming increasingly well documented in the scientific literature, clear mechanistic links are often difficult to establish. An integrated science approach and robust modeling tools are necessary to make predictions and determine resiliency to change. We provide a conceptual framework and introduce examples relevant for developing wildlife forecasts useful to management decisions.
Aerial Observation Needs Workshop, May 13-14, 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nasiri, Shaima; Serbin, Shawn; Lesmes, David

2015-10-01

The mission of the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER) within the U.S. Department of Energy's (DOE) Office of Science is "to advance a robust, predictive understanding of Earth's climate and environmental systems and to inform the development of sustainable solutions to the nation's energy and environmental challenges." Accomplishing this mission requires aerial observations of the atmospheric and terrestrial components of the climate system. CESD is assessing its current and future aerial observation needs to develop a strategy and roadmap of capability requirements for the next decade. To facilitate this process,more » a workshop was convened that consisted of invited experts in the atmospheric and terrestrial sciences, airborne observations, and modeling. This workshop report summarizes the community input prior to and during the workshop on research challenges and opportunities, as well as specific science questions and observational needs that require aerial observations to address.« less
DOE Office of Scientific and Technical Information (OSTI.GOV)

Bony, Sandrine; Stevens, Bjorn; Coppin, David

General circulation models show that as the surface temperature increases, the convective anvil clouds shrink. By analyzing radiative–convective equilibrium simulations, our work shows that this behavior is rooted in basic energetic and thermodynamic properties of the atmosphere: As the climate warms, the clouds rise and remain at nearly the same temperature, but find themselves in a more stable atmosphere; this enhanced stability reduces the convective outflow in the upper troposphere and decreases the anvil cloud fraction. By warming the troposphere and increasing the upper-tropospheric stability, the clustering of deep convection also reduces the convective outflow and the anvil cloud fraction.more » When clouds are radiatively active, this robust coupling between temperature, high clouds, and circulation exerts a positive feedback on convective aggregation and favors the maintenance of strongly aggregated atmospheric states at high temperatures. This stability iris mechanism likely contributes to the narrowing of rainy areas as the climate warms. Whether or not it influences climate sensitivity requires further investigation.« less
Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network

PubMed Central

Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui

2012-01-01

This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production. PMID:22778587
Multi-locus phylogeny and divergence time estimates of Enallagma damselflies (Odonata: Coenagrionidae).

PubMed

Callahan, Melissa S; McPeek, Mark A

2016-01-01

Reconstructing evolutionary patterns of species and populations provides a framework for asking questions about the impacts of climate change. Here we use a multilocus dataset to estimate gene trees under maximum likelihood and Bayesian models to obtain a robust estimate of relationships for a genus of North American damselflies, Enallagma. Using a relaxed molecular clock, we estimate the divergence times for this group. Furthermore, to account for the fact that gene tree analyses can overestimate ages of population divergences, we use a multi-population coalescent model to gain a more accurate estimate of divergence times. We also infer diversification rates using a method that allows for variation in diversification rate through time and among lineages. Our results reveal a complex evolutionary history of Enallagma, in which divergence events both predate and occur during Pleistocene climate fluctuations. There is also evidence of diversification rate heterogeneity across the tree. These divergence time estimates provide a foundation for addressing the relative significance of historical climatic events in the diversification of this genus. Copyright © 2015 Elsevier Inc. All rights reserved.
Inconvenient Truth or Convenient Fiction? Probable Maximum Precipitation and Nonstationarity

NASA Astrophysics Data System (ADS)

Nielsen-Gammon, J. W.

2017-12-01

According to the inconvenient truth that Probable Maximum Precipitation (PMP) represents a non-deterministic, statistically very rare event, future changes in PMP involve a complex interplay between future frequencies of storm type, storm morphology, and environmental characteristics, many of which are poorly constrained by global climate models. On the other hand, according to the convenient fiction that PMP represents an estimate of the maximum possible precipitation that can occur at a given location, as determined by storm maximization and transposition, the primary climatic driver of PMP change is simply a change in maximum moisture availability. Increases in boundary-layer and total-column moisture have been observed globally, are anticipated from basic physical principles, and are robustly projected to continue by global climate models. Thus, using the same techniques that are used within the PMP storm maximization process itself, future PMP values may be projected. The resulting PMP trend projections are qualitatively consistent with observed trends of extreme rainfall within Texas, suggesting that in this part of the world the inconvenient truth is congruent with the convenient fiction.
Development of fuzzy multi-criteria approach to prioritize locations of treated wastewater use considering climate change scenarios.

PubMed

Chung, Eun-Sung; Kim, Yeonjoo

2014-12-15

This study proposed a robust prioritization framework to identify the priorities of treated wastewater (TWW) use locations with consideration of various uncertainties inherent in the climate change scenarios and the decision-making process. First, a fuzzy concept was applied because future forecast precipitation and their hydrological impact analysis results displayed significant variances when considering various climate change scenarios and long periods (e.g., 2010-2099). Second, various multi-criteria decision making (MCDM) techniques including weighted sum method (WSM), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and fuzzy TOPSIS were introduced to robust prioritization because different MCDM methods use different decision philosophies. Third, decision making method under complete uncertainty (DMCU) including maximin, maximax, minimax regret, Hurwicz, and equal likelihood were used to find robust final rankings. This framework is then applied to a Korean urban watershed. As a result, different rankings were obviously appeared between fuzzy TOPSIS and non-fuzzy MCDMs (e.g., WSM and TOPSIS) because the inter-annual variability in effectiveness was considered only with fuzzy TOPSIS. Then, robust prioritizations were derived based on 18 rankings from nine decadal periods of RCP4.5 and RCP8.5. For more robust rankings, five DMCU approaches using the rankings from fuzzy TOPSIS were derived. This framework combining fuzzy TOPSIS with DMCU approaches can be rendered less controversial among stakeholders under complete uncertainty of changing environments. Copyright © 2014 Elsevier Ltd. All rights reserved.
Evaluation of a new CNRM-CM6 model version for seasonal climate predictions

NASA Astrophysics Data System (ADS)

Volpi, Danila; Ardilouze, Constantin; Batté, Lauriane; Dorel, Laurant; Guérémy, Jean-François; Déqué, Michel

2017-04-01

This work presents the quality assessment of a new version of the Météo-France coupled climate prediction system, which has been developed in the EU COPERNICUS Climate Change Services framework to carry out seasonal forecast. The system is based on the CNRM-CM6 model, with Arpege-Surfex 6.2.2 as atmosphere/land component and Nemo 3.2 as ocean component, which has directly embedded the sea-ice component Gelato 6.0. In order to have a robust diagnostic, the experiment is composed by 60 ensemble members generated with stochastic dynamic perturbations. The experiment has been performed over a 37-year re-forecast period from 1979 to 2015, with two start dates per year, respectively in May 1st and November 1st. The evaluation of the predictive skill of the model is shown under two perspectives: on the one hand, the ability of the model to faithfully respond to positive or negative ENSO, NAO and QBO events, independently of the predictability of these events. Such assessment is carried out through a composite analysis, and shows that the model succeeds in reproducing the main patterns for 2-meter temperature, precipitation and geopotential height at 500 hPa during the winter season. On the other hand, the model predictive skill of the same events (positive and negative ENSO, NAO and QBO) is evaluated.
Decline and poleward shift in Indian summer monsoon synoptic activity in a warming climate.

PubMed

Sandeep, S; Ajayamohan, R S; Boos, William R; Sabin, T P; Praveen, V

2018-03-13

Cyclonic atmospheric vortices of varying intensity, collectively known as low-pressure systems (LPS), travel northwest across central India and produce more than half of the precipitation received by that fertile region and its ∼600 million inhabitants. Yet, future changes in LPS activity are poorly understood, due in part to inadequate representation of these storms in current climate models. Using a high-resolution atmospheric general circulation model that realistically simulates the genesis distribution of LPS, here we show that Indian monsoon LPS activity declines about 45% by the late 21st century in simulations of a business-as-usual emission scenario. The distribution of LPS genesis shifts poleward as it weakens, with oceanic genesis decreasing by ∼60% and continental genesis increasing by ∼10%; over land the increase in storm counts is accompanied by a shift toward lower storm wind speeds. The weakening and poleward shift of the genesis distribution in a warmer climate are confirmed and attributed, via a statistical model, to the reduction and poleward shift of low-level absolute vorticity over the monsoon region, which in turn are robust features of most coupled model projections. The poleward shift in LPS activity results in an increased frequency of extreme precipitation events over northern India. Copyright © 2018 the Author(s). Published by PNAS.
Decline and poleward shift in Indian summer monsoon synoptic activity in a warming climate

PubMed Central

Boos, William R.; Sabin, T. P.; Praveen, V.

2018-01-01

Cyclonic atmospheric vortices of varying intensity, collectively known as low-pressure systems (LPS), travel northwest across central India and produce more than half of the precipitation received by that fertile region and its ∼600 million inhabitants. Yet, future changes in LPS activity are poorly understood, due in part to inadequate representation of these storms in current climate models. Using a high-resolution atmospheric general circulation model that realistically simulates the genesis distribution of LPS, here we show that Indian monsoon LPS activity declines about 45% by the late 21st century in simulations of a business-as-usual emission scenario. The distribution of LPS genesis shifts poleward as it weakens, with oceanic genesis decreasing by ∼60% and continental genesis increasing by ∼10%; over land the increase in storm counts is accompanied by a shift toward lower storm wind speeds. The weakening and poleward shift of the genesis distribution in a warmer climate are confirmed and attributed, via a statistical model, to the reduction and poleward shift of low-level absolute vorticity over the monsoon region, which in turn are robust features of most coupled model projections. The poleward shift in LPS activity results in an increased frequency of extreme precipitation events over northern India. PMID:29483270
Future Changes to ENSO Temperature and Precipitation Teleconnections Under Warming

NASA Astrophysics Data System (ADS)

Perry, S.; McGregor, S.; Sen Gupta, A.; England, M. H.

2016-12-01

As the dominant mode of interannual climate variability, the El Niño-Southern Oscillation (ENSO) modulates temperature and rainfall globally, additionally contributing to weather extremes. Anthropogenic climate change has the potential to alter the strength and frequency of ENSO and may also alter ENSO-driven atmospheric teleconnections, affecting ecosystems and human activity in regions far removed from the tropical Pacific. State-of-art climate models exhibit considerable disagreement in projections of future changes in ENSO sea surface temperature variability. Despite this uncertainty, recent model studies suggest that the precipitation response to ENSO will be enhanced in the tropical Pacific under future warming, and as such the societal impacts of ENSO will increase. Here we use temperature and precipitation data from an ensemble of 41 CMIP5 models to show where ENSO teleconnections are being enhanced and dampened in a high-emission future scenario (RCP8.5) focusing on the changes that are occurring over land areas globally. Although there is some spread between the model projections, robust changes with strong ensemble agreement are found in certain locations, including amplification of teleconnections in southeast Australia, South America and the Maritime Continent. Our results suggest that in these regions future ENSO events will lead to more extreme temperature and rainfall responses.
Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown

NASA Astrophysics Data System (ADS)

Smith, Doug M.; Booth, Ben B. B.; Dunstone, Nick J.; Eade, Rosie; Hermanson, Leon; Jones, Gareth S.; Scaife, Adam A.; Sheen, Katy L.; Thompson, Vikki

2016-10-01

The rate of global mean surface temperature (GMST) warming has slowed this century despite the increasing concentrations of greenhouse gases. Climate model experiments show that this slowdown was largely driven by a negative phase of the Pacific Decadal Oscillation (PDO), with a smaller external contribution from solar variability, and volcanic and anthropogenic aerosols. The prevailing view is that this negative PDO occurred through internal variability. However, here we show that coupled models from the Fifth Coupled Model Intercomparison Project robustly simulate a negative PDO in response to anthropogenic aerosols implying a potentially important role for external human influences. The recovery from the eruption of Mount Pinatubo in 1991 also contributed to the slowdown in GMST trends. Our results suggest that a slowdown in GMST trends could have been predicted in advance, and that future reduction of anthropogenic aerosol emissions, particularly from China, would promote a positive PDO and increased GMST trends over the coming years. Furthermore, the overestimation of the magnitude of recent warming by models is substantially reduced by using detection and attribution analysis to rescale their response to external factors, especially cooling following volcanic eruptions. Improved understanding of external influences on climate is therefore crucial to constrain near-term climate predictions.
Decline and poleward shift in Indian summer monsoon synoptic activity in a warming climate

NASA Astrophysics Data System (ADS)

Sandeep, S.; Ajayamohan, R. S.; Boos, William R.; Sabin, T. P.; Praveen, V.

2018-03-01

Cyclonic atmospheric vortices of varying intensity, collectively known as low-pressure systems (LPS), travel northwest across central India and produce more than half of the precipitation received by that fertile region and its ˜600 million inhabitants. Yet, future changes in LPS activity are poorly understood, due in part to inadequate representation of these storms in current climate models. Using a high-resolution atmospheric general circulation model that realistically simulates the genesis distribution of LPS, here we show that Indian monsoon LPS activity declines about 45% by the late 21st century in simulations of a business-as-usual emission scenario. The distribution of LPS genesis shifts poleward as it weakens, with oceanic genesis decreasing by ˜60% and continental genesis increasing by ˜10%; over land the increase in storm counts is accompanied by a shift toward lower storm wind speeds. The weakening and poleward shift of the genesis distribution in a warmer climate are confirmed and attributed, via a statistical model, to the reduction and poleward shift of low-level absolute vorticity over the monsoon region, which in turn are robust features of most coupled model projections. The poleward shift in LPS activity results in an increased frequency of extreme precipitation events over northern India.
An observationally centred method to quantify local climate change as a distribution

NASA Astrophysics Data System (ADS)

Stainforth, David; Chapman, Sandra; Watkins, Nicholas

2013-04-01

For planning and adaptation, guidance on trends in local climate is needed at the specific thresholds relevant to particular impact or policy endeavours. This requires quantifying trends at specific quantiles in distributions of variables such as daily temperature or precipitation. These non-normal distributions vary both geographically and in time. The trends in the relevant quantiles may not simply follow the trend in the distribution mean. We present a method[1] for analysing local climatic timeseries data to assess which quantiles of the local climatic distribution show the greatest and most robust trends. We demonstrate this approach using E-OBS gridded data[2] timeseries of local daily temperature from specific locations across Europe over the last 60 years. Our method extracts the changing cumulative distribution function over time and uses a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. This deconstruction facilitates an assessment of the sensitivity of different quantiles of the distributions to changing climate. Geographical location and temperature are treated as independent variables, we thus obtain as outputs how the trend or sensitivity varies with temperature (or occurrence likelihood), and with geographical location. These sensitivities are found to be geographically varying across Europe; as one would expect given the different influences on local climate between, say, Western Scotland and central Italy. We find as an output many regionally consistent patterns of response of potential value in adaptation planning. We discuss methods to quantify the robustness of these observed sensitivities and their statistical likelihood. This also quantifies the level of detail needed from climate models if they are to be used as tools to assess climate change impact. [1] S C Chapman, D A Stainforth, N W Watkins, 2013, On Estimating Local Long Term Climate Trends, Phil. Trans. R. Soc. A, in press [2] Haylock, M.R., N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones and M. New. 2008: A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res (Atmospheres), 113, D20119, doi:10.1029/2008JD10201
Undergraduate Students as Climate Communicators

NASA Astrophysics Data System (ADS)

Sharif, H. O.; Joseph, J.; Mullendore, G. L.

2012-12-01

The University of Texas at San Antonio (UTSA), San Antonio College (SAC), and the University of North Dakota (UND) are partnering with NASA to provide underrepresented undergraduates from UTSA, SAC, and other community colleges climate-related research and education experiences. The program aims to develop a robust response to climate change by providing K-16 climate change education; enhance the effectiveness of K-16 education particularly in engineering and other STEM disciplines by use of new instructional technologies; increase the enrollment in engineering programs and the number of engineering degrees awarded by showing engineering's usefulness in relation to the much-discussed contemporary issue of climate change; increase persistence in STEM degrees by providing student research opportunities; and increase the ethnic diversity of those receiving engineering degrees and help ensure an ethnically diverse response to climate change. Students will have the opportunity to participate in guided research experiences aligned with NASA Science Plan objectives for climate and Earth system science and the educational objectives of the three institutions. An integral part of the learning process will include training in modern media technology (webcasts), and in using this technology to communicate the information on climate change to others, especially high school students, culminating in production of a webcast about investigating aspects of climate change using NASA data. Content developed is leveraged by NASA Earth observation data and NASA Earth system models and tools. Several departments are involved in the educational program.
Water Stress on U.S. Power Production at Decadal Time Horizons

NASA Astrophysics Data System (ADS)

Ganguli, P.; Kumar, D.; Yun, J.; Short, G.; Klausner, J.; Ganguly, A. R.

2014-12-01

Thermoelectric power production at risk, owing to current and projected water scarcity and rising stream temperatures, is assessed for the continental United States (US) at decadal scales. Regional water scarcity is driven by climate variability and change, as well as by multi-sector water demand. While a planning horizon of zero to about thirty years is occasionally prescribed by stakeholders, the challenges to risk assessment at these scales include the difficulty in delineating decadal climate trends from intrinsic natural or multiple model variability. Current generation global climate or earth system models are not credible at the spatial resolutions of power plants, especially for surface water quantity and stream temperatures, which further exacerbates the assessment challenge. Population changes, which are anyway difficult to project, cannot serve as adequate proxies for changes in the water demand across sectors. The hypothesis that robust assessments of power production at risks are possible, despite the uncertainties, has been examined as a proof of concept. An approach is presented for delineating water scarcity and temperature from climate models, observations and population storylines, as well as for assessing power production at risk by examining geospatial correlations of power plant locations within regions where the usable water supply for energy production happens to be scarcer and warmer. Acknowledgment: Funding provided by US DOE's ARPA-E through Award DE-AR0000374.
Can small island mountains provide relief from the Subtropical Precipitation Decline? Simulating future precipitation regimes for small island nations using high resolution Regional Climate Models.

NASA Astrophysics Data System (ADS)

Bowden, J.; Terando, A. J.; Misra, V.; Wootten, A.

2017-12-01

Small island nations are vulnerable to changes in the hydrologic cycle because of their limited water resources. This risk to water security is likely even higher in sub-tropical regions where anthropogenic forcing of the climate system is expected to lead to a drier future (the so-called `dry-get-drier' pattern). However, high-resolution numerical modeling experiments have also shown an enhancement of existing orographically-influenced precipitation patterns on islands with steep topography, potentially mitigating subtropical drying on windward mountain sides. Here we explore the robustness of the near-term (25-45 years) subtropical precipitation decline (SPD) across two island groupings in the Caribbean, Puerto Rico and the U.S. Virgin Islands. These islands, forming the boundary between the Greater and Lesser Antilles, significantly differ in size, topographic relief, and orientation to prevailing winds. Two 2-km horizontal resolution regional climate model simulations are used to downscale a total of three different GCMs under the RCP8.5 emissions scenario. Results indicate some possibility for modest increases in precipitation at the leading edge of the Luquillo Mountains in Puerto Rico, but consistent declines elsewhere. We conclude with a discussion of potential explanations for these patterns and the attendant risks to water security that subtropical small island nations could face as the climate warms.
Persistent cold air outbreaks over North America in a warming climate

DOE PAGES

Gao, Yang; Leung, L. Ruby; Lu, Jian; ...

2015-03-30

This study examines future changes of cold air outbreaks (CAO) using a multi-model ensemble of global climate simulations from the Coupled Model Intercomparison Project Phase 5 as well as regional high resolution climate simulations. In the future, while robust decrease of CAO duration dominates in most regions, the magnitude of decrease over northwestern U.S. is much smaller than the surrounding regions. We identified statistically significant increases in sea level pressure during CAO events centering over Yukon, Alaska, and Gulf of Alaska that advects continental cold air to northwestern U.S., leading to blocking and CAO events. Changes in large scale circulationmore » contribute to about 50% of the enhanced sea level pressure anomaly conducive to CAO in northwestern U.S. in the future. High resolution regional simulations revealed potential contributions of increased existing snowpack to increased CAO in the near future over the Rocky Mountain, southwestern U.S., and Great Lakes areas through surface albedo effects, despite winter mean snow water equivalent decreases in the future. Overall, the multi-model projections emphasize that cold extremes do not completely disappear in a warming climate. Concomitant with the relatively smaller reduction in CAO events in northwestern U.S., the top 5 most extreme CAO events may still occur in the future, and wind chill warning will continue to have societal impacts in that region.« less
Performance assessment and parameterization of the SWAP-WOFOST model for peat soil under agricultural use in northern Europe.

NASA Astrophysics Data System (ADS)

Bertram, Sascha; Bechtold, Michel; Hendriks, Rob; Piayda, Arndt; Regina, Kristiina; Myllys, Merja; Tiemeyer, Bärbel

2017-04-01

Peat soils form a major share of soil suitable for agriculture in northern Europe. Successful agricultural production depends on hydrological and pedological conditions, local climate and agricultural management. Climate change impact assessment on food production and development of mitigation and adaptation strategies require reliable yield forecasts under given emission scenarios. Coupled soil hydrology - crop growth models, driven by regionalized future climate scenarios are a valuable tool and widely used for this purpose. Parameterization on local peat soil conditions and crop breed or grassland specie performance, however, remains a major challenge. The subject of this study is to evaluate the performance and sensitivity of the SWAP-WOFOST coupled soil hydrology and plant growth model with respect to the application on peat soils under different regional conditions across northern Europe. Further, the parameterization of region-specific crop and grass species is discussed. First results of the model application and parameterization at deep peat sites in southern Finland are presented. The model performed very well in reproducing two years of observed, daily ground water level data on four hydrologically contrasting sites. Naturally dry and wet sites could be modelled with the same performance as sites with active water table management by regulated drains in order to improve peat conservation. A simultaneous multi-site calibration scheme was used to estimate plant growth parameters of the local oat breed. Cross-site validation of the modelled yields against two years of observations proved the robustness of the chosen parameter set and gave no indication of possible overparameterization. This study proves the suitability of the coupled SWAP-WOFOST model for the prediction of crop yields and water table dynamics of peat soils in agricultural use under given climate conditions.

Carbon cycle observations: gaps threaten climate mitigation policies

Treesearch

Richard Birdsey; Nick Bates; MIke Behrenfeld; Kenneth Davis; Scott C. Doney; Richard Feely; Dennis Hansell; Linda Heath; et al.

2009-01-01

Successful management of carbon dioxide (CO2) requires robust and sustained carbon cycle observations. Yet key elements of a national observation network are lacking or at risk. A U.S. National Research Council review of the U.S. Climate Change Science Program earlier this year highlighted the critical need for a U.S. climate observing system to...
Assessing the role of internal climate variability in Antarctica's contribution to future sea-level rise

NASA Astrophysics Data System (ADS)

Tsai, C. Y.; Forest, C. E.; Pollard, D.

2017-12-01

The Antarctic ice sheet (AIS) has the potential to be a major contributor to future sea-level rise (SLR). Current projections of SLR due to AIS mass loss remain highly uncertain. Better understanding of how ice sheets respond to future climate forcing and variability is essential for assessing the long-term risk of SLR. However, the predictability of future climate is limited by uncertainties from emission scenarios, model structural differences, and the internal variability that is inherently generated within the fully coupled climate system. Among those uncertainties, the impact of internal variability on the AIS changes has not been explicitly assessed. In this study, we quantify the effect of internal variability on the AIS evolutions by using climate fields from two large-ensemble experiments using the Community Earth System Model to force a three-dimensional ice sheet model. We find that internal variability of climate fields, particularly atmospheric fields, among ensemble members leads to significantly different AIS responses. Our results show that the internal variability can cause about 80 mm differences of AIS contribution to SLR by 2100 compared to the ensemble-mean contribution of 380-450 mm. Moreover, using ensemble-mean climate fields as the forcing in the ice sheet model does not produce realistic simulations of the ice loss. Instead, it significantly delays the onset of retreat of the West Antarctic Ice Sheet for up to 20 years and significantly underestimates the AIS contribution to SLR by 0.07-0.11 m in 2100 and up to 0.34 m in the 2250's. Therefore, because the uncertainty caused by internal variability is irreducible, we seek to highlight a critical need to assess the role of internal variability in projecting the AIS loss over the next few centuries. By quantifying the impact of internal variability on AIS contribution to SLR, policy makers can obtain more robust estimates of SLR and implement suitable adaptation strategies.
Using simple chaotic models to interpret climate under climate change: Implications for probabilistic climate prediction

NASA Astrophysics Data System (ADS)

Daron, Joseph

2010-05-01

Exploring the reliability of model based projections is an important pre-cursor to evaluating their societal relevance. In order to better inform decisions concerning adaptation (and mitigation) to climate change, we must investigate whether or not our models are capable of replicating the dynamic nature of the climate system. Whilst uncertainty is inherent within climate prediction, establishing and communicating what is plausible as opposed to what is likely is the first step to ensuring that climate sensitive systems are robust to climate change. Climate prediction centers are moving towards probabilistic projections of climate change at regional and local scales (Murphy et al., 2009). It is therefore important to understand what a probabilistic forecast means for a chaotic nonlinear dynamic system that is subject to changing forcings. It is in this context that we present the results of experiments using simple models that can be considered analogous to the more complex climate system, namely the Lorenz 1963 and Lorenz 1984 models (Lorenz, 1963; Lorenz, 1984). Whilst the search for a low-dimensional climate attractor remains illusive (Fraedrich, 1986; Sahay and Sreenivasan, 1996) the characterization of the climate system in such terms can be useful for conceptual and computational simplicity. Recognising that a change in climate is manifest in a change in the distribution of a particular climate variable (Stainforth et al., 2007), we first establish the equilibrium distributions of the Lorenz systems for certain parameter settings. Allowing the parameters to vary in time, we investigate the dependency of such distributions to initial conditions and discuss the implications for climate prediction. We argue that the role of chaos and nonlinear dynamic behaviour ought to have more prominence in the discussion of the forecasting capabilities in climate prediction. References: Fraedrich, K. Estimating the dimensions of weather and climate attractors. J. Atmos. Sci, 43, 419-432, 1986. Lorenz, E. N. Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130-141, 1963. Lorenz, E. N. Irregularity: a fundamental property of the atmosphere. Tellus, 36A, 98-110, 1984. Murphy, J. M., D. M. H. Sexton, G. J. Jenkins, B. B. B. Booth, C. C. Brown, R. T. Clark, M. Collins, G. R. Harris, E. J. Kendon, R. A. Betts, S. J. Brown, P. Boorman, T. P. Howard, K. A. Humphrey, M. P. McCarthy, R. E. McDonald, A. Stephens, C. Wallace, R. Warren, R. Wilby, and R. A. Wood. Uk climate projections science report: Climate change projections. 2009. Sahay, A. and K. R. Sreenivasan. The search for a low-dimensional characterization of a local climate system. Phil. Trans. R. Soc. A., 354, 1715-1750, 1996. Stainforth, D. A., M. R. Allen, E. R. Tredger, and L. A. Smith. Confidence, uncertainty and decision-support relevance in climate predictions. Phil. Trans. R. Soc. A, 365, 2145-2161, 2007.
A Framework for the Cross-Sectoral Integration of Multi-Model Impact Projections: Land Use Decisions Under Climate Impacts Uncertainties

NASA Technical Reports Server (NTRS)

Frieler, K.; Elliott, Joshua; Levermann, A.; Heinke, J.; Arneth, A.; Bierkens, M. F. P.; Ciais, P.; Clark, D. B.; Deryng, D.; Doll, P.;

2015-01-01

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impactmodel setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making

Increasing precipitation volatility in twenty-first-century California

NASA Astrophysics Data System (ADS)

Swain, Daniel L.; Langenbrunner, Baird; Neelin, J. David; Hall, Alex

2018-05-01

Mediterranean climate regimes are particularly susceptible to rapid shifts between drought and flood—of which, California's rapid transition from record multi-year dryness between 2012 and 2016 to extreme wetness during the 2016-2017 winter provides a dramatic example. Projected future changes in such dry-to-wet events, however, remain inadequately quantified, which we investigate here using the Community Earth System Model Large Ensemble of climate model simulations. Anthropogenic forcing is found to yield large twenty-first-century increases in the frequency of wet extremes, including a more than threefold increase in sub-seasonal events comparable to California's `Great Flood of 1862'. Smaller but statistically robust increases in dry extremes are also apparent. As a consequence, a 25% to 100% increase in extreme dry-to-wet precipitation events is projected, despite only modest changes in mean precipitation. Such hydrological cycle intensification would seriously challenge California's existing water storage, conveyance and flood control infrastructure.
Asymmetry of projected increases in extreme temperature distributions

PubMed Central

Kodra, Evan; Ganguly, Auroop R.

2014-01-01

A statistical analysis reveals projections of consistently larger increases in the highest percentiles of summer and winter temperature maxima and minima versus the respective lowest percentiles, resulting in a wider range of temperature extremes in the future. These asymmetric changes in tail distributions of temperature appear robust when explored through 14 CMIP5 climate models and three reanalysis datasets. Asymmetry of projected increases in temperature extremes generalizes widely. Magnitude of the projected asymmetry depends significantly on region, season, land-ocean contrast, and climate model variability as well as whether the extremes of consideration are seasonal minima or maxima events. An assessment of potential physical mechanisms provides support for asymmetric tail increases and hence wider temperature extremes ranges, especially for northern winter extremes. These results offer statistically grounded perspectives on projected changes in the IPCC-recommended extremes indices relevant for impacts and adaptation studies. PMID:25073751
Testing hypotheses on distribution shifts and changes in phenology of imperfectly detectable species

USGS Publications Warehouse

Chambert, Thierry A.; Kendall, William L.; Hines, James E.; Nichols, James D.; Pedrini, Paolo; Waddle, J. Hardin; Tavecchia, Giacomo; Walls, Susan C.; Tenan, Simone

2015-01-01

With ongoing climate change, many species are expected to shift their spatial and temporal distributions. To document changes in species distribution and phenology, detection/non-detection data have proven very useful. Occupancy models provide a robust way to analyse such data, but inference is usually focused on species spatial distribution, not phenology.We present a multi-season extension of the staggered-entry occupancy model of Kendall et al. (2013, Ecology, 94, 610), which permits inference about the within-season patterns of species arrival and departure at sampling sites. The new model presented here allows investigation of species phenology and spatial distribution across years, as well as site extinction/colonization dynamics.We illustrate the model with two data sets on European migratory passerines and one data set on North American treefrogs. We show how to derive several additional phenological parameters, such as annual mean arrival and departure dates, from estimated arrival and departure probabilities.Given the extent of detection/non-detection data that are available, we believe that this modelling approach will prove very useful to further understand and predict species responses to climate change.
An improved empirical dynamic control system model of global mean sea level rise and surface temperature change

NASA Astrophysics Data System (ADS)

Wu, Qing; Luu, Quang-Hung; Tkalich, Pavel; Chen, Ge

2018-04-01

Having great impacts on human lives, global warming and associated sea level rise are believed to be strongly linked to anthropogenic causes. Statistical approach offers a simple and yet conceptually verifiable combination of remotely connected climate variables and indices, including sea level and surface temperature. We propose an improved statistical reconstruction model based on the empirical dynamic control system by taking into account the climate variability and deriving parameters from Monte Carlo cross-validation random experiments. For the historic data from 1880 to 2001, we yielded higher correlation results compared to those from other dynamic empirical models. The averaged root mean square errors are reduced in both reconstructed fields, namely, the global mean surface temperature (by 24-37%) and the global mean sea level (by 5-25%). Our model is also more robust as it notably diminished the unstable problem associated with varying initial values. Such results suggest that the model not only enhances significantly the global mean reconstructions of temperature and sea level but also may have a potential to improve future projections.
Climate Sensitivity Runs and Regional Hydrologic Modeling for Predicting the Response of the Greater Florida Everglades Ecosystem to Climate Change

NASA Astrophysics Data System (ADS)

Obeysekera, Jayantha; Barnes, Jenifer; Nungesser, Martha

2015-04-01

It is important to understand the vulnerability of the water management system in south Florida and to determine the resilience and robustness of greater Everglades restoration plans under future climate change. The current climate models, at both global and regional scales, are not ready to deliver specific climatic datasets for water resources investigations involving future plans and therefore a scenario based approach was adopted for this first study in restoration planning. We focused on the general implications of potential changes in future temperature and associated changes in evapotranspiration, precipitation, and sea levels at the regional boundary. From these, we developed a set of six climate and sea level scenarios, used them to simulate the hydrologic response of the greater Everglades region including agricultural, urban, and natural areas, and compared the results to those from a base run of current conditions. The scenarios included a 1.5 °C increase in temperature, ±10 % change in precipitation, and a 0.46 m (1.5 feet) increase in sea level for the 50-year planning horizon. The results suggested that, depending on the rainfall and temperature scenario, there would be significant changes in water budgets, ecosystem performance, and in water supply demands met. The increased sea level scenarios also show that the ground water levels would increase significantly with associated implications for flood protection in the urbanized areas of southeastern Florida.
Response of the Indian Creek alluvial fan, Nevada, to glacial-interglacial climate change

NASA Astrophysics Data System (ADS)

D'Arcy, Mitch; Roda-Boluda, Duna; Whittaker, Alexander; Brooke, Sam

2017-04-01

Alluvial fans have been shown to record signals of glacial-interglacial climate changes. Specifically, it has been suggested that their down-system grain size fining patterns may record changes in sediment flux. However, very few field studies have tested this because they require (i) robust fan chronologies, (ii) constraints on basin subsidence and 3D fan geometry, and (iii) a suitable model for inverting grain size fining for sediment flux. Here, we present a case study from the fluvially-dominated Indian Creek fan system in Fish Lake Valley, Nevada, which satisfies these criteria. We measure grain size fining patterns on a surface dating to the mid-glacial period ˜71 kyr ago, and a surface dating to the Holocene, which between them represent an overall warming (˜3 ˚ C) and drying (˜30%) of the regional climate. We use constraints on basin subsidence and a self-similar model of grain size fining to reconstruct sediment fluxes to the alluvial fan during the time periods captured by the two surfaces. Our results indicate a decline in sediment flux of ˜38% between the deposition of the ˜71 kyr and Holocene surfaces, implying significant sensitivity to climatic forcing over time periods of >10 kyr. This could represent a decrease in catchment erosion rates and/or a decrease in sediment export as the climate dried. Our results offer quantitative new constraints on how simple landscapes react to known glacial-interglacial climate shifts.
Shrub growth response to climate across the North Slope of Alaska

NASA Astrophysics Data System (ADS)

Ackerman, D.; Griffin, D.; Finlay, J. C.; Hobbie, S. E.

2016-12-01

Warmer temperatures at high latitudes are driving the expansion of woody shrubs in arctic tundra, yielding feedbacks to regional carbon cycling. Accounting for these feedbacks in global climate models will require accurate predictions of the spatial extent of shrub expansion within arctic tundra. While dendroecological approaches have proven useful in understanding how shrubs respond to climate, empirical studies to date are limited in spatial extent, often to just one or two sites within a landscape. A recent meta-analysis of such dendroecological studies hypothesizes that soil moisture is a key variable in determining climate sensitivity of arctic shrub growth. We present the first regional-scale empirical test of this hypothesis by analyzing inter-annual radial growth of deciduous shrubs across soil moisture gradients throughout the North Slope of Alaska. Contrary to expectation, riparian shrubs in high-moisture environments showed no climate sensitivity, while shrubs growing in drier upland sites showed a strong positive growth response to summer temperature. These results proved robust to a variety of detrending functions ranging from conservative (negative exponential) to data adaptive (20-year cubic smoothing spline). These findings call into question the role of soil moisture in determining the climate sensitivity of arctic shrubs and further highlight the importance of unified, regional-scale sampling strategies in understanding climate-vegetation links.
Development and testing of transfer functions for generating quantitative climatic estimates from Australian pollen data

NASA Astrophysics Data System (ADS)

Cook, Ellyn J.; van der Kaars, Sander

2006-10-01

We review attempts to derive quantitative climatic estimates from Australian pollen data, including the climatic envelope, climatic indicator and modern analogue approaches, and outline the need to pursue alternatives for use as input to, or validation of, simulations by models of past, present and future climate patterns. To this end, we have constructed and tested modern pollen-climate transfer functions for mainland southeastern Australia and Tasmania using the existing southeastern Australian pollen database and for northern Australia using a new pollen database we are developing. After testing for statistical significance, 11 parameters were selected for mainland southeastern Australia, seven for Tasmania and six for northern Australia. The functions are based on weighted-averaging partial least squares regression and their predictive ability evaluated against modern observational climate data using leave-one-out cross-validation. Functions for summer, annual and winter rainfall and temperatures are most robust for southeastern Australia, while in Tasmania functions for minimum temperature of the coldest period, mean winter and mean annual temperature are the most reliable. In northern Australia, annual and summer rainfall and annual and summer moisture indexes are the strongest. The validation of all functions means all can be applied to Quaternary pollen records from these three areas with confidence. Copyright
On the Representation of Cloud Phase in Global Climate Models, and its Importance for Simulations of Climate Forcings and Feedbacks

NASA Astrophysics Data System (ADS)

Storelvmo, Trude; Sagoo, Navjit; Tan, Ivy

2016-04-01

Despite the growing effort in improving the cloud microphysical schemes in GCMs, most of this effort has not focused on improving the ability of GCMs to accurately simulate phase partitioning in mixed-phase clouds. Getting the relative proportion of liquid droplets and ice crystals in clouds right in GCMs is critical for the representation of cloud radiative forcings and cloud-climate feedbacks. Here, we first present satellite observations of cloud phase obtained by NASA's CALIOP instrument, and report on robust statistical relationships between cloud phase and several aerosols species that have been demonstrated to act as ice nuclei (IN) in laboratory studies. We then report on results from model intercomparison projects that reveal that GCMs generally underestimate the amount of supercooled liquid in clouds. For a selected GCM (NCAR 's CAM5), we thereafter show that the underestimate can be attributed to two main factors: i) the presence of IN in the mixed-phase temperature range, and ii) the Wegener-Bergeron-Findeisen process, which converts liquid to ice once ice crystals have formed. Finally, we show that adjusting these two processes such that the GCM's cloud phase is in agreement with the observed has a substantial impact on the simulated radiative forcing due to IN perturbations, as well as on the cloud-climate feedbacks and ultimately climate sensitivity simulated by the GCM.
Multi-Site and Multi-Variables Statistical Downscaling Technique in the Monsoon Dominated Region of Pakistan

NASA Astrophysics Data System (ADS)

Khan, Firdos; Pilz, Jürgen

2016-04-01

South Asia is under the severe impacts of changing climate and global warming. The last two decades showed that climate change or global warming is happening and the first decade of 21st century is considered as the warmest decade over Pakistan ever in history where temperature reached 53 0C in 2010. Consequently, the spatio-temporal distribution and intensity of precipitation is badly effected and causes floods, cyclones and hurricanes in the region which further have impacts on agriculture, water, health etc. To cope with the situation, it is important to conduct impact assessment studies and take adaptation and mitigation remedies. For impact assessment studies, we need climate variables at higher resolution. Downscaling techniques are used to produce climate variables at higher resolution; these techniques are broadly divided into two types, statistical downscaling and dynamical downscaling. The target location of this study is the monsoon dominated region of Pakistan. One reason for choosing this area is because the contribution of monsoon rains in this area is more than 80 % of the total rainfall. This study evaluates a statistical downscaling technique which can be then used for downscaling climatic variables. Two statistical techniques i.e. quantile regression and copula modeling are combined in order to produce realistic results for climate variables in the area under-study. To reduce the dimension of input data and deal with multicollinearity problems, empirical orthogonal functions will be used. Advantages of this new method are: (1) it is more robust to outliers as compared to ordinary least squares estimates and other estimation methods based on central tendency and dispersion measures; (2) it preserves the dependence among variables and among sites and (3) it can be used to combine different types of distributions. This is important in our case because we are dealing with climatic variables having different distributions over different meteorological stations. The proposed model will be validated by using the (National Centers for Environmental Prediction / National Center for Atmospheric Research) NCEP/NCAR predictors for the period of 1960-1990 and validated for 1990-2000. To investigate the efficiency of the proposed model, it will be compared with the multivariate multiple regression model and with dynamical downscaling climate models by using different climate indices that describe the frequency, intensity and duration of the variables of interest. KEY WORDS: Climate change, Copula, Monsoon, Quantile regression, Spatio-temporal distribution.
Influence of Climate Oscillations on Extreme Precipitation in Texas

NASA Astrophysics Data System (ADS)

Bhatia, N.; Singh, V. P.; Srivastav, R. K.

2016-12-01

Much research in the field of hydroclimatology is focusing on the impact of climate variability on hydrologic extremes. Recent studies show that the unique geographical location and the enormous areal extent, coupled with extensive variations in climate oscillations, have intensified the regional hydrologic cycle of Texas. The state-wide extreme precipitation events can actually be attributed to sea-surface pressure and temperature anomalies, such as Bermuda High and Jet Streams, which are further triggered by such climate oscillations. This study aims to quantify the impact of five major Atlantic and Pacific Ocean related climate oscillations: (i) Atlantic Multidecadal Oscillation (AMO), (ii) North Atlantic Oscillation (NAO), (iii) Pacific Decadal Oscillation (PDO), (iv) Pacific North American Pattern (PNA), and (v) Southern Oscillation Index (SOI), on extreme precipitation in Texas. Their respective effects will be determined for both climate divisions delineated by the National Climatic Data Centre (NCDC) and climate regions defined by the Köppen Climate Classification System. This study will adopt a weighted correlation approach to attain the robust correlation coefficients while addressing the regionally variable data outliers for extreme precipitation. Further, the variation of robust correlation coefficients across Texas is found to be related to the station elevation, historical average temperature, and total precipitation in the months of extremes. The research will shed light on the relationship between precipitation extremes and climate variability, thus aiding regional water boards in planning, designing, and managing the respective systems as per the future climate change.
The western Pacific monsoon in CMIP5 models: Model evaluation and projections

NASA Astrophysics Data System (ADS)

Brown, Josephine R.; Colman, Robert A.; Moise, Aurel F.; Smith, Ian N.

2013-11-01

ability of 35 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to simulate the western Pacific (WP) monsoon is evaluated over four representative regions around Timor, New Guinea, the Solomon Islands and Palau. Coupled model simulations are compared with atmosphere-only model simulations (with observed sea surface temperatures, SSTs) to determine the impact of SST biases on model performance. Overall, the CMIP5 models simulate the WP monsoon better than previous-generation Coupled Model Intercomparison Project Phase 3 (CMIP3) models, but some systematic biases remain. The atmosphere-only models are better able to simulate the seasonal cycle of zonal winds than the coupled models, but display comparable biases in the rainfall. The CMIP5 models are able to capture features of interannual variability in response to the El Niño-Southern Oscillation. In climate projections under the RCP8.5 scenario, monsoon rainfall is increased over most of the WP monsoon domain, while wind changes are small. Widespread rainfall increases at low latitudes in the summer hemisphere appear robust as a large majority of models agree on the sign of the change. There is less agreement on rainfall changes in winter. Interannual variability of monsoon wet season rainfall is increased in a warmer climate, particularly over Palau, Timor and the Solomon Islands. A subset of the models showing greatest skill in the current climate confirms the overall projections, although showing markedly smaller rainfall increases in the western equatorial Pacific. The changes found here may have large impacts on Pacific island countries influenced by the WP monsoon.
Robust multiscale prediction of Po River discharge using a twofold AR-NN approach

NASA Astrophysics Data System (ADS)

Alessio, Silvia; Taricco, Carla; Rubinetti, Sara; Zanchettin, Davide; Rubino, Angelo; Mancuso, Salvatore

2017-04-01

The Mediterranean area is among the regions most exposed to hydroclimatic changes, with a likely increase of frequency and duration of droughts in the last decades and potentially substantial future drying according to climate projections. However, significant decadal variability is often superposed or even dominates these long-term hydrological trend as observed, for instance, in North Italian precipitation and river discharge records. The capability to accurately predict such decadal changes is, therefore, of utmost environmental and social importance. In order to forecast short and noisy hydroclimatic time series, we apply a twofold statistical approach that we improved with respect to previous works [1]. Our prediction strategy consists in the application of two independent methods that use autoregressive models and feed-forward neural networks. Since all prediction methods work better on clean signals, the predictions are not performed directly on the series, but rather on each significant variability components extracted with Singular Spectrum Analysis (SSA). In this contribution, we will illustrate the multiscale prediction approach and its application to the case of decadal prediction of annual-average Po River discharges (Italy). The discharge record is available for the last 209 years and allows to work with both interannual and decadal time-scale components. Fifteen-year forecasts obtained with both methods robustly indicate a prominent dry period in the second half of the 2020s. We will discuss advantages and limitations of the proposed statistical approach in the light of the current capabilities of decadal climate prediction systems based on numerical climate models, toward an integrated dynamical and statistical approach for the interannual-to-decadal prediction of hydroclimate variability in medium-size river basins. [1] Alessio et. al., Natural variability and anthropogenic effects in a Central Mediterranean core, Clim. of the Past, 8, 831-839, 2012.
Climate Change Impacts on Transportation; Groundwater Elevation, Road Performance, and Robust Adaptation

NASA Astrophysics Data System (ADS)

Kirshen, P. H.; Knott, J. F.; Ray, P.; Elshaer, M.; Daniel, J.; Jacobs, J. M.

2016-12-01

Transportation climate change vulnerability and adaptation studies have primarily focused on surface-water flooding from sea-level rise (SLR); little attention has been given to the effects of climate change and SLR on groundwater and subsequent impacts on the unbound foundation layers of coastal-road infrastructure. The magnitude of service-life reduction depends on the height of the groundwater in the unbound pavement materials, the pavement structure itself, and the loading. Using a steady-state groundwater model, and a multi-layer elastic pavement evaluation model, the strain changes in the layers can be determined as a function of parameter values and the strain changes translated into failure as measured by number of loading cycles to failure. For a section of a major coastal road in New Hampshire, future changes in sea-level, precipitation, temperature, land use, and groundwater pumping are characterized by deep uncertainty. Parameters that describe the groundwater system such as hydraulic conductivity can be probabilistically described while road characteristics are assumed to be deterministic. To understand the vulnerability of this road section, a bottom-up planning approach was employed over time where the combinations of parameter values that cause failure were determined and their plausibility of their occurring was analyzed. To design a robust adaptation strategy that will function reasonably well in the present and the future given the large number of uncertain parameter values, performance of adaptation options were investigated. Adaptation strategies that were considered include raising the road, load restrictions, increasing pavement layer thicknesses, replacing moisture-sensitive materials with materials that are not moisture sensitive, improving drainage systems, and treatment of the underlying materials.
Climate Vulnerability of Hydro-power infrastructure in the Eastern African Power Pool

NASA Astrophysics Data System (ADS)

Sridharan, Vignesh

2017-04-01

At present there is around 6000 MW of installed hydropower capacity in the Eastern African power pool (EAPP)[1]. With countries aggressively planning to achieve the Sustainable development goal (SDG) of ensuring access to affordable electricity for all, a three-fold increase in hydropower capacity is expected by 2040 [1]. Most of the existing and planned infrastructure lie inside the Nile River Basin. The latest assessment report (AR 5) from the Intergovernmental Panel on Climate Change (IPCC) indicates a high level of climatic uncertainty in the Nile Basin. The Climate Moisture index (CMI) for the Eastern Nile region and the Nile Equatorial lakes varies significantly across the different General Circulation Models (GCM)[2]. Such high uncertainty casts a shadow on the plans to expand hydropower capacity, doubting whether hydropower expansion can contribute to the goal of improving access to electricity or end up as sunk investments. In this assessment, we analyze adaptation strategies for national energy systems in the Eastern African Power Pool (EAPP), which minimize the regret that could potentially arise from impacts of a changed climate. An energy systems model of the EAPP is developed representing national electricity supply infrastructure. Cross border transmission and hydropower infrastructure is defined at individual project level. The energy systems model is coupled with a water systems management model of the Nile River Basin that calculates the water availability at different hydropower infrastructures under a range of climate scenarios. The results suggest that a robust adaptation strategy consisting of investments in cross border electricity transmission infrastructure and diversifying sources of electricity supply will require additional investments of USD 4.2 billion by 2050. However, this leads to fuel and operational cost savings of up to USD 22.6 billion, depending on the climate scenario. [1] "Platts, 2016. World Electric Power Plants Database," World Electric Power Plants Database. [Online]. Available: http://www.platts.com/Products/worldelectricpowerplantsdatabase. [Accessed: 01-Mar-2016]. [2] Brent Boehlert, Kenneth M. Strzepek, David Groves, and Bruce Hewitson, Chris Jack, "Climate Change Projections in Africa-Chapter 3," in Enhancing the Climate Resilience of Africa's Infrastructure : The Power and Water Sectors, Washington DC: The World Bank, 2016, p. 219.
Agriculture in West Africa in the Twenty-First Century: Climate Change and Impacts Scenarios, and Potential for Adaptation

PubMed Central

Sultan, Benjamin; Gaetani, Marco

2016-01-01

West Africa is known to be particularly vulnerable to climate change due to high climate variability, high reliance on rain-fed agriculture, and limited economic and institutional capacity to respond to climate variability and change. In this context, better knowledge of how climate will change in West Africa and how such changes will impact crop productivity is crucial to inform policies that may counteract the adverse effects. This review paper provides a comprehensive overview of climate change impacts on agriculture in West Africa based on the recent scientific literature. West Africa is nowadays experiencing a rapid climate change, characterized by a widespread warming, a recovery of the monsoonal precipitation, and an increase in the occurrence of climate extremes. The observed climate tendencies are also projected to continue in the twenty-first century under moderate and high emission scenarios, although large uncertainties still affect simulations of the future West African climate, especially regarding the summer precipitation. However, despite diverging future projections of the monsoonal rainfall, which is essential for rain-fed agriculture, a robust evidence of yield loss in West Africa emerges. This yield loss is mainly driven by increased mean temperature while potential wetter or drier conditions as well as elevated CO2 concentrations can modulate this effect. Potential for adaptation is illustrated for major crops in West Africa through a selection of studies based on process-based crop models to adjust cropping systems (change in varieties, sowing dates and density, irrigation, fertilizer management) to future climate. Results of the cited studies are crop and region specific and no clear conclusions can be made regarding the most effective adaptation options. Further efforts are needed to improve modeling of the monsoon system and to better quantify the uncertainty in its changes under a warmer climate, in the response of the crops to such changes and in the potential for adaptation. PMID:27625660

Some links on this page may take you to non-federal websites. Their policies may differ from this site.