Statistical bias correction method applied on CMIP5 datasets over the Indian region during the summer monsoon season for climate change applications

NASA Astrophysics Data System (ADS)

Prasanna, V.

2018-01-01

This study makes use of temperature and precipitation from CMIP5 climate model output for climate change application studies over the Indian region during the summer monsoon season (JJAS). Bias correction of temperature and precipitation from CMIP5 GCM simulation results with respect to observation is discussed in detail. The non-linear statistical bias correction is a suitable bias correction method for climate change data because it is simple and does not add up artificial uncertainties to the impact assessment of climate change scenarios for climate change application studies (agricultural production changes) in the future. The simple statistical bias correction uses observational constraints on the GCM baseline, and the projected results are scaled with respect to the changing magnitude in future scenarios, varying from one model to the other. Two types of bias correction techniques are shown here: (1) a simple bias correction using a percentile-based quantile-mapping algorithm and (2) a simple but improved bias correction method, a cumulative distribution function (CDF; Weibull distribution function)-based quantile-mapping algorithm. This study shows that the percentile-based quantile mapping method gives results similar to the CDF (Weibull)-based quantile mapping method, and both the methods are comparable. The bias correction is applied on temperature and precipitation variables for present climate and future projected data to make use of it in a simple statistical model to understand the future changes in crop production over the Indian region during the summer monsoon season. In total, 12 CMIP5 models are used for Historical (1901-2005), RCP4.5 (2005-2100), and RCP8.5 (2005-2100) scenarios. The climate index from each CMIP5 model and the observed agricultural yield index over the Indian region are used in a regression model to project the changes in the agricultural yield over India from RCP4.5 and RCP8.5 scenarios. The results revealed a better convergence of model projections in the bias corrected data compared to the uncorrected data. The study can be extended to localized regional domains aimed at understanding the changes in the agricultural productivity in the future with an agro-economy or a simple statistical model. The statistical model indicated that the total food grain yield is going to increase over the Indian region in the future, the increase in the total food grain yield is approximately 50 kg/ ha for the RCP4.5 scenario from 2001 until the end of 2100, and the increase in the total food grain yield is approximately 90 kg/ha for the RCP8.5 scenario from 2001 until the end of 2100. There are many studies using bias correction techniques, but this study applies the bias correction technique to future climate scenario data from CMIP5 models and applied it to crop statistics to find future crop yield changes over the Indian region.

Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil

NASA Astrophysics Data System (ADS)

de Carvalho, Luiz G.; de Carvalho Alves, Marcelo; de Oliveira, Marcelo S.; Vianello, Rubens L.; Sediyama, Gilberto C.; de Carvalho, Luis M. T.

2010-11-01

The objective of the present study was to assess for Minas Gerais the cokriging methodology, in order to characterize the spatial variability of Thornthwaite annual moisture index, annual rainfall, and average annual air temperature, based on geographical coordinates, altitude, latitude, and longitude. The climatic element data referred to 39 INMET climatic stations located in the state of Minas Gerais and in nearby areas and the covariables altitude, latitude, and longitude to the SRTM digital elevation model. Spatial dependence of data was observed through spherical cross semivariograms and cross covariance models. Box-Cox and log transformation were applied to the positive variables. In these situations, kriged predictions were back-transformed and returned to the same scale as the original data. Trend was removed using global polynomial interpolation. Universal simple cokriging best characterized the climate variables without tendentiousness and with high accuracy and precision when compared to simple cokriging. Considering the satisfactory implementation of universal simple cokriging for the monitoring of climatic elements, this methodology presents enormous potential for the characterization of climate change impact in Minas Gerais state.

Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers.

PubMed

Axelsen, Jacob Bock; Yaari, Rami; Grenfell, Bryan T; Stone, Lewi

2014-07-01

Human influenza occurs annually in most temperate climatic zones of the world, with epidemics peaking in the cold winter months. Considerable debate surrounds the relative role of epidemic dynamics, viral evolution, and climatic drivers in driving year-to-year variability of outbreaks. The ultimate test of understanding is prediction; however, existing influenza models rarely forecast beyond a single year at best. Here, we use a simple epidemiological model to reveal multiannual predictability based on high-quality influenza surveillance data for Israel; the model fit is corroborated by simple metapopulation comparisons within Israel. Successful forecasts are driven by temperature, humidity, antigenic drift, and immunity loss. Essentially, influenza dynamics are a balance between large perturbations following significant antigenic jumps, interspersed with nonlinear epidemic dynamics tuned by climatic forcing.

Improved Analysis of Earth System Models and Observations using Simple Climate Models

NASA Astrophysics Data System (ADS)

Nadiga, B. T.; Urban, N. M.

2016-12-01

Earth system models (ESM) are the most comprehensive tools we have to study climate change and develop climate projections. However, the computational infrastructure required and the cost incurred in running such ESMs precludes direct use of such models in conjunction with a wide variety of tools that can further our understanding of climate. Here we are referring to tools that range from dynamical systems tools that give insight into underlying flow structure and topology to tools that come from various applied mathematical and statistical techniques and are central to quantifying stability, sensitivity, uncertainty and predictability to machine learning tools that are now being rapidly developed or improved. Our approach to facilitate the use of such models is to analyze output of ESM experiments (cf. CMIP) using a range of simpler models that consider integral balances of important quantities such as mass and/or energy in a Bayesian framework.We highlight the use of this approach in the context of the uptake of heat by the world oceans in the ongoing global warming. Indeed, since in excess of 90% of the anomalous radiative forcing due greenhouse gas emissions is sequestered in the world oceans, the nature of ocean heat uptake crucially determines the surface warming that is realized (cf. climate sensitivity). Nevertheless, ESMs themselves are never run long enough to directly assess climate sensitivity. So, we consider a range of models based on integral balances--balances that have to be realized in all first-principles based models of the climate system including the most detailed state-of-the art climate simulations. The models range from simple models of energy balance to those that consider dynamically important ocean processes such as the conveyor-belt circulation (Meridional Overturning Circulation, MOC), North Atlantic Deep Water (NADW) formation, Antarctic Circumpolar Current (ACC) and eddy mixing. Results from Bayesian analysis of such models using both ESM experiments and actual observations are presented. One such result points to the importance of direct sequestration of heat below 700 m, a process that is not allowed for in the simple models that have been traditionally used to deduce climate sensitivity.

A commentary on the Atlantic meridional overturning circulation stability in climate models

NASA Astrophysics Data System (ADS)

Gent, Peter R.

2018-02-01

The stability of the Atlantic meridional overturning circulation (AMOC) in ocean models depends quite strongly on the model formulation, especially the vertical mixing, and whether it is coupled to an atmosphere model. A hysteresis loop in AMOC strength with respect to freshwater forcing has been found in several intermediate complexity climate models and in one fully coupled climate model that has very coarse resolution. Over 40% of modern climate models are in a bistable AMOC state according to the very frequently used simple stability criterion which is based solely on the sign of the AMOC freshwater transport across 33° S. In a recent freshwater hosing experiment in a climate model with an eddy-permitting ocean component, the change in the gyre freshwater transport across 33° S is larger than the AMOC freshwater transport change. This casts very strong doubt on the usefulness of this simple AMOC stability criterion. If a climate model uses large surface flux adjustments, then these adjustments can interfere with the atmosphere-ocean feedbacks, and strongly change the AMOC stability properties. AMOC can be shut off for many hundreds of years in modern fully coupled climate models if the hosing or carbon dioxide forcing is strong enough. However, in one climate model the AMOC recovers after between 1000 and 1400 years. Recent 1% increasing carbon dioxide runs and RCP8.5 future scenario runs have shown that the AMOC reduction is smaller using an eddy-resolving ocean component than in the comparable standard 1° ocean climate models.

Constrained range expansion and climate change assessments

Treesearch

Yohay Carmel; Curtis H. Flather

2006-01-01

Modeling the future distribution of keystone species has proved to be an important approach to assessing the potential ecological consequences of climate change (Loehle and LeBlanc 1996; Hansen et al. 2001). Predictions of range shifts are typically based on empirical models derived from simple correlative relationships between climatic characteristics of occupied and...

Predictability in community dynamics.

PubMed

Blonder, Benjamin; Moulton, Derek E; Blois, Jessica; Enquist, Brian J; Graae, Bente J; Macias-Fauria, Marc; McGill, Brian; Nogué, Sandra; Ordonez, Alejandro; Sandel, Brody; Svenning, Jens-Christian

2017-03-01

The coupling between community composition and climate change spans a gradient from no lags to strong lags. The no-lag hypothesis is the foundation of many ecophysiological models, correlative species distribution modelling and climate reconstruction approaches. Simple lag hypotheses have become prominent in disequilibrium ecology, proposing that communities track climate change following a fixed function or with a time delay. However, more complex dynamics are possible and may lead to memory effects and alternate unstable states. We develop graphical and analytic methods for assessing these scenarios and show that these dynamics can appear in even simple models. The overall implications are that (1) complex community dynamics may be common and (2) detailed knowledge of past climate change and community states will often be necessary yet sometimes insufficient to make predictions of a community's future state. © 2017 John Wiley & Sons Ltd/CNRS.

An eco-hydrologic model of malaria outbreaks

NASA Astrophysics Data System (ADS)

Montosi, E.; Manzoni, S.; Porporato, A.; Montanari, A.

2012-03-01

Malaria is a geographically widespread infectious disease that is well known to be affected by climate variability at both seasonal and interannual timescales. In an effort to identify climatic factors that impact malaria dynamics, there has been considerable research focused on the development of appropriate disease models for malaria transmission and their consideration alongside climatic datasets. These analyses have focused largely on variation in temperature and rainfall as direct climatic drivers of malaria dynamics. Here, we further these efforts by considering additionally the role that soil water content may play in driving malaria incidence. Specifically, we hypothesize that hydro-climatic variability should be an important factor in controlling the availability of mosquito habitats, thereby governing mosquito growth rates. To test this hypothesis, we reduce a nonlinear eco-hydrologic model to a simple linear model through a series of consecutive assumptions and apply this model to malaria incidence data from three South African provinces. Despite the assumptions made in the reduction of the model, we show that soil water content can account for a significant portion of malaria's case variability beyond its seasonal patterns, whereas neither temperature nor rainfall alone can do so. Future work should therefore consider soil water content as a simple and computable variable for incorporation into climate-driven disease models of malaria and other vector-borne infectious diseases.

An ecohydrological model of malaria outbreaks

NASA Astrophysics Data System (ADS)

Montosi, E.; Manzoni, S.; Porporato, A.; Montanari, A.

2012-08-01

Malaria is a geographically widespread infectious disease that is well known to be affected by climate variability at both seasonal and interannual timescales. In an effort to identify climatic factors that impact malaria dynamics, there has been considerable research focused on the development of appropriate disease models for malaria transmission driven by climatic time series. These analyses have focused largely on variation in temperature and rainfall as direct climatic drivers of malaria dynamics. Here, we further these efforts by considering additionally the role that soil water content may play in driving malaria incidence. Specifically, we hypothesize that hydro-climatic variability should be an important factor in controlling the availability of mosquito habitats, thereby governing mosquito growth rates. To test this hypothesis, we reduce a nonlinear ecohydrological model to a simple linear model through a series of consecutive assumptions and apply this model to malaria incidence data from three South African provinces. Despite the assumptions made in the reduction of the model, we show that soil water content can account for a significant portion of malaria's case variability beyond its seasonal patterns, whereas neither temperature nor rainfall alone can do so. Future work should therefore consider soil water content as a simple and computable variable for incorporation into climate-driven disease models of malaria and other vector-borne infectious diseases.

Simple three-pool model accurately describes patterns of long-term litter decomposition in diverse climates

Treesearch

E. Carol Adair; William J. Parton; Steven J. Del Grosso; Shendee L. Silver; Mark E. Harmon; Sonia A. Hall; Ingrid C. Burke; Stephen C. Hart

2008-01-01

As atmospheric CO2 increases, ecosystem carbon sequestration will largely depend on how global changes in climate will alter the balance between net primary production and decomposition. The response of primary production to climatic change has been examined using well-validated mechanistic models, but the same is not true for decomposition, a...

Comparing and combining process-based crop models and statistical models with some implications for climate change

NASA Astrophysics Data System (ADS)

Roberts, Michael J.; Braun, Noah O.; Sinclair, Thomas R.; Lobell, David B.; Schlenker, Wolfram

2017-09-01

We compare predictions of a simple process-based crop model (Soltani and Sinclair 2012), a simple statistical model (Schlenker and Roberts 2009), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields in the Corn Belt region of the United States. After statistical post-model calibration, the process model (Simple Simulation Model, or SSM) predicts actual outcomes slightly better than the statistical model, but the combined model performs significantly better than either model. The SSM, statistical model and combined model all show similar relationships with precipitation, while the SSM better accounts for temporal patterns of precipitation, vapor pressure deficit and solar radiation. The statistical and combined models show a more negative impact associated with extreme heat for which the process model does not account. Due to the extreme heat effect, predicted impacts under uniform climate change scenarios are considerably more severe for the statistical and combined models than for the process-based model.

A Global Climate Model for Instruction.

ERIC Educational Resources Information Center

Burt, James E.

This paper describes a simple global climate model useful in a freshman or sophomore level course in climatology. There are three parts to the paper. The first part describes the model, which is a global model of surface air temperature averaged over latitude and longitude. Samples of the types of calculations performed in the model are provided.…

A simple integrated assessment approach to global change simulation and evaluation

NASA Astrophysics Data System (ADS)

Ogutu, Keroboto; D'Andrea, Fabio; Ghil, Michael

2016-04-01

We formulate and study the Coupled Climate-Economy-Biosphere (CoCEB) model, which constitutes the basis of our idealized integrated assessment approach to simulating and evaluating global change. CoCEB is composed of a physical climate module, based on Earth's energy balance, and an economy module that uses endogenous economic growth with physical and human capital accumulation. A biosphere model is likewise under study and will be coupled to the existing two modules. We concentrate on the interactions between the two subsystems: the effect of climate on the economy, via damage functions, and the effect of the economy on climate, via a control of the greenhouse gas emissions. Simple functional forms of the relation between the two subsystems permit simple interpretations of the coupled effects. The CoCEB model is used to make hypotheses on the long-term effect of investment in emission abatement, and on the comparative efficacy of different approaches to abatement, in particular by investing in low carbon technology, in deforestation reduction or in carbon capture and storage (CCS). The CoCEB model is very flexible and transparent, and it allows one to easily formulate and compare different functional representations of climate change mitigation policies. Using different mitigation measures and their cost estimates, as found in the literature, one is able to compare these measures in a coherent way.

Climatic impact of Amazon deforestation - a mechanistic model study

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

Ning Zeng; Dickinson, R.E.; Xubin Zeng

1996-04-01

Recent general circulation model (GCM) experiments suggest a drastic change in the regional climate, especially the hydrological cycle, after hypothesized Amazon basinwide deforestation. To facilitate the theoretical understanding os such a change, we develop an intermediate-level model for tropical climatology, including atmosphere-land-ocean interaction. The model consists of linearized steady-state primitive equations with simplified thermodynamics. A simple hydrological cycle is also included. Special attention has been paid to land-surface processes. It generally better simulates tropical climatology and the ENSO anomaly than do many of the previous simple models. The climatic impact of Amazon deforestation is studied in the context of thismore » model. Model results show a much weakened Atlantic Walker-Hadley circulation as a result of the existence of a strong positive feedback loop in the atmospheric circulation system and the hydrological cycle. The regional climate is highly sensitive to albedo change and sensitive to evapotranspiration change. The pure dynamical effect of surface roughness length on convergence is small, but the surface flow anomaly displays intriguing features. Analysis of the thermodynamic equation reveals that the balance between convective heating, adiabatic cooling, and radiation largely determines the deforestation response. Studies of the consequences of hypothetical continuous deforestation suggest that the replacement of forest by desert may be able to sustain a dry climate. Scaling analysis motivated by our modeling efforts also helps to interpret the common results of many GCM simulations. When a simple mixed-layer ocean model is coupled with the atmospheric model, the results suggest a 1{degrees}C decrease in SST gradient across the equatorial Atlantic Ocean in response to Amazon deforestation. The magnitude depends on the coupling strength. 66 refs., 16 figs., 4 tabs.« less

Assessment of Climate Suitability of Maize in South Korea

NASA Astrophysics Data System (ADS)

Hyun, S.; Choi, D.; Seo, B.

2017-12-01

Assessing suitable areas for crops would be useful to design alternate cropping systems as an adaptation option to climate change adaptation. Although suitable areas could be identified by using a crop growth model, it would require a number of input parameters including cultivar and soil. Instead, a simple climate suitability model, e.g., EcoCrop model, could be used for an assessment of climate suitability for a major grain crop. The objective of this study was to assess of climate suitability for maize using the EcoCrop model under climate change conditions in Korea. A long term climate data from 2000 - 2100 were compiled from weather data source. The EcoCrop model implemented in R was used to determine climate suitability index at each grid cell. Overall, the EcoCrop model tended to identify suitable areas for maize production near the coastal areas whereas the actual major production areas located in inland areas. It is likely that the discrepancy between assessed and actual crop production areas would result from the socioeconomic aspects of maize production. Because the price of maize is considerably low, maize has been grown in an area where moisture and temperature conditions would be less than optimum. In part, a simple algorithm to predict climate suitability for maize would caused a relatively large error in climate suitability assessment under the present climate conditions. In 2050s, the climate suitability for maize increased in a large areas in southern and western part of Korea. In particular, the plain areas near the coastal region had considerably greater suitability index in the future compared with mountainous areas. The expansion of suitable areas for maize would help crop production policy making such as the allocation of rice production area for other crops due to considerably less demand for the rice in Korea.

Heat Transport Compensation in Atmosphere and Ocean over the Past 22,000 Years

PubMed Central

Yang, Haijun; Zhao, Yingying; Liu, Zhengyu; Li, Qing; He, Feng; Zhang, Qiong

2015-01-01

The Earth’s climate has experienced dramatic changes over the past 22,000 years; however, the total meridional heat transport (MHT) of the climate system remains stable. A 22,000-year-long simulation using an ocean-atmosphere coupled model shows that the changes in atmosphere and ocean MHT are significant but tend to be out of phase in most regions, mitigating the total MHT change, which helps to maintain the stability of the Earth’s overall climate. A simple conceptual model is used to understand the compensation mechanism. The simple model can reproduce qualitatively the evolution and compensation features of the MHT over the past 22,000 years. We find that the global energy conservation requires the compensation changes in the atmosphere and ocean heat transports. The degree of compensation is mainly determined by the local climate feedback between surface temperature and net radiation flux at the top of the atmosphere. This study suggests that an internal mechanism may exist in the climate system, which might have played a role in constraining the global climate change over the past 22,000 years. PMID:26567710

Predicting the response of seven Asian glaciers to future climate scenarios using a simple linear glacier model

NASA Astrophysics Data System (ADS)

Ren, Diandong; Karoly, David J.

2008-03-01

Observations from seven Central Asian glaciers (35-55°N; 70-95°E) are used, together with regional temperature data, to infer uncertain parameters for a simple linear model of the glacier length variations. The glacier model is based on first order glacier dynamics and requires the knowledge of reference states of forcing and glacier perturbation magnitude. An adjoint-based variational method is used to optimally determine the glacier reference states in 1900 and the uncertain glacier model parameters. The simple glacier model is then used to estimate the glacier length variations until 2060 using regional temperature projections from an ensemble of climate model simulations for a future climate change scenario (SRES A2). For the period 2000-2060, all glaciers are projected to experience substantial further shrinkage, especially those with gentle slopes (e.g., Glacier Chogo Lungma retreats ˜4 km). Although nearly one-third of the year 2000 length will be reduced for some small glaciers, the existence of the glaciers studied here is not threatened by year 2060. The differences between the individual glacier responses are large. No straightforward relationship is found between glacier size and the projected fractional change of its length.

Detection of greenhouse-gas-induced climatic change. Progress report, 1 December 1991--30 June 1994

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

Wigley, T.M.L.; Jones, P.D.

1994-07-01

In addition to changes due to variations in greenhouse gas concentrations, the global climate system exhibits a high degree of internally-generated and externally-forced natural variability. To detect the enhanced greenhouse effect, its signal must be isolated from the ``noise`` of this natural climatic variability. A high quality, spatially extensive data base is required to define the noise and its spatial characteristics. To facilitate this, available land and marine data bases will be updated and expanded. The data will be analyzed to determine the potential effects on climate of greenhouse gas concentration changes and other factors. Analyses will be guided bymore » a variety of models, from simple energy balance climate models to ocean General Circulation Models. Appendices A--G contain the following seven papers: (A) Recent global warmth moderated by the effects of the Mount Pinatubo eruption; (B) Recent warming in global temperature series; (C) Correlation methods in fingerprint detection studies; (D) Balancing the carbon budget. Implications for projections of future carbon dioxide concentration changes; (E) A simple model for estimating methane concentration and lifetime variations; (F) Implications for climate and sea level of revised IPCC emissions scenarios; and (G) Sulfate aerosol and climatic change.« less

Methods for developing time-series climate surfaces to drive topographically distributed energy- and water-balance models

USGS Publications Warehouse

Susong, D.; Marks, D.; Garen, D.

1999-01-01

Topographically distributed energy- and water-balance models can accurately simulate both the development and melting of a seasonal snowcover in the mountain basins. To do this they require time-series climate surfaces of air temperature, humidity, wind speed, precipitation, and solar and thermal radiation. If data are available, these parameters can be adequately estimated at time steps of one to three hours. Unfortunately, climate monitoring in mountain basins is very limited, and the full range of elevations and exposures that affect climate conditions, snow deposition, and melt is seldom sampled. Detailed time-series climate surfaces have been successfully developed using limited data and relatively simple methods. We present a synopsis of the tools and methods used to combine limited data with simple corrections for the topographic controls to generate high temporal resolution time-series images of these climate parameters. Methods used include simulations, elevational gradients, and detrended kriging. The generated climate surfaces are evaluated at points and spatially to determine if they are reasonable approximations of actual conditions. Recommendations are made for the addition of critical parameters and measurement sites into routine monitoring systems in mountain basins.Topographically distributed energy- and water-balance models can accurately simulate both the development and melting of a seasonal snowcover in the mountain basins. To do this they require time-series climate surfaces of air temperature, humidity, wind speed, precipitation, and solar and thermal radiation. If data are available, these parameters can be adequately estimated at time steps of one to three hours. Unfortunately, climate monitoring in mountain basins is very limited, and the full range of elevations and exposures that affect climate conditions, snow deposition, and melt is seldom sampled. Detailed time-series climate surfaces have been successfully developed using limited data and relatively simple methods. We present a synopsis of the tools and methods used to combine limited data with simple corrections for the topographic controls to generate high temporal resolution time-series images of these climate parameters. Methods used include simulations, elevational gradients, and detrended kriging. The generated climate surfaces are evaluated at points and spatially to determine if they are reasonable approximations of actual conditions. Recommendations are made for the addition of critical parameters and measurement sites into routine monitoring systems in mountain basins.

Upgrades to the REA method for producing probabilistic climate change projections

NASA Astrophysics Data System (ADS)

Xu, Ying; Gao, Xuejie; Giorgi, Filippo

2010-05-01

We present an augmented version of the Reliability Ensemble Averaging (REA) method designed to generate probabilistic climate change information from ensembles of climate model simulations. Compared to the original version, the augmented one includes consideration of multiple variables and statistics in the calculation of the performance-based weights. In addition, the model convergence criterion previously employed is removed. The method is applied to the calculation of changes in mean and variability for temperature and precipitation over different sub-regions of East Asia based on the recently completed CMIP3 multi-model ensemble. Comparison of the new and old REA methods, along with the simple averaging procedure, and the use of different combinations of performance metrics shows that at fine sub-regional scales the choice of weighting is relevant. This is mostly because the models show a substantial spread in performance for the simulation of precipitation statistics, a result that supports the use of model weighting as a useful option to account for wide ranges of quality of models. The REA method, and in particular the upgraded one, provides a simple and flexible framework for assessing the uncertainty related to the aggregation of results from ensembles of models in order to produce climate change information at the regional scale. KEY WORDS: REA method, Climate change, CMIP3

Overview of global climate change and carbon sequestration

Treesearch

Kurt Johnsen

2004-01-01

The potential influence of global climate change on southern forests is uncertain. Outputs of climate change models differ considerably in their projections for precipitation and other variables that affect forests. Forest responses, particularly effects on competition among species, are difficult to assess. Even the responses of relatively simple ecosystems, such as...

Climate change unlikely to increase malaria burden in West Africa

NASA Astrophysics Data System (ADS)

Yamana, Teresa K.; Bomblies, Arne; Eltahir, Elfatih A. B.

2016-11-01

The impact of climate change on malaria transmission has been hotly debated. Recent conclusions have been drawn using relatively simple biological models and statistical approaches, with inconsistent predictions. Consequently, the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5) echoes this uncertainty, with no clear guidance for the impacts of climate change on malaria transmission, yet recognizing a strong association between local climate and malaria. Here, we present results from a decade-long study involving field observations and a sophisticated model simulating village-scale transmission. We drive the malaria model using select climate models that correctly reproduce historical West African climate, and project reduced malaria burden in a western sub-region and insignificant impact in an eastern sub-region. Projected impacts of climate change on malaria transmission in this region are not of serious concern.

Oscillations in a simple climate-vegetation model

NASA Astrophysics Data System (ADS)

Rombouts, J.; Ghil, M.

2015-05-01

We formulate and analyze a simple dynamical systems model for climate-vegetation interaction. The planet we consider consists of a large ocean and a land surface on which vegetation can grow. The temperature affects vegetation growth on land and the amount of sea ice on the ocean. Conversely, vegetation and sea ice change the albedo of the planet, which in turn changes its energy balance and hence the temperature evolution. Our highly idealized, conceptual model is governed by two nonlinear, coupled ordinary differential equations, one for global temperature, the other for vegetation cover. The model exhibits either bistability between a vegetated and a desert state or oscillatory behavior. The oscillations arise through a Hopf bifurcation off the vegetated state, when the death rate of vegetation is low enough. These oscillations are anharmonic and exhibit a sawtooth shape that is characteristic of relaxation oscillations, as well as suggestive of the sharp deglaciations of the Quaternary. Our model's behavior can be compared, on the one hand, with the bistability of even simpler, Daisyworld-style climate-vegetation models. On the other hand, it can be integrated into the hierarchy of models trying to simulate and explain oscillatory behavior in the climate system. Rigorous mathematical results are obtained that link the nature of the feedbacks with the nature and the stability of the solutions. The relevance of model results to climate variability on various timescales is discussed.

Oscillations in a simple climate-vegetation model

NASA Astrophysics Data System (ADS)

Rombouts, J.; Ghil, M.

2015-02-01

We formulate and analyze a simple dynamical systems model for climate-vegetation interaction. The planet we consider consists of a large ocean and a land surface on which vegetation can grow. The temperature affects vegetation growth on land and the amount of sea ice on the ocean. Conversely, vegetation and sea ice change the albedo of the planet, which in turn changes its energy balance and hence the temperature evolution. Our highly idealized, conceptual model is governed by two nonlinear, coupled ordinary differential equations, one for global temperature, the other for vegetation cover. The model exhibits either bistability between a vegetated and a desert state or oscillatory behavior. The oscillations arise through a Hopf bifurcation off the vegetated state, when the death rate of vegetation is low enough. These oscillations are anharmonic and exhibit a sawtooth shape that is characteristic of relaxation oscillations, as well as suggestive of the sharp deglaciations of the Quaternary. Our model's behavior can be compared, on the one hand, with the bistability of even simpler, Daisyworld-style climate-vegetation models. On the other hand, it can be integrated into the hierarchy of models trying to simulate and explain oscillatory behavior in the climate system. Rigorous mathematical results are obtained that link the nature of the feedbacks with the nature and the stability of the solutions. The relevance of model results to climate variability on various time scales is discussed.

A Simple Water Balance Model Adapted for Arctic Hydrology Reveals Glacier and Streamflow Responses to Climate Change in the Copper River, Alaska

NASA Astrophysics Data System (ADS)

Valentin, M. M.; Hay, L.; Van Beusekom, A. E.; Viger, R. J.; Hogue, T. S.

2016-12-01

Forecasting the hydrologic response to climate change in Alaska's glaciated watersheds remains daunting for hydrologists due to sparse field data and few modeling tools, which frustrates efforts to manage and protect critical aquatic habitat. Approximately 20% of the 64,000 square kilometer Copper River watershed is glaciated, and its glacier-fed tributaries support renowned salmon fisheries that are economically, culturally, and nutritionally invaluable to the local communities. This study adapts a simple, yet powerful, conceptual hydrologic model to simulate changes in the timing and volume of streamflow in the Copper River, Alaska as glaciers change under plausible future climate scenarios. The USGS monthly water balance model (MWBM), a hydrologic tool used for two decades to evaluate a broad range of hydrologic questions in the contiguous U.S., was enhanced to include glacier melt simulations and remotely sensed data. In this presentation we summarize the technical details behind our MWBM adaptation and demonstrate its use in the Copper River Basin to evaluate glacier and streamflow responses to climate change.

Application of synthetic scenarios to address water resource concerns: A management-guided case study from the Upper Colorado River Basin

USGS Publications Warehouse

McAfee, Stephanie A.; Pederson, Gregory T.; Woodhouse, Connie A.; McCabe, Gregory

2017-01-01

Water managers are increasingly interested in better understanding and planning for projected resource impacts from climate change. In this management-guided study, we use a very large suite of synthetic climate scenarios in a statistical modeling framework to simultaneously evaluate how (1) average temperature and precipitation changes, (2) initial basin conditions, and (3) temporal characteristics of the input climate data influence water-year flow in the Upper Colorado River. The results here suggest that existing studies may underestimate the degree of uncertainty in future streamflow, particularly under moderate temperature and precipitation changes. However, we also find that the relative severity of future flow projections within a given climate scenario can be estimated with simple metrics that characterize the input climate data and basin conditions. These results suggest that simple testing, like the analyses presented in this paper, may be helpful in understanding differences between existing studies or in identifying specific conditions for physically based mechanistic modeling. Both options could reduce overall cost and improve the efficiency of conducting climate change impacts studies.

Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*

DOE PAGES

Castruccio, Stefano; McInerney, David J.; Stein, Michael L.; ...

2014-02-24

The authors describe a new approach for emulating the output of a fully coupled climate model under arbitrary forcing scenarios that is based on a small set of precomputed runs from the model. Temperature and precipitation are expressed as simple functions of the past trajectory of atmospheric CO 2 concentrations, and a statistical model is fit using a limited set of training runs. The approach is demonstrated to be a useful and computationally efficient alternative to pattern scaling and captures the nonlinear evolution of spatial patterns of climate anomalies inherent in transient climates. The approach does as well as patternmore » scaling in all circumstances and substantially better in many; it is not computationally demanding; and, once the statistical model is fit, it produces emulated climate output effectively instantaneously. In conclusion, it may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections.« less

Using Four Downscaling Techniques to Characterize Uncertainty in Updating Intensity-Duration-Frequency Curves Under Climate Change

NASA Astrophysics Data System (ADS)

Cook, L. M.; Samaras, C.; McGinnis, S. A.

2017-12-01

Intensity-duration-frequency (IDF) curves are a common input to urban drainage design, and are used to represent extreme rainfall in a region. As rainfall patterns shift into a non-stationary regime as a result of climate change, these curves will need to be updated with future projections of extreme precipitation. Many regions have begun to update these curves to reflect the trends from downscaled climate models; however, few studies have compared the methods for doing so, as well as the uncertainty that results from the selection of the native grid scale and temporal resolution of the climate model. This study examines the variability in updated IDF curves for Pittsburgh using four different methods for adjusting gridded regional climate model (RCM) outputs into station scale precipitation extremes: (1) a simple change factor applied to observed return levels, (2) a naïve adjustment of stationary and non-stationary Generalized Extreme Value (GEV) distribution parameters, (3) a transfer function of the GEV parameters from the annual maximum series, and (4) kernel density distribution mapping bias correction of the RCM time series. Return level estimates (rainfall intensities) and confidence intervals from these methods for the 1-hour to 48-hour duration are tested for sensitivity to the underlying spatial and temporal resolution of the climate ensemble from the NA-CORDEX project, as well as, the future time period for updating. The first goal is to determine if uncertainty is highest for: (i) the downscaling method, (ii) the climate model resolution, (iii) the climate model simulation, (iv) the GEV parameters, or (v) the future time period examined. Initial results of the 6-hour, 10-year return level adjusted with the simple change factor method using four climate model simulations of two different spatial resolutions show that uncertainty is highest in the estimation of the GEV parameters. The second goal is to determine if complex downscaling methods and high-resolution climate models are necessary for updating, or if simpler methods and lower resolution climate models will suffice. The final results can be used to inform the most appropriate method and climate model resolutions to use for updating IDF curves for urban drainage design.

A new paradigm for predicting zonal-mean climate and climate change

NASA Astrophysics Data System (ADS)

Armour, K.; Roe, G.; Donohoe, A.; Siler, N.; Markle, B. R.; Liu, X.; Feldl, N.; Battisti, D. S.; Frierson, D. M.

2016-12-01

How will the pole-to-equator temperature gradient, or large-scale patterns of precipitation, change under global warming? Answering such questions typically involves numerical simulations with comprehensive general circulation models (GCMs) that represent the complexities of climate forcing, radiative feedbacks, and atmosphere and ocean dynamics. Yet, our understanding of these predictions hinges on our ability to explain them through the lens of simple models and physical theories. Here we present evidence that zonal-mean climate, and its changes, can be understood in terms of a moist energy balance model that represents atmospheric heat transport as a simple diffusion of latent and sensible heat (as a down-gradient transport of moist static energy, with a diffusivity coefficient that is nearly constant with latitude). We show that the theoretical underpinnings of this model derive from the principle of maximum entropy production; that its predictions are empirically supported by atmospheric reanalyses; and that it successfully predicts the behavior of a hierarchy of climate models - from a gray radiation aquaplanet moist GCM, to comprehensive GCMs participating in CMIP5. As an example of the power of this paradigm, we show that, given only patterns of local radiative feedbacks and climate forcing, the moist energy balance model accurately predicts the evolution of zonal-mean temperature and atmospheric heat transport as simulated by the CMIP5 ensemble. These results suggest that, despite all of its dynamical complexity, the atmosphere essentially responds to energy imbalances by simply diffusing latent and sensible heat down-gradient; this principle appears to explain zonal-mean climate and its changes under global warming.

Energy-balance climate models

NASA Technical Reports Server (NTRS)

North, G. R.; Cahalan, R. F.; Coakley, J. A., Jr.

1980-01-01

An introductory survey of the global energy balance climate models is presented with an emphasis on analytical results. A sequence of increasingly complicated models involving ice cap and radiative feedback processes are solved and the solutions and parameter sensitivities are studied. The model parameterizations are examined critically in light of many current uncertainties. A simple seasonal model is used to study the effects of changes in orbital elements on the temperature field. A linear stability theorem and a complete nonlinear stability analysis for the models are developed. Analytical solutions are also obtained for the linearized models driven by stochastic forcing elements. In this context the relation between natural fluctuation statistics and climate sensitivity is stressed.

Energy balance climate models

NASA Technical Reports Server (NTRS)

North, G. R.; Cahalan, R. F.; Coakley, J. A., Jr.

1981-01-01

An introductory survey of the global energy balance climate models is presented with an emphasis on analytical results. A sequence of increasingly complicated models involving ice cap and radiative feedback processes are solved, and the solutions and parameter sensitivities are studied. The model parameterizations are examined critically in light of many current uncertainties. A simple seasonal model is used to study the effects of changes in orbital elements on the temperature field. A linear stability theorem and a complete nonlinear stability analysis for the models are developed. Analytical solutions are also obtained for the linearized models driven by stochastic forcing elements. In this context the relation between natural fluctuation statistics and climate sensitivity is stressed.

Integration of Linear Dynamic Emission and Climate Models with Air Traffic Simulations

NASA Technical Reports Server (NTRS)

Sridhar, Banavar; Ng, Hok K.; Chen, Neil Y.

2012-01-01

Future air traffic management systems are required to balance the conflicting objectives of maximizing safety and efficiency of traffic flows while minimizing the climate impact of aviation emissions and contrails. Integrating emission and climate models together with air traffic simulations improve the understanding of the complex interaction between the physical climate system, carbon and other greenhouse gas emissions and aviation activity. This paper integrates a national-level air traffic simulation and optimization capability with simple climate models and carbon cycle models, and climate metrics to assess the impact of aviation on climate. The capability can be used to make trade-offs between extra fuel cost and reduction in global surface temperature change. The parameters in the simulation can be used to evaluate the effect of various uncertainties in emission models and contrails and the impact of different decision horizons. Alternatively, the optimization results from the simulation can be used as inputs to other tools that monetize global climate impacts like the FAA s Aviation Environmental Portfolio Management Tool for Impacts.

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

PubMed Central

Lange, Stefan; Donges, Jonathan F.; Volkholz, Jan; Kurths, Jürgen

2015-01-01

A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation. Building on a recent study by Feldhoff et al. [1] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system. Three types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge- and node-weighted graphs are discussed. PMID:25856374

Local difference measures between complex networks for dynamical system model evaluation.

PubMed

Lange, Stefan; Donges, Jonathan F; Volkholz, Jan; Kurths, Jürgen

2015-01-01

A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation.Building on a recent study by Feldhoff et al. [8] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system [corrected]. types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge- and node-weighted graphs are discussed.

Probabilistic Integrated Assessment of ``Dangerous'' Climate Change

NASA Astrophysics Data System (ADS)

Mastrandrea, Michael D.; Schneider, Stephen H.

2004-04-01

Climate policy decisions are being made despite layers of uncertainty. Such decisions directly influence the potential for ``dangerous anthropogenic interference with the climate system.'' We mapped a metric for this concept, based on Intergovernmental Panel on Climate Change assessment of climate impacts, onto probability distributions of future climate change produced from uncertainty in key parameters of the coupled social-natural system-climate sensitivity, climate damages, and discount rate. Analyses with a simple integrated assessment model found that, under midrange assumptions, endogenously calculated, optimal climate policy controls can reduce the probability of dangerous anthropogenic interference from ~45% under minimal controls to near zero.

Benchmarking novel approaches for modelling species range dynamics

PubMed Central

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H.; Moore, Kara A.; Zimmermann, Niklaus E.

2016-01-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasise several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. PMID:26872305

Benchmarking novel approaches for modelling species range dynamics.

PubMed

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H; Moore, Kara A; Zimmermann, Niklaus E

2016-08-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. © 2016 John Wiley & Sons Ltd.

Probabilistic Evaluation of Competing Climate Models

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Chatterjee, S.; Heyman, M.; Cressie, N.

2017-12-01

A standard paradigm for assessing the quality of climate model simulations is to compare what these models produce for past and present time periods, to observations of the past and present. Many of these comparisons are based on simple summary statistics called metrics. Here, we propose an alternative: evaluation of competing climate models through probabilities derived from tests of the hypothesis that climate-model-simulated and observed time sequences share common climate-scale signals. The probabilities are based on the behavior of summary statistics of climate model output and observational data, over ensembles of pseudo-realizations. These are obtained by partitioning the original time sequences into signal and noise components, and using a parametric bootstrap to create pseudo-realizations of the noise sequences. The statistics we choose come from working in the space of decorrelated and dimension-reduced wavelet coefficients. We compare monthly sequences of CMIP5 model output of average global near-surface temperature anomalies to similar sequences obtained from the well-known HadCRUT4 data set, as an illustration.

Modeling Impact of Urbanization in US Cities Using Simple Biosphere Model SiB2

NASA Technical Reports Server (NTRS)

Zhang, Ping; Bounoua, Lahouari; Thome, Kurtis; Wolfe, Robert

2016-01-01

We combine Landsat- and the Moderate Resolution Imaging Spectroradiometer (MODIS)-based products, as well as climate drivers from Phase 2 of the North American Land Data Assimilation System (NLDAS-2) in a Simple Biosphere land surface model (SiB2) to assess the impact of urbanization in continental USA (excluding Alaska and Hawaii). More than 300 cities and their surrounding suburban and rural areas are defined in this study to characterize the impact of urbanization on surface climate including surface energy, carbon budget, and water balance. These analyses reveal an uneven impact of urbanization across the continent that should inform upon policy options for improving urban growth including heat mitigation and energy use, carbon sequestration and flood prevention.

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.

Predicting ecosystem shifts requires new approaches that integrate the effects of climate change across entire systems

PubMed Central

Russell, Bayden D.; Harley, Christopher D. G.; Wernberg, Thomas; Mieszkowska, Nova; Widdicombe, Stephen; Hall-Spencer, Jason M.; Connell, Sean D.

2012-01-01

Most studies that forecast the ecological consequences of climate change target a single species and a single life stage. Depending on climatic impacts on other life stages and on interacting species, however, the results from simple experiments may not translate into accurate predictions of future ecological change. Research needs to move beyond simple experimental studies and environmental envelope projections for single species towards identifying where ecosystem change is likely to occur and the drivers for this change. For this to happen, we advocate research directions that (i) identify the critical species within the target ecosystem, and the life stage(s) most susceptible to changing conditions and (ii) the key interactions between these species and components of their broader ecosystem. A combined approach using macroecology, experimentally derived data and modelling that incorporates energy budgets in life cycle models may identify critical abiotic conditions that disproportionately alter important ecological processes under forecasted climates. PMID:21900317

Desert dust and anthropogenic aerosol interactions in the Community Climate System Model coupled-carbon-climate model

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

Mahowald, Natalie; Rothenberg, D.; Lindsay, Keith

2011-02-01

Coupled-carbon-climate simulations are an essential tool for predicting the impact of human activity onto the climate and biogeochemistry. Here we incorporate prognostic desert dust and anthropogenic aerosols into the CCSM3.1 coupled carbon-climate model and explore the resulting interactions with climate and biogeochemical dynamics through a series of transient anthropogenic simulations (20th and 21st centuries) and sensitivity studies. The inclusion of prognostic aerosols into this model has a small net global cooling effect on climate but does not significantly impact the globally averaged carbon cycle; we argue that this is likely to be because the CCSM3.1 model has a small climatemore » feedback onto the carbon cycle. We propose a mechanism for including desert dust and anthropogenic aerosols into a simple carbon-climate feedback analysis to explain the results of our and previous studies. Inclusion of aerosols has statistically significant impacts on regional climate and biogeochemistry, in particular through the effects on the ocean nitrogen cycle and primary productivity of altered iron inputs from desert dust deposition.« less

The economics and ethics of aerosol geoengineering strategies

NASA Astrophysics Data System (ADS)

Goes, Marlos; Keller, Klaus; Tuana, Nancy

2010-05-01

Anthropogenic greenhouse gas emissions are changing the Earth's climate and impose substantial risks for current and future generations. What are scientifically sound, economically viable, and ethically defendable strategies to manage these climate risks? Ratified international agreements call for a reduction of greenhouse gas emissions to avoid dangerous anthropogenic interference with the climate system. Recent proposals, however, call for a different approach: geoengineering climate by injecting aerosol precursors into the stratosphere. Published economic studies typically neglect the risks of aerosol geoengineering due to (i) a potential failure to sustain the aerosol forcing and (ii) due to potential negative impacts associated with aerosol forcings. Here we use a simple integrated assessment model of climate change to analyze potential economic impacts of aerosol geoengineering strategies over a wide range of uncertain parameters such as climate sensitivity, the economic damages due to climate change, and the economic damages due to aerosol geoengineering forcings. The simplicity of the model provides the advantages of parsimony and transparency, but it also imposes considerable caveats. For example, the analysis is based on a globally aggregated model and is hence silent on intragenerational distribution of costs and benefits. In addition, the analysis neglects the effects of future learning and is based on a simple representation of climate change impacts. We use this integrated assessment model to show three main points. First, substituting aerosol geoengineering for the reduction of greenhouse gas emissions can fail the test of economic efficiency. One key to this finding is that a failure to sustain the aerosol forcing can lead to sizeable and abrupt climatic changes. The monetary damages due to such a discontinuous aerosol geoengineering can dominate the cost-benefit analysis because the monetary damages of climate change are expected to increase with the rate of change. Second, the relative contribution of aerosol geoengineering to an economically optimal portfolio hinges critically on deeply uncertain estimates of the damages due to aerosol forcing. Even if we assume that aerosol forcing could be deployed continuously, the aerosol geoengineering does not considerably displace the reduction of greenhouse gas emissions in the simple economic optimal growth model until the damages due to the aerosol forcing are rather low. Third, deploying aerosol geoengineering may also fail an ethical test regarding issues of intergenerational justice. Substituting aerosol geoengineering for reducing greenhouse gas emissions constitutes a conscious risk transfer to future generations, for example due to the increased risk of future abrupt climate change. This risk transfer is in tension with the requirement of intergenerational justice that present generations should not create benefits for themselves in exchange for burdens on future generations.

Real-Time Climate Simulations in the Interactive 3D Game Universe Sandbox ²

NASA Astrophysics Data System (ADS)

Goldenson, N. L.

2014-12-01

Exploration in an open-ended computer game is an engaging way to explore climate and climate change. Everyone can explore physical models with real-time visualization in the educational simulator Universe Sandbox ² (universesandbox.com/2), which includes basic climate simulations on planets. I have implemented a time-dependent, one-dimensional meridional heat transport energy balance model to run and be adjustable in real time in the midst of a larger simulated system. Universe Sandbox ² is based on the original game - at its core a gravity simulator - with other new physically-based content for stellar evolution, and handling collisions between bodies. Existing users are mostly science enthusiasts in informal settings. We believe that this is the first climate simulation to be implemented in a professionally developed computer game with modern 3D graphical output in real time. The type of simple climate model we've adopted helps us depict the seasonal cycle and the more drastic changes that come from changing the orbit or other external forcings. Users can alter the climate as the simulation is running by altering the star(s) in the simulation, dragging to change orbits and obliquity, adjusting the climate simulation parameters directly or changing other properties like CO2 concentration that affect the model parameters in representative ways. Ongoing visuals of the expansion and contraction of sea ice and snow-cover respond to the temperature calculations, and make it accessible to explore a variety of scenarios and intuitive to understand the output. Variables like temperature can also be graphed in real time. We balance computational constraints with the ability to capture the physical phenomena we wish to visualize, giving everyone access to a simple open-ended meridional energy balance climate simulation to explore and experiment with. The software lends itself to labs at a variety of levels about climate concepts including seasons, the Greenhouse effect, reservoirs and flows, albedo feedback, Snowball Earth, climate sensitivity, and model experiment design. Climate calculations are extended to Mars with some modifications to the Earth climate component, and could be used in lessons about the Mars atmosphere, and exploring scenarios of Mars climate history.

Paleoclimate diagnostics: consistent large-scale temperature responses in warm and cold climates

NASA Astrophysics Data System (ADS)

Izumi, Kenji; Bartlein, Patrick; Harrison, Sandy

2015-04-01

The CMIP5 model simulations of the large-scale temperature responses to increased raditative forcing include enhanced land-ocean contrast, stronger response at higher latitudes than in the tropics, and differential responses in warm and cool season climates to uniform forcing. Here we show that these patterns are also characteristic of CMIP5 model simulations of past climates. The differences in the responses over land as opposed to over the ocean, between high and low latitudes, and between summer and winter are remarkably consistent (proportional and nearly linear) across simulations of both cold and warm climates. Similar patterns also appear in historical observations and paleoclimatic reconstructions, implying that such responses are characteristic features of the climate system and not simple model artifacts, thereby increasing our confidence in the ability of climate models to correctly simulate different climatic states. We also show the possibility that a small set of common mechanisms control these large-scale responses of the climate system across multiple states.

Energy Balance Models and Planetary Dynamics

NASA Technical Reports Server (NTRS)

Domagal-Goldman, Shawn

2012-01-01

We know that planetary dynamics can have a significant affect on the climate of planets. Planetary dynamics dominate the glacial-interglacial periods on Earth, leaving a significant imprint on the geological record. They have also been demonstrated to have a driving influence on the climates of other planets in our solar system. We should therefore expect th.ere to be similar relationships on extrasolar planets. Here we describe a simple energy balance model that can predict the growth and thickness of glaciers, and their feedbacks on climate. We will also describe model changes that we have made to include planetary dynamics effects. This is the model we will use at the start of our collaboration to handle the influence of dynamics on climate.

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.].

Performance of the Hydrological Portion of a Simple Water Quality Model in Different Climatic Regions

NASA Astrophysics Data System (ADS)

Moore, K.; Pierson, D.; Pettersson, K.; Naden, P.; Allott, N.; Jennings, E.; Tamm, T.; Järvet, A.; Nickus, U.; Thies, H.; Arvola, L.; Järvinen, M.; Schneiderman, E.; Zion, M.; Lounsbury, D.

2004-05-01

We are applying an existing watershed model in the EU CLIME (Climate and Lake Impacts in Europe) project to evaluate the effects of weather on seasonal and annual delivery of N, P, and DOC to lakes. Model calibration is based on long-term records of weather and water quality data collected from sites in different climatic regions spread across Europe and in New York State. The overall aim of the CLIME project is to develop methods and models to support lake and catchment management under current climate conditions and make predictions under future climate scenarios. Scientists from 10 partner countries are collaborating on developing a consistent approach to defining model parameters for the Generalized Watershed Loading Functions (GWLF) model, one of a larger suite of models used in the project. An example of the approach for the hydrological portion of the GWLF model will be presented, with consideration of the balance between model simplicity, ease of use, data requirements, and realistic predictions.

Combining Statistics and Physics to Improve Climate Downscaling

NASA Astrophysics Data System (ADS)

Gutmann, E. D.; Eidhammer, T.; Arnold, J.; Nowak, K.; Clark, M. P.

2017-12-01

Getting useful information from climate models is an ongoing problem that has plagued climate science and hydrologic prediction for decades. While it is possible to develop statistical corrections for climate models that mimic current climate almost perfectly, this does not necessarily guarantee that future changes are portrayed correctly. In contrast, convection permitting regional climate models (RCMs) have begun to provide an excellent representation of the regional climate system purely from first principles, providing greater confidence in their change signal. However, the computational cost of such RCMs prohibits the generation of ensembles of simulations or long time periods, thus limiting their applicability for hydrologic applications. Here we discuss a new approach combining statistical corrections with physical relationships for a modest computational cost. We have developed the Intermediate Complexity Atmospheric Research model (ICAR) to provide a climate and weather downscaling option that is based primarily on physics for a fraction of the computational requirements of a traditional regional climate model. ICAR also enables the incorporation of statistical adjustments directly within the model. We demonstrate that applying even simple corrections to precipitation while the model is running can improve the simulation of land atmosphere feedbacks in ICAR. For example, by incorporating statistical corrections earlier in the modeling chain, we permit the model physics to better represent the effect of mountain snowpack on air temperature changes.

Quantifying climate feedbacks in polar regions.

PubMed

Goosse, Hugues; Kay, Jennifer E; Armour, Kyle C; Bodas-Salcedo, Alejandro; Chepfer, Helene; Docquier, David; Jonko, Alexandra; Kushner, Paul J; Lecomte, Olivier; Massonnet, François; Park, Hyo-Seok; Pithan, Felix; Svensson, Gunilla; Vancoppenolle, Martin

2018-05-15

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range of feedbacks, offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.

A simple scaling approach to produce climate scenarios of local precipitation extremes for the Netherlands

NASA Astrophysics Data System (ADS)

Lenderink, Geert; Attema, Jisk

2015-08-01

Scenarios of future changes in small scale precipitation extremes for the Netherlands are presented. These scenarios are based on a new approach whereby changes in precipitation extremes are set proportional to the change in water vapor amount near the surface as measured by the 2m dew point temperature. This simple scaling framework allows the integration of information derived from: (i) observations, (ii) a new unprecedentedly large 16 member ensemble of simulations with the regional climate model RACMO2 driven by EC-Earth, and (iii) short term integrations with a non-hydrostatic model Harmonie. Scaling constants are based on subjective weighting (expert judgement) of the three different information sources taking also into account previously published work. In all scenarios local precipitation extremes increase with warming, yet with broad uncertainty ranges expressing incomplete knowledge of how convective clouds and the atmospheric mesoscale circulation will react to climate change.

On Diffusive Climatological Models.

NASA Astrophysics Data System (ADS)

Griffel, D. H.; Drazin, P. G.

1981-11-01

A simple, zonally and annually averaged, energy-balance climatological model with diffusive heat transport and nonlinear albedo feedback is solved numerically. Some parameters of the model are varied, one by one, to find the resultant effects on the steady solution representing the climate. In particular, the outward radiation flux, the insulation distribution and the albedo parameterization are varied. We have found an accurate yet simple analytic expression for the mean annual insolation as a function of latitude and the obliquity of the Earth's rotation axis; this has enabled us to consider the effects of the oscillation of the obliquity. We have used a continuous albedo function which fits the observed values; it considerably reduces the sensitivity of the model. Climatic cycles, calculated by solving the time-dependent equation when parameters change slowly and periodically, are compared qualitatively with paleoclimatic records.

Projections of increased and decreased dengue incidence under climate change.

PubMed

Williams, C R; Mincham, G; Faddy, H; Viennet, E; Ritchie, S A; Harley, D

2016-10-01

Dengue is the world's most prevalent mosquito-borne disease, with more than 200 million people each year becoming infected. We used a mechanistic virus transmission model to determine whether climate warming would change dengue transmission in Australia. Using two climate models each with two carbon emission scenarios, we calculated future dengue epidemic potential for the period 2046-2064. Using the ECHAM5 model, decreased dengue transmission was predicted under the A2 carbon emission scenario, whereas some increases are likely under the B1 scenario. Dengue epidemic potential may decrease under climate warming due to mosquito breeding sites becoming drier and mosquito survivorship declining. These results contradict most previous studies that use correlative models to show increased dengue transmission under climate warming. Dengue epidemiology is determined by a complex interplay between climatic, human host, and pathogen factors. It is therefore naive to assume a simple relationship between climate and incidence, and incorrect to state that climate warming will uniformly increase dengue transmission, although in general the health impacts of climate change will be negative.

A simple framework for relating variations in runoff to variations in climatic conditions and catchment properties

NASA Astrophysics Data System (ADS)

Roderick, Michael L.; Farquhar, Graham D.

2011-12-01

We use the Budyko framework to calculate catchment-scale evapotranspiration (E) and runoff (Q) as a function of two climatic factors, precipitation (P) and evaporative demand (Eo = 0.75 times the pan evaporation rate), and a third parameter that encodes the catchment properties (n) and modifies how P is partitioned between E and Q. This simple theory accurately predicted the long-term evapotranspiration (E) and runoff (Q) for the Murray-Darling Basin (MDB) in southeast Australia. We extend the theory by developing a simple and novel analytical expression for the effects on E and Q of small perturbations in P, Eo, and n. The theory predicts that a 10% change in P, with all else constant, would result in a 26% change in Q in the MDB. Future climate scenarios (2070-2099) derived using Intergovernmental Panel on Climate Change AR4 climate model output highlight the diversity of projections for P (±30%) with a correspondingly large range in projections for Q (±80%) in the MDB. We conclude with a qualitative description about the impact of changes in catchment properties on water availability and focus on the interaction between vegetation change, increasing atmospheric [CO2], and fire frequency. We conclude that the modern version of the Budyko framework is a useful tool for making simple and transparent estimates of changes in water availability.

A Simple Exploration of Complexity at the Climate-Weather-Social-Conflict Nexus

NASA Astrophysics Data System (ADS)

Shaw, M.

2017-12-01

The conceptualization, exploration, and prediction of interplay between climate, weather, important resources, and social and economic - so political - human behavior is cast, and analyzed, in terms familiar from statistical physics and nonlinear dynamics. A simple threshold toy model is presented which emulates human tendencies to either actively engage in responses deriving, in part, from environmental circumstances or to maintain some semblance of status quo, formulated based on efforts drawn from the sociophysics literature - more specifically vis a vis a model akin to spin glass depictions of human behavior - with threshold/switching of individual and collective dynamics influenced by relatively more detailed weather and land surface model (hydrological) analyses via a land data assimilation system (a custom rendition of the NASA GSFC Land Information System). Parameters relevant to human systems' - e.g., individual and collective switching - sensitivity to hydroclimatology are explored towards investigation of overall system behavior; i.e., fixed points/equilibria, oscillations, and bifurcations of systems composed of human interactions and responses to climate and weather through, e.g., agriculture. We discuss implications in terms of conceivable impacts of climate change and associated natural disasters on socioeconomics, politics, and power transfer, drawing from relatively recent literature concerning human conflict.

Automated parameter tuning applied to sea ice in a global climate model

NASA Astrophysics Data System (ADS)

Roach, Lettie A.; Tett, Simon F. B.; Mineter, Michael J.; Yamazaki, Kuniko; Rae, Cameron D.

2018-01-01

This study investigates the hypothesis that a significant portion of spread in climate model projections of sea ice is due to poorly-constrained model parameters. New automated methods for optimization are applied to historical sea ice in a global coupled climate model (HadCM3) in order to calculate the combination of parameters required to reduce the difference between simulation and observations to within the range of model noise. The optimized parameters result in a simulated sea-ice time series which is more consistent with Arctic observations throughout the satellite record (1980-present), particularly in the September minimum, than the standard configuration of HadCM3. Divergence from observed Antarctic trends and mean regional sea ice distribution reflects broader structural uncertainty in the climate model. We also find that the optimized parameters do not cause adverse effects on the model climatology. This simple approach provides evidence for the contribution of parameter uncertainty to spread in sea ice extent trends and could be customized to investigate uncertainties in other climate variables.

Variations in planetary convection via the effect of climate on damage

NASA Astrophysics Data System (ADS)

Landuyt, W.; Bercovici, D.

2008-12-01

The generation of plate tectonics on Earth and its absence on the other terrestrial planets (especially Venus) remains a significant conundrum in geophysics. We propose a model for the generation of plate tectonics that suggests an important interaction between a planet's climate and its lithospheric damage behavior; and thus provides a simple explanation for the tectonic difference between Earth and Venus. We propose that high surface temperatures will lead to higher healing rates (e.g. grain growth) in the lithosphere that will act to suppress localization, plate boundary formation, and subduction. This leads to episodic or stagnant lid convection on Venus because of its hotter climate. In contrast, Earth's cooler climate promotes damage and plate boundary formation. The damage rheology presented in this paper attempts to describe the evolution of grain size by allowing for grain reduction via deformational work input and grain growth via surface tension- driven coarsening. We present results from convection simulations and a simple "drip-instability" model to test our hypothesis. The results suggest the feasibility of our proposed hypothesis that the influence of climate on damage may control the mode of tectonics on a planet.

Detection of greenhouse-gas-induced climatic change. Progress report, July 1, 1994--July 31, 1995

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

Jones, P.D.; Wigley, T.M.L.

1995-07-21

The objective of this research is to assembly and analyze instrumental climate data and to develop and apply climate models as a basis for detecting greenhouse-gas-induced climatic change, and validation of General Circulation Models. In addition to changes due to variations in anthropogenic forcing, including greenhouse gas and aerosol concentration changes, the global climate system exhibits a high degree of internally-generated and externally-forced natural variability. To detect the anthropogenic effect, its signal must be isolated from the ``noise`` of this natural climatic variability. A high quality, spatially extensive data base is required to define the noise and its spatial characteristics.more » To facilitate this, available land and marine data bases will be updated and expanded. The data will be analyzed to determine the potential effects on climate of greenhouse gas and aerosol concentration changes and other factors. Analyses will be guided by a variety of models, from simple energy balance climate models to coupled atmosphere ocean General Circulation Models. These analyses are oriented towards obtaining early evidence of anthropogenic climatic change that would lead either to confirmation, rejection or modification of model projections, and towards the statistical validation of General Circulation Model control runs and perturbation experiments.« less

Continuous versus discontinuous albedo representations in a simple diffusive climate model

NASA Astrophysics Data System (ADS)

Simmons, P. A.; Griffel, D. H.

1988-07-01

A one-dimensional annually and zonally averaged energy-balance model, with diffusive meridional heat transport and including icealbedo feedback, is considered. This type of model is found to be very sensitive to the form of albedo used. The solutions for a discontinuous step-function albedo are compared to those for a more realistic smoothly varying albedo. The smooth albedo gives a closer fit to present conditions, but the discontinuous form gives a better representation of climates in earlier epochs.

Comparing convective heat fluxes derived from thermodynamics to a radiative-convective model and GCMs

NASA Astrophysics Data System (ADS)

Dhara, Chirag; Renner, Maik; Kleidon, Axel

2015-04-01

The convective transport of heat and moisture plays a key role in the climate system, but the transport is typically parameterized in models. Here, we aim at the simplest possible physical representation and treat convective heat fluxes as the result of a heat engine. We combine the well-known Carnot limit of this heat engine with the energy balances of the surface-atmosphere system that describe how the temperature difference is affected by convective heat transport, yielding a maximum power limit of convection. This results in a simple analytic expression for convective strength that depends primarily on surface solar absorption. We compare this expression with an idealized grey atmosphere radiative-convective (RC) model as well as Global Circulation Model (GCM) simulations at the grid scale. We find that our simple expression as well as the RC model can explain much of the geographic variation of the GCM output, resulting in strong linear correlations among the three approaches. The RC model, however, shows a lower bias than our simple expression. We identify the use of the prescribed convective adjustment in RC-like models as the reason for the lower bias. The strength of our model lies in its ability to capture the geographic variation of convective strength with a parameter-free expression. On the other hand, the comparison with the RC model indicates a method for improving the formulation of radiative transfer in our simple approach. We also find that the latent heat fluxes compare very well among the approaches, as well as their sensitivity to surface warming. What our comparison suggests is that the strength of convection and their sensitivity in the climatic mean can be estimated relatively robustly by rather simple approaches.

Estimation of the fractional coverage of rainfall in climate models

NASA Technical Reports Server (NTRS)

Eltahir, E. A. B.; Bras, R. L.

1993-01-01

The fraction of the grid cell area covered by rainfall, mu, is an essential parameter in descriptions of land surface hydrology in climate models. A simple procedure is presented for estimating this fraction, based on extensive observations of storm areas and rainfall volumes. Storm area and rainfall volume are often linearly related; this relation can be used to compute the storm area from the volume of rainfall simulated by a climate model. A formula is developed for computing mu, which describes the dependence of the fractional coverage of rainfall on the season of the year, the geographical region, rainfall volume, and the spatial and temporal resolution of the model. The new formula is applied in computing mu over the Amazon region. Significant temporal variability in the fractional coverage of rainfall is demonstrated. The implications of this variability for the modeling of land surface hydrology in climate models are discussed.

Quantifying climate feedbacks in polar regions

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

Goosse, Hugues; Kay, Jennifer E.; Armour, Kyle C.

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range ofmore » feedbacks, thus offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.« less

Quantifying climate feedbacks in polar regions

DOE PAGES

Goosse, Hugues; Kay, Jennifer E.; Armour, Kyle C.; ...

2018-05-15

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range ofmore » feedbacks, thus offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.« less

Correlated k-distribution method for radiative transfer in climate models: Application to effect of cirrus clouds on climate

NASA Technical Reports Server (NTRS)

Lacis, A. A.; Wang, W. C.; Hansen, J. E.

1979-01-01

A radiative transfer method appropriate for use in simple climate models and three dimensional global climate models was developed. It is fully interactive with climate changes, such as in the temperature-pressure profile, cloud distribution, and atmospheric composition, and it is accurate throughout the troposphere and stratosphere. The vertical inhomogeneity of the atmosphere is accounted for by assuming a correlation of gaseous k-distributions of different pressures and temperatures. Line-by-line calculations are made to demonstrate that The method is remarkably accurate. The method is then used in a one-dimensional radiative-convective climate model to study the effect of cirrus clouds on surface temperature. It is shown that an increase in cirrus cloud cover can cause a significant warming of the troposphere and the Earth's surface, by the mechanism of an enhanced green-house effect. The dependence of this phenomenon on cloud optical thickness, altitude, and latitude is investigated.

Attributing impacts to emissions traced to major fossil energy and cement producers over specific historical time periods

NASA Astrophysics Data System (ADS)

Ekwurzel, B.; Frumhoff, P. C.; Allen, M. R.; Boneham, J.; Heede, R.; Dalton, M. W.; Licker, R.

2017-12-01

Given the progress in climate change attribution research over the last decade, attribution studies can inform policymakers guided by the UNFCCC principle of "common but differentiated responsibilities." Historically this has primarily focused on nations, yet requests for information on the relative role of the fossil energy sector are growing. We present an approach that relies on annual CH4 and CO2 emissions from production through to the sale of products from the largest industrial fossil fuel and cement production company records from the mid-nineteenth century to present (Heede 2014). Analysis of the global trends with all the natural and human drivers compared with a scenario without the emissions traced to major carbon producers over full historical versus select periods of recent history can be policy relevant. This approach can be applied with simple climate models and earth system models depending on the type of climate impacts being investigated. For example, results from a simple climate model, using best estimate parameters and emissions traced to 90 largest carbon producers, illustrate the relative difference in global mean surface temperature increase over 1880-2010 after removing these emissions from 1980-2010 (29-35%) compared with removing these emissions over 1880-2010 (42-50%). The changing relative contributions from the largest climate drivers can be important to help assess the changing risks for stakeholders adapting to and reducing exposure and vulnerability to regional climate change impacts.

Application of empirical and dynamical closure methods to simple climate models

NASA Astrophysics Data System (ADS)

Padilla, Lauren Elizabeth

This dissertation applies empirically- and physically-based methods for closure of uncertain parameters and processes to three model systems that lie on the simple end of climate model complexity. Each model isolates one of three sources of closure uncertainty: uncertain observational data, large dimension, and wide ranging length scales. They serve as efficient test systems toward extension of the methods to more realistic climate models. The empirical approach uses the Unscented Kalman Filter (UKF) to estimate the transient climate sensitivity (TCS) parameter in a globally-averaged energy balance model. Uncertainty in climate forcing and historical temperature make TCS difficult to determine. A range of probabilistic estimates of TCS computed for various assumptions about past forcing and natural variability corroborate ranges reported in the IPCC AR4 found by different means. Also computed are estimates of how quickly uncertainty in TCS may be expected to diminish in the future as additional observations become available. For higher system dimensions the UKF approach may become prohibitively expensive. A modified UKF algorithm is developed in which the error covariance is represented by a reduced-rank approximation, substantially reducing the number of model evaluations required to provide probability densities for unknown parameters. The method estimates the state and parameters of an abstract atmospheric model, known as Lorenz 96, with accuracy close to that of a full-order UKF for 30-60% rank reduction. The physical approach to closure uses the Multiscale Modeling Framework (MMF) to demonstrate closure of small-scale, nonlinear processes that would not be resolved directly in climate models. A one-dimensional, abstract test model with a broad spatial spectrum is developed. The test model couples the Kuramoto-Sivashinsky equation to a transport equation that includes cloud formation and precipitation-like processes. In the test model, three main sources of MMF error are evaluated independently. Loss of nonlinear multi-scale interactions and periodic boundary conditions in closure models were dominant sources of error. Using a reduced order modeling approach to maximize energy content allowed reduction of the closure model dimension up to 75% without loss in accuracy. MMF and a comparable alternative model peformed equally well compared to direct numerical simulation.

Cloudy Windows: What GCM Ensembles, Reanalyses and Observations Tell Us About Uncertainty in Greenland's Future Climate and Surface Melting

NASA Astrophysics Data System (ADS)

Reusch, D. B.

2016-12-01

Any analysis that wants to use a GCM-based scenario of future climate benefits from knowing how much uncertainty the GCM's inherent variability adds to the development of climate change predictions. This is extra relevant in the polar regions due to the potential of global impacts (e.g., sea level rise) from local (ice sheet) climate changes such as more frequent/intense surface melting. High-resolution, regional-scale models using GCMs for boundary/initial conditions in future scenarios inherit a measure of GCM-derived externally-driven uncertainty. We investigate these uncertainties for the Greenland ice sheet using the 30-member CESM1.0-CAM5-BGC Large Ensemble (CESMLE) for recent (1981-2000) and future (2081-2100, RCP 8.5) decades. Recent simulations are skill-tested against the ERA-Interim reanalysis and AWS observations with results informing future scenarios. We focus on key variables influencing surface melting through decadal climatologies, nonlinear analysis of variability with self-organizing maps (SOMs), regional-scale modeling (Polar WRF), and simple melt models. Relative to the ensemble average, spatially averaged climatological July temperature anomalies over a Greenland ice-sheet/ocean domain are mostly between +/- 0.2 °C. The spatial average hides larger local anomalies of up to +/- 2 °C. The ensemble average itself is 2 °C cooler than ERA-Interim. SOMs extend our diagnostics by providing a concise, objective summary of model variability as a set of generalized patterns. For CESMLE, the SOM patterns summarize the variability of multiple realizations of climate. Changes in pattern frequency by ensemble member show the influence of initial conditions. For example, basic statistical analysis of pattern frequency yields interquartile ranges of 2-4% for individual patterns across the ensemble. In climate terms, this tells us about climate state variability through the range of the ensemble, a potentially significant source of melt-prediction uncertainty. SOMs can also capture the different trajectories of climate due to intramodel variability over time. Polar WRF provides higher resolution regional modeling with improved, polar-centric model physics. Simple melt models allow us to characterize impacts of the upstream uncertainties on estimates of surface melting.

Modeling aeolian transport in response to succession, disturbance and future climate: Dynamic long-term risk assessment for contaminant redistribution

USGS Publications Warehouse

Breshears, D.D.; Kirchner, T.B.; Whicker, J.J.; Field, J.P.; Allen, Craig D.

2012-01-01

Aeolian sediment transport is a fundamental process redistributing sediment, nutrients, and contaminants in dryland ecosystems. Over time frames of centuries or longer, horizontal sediment fluxes and associated rates of contaminant transport are likely to be influenced by succession, disturbances, and changes in climate, yet models of horizontal sediment transport that account for these fundamental factors are lacking, precluding in large part accurate assessment of human health risks associated with persistent soil-bound contaminants. We present a simple model based on empirical measurements of horizontal sediment transport (predominantly saltation) to predict potential contaminant transport rates for recently disturbed sites such as a landfill cover. Omnidirectional transport is estimated within vegetation that changes using a simple Markov model that simulates successional trajectory and considers three types of short-term disturbances (surface fire, crown fire, and drought-induced plant mortality) under current and projected climates. The model results highlight that movement of contaminated soil is sensitive to vegetation dynamics and increases substantially (e.g., > fivefold) when disturbance and/or future climate are considered. The time-dependent responses in horizontal sediment fluxes and associated contaminant fluxes were sensitive to variability in the timing of disturbance, with longer intervals between disturbance allowing woody plants to become dominant and crown fire and drought abruptly reducing woody plant cover. Our results, which have direct implications for contaminant transport and landfill management in the specific context of our assessment, also have general relevance because they highlight the need to more fully account for vegetation dynamics, disturbance, and changing climate in aeolian process studies.

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

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.

Modeling Surface Climate in US Cities Using Simple Biosphere Model Sib2

NASA Technical Reports Server (NTRS)

Zhang, Ping; Bounoua, Lahouari; Thome, Kurtis; Wolfe, Robert; Imhoff, Marc

2015-01-01

We combine Landsat- and the Moderate Resolution Imaging Spectroradiometer (MODIS)-based products in the Simple Biosphere model (SiB2) to assess the effects of urbanized land on the continental US (CONUS) surface climate. Using National Land Cover Database (NLCD) Impervious Surface Area (ISA), we define more than 300 urban settlements and their surrounding suburban and rural areas over the CONUS. The SiB2 modeled Gross Primary Production (GPP) over the CONUS of 7.10 PgC (1 PgC= 10(exp 15) grams of Carbon) is comparable to the MODIS improved GPP of 6.29 PgC. At state level, SiB2 GPP is highly correlated with MODIS GPP with a correlation coefficient of 0.94. An increasing horizontal GPP gradient is shown from the urban out to the rural area, with, on average, rural areas fixing 30% more GPP than urbans. Cities built in forested biomes have stronger UHI magnitude than those built in short vegetation with low biomass. Mediterranean climate cities have a stronger UHI in wet season than dry season. Our results also show that for urban areas built within forests, 39% of the precipitation is discharged as surface runoff during summer versus 23% in rural areas.

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.

Long-term climate change and the geochemical cycle of carbon

NASA Technical Reports Server (NTRS)

Marshall, Hal G.; Walker, James C. G.; Kuhn, William R.

1988-01-01

The response of the coupled climate-geochemical system to changes in paleography is examined in terms of the biogeochemical carbon cycle. The simple, zonally averaged energy balance climate model combined with a geochemical carbon cycle model, which was developed to study climate changes, is described. The effects of latitudinal distributions of the continents on the carbon cycle are investigated, and the global silicate weathering rate as a function of latitude is measured. It is observed that a concentration of land area at high altitudes results in a high CO2 partial pressure and a high global average temperature, and for land at low latitudes a cold globe and ice are detected. It is noted that the CO2 greenhouse feedback effect is potentially strong and has a stabilizing effect on the climate system.

Investigation of Climate Change Impact on Water Resources for an Alpine Basin in Northern Italy: Implications for Evapotranspiration Modeling Complexity

PubMed Central

Ravazzani, Giovanni; Ghilardi, Matteo; Mendlik, Thomas; Gobiet, Andreas; Corbari, Chiara; Mancini, Marco

2014-01-01

Assessing the future effects of climate change on water availability requires an understanding of how precipitation and evapotranspiration rates will respond to changes in atmospheric forcing. Use of simplified hydrological models is required beacause of lack of meteorological forcings with the high space and time resolutions required to model hydrological processes in mountains river basins, and the necessity of reducing the computational costs. The main objective of this study was to quantify the differences between a simplified hydrological model, which uses only precipitation and temperature to compute the hydrological balance when simulating the impact of climate change, and an enhanced version of the model, which solves the energy balance to compute the actual evapotranspiration. For the meteorological forcing of future scenario, at-site bias-corrected time series based on two regional climate models were used. A quantile-based error-correction approach was used to downscale the regional climate model simulations to a point scale and to reduce its error characteristics. The study shows that a simple temperature-based approach for computing the evapotranspiration is sufficiently accurate for performing hydrological impact investigations of climate change for the Alpine river basin which was studied. PMID:25285917

Investigation of climate change impact on water resources for an Alpine basin in northern Italy: implications for evapotranspiration modeling complexity.

PubMed

Ravazzani, Giovanni; Ghilardi, Matteo; Mendlik, Thomas; Gobiet, Andreas; Corbari, Chiara; Mancini, Marco

2014-01-01

Assessing the future effects of climate change on water availability requires an understanding of how precipitation and evapotranspiration rates will respond to changes in atmospheric forcing. Use of simplified hydrological models is required because of lack of meteorological forcings with the high space and time resolutions required to model hydrological processes in mountains river basins, and the necessity of reducing the computational costs. The main objective of this study was to quantify the differences between a simplified hydrological model, which uses only precipitation and temperature to compute the hydrological balance when simulating the impact of climate change, and an enhanced version of the model, which solves the energy balance to compute the actual evapotranspiration. For the meteorological forcing of future scenario, at-site bias-corrected time series based on two regional climate models were used. A quantile-based error-correction approach was used to downscale the regional climate model simulations to a point scale and to reduce its error characteristics. The study shows that a simple temperature-based approach for computing the evapotranspiration is sufficiently accurate for performing hydrological impact investigations of climate change for the Alpine river basin which was studied.

Quantifying characteristic growth dynamics in a semiarid grassland ecosystem by predicting short-term NDVI phenology from daily rainfall: a simple 4 parameter coupled-reservoir model

USDA-ARS?s Scientific Manuscript database

Predicting impacts of the magnitude and seasonal timing of rainfall pulses in water-limited grassland ecosystems concerns ecologists, climate scientists, hydrologists, and a variety of stakeholders. This report describes a simple, effective procedure to emulate the seasonal response of grassland bio...

Emergent dynamics of the climate-economy system in the Anthropocene.

PubMed

Kellie-Smith, Owen; Cox, Peter M

2011-03-13

Global CO(2) emissions are understood to be the largest contributor to anthropogenic climate change, and have, to date, been highly correlated with economic output. However, there is likely to be a negative feedback between climate change and human wealth: economic growth is typically associated with an increase in CO(2) emissions and global warming, but the resulting climate change may lead to damages that suppress economic growth. This climate-economy feedback is assumed to be weak in standard climate change assessments. When the feedback is incorporated in a transparently simple model it reveals possible emergent behaviour in the coupled climate-economy system. Formulae are derived for the critical rates of growth of global CO(2) emissions that cause damped or long-term boom-bust oscillations in human wealth, thereby preventing a soft landing of the climate-economy system. On the basis of this model, historical rates of economic growth and decarbonization appear to put the climate-economy system in a potentially damaging oscillatory regime.

Large-scale coastal and fluvial models constrain the late Holocene evolution of the Ebro Delta

NASA Astrophysics Data System (ADS)

Nienhuis, Jaap H.; Ashton, Andrew D.; Kettner, Albert J.; Giosan, Liviu

2017-09-01

The distinctive plan-view shape of the Ebro Delta coast reveals a rich morphologic history. The degree to which the form and depositional history of the Ebro and other deltas represent autogenic (internal) dynamics or allogenic (external) forcing remains a prominent challenge for paleo-environmental reconstructions. Here we use simple coastal and fluvial morphodynamic models to quantify paleo-environmental changes affecting the Ebro Delta over the late Holocene. Our findings show that these models are able to broadly reproduce the Ebro Delta morphology, with simple fluvial and wave climate histories. Based on numerical model experiments and the preserved and modern shape of the Ebro Delta plain, we estimate that a phase of rapid shoreline progradation began approximately 2100 years BP, requiring approximately a doubling in coarse-grained fluvial sediment supply to the delta. River profile simulations suggest that an instantaneous and sustained increase in coarse-grained sediment supply to the delta requires a combined increase in both flood discharge and sediment supply from the drainage basin. The persistence of rapid delta progradation throughout the last 2100 years suggests an anthropogenic control on sediment supply and flood intensity. Using proxy records of the North Atlantic Oscillation, we do not find evidence that changes in wave climate aided this delta expansion. Our findings highlight how scenario-based investigations of deltaic systems using simple models can assist first-order quantitative paleo-environmental reconstructions, elucidating the effects of past human influence and climate change, and allowing a better understanding of the future of deltaic landforms.

Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States

USGS Publications Warehouse

Magarey, Roger; Newton, Leslie; Hong, Seung C.; Takeuchi, Yu; Christie, Dave; Jarnevich, Catherine S.; Kohl, Lisa; Damus, Martin; Higgins, Steven I.; Miller, Leah; Castro, Karen; West, Amanda; Hastings, John; Cook, Gericke; Kartesz, John; Koop, Anthony

2018-01-01

This study compares four models for predicting the potential distribution of non-indigenous weed species in the conterminous U.S. The comparison focused on evaluating modeling tools and protocols as currently used for weed risk assessment or for predicting the potential distribution of invasive weeds. We used six weed species (three highly invasive and three less invasive non-indigenous species) that have been established in the U.S. for more than 75 years. The experiment involved providing non-U. S. location data to users familiar with one of the four evaluated techniques, who then developed predictive models that were applied to the United States without knowing the identity of the species or its U.S. distribution. We compared a simple GIS climate matching technique known as Proto3, a simple climate matching tool CLIMEX Match Climates, the correlative model MaxEnt, and a process model known as the Thornley Transport Resistance (TTR) model. Two experienced users ran each modeling tool except TTR, which had one user. Models were trained with global species distribution data excluding any U.S. data, and then were evaluated using the current known U.S. distribution. The influence of weed species identity and modeling tool on prevalence and sensitivity effects was compared using a generalized linear mixed model. Each modeling tool itself had a low statistical significance, while weed species alone accounted for 69.1 and 48.5% of the variance for prevalence and sensitivity, respectively. These results suggest that simple modeling tools might perform as well as complex ones in the case of predicting potential distribution for a weed not yet present in the United States. Considerations of model accuracy should also be balanced with those of reproducibility and ease of use. More important than the choice of modeling tool is the construction of robust protocols and testing both new and experienced users under blind test conditions that approximate operational conditions.

Sources and Impacts of Modeled and Observed Low-Frequency Climate Variability

NASA Astrophysics Data System (ADS)

Parsons, Luke Alexander

Here we analyze climate variability using instrumental, paleoclimate (proxy), and the latest climate model data to understand more about the sources and impacts of low-frequency climate variability. Understanding the drivers of climate variability at interannual to century timescales is important for studies of climate change, including analyses of detection and attribution of climate change impacts. Additionally, correctly modeling the sources and impacts of variability is key to the simulation of abrupt change (Alley et al., 2003) and extended drought (Seager et al., 2005; Pelletier and Turcotte, 1997; Ault et al., 2014). In Appendix A, we employ an Earth system model (GFDL-ESM2M) simulation to study the impacts of a weakening of the Atlantic meridional overturning circulation (AMOC) on the climate of the American Tropics. The AMOC drives some degree of local and global internal low-frequency climate variability (Manabe and Stouffer, 1995; Thornalley et al., 2009) and helps control the position of the tropical rainfall belt (Zhang and Delworth, 2005). We find that a major weakening of the AMOC can cause large-scale temperature, precipitation, and carbon storage changes in Central and South America. Our results suggest that possible future changes in AMOC strength alone will not be sufficient to drive a large-scale dieback of the Amazonian forest, but this key natural ecosystem is sensitive to dry-season length and timing of rainfall (Parsons et al., 2014). In Appendix B, we compare a paleoclimate record of precipitation variability in the Peruvian Amazon to climate model precipitation variability. The paleoclimate (Lake Limon) record indicates that precipitation variability in western Amazonia is 'red' (i.e., increasing variability with timescale). By contrast, most state-of-the-art climate models indicate precipitation variability in this region is nearly 'white' (i.e., equally variability across timescales). This paleo-model disagreement in the overall structure of the variance spectrum has important consequences for the probability of multi-year drought. Our lake record suggests there is a significant background threat of multi-year, and even decade-length, drought in western Amazonia, whereas climate model simulations indicate most droughts likely last no longer than one to three years. These findings suggest climate models may underestimate the future risk of extended drought in this important region. In Appendix C, we expand our analysis of climate variability beyond South America. We use observations, well-constrained tropical paleoclimate, and Earth system model data to examine the overall shape of the climate spectrum across interannual to century frequencies. We find a general agreement among observations and models that temperature variability increases with timescale across most of the globe outside the tropics. However, as compared to paleoclimate records, climate models generate too little low-frequency variability in the tropics (e.g., Laepple and Huybers, 2014). When we compare the shape of the simulated climate spectrum to the spectrum of a simple autoregressive process, we find much of the modeled surface temperature variability in the tropics could be explained by ocean smoothing of weather noise. Importantly, modeled precipitation tends to be similar to white noise across much of the globe. By contrast, paleoclimate records of various types from around the globe indicate that both temperature and precipitation variability should experience much more low-frequency variability than a simple autoregressive or white-noise process. In summary, state-of-the-art climate models generate some degree of dynamically driven low-frequency climate variability, especially at high latitudes. However, the latest climate models, observations, and paleoclimate data provide us with drastically different pictures of the background climate system and its associated risks. This research has important consequences for improving how we simulate climate extremes as we enter a warmer (and often drier) world in the coming centuries; if climate models underestimate low-frequency variability, we will underestimate the risk of future abrupt change and extreme events, such as megadroughts.

GCSS/WGNE Pacific Cross-section Intercomparison: Tropical and Subtropical Cloud Transitions

NASA Astrophysics Data System (ADS)

Teixeira, J.

2008-12-01

In this presentation I will discuss the role of the GEWEX Cloud Systems Study (GCSS) working groups in paving the way for substantial improvements in cloud parameterization in weather and climate models. The GCSS/WGNE Pacific Cross-section Intercomparison (GPCI) is an extension of GCSS and is a different type of model evaluation where climate models are analyzed along a Pacific Ocean transect from California to the equator. This approach aims at complementing the more traditional efforts in GCSS by providing a simple framework for the evaluation of models that encompasses several fundamental cloud regimes such as stratocumulus, shallow cumulus and deep cumulus, as well as the transitions between them. Currently twenty four climate and weather prediction models are participating in GPCI. We will present results of the comparison between models and recent satellite data. In particular, we will explore in detail the potential of the Atmospheric Infrared Sounder (AIRS) and CloudSat data for the evaluation of the representation of clouds and convection in climate models.

Intercomparison of the capabilities of simplified climate models to project the effects of aviation CO2 on climate

NASA Astrophysics Data System (ADS)

Khodayari, Arezoo; Wuebbles, Donald J.; Olsen, Seth C.; Fuglestvedt, Jan S.; Berntsen, Terje; Lund, Marianne T.; Waitz, Ian; Wolfe, Philip; Forster, Piers M.; Meinshausen, Malte; Lee, David S.; Lim, Ling L.

2013-08-01

This study evaluates the capabilities of the carbon cycle and energy balance treatments relative to the effect of aviation CO2 emissions on climate in several existing simplified climate models (SCMs) that are either being used or could be used for evaluating the effects of aviation on climate. Since these models are used in policy-related analyses, it is important that the capabilities of such models represent the state of understanding of the science. We compare the Aviation Environmental Portfolio Management Tool (APMT) Impacts climate model, two models used at the Center for International Climate and Environmental Research-Oslo (CICERO-1 and CICERO-2), the Integrated Science Assessment Model (ISAM) model as described in Jain et al. (1994), the simple Linear Climate response model (LinClim) and the Model for the Assessment of Greenhouse-gas Induced Climate Change version 6 (MAGICC6). In this paper we select scenarios to illustrate the behavior of the carbon cycle and energy balance models in these SCMs. This study is not intended to determine the absolute and likely range of the expected climate response in these models but to highlight specific features in model representations of the carbon cycle and energy balance models that need to be carefully considered in studies of aviation effects on climate. These results suggest that carbon cycle models that use linear impulse-response-functions (IRF) in combination with separate equations describing air-sea and air-biosphere exchange of CO2 can account for the dominant nonlinearities in the climate system that would otherwise not have been captured with an IRF alone, and hence, produce a close representation of more complex carbon cycle models. Moreover, results suggest that an energy balance model with a 2-box ocean sub-model and IRF tuned to reproduce the response of coupled Earth system models produces a close representation of the globally-averaged temperature response of more complex energy balance models.

Ensembles of 21st Century Colorado River Flow Projections Exhibit Substantial Diversity in Response to Seasonal Hydroclimatic Scenarios

NASA Astrophysics Data System (ADS)

McAfee, S. A.; Woodhouse, C. A.; McCabe, G. J., Jr.; Pederson, G. T.

2016-12-01

Approximately 40 million people depend on the Colorado River, and that number is likely to grow in the future, making the River's response to projected increases in temperature and possible changes in precipitation a critical societal issue. By far the most common way of approaching the problem is synthesize results obtained by forcing a hydrological model with a set of downscaled future climate scenarios. One weakness with this type of analysis is that full hydrologic model simulations can be computationally demanding, and so the number of potential climate futures is generally somewhat limited. Here we sidestep that issue by using a very large set of synthetic climate futures to drive a simple statistical model of water year flow at Lees Ferry. 62,500 climate series, comprising 500 iterations of 125 unique combinations of summer temperature changes ranging from 0 to +4°C and summer and winter precipitation changes ranging from -20 to +20% were input into the flow model. Without substantial temperature increases, significant increases in the occurrence of very low flows (<75%) were unlikely, even with sharp decreases in temperature. Conversely, increases in precipitation, could buffer the effect of summer temperature increases up to about 3°C on mean water year flows. While very simple models like this one are inappropriate for some questions, they do provide an effective way of prioritizing and framing more complex investigations, and facilitate conversations with stakeholders about research directions.

Development of a Simple Framework to Assess Hydrological Extremes using Solely Climate Data

NASA Astrophysics Data System (ADS)

Foulon, E.; Gagnon, P.; Rousseau, A. N.

2014-12-01

Extreme flow conditions such as droughts and floods are in general the direct consequences of short- to long-term weather/climate anomalies. For example, in southern Quebec, Canada, winter and summer 7-day low flows are due to summer and fall precipitations. Which prompts the question: is it possible to assess future extreme flow conditions from meteorological/climate indices or should we rely on the classical approach of using outputs of climate models as input to a hydrological model? The objective of this study is to assess six hydrological indices describing extreme flows at the watershed scale (Qmax, Qmin;7d, Qmin;30d for two seasons: winter and summer) using local climate indices without relying on the aforementioned classical approach. To establish the relationship between climate and hydrological indices, daily precipitations, minimum and maximum temperatures from 89 climate projections are used as inputs to a distributed hydrological model. River flows are simulated at the outlet of the Yamaska and Bécancour watersheds in Québec for the 1961-2100 periods. To identify the best predictors, hydrological indices are extracted from the flow series, and climate indices are computed for different time intervals (from a day up to four years). The difference between four-month, cumulative, climatic demand (P-ETP) explains 69% of the 7-day summer low flow during the calibration process. For both watersheds, preliminary findings indicate that the selected indices explain, on average, 38 and 60% of the variability of high- and low-flow indices, respectively. Overall, the results clearly illustrate that the change in the hydrological indices can be detected through the concurrent trends in the climate indices. The use of many climate projections ensures the relationships are not simulation-dependent and shows summer events are particularly at risk with increasing high flows and decreasing low flows. The development of a simple predictive tool to assess the impact of climate change on flows represents one of the major spin-off benefits of this study and may prooveto be useful to municipalities concerned with source water and flood management. Future work includes development of additional climate indices and application of the framework to more watersheds.

Climatological temperature senstivity of soil carbon turnover: Observations, simple scaling models, and ESMs

NASA Astrophysics Data System (ADS)

Koven, C. D.; Hugelius, G.; Lawrence, D. M.; Wieder, W. R.

2016-12-01

The projected loss of soil carbon to the atmosphere resulting from climate change is a potentially large but highly uncertain feedback to warming. The magnitude of this feedback is poorly constrained by observations and theory, and is disparately represented in Earth system models. To assess the likely long-term response of soils to climate change, spatial gradients in soil carbon turnover times can identify broad-scale and long-term controls on the rate of carbon cycling as a function of climate and other factors. Here we show that the climatological temperature control on carbon turnover in the top meter of global soils is more sensitive in cold climates than in warm ones. We present a simplified model that explains the high cold-climate sensitivity using only the physical scaling of soil freeze-thaw state across climate gradients. Critically, current Earth system models (ESMs) fail to capture this pattern, however it emerges from an ESM that explicitly resolves vertical gradients in soil climate and turnover. The weak tropical temperature sensitivity emerges from a different model that explicitly resolves mineralogical control on decomposition. These results support projections of strong future carbon-climate feedbacks from northern soils and demonstrate a method for ESMs to capture this emergent behavior.

Mathematics applied to the climate system: outstanding challenges and recent progress

PubMed Central

Williams, Paul D.; Cullen, Michael J. P.; Davey, Michael K.; Huthnance, John M.

2013-01-01

The societal need for reliable climate predictions and a proper assessment of their uncertainties is pressing. Uncertainties arise not only from initial conditions and forcing scenarios, but also from model formulation. Here, we identify and document three broad classes of problems, each representing what we regard to be an outstanding challenge in the area of mathematics applied to the climate system. First, there is the problem of the development and evaluation of simple physically based models of the global climate. Second, there is the problem of the development and evaluation of the components of complex models such as general circulation models. Third, there is the problem of the development and evaluation of appropriate statistical frameworks. We discuss these problems in turn, emphasizing the recent progress made by the papers presented in this Theme Issue. Many pressing challenges in climate science require closer collaboration between climate scientists, mathematicians and statisticians. We hope the papers contained in this Theme Issue will act as inspiration for such collaborations and for setting future research directions. PMID:23588054

The use of simple inflow- and storage-based heuristics equations to represent reservoir behavior in California for investigating human impacts on the water cycle

NASA Astrophysics Data System (ADS)

Solander, K.; David, C. H.; Reager, J. T.; Famiglietti, J. S.

2013-12-01

The ability to reasonably replicate reservoir behavior in terms of storage and outflow is important for studying the potential human impacts on the terrestrial water cycle. Developing a simple method for this purpose could facilitate subsequent integration in a land surface or global climate model. This study attempts to simulate monthly reservoir outflow and storage using a simple, temporally-varying set of heuristics equations with input consisting of in situ records of reservoir inflow and storage. Equations of increasing complexity relative to the number of parameters involved were tested. Only two parameters were employed in the final equations used to predict outflow and storage in an attempt to best mimic seasonal reservoir behavior while still preserving model parsimony. California reservoirs were selected for model development due to the high level of data availability and intensity of water resource management in this region relative to other areas. Calibration was achieved using observations from eight major reservoirs representing approximately 41% of the 107 largest reservoirs in the state. Parameter optimization was accomplished using the minimum RMSE between observed and modeled storage and outflow as the main objective function. Initial results obtained for a multi-reservoir average of the correlation coefficient between observed and modeled storage (resp. outflow) is of 0.78 (resp. 0.75). These results combined with the simplicity of the equations being used show promise for integration into a land surface or a global climate model. This would be invaluable for evaluations of reservoir management impacts on the flow regime and associated ecosystems as well as on the climate at both regional and global scales.

Predicting Coupled Ocean-Atmosphere Modes with a Climate Modeling Hierarchy -- Final Report

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

Michael Ghil, UCLA; Andrew W. Robertson, IRI, Columbia Univ.; Sergey Kravtsov, U. of Wisconsin, Milwaukee

The goal of the project was to determine midlatitude climate predictability associated with tropical-extratropical interactions on interannual-to-interdecadal time scales. Our strategy was to develop and test a hierarchy of climate models, bringing together large GCM-based climate models with simple fluid-dynamical coupled ocean-ice-atmosphere models, through the use of advanced probabilistic network (PN) models. PN models were used to develop a new diagnostic methodology for analyzing coupled ocean-atmosphere interactions in large climate simulations made with the NCAR Parallel Climate Model (PCM), and to make these tools user-friendly and available to other researchers. We focused on interactions between the tropics and extratropics throughmore » atmospheric teleconnections (the Hadley cell, Rossby waves and nonlinear circulation regimes) over both the North Atlantic and North Pacific, and the ocean’s thermohaline circulation (THC) in the Atlantic. We tested the hypothesis that variations in the strength of the THC alter sea surface temperatures in the tropical Atlantic, and that the latter influence the atmosphere in high latitudes through an atmospheric teleconnection, feeding back onto the THC. The PN model framework was used to mediate between the understanding gained with simplified primitive equations models and multi-century simulations made with the PCM. The project team is interdisciplinary and built on an existing synergy between atmospheric and ocean scientists at UCLA, computer scientists at UCI, and climate researchers at the IRI.« less

Tempo and mode of climatic niche evolution in Primates.

PubMed

Duran, Andressa; Pie, Marcio R

2015-09-01

Climatic niches have increasingly become a nexus in our understanding of a variety of ecological and evolutionary phenomena, from species distributions to latitudinal diversity gradients. Despite the increasing availability of comprehensive datasets on species ranges, phylogenetic histories, and georeferenced environmental conditions, studies on the evolution of climate niches have only begun to understand how niches evolve over evolutionary timescales. Here, using primates as a model system, we integrate recently developed phylogenetic comparative methods, species distribution patterns, and climatic data to explore primate climatic niche evolution, both among clades and over time. In general, we found that simple, constant-rate models provide a poor representation of how climatic niches evolve. For instance, there have been shifts in the rate of climatic niche evolution in several independent clades, particularly in response to the increasingly cooler climates of the past 10 My. Interestingly, rate accelerations greatly outnumbered rate decelerations. These results highlight the importance of considering more realistic evolutionary models that allow for the detection of heterogeneity in the tempo and mode of climatic niche evolution, as well as to infer possible constraining factors for species distributions in geographical space. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Interactive, process-oriented climate modeling with CLIMLAB

NASA Astrophysics Data System (ADS)

Rose, B. E. J.

2016-12-01

Global climate is a complex emergent property of the rich interactions between simpler components of the climate system. We build scientific understanding of this system by breaking it down into component process models (e.g. radiation, large-scale dynamics, boundary layer turbulence), understanding each components, and putting them back together. Hands-on experience and freedom to tinker with climate models (whether simple or complex) is invaluable for building physical understanding. CLIMLAB is an open-ended software engine for interactive, process-oriented climate modeling. With CLIMLAB you can interactively mix and match model components, or combine simpler process models together into a more comprehensive model. It was created primarily to support classroom activities, using hands-on modeling to teach fundamentals of climate science at both undergraduate and graduate levels. CLIMLAB is written in Python and ties in with the rich ecosystem of open-source scientific Python tools for numerics and graphics. The Jupyter Notebook format provides an elegant medium for distributing interactive example code. I will give an overview of the current capabilities of CLIMLAB, the curriculum we have developed thus far, and plans for the future. Using CLIMLAB requires some basic Python coding skills. We consider this an educational asset, as we are targeting upper-level undergraduates and Python is an increasingly important language in STEM fields.

Weighting climate model projections using observational constraints.

PubMed

Gillett, Nathan P

2015-11-13

Projected climate change integrates the net response to multiple climate feedbacks. Whereas existing long-term climate change projections are typically based on unweighted individual climate model simulations, as observed climate change intensifies it is increasingly becoming possible to constrain the net response to feedbacks and hence projected warming directly from observed climate change. One approach scales simulated future warming based on a fit to observations over the historical period, but this approach is only accurate for near-term projections and for scenarios of continuously increasing radiative forcing. For this reason, the recent Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) included such observationally constrained projections in its assessment of warming to 2035, but used raw model projections of longer term warming to 2100. Here a simple approach to weighting model projections based on an observational constraint is proposed which does not assume a linear relationship between past and future changes. This approach is used to weight model projections of warming in 2081-2100 relative to 1986-2005 under the Representative Concentration Pathway 4.5 forcing scenario, based on an observationally constrained estimate of the Transient Climate Response derived from a detection and attribution analysis. The resulting observationally constrained 5-95% warming range of 0.8-2.5 K is somewhat lower than the unweighted range of 1.1-2.6 K reported in the IPCC AR5. © 2015 The Authors.

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.

Decadal climate predictability in the southern Indian Ocean captured by SINTEX-F using a simple SST-nudging scheme.

PubMed

Morioka, Yushi; Doi, Takeshi; Behera, Swadhin K

2018-01-26

Decadal climate variability in the southern Indian Ocean has great influences on southern African climate through modulation of atmospheric circulation. Although many efforts have been made to understanding physical mechanisms, predictability of the decadal climate variability, in particular, the internally generated variability independent from external atmospheric forcing, remains poorly understood. This study investigates predictability of the decadal climate variability in the southern Indian Ocean using a coupled general circulation model, called SINTEX-F. The ensemble members of the decadal reforecast experiments were initialized with a simple sea surface temperature (SST) nudging scheme. The observed positive and negative peaks during late 1990s and late 2000s are well reproduced in the reforecast experiments initiated from 1994 and 1999, respectively. The experiments initiated from 1994 successfully capture warm SST and high sea level pressure anomalies propagating from the South Atlantic to the southern Indian Ocean. Also, the other experiments initiated from 1999 skillfully predict phase change from a positive to negative peak. These results suggest that the SST-nudging initialization has the essence to capture the predictability of the internally generated decadal climate variability in the southern Indian Ocean.

A simple model that identifies potential effects of sea-level rise on estuarine and estuary-ecotone habitat locations for salmonids in Oregon, USA

Treesearch

Rebecca Flitcroft; Kelly Burnett; Kelly Christiansen

2013-01-01

Diadromous aquatic species that cross a diverse range of habitats (including marine, estuarine, and freshwater) face different effects of climate change in each environment. One such group of species is the anadromous Pacific salmon (Oncorhynchus spp.). Studies of the potential effects of climate change on salmonids have focused on both marine and...

Atmospheric water vapor transport: Estimation of continental precipitation recycling and parameterization of a simple climate model. M.S. Thesis

NASA Technical Reports Server (NTRS)

Brubaker, Kaye L.; Entekhabi, Dara; Eagleson, Peter S.

1991-01-01

The advective transport of atmospheric water vapor and its role in global hydrology and the water balance of continental regions are discussed and explored. The data set consists of ten years of global wind and humidity observations interpolated onto a regular grid by objective analysis. Atmospheric water vapor fluxes across the boundaries of selected continental regions are displayed graphically. The water vapor flux data are used to investigate the sources of continental precipitation. The total amount of water that precipitates on large continental regions is supplied by two mechanisms: (1) advection from surrounding areas external to the region; and (2) evaporation and transpiration from the land surface recycling of precipitation over the continental area. The degree to which regional precipitation is supplied by recycled moisture is a potentially significant climate feedback mechanism and land surface-atmosphere interaction, which may contribute to the persistence and intensification of droughts. A simplified model of the atmospheric moisture over continents and simultaneous estimates of regional precipitation are employed to estimate, for several large continental regions, the fraction of precipitation that is locally derived. In a separate, but related, study estimates of ocean to land water vapor transport are used to parameterize an existing simple climate model, containing both land and ocean surfaces, that is intended to mimic the dynamics of continental climates.

Feedbacks between climate change and biosphere integrity

NASA Astrophysics Data System (ADS)

Lade, Steven; Anderies, J. Marty; Donges, Jonathan; Steffen, Will; Rockström, Johan; Richardson, Katherine; Cornell, Sarah; Norberg, Jon; Fetzer, Ingo

2017-04-01

The terrestrial and marine biospheres sink substantial fractions of human fossil fuel emissions. How the biosphere's capacity to sink carbon depends on biodiversity and other measures of biosphere integrity is however poorly understood. Here, we (1): review assumptions from literature regarding the relationships between the carbon cycle and the terrestrial and marine biospheres; and (2) explore the consequences of these different assumptions for climate feedbacks using the stylised carbon cycle model PB-INT. We find that: terrestrial biodiversity loss could significantly dampen climate-carbon cycle feedbacks; direct biodiversity effects, if they exist, could rival temperature increases from low-emission trajectories; and the response of the marine biosphere is critical for longer term climate change. Simple, low-dimensional climate models such as PB-INT can help assess the importance of still unknown or controversial earth system processes such as biodiversity loss for climate feedbacks. This study constitutes the first detailed study of the interactions between climate change and biosphere integrity, two of the 'planetary boundaries'.

Framework for Detection and Localization of Extreme Climate Event with Pixel Recursive Super Resolution

NASA Astrophysics Data System (ADS)

Kim, S. K.; Lee, J.; Zhang, C.; Ames, S.; Williams, D. N.

2017-12-01

Deep learning techniques have been successfully applied to solve many problems in climate and geoscience using massive-scaled observed and modeled data. For extreme climate event detections, several models based on deep neural networks have been recently proposed and attend superior performance that overshadows all previous handcrafted expert based method. The issue arising, though, is that accurate localization of events requires high quality of climate data. In this work, we propose framework capable of detecting and localizing extreme climate events in very coarse climate data. Our framework is based on two models using deep neural networks, (1) Convolutional Neural Networks (CNNs) to detect and localize extreme climate events, and (2) Pixel recursive recursive super resolution model to reconstruct high resolution climate data from low resolution climate data. Based on our preliminary work, we have presented two CNNs in our framework for different purposes, detection and localization. Our results using CNNs for extreme climate events detection shows that simple neural nets can capture the pattern of extreme climate events with high accuracy from very coarse reanalysis data. However, localization accuracy is relatively low due to the coarse resolution. To resolve this issue, the pixel recursive super resolution model reconstructs the resolution of input of localization CNNs. We present a best networks using pixel recursive super resolution model that synthesizes details of tropical cyclone in ground truth data while enhancing their resolution. Therefore, this approach not only dramat- ically reduces the human effort, but also suggests possibility to reduce computing cost required for downscaling process to increase resolution of data.

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

Li, Hongyi; Sivapalan, Murugesu; Tian, Fuqiang

Inspired by the Dunne diagram, the climatic and landscape controls on the partitioning of annual runoff into its various components (Hortonian and Dunne overland flow and subsurface stormflow) are assessed quantitatively, from a purely theoretical perspective. A simple distributed hydrologic model has been built sufficient to simulate the effects of different combinations of climate, soil, and topography on the runoff generation processes. The model is driven by a sequence of simple hypothetical precipitation events, for a large combination of climate and landscape properties, and hydrologic responses at the catchment scale are obtained through aggregation of grid-scale responses. It is found,more » first, that the water balance responses, including relative contributions of different runoff generation mechanisms, could be related to a small set of dimensionless similarity parameters. These capture the competition between the wetting, drying, storage, and drainage functions underlying the catchment responses, and in this way, provide a quantitative approximation of the conceptual Dunne diagram. Second, only a subset of all hypothetical catchment/climate combinations is found to be ‘‘behavioral,’’ in terms of falling sufficiently close to the Budyko curve, describing mean annual runoff as a function of climate aridity. Furthermore, these behavioral combinations are mostly consistent with the qualitative picture presented in the Dunne diagram, indicating clearly the commonality between the Budyko curve and the Dunne diagram. These analyses also suggest clear interrelationships amongst the ‘‘behavioral’’ climate, soil, and topography parameter combinations, implying these catchment properties may be constrained to be codependent in order to satisfy the Budyko curve.« less

Landscape structure and climate influences on hydrologic response

NASA Astrophysics Data System (ADS)

Nippgen, Fabian; McGlynn, Brian L.; Marshall, Lucy A.; Emanuel, Ryan E.

2011-12-01

Climate variability and catchment structure (topography, geology, vegetation) have a significant influence on the timing and quantity of water discharged from mountainous catchments. How these factors combine to influence runoff dynamics is poorly understood. In this study we linked differences in hydrologic response across catchments and across years to metrics of landscape structure and climate using a simple transfer function rainfall-runoff modeling approach. A transfer function represents the internal catchment properties that convert a measured input (rainfall/snowmelt) into an output (streamflow). We examined modeled mean response time, defined as the average time that it takes for a water input to leave the catchment outlet from the moment it reaches the ground surface. We combined 12 years of precipitation and streamflow data from seven catchments in the Tenderfoot Creek Experimental Forest (Little Belt Mountains, southwestern Montana) with landscape analyses to quantify the first-order controls on mean response times. Differences between responses across the seven catchments were related to the spatial variability in catchment structure (e.g., slope, flowpath lengths, tree height). Annual variability was largely a function of maximum snow water equivalent. Catchment averaged runoff ratios exhibited strong correlations with mean response time while annually averaged runoff ratios were not related to climatic metrics. These results suggest that runoff ratios in snowmelt dominated systems are mainly controlled by topography and not by climatic variability. This approach provides a simple tool for assessing differences in hydrologic response across diverse watersheds and climate conditions.

A Simple Model Framework to Explore the Deeply Uncertain, Local Sea Level Response to Climate Change. A Case Study on New Orleans, Louisiana

NASA Astrophysics Data System (ADS)

Bakker, Alexander; Louchard, Domitille; Keller, Klaus

2016-04-01

Sea-level rise threatens many coastal areas around the world. The integrated assessment of potential adaptation and mitigation strategies requires a sound understanding of the upper tails and the major drivers of the uncertainties. Global warming causes sea-level to rise, primarily due to thermal expansion of the oceans and mass loss of the major ice sheets, smaller ice caps and glaciers. These components show distinctly different responses to temperature changes with respect to response time, threshold behavior, and local fingerprints. Projections of these different components are deeply uncertain. Projected uncertainty ranges strongly depend on (necessary) pragmatic choices and assumptions; e.g. on the applied climate scenarios, which processes to include and how to parameterize them, and on error structure of the observations. Competing assumptions are very hard to objectively weigh. Hence, uncertainties of sea-level response are hard to grasp in a single distribution function. The deep uncertainty can be better understood by making clear the key assumptions. Here we demonstrate this approach using a relatively simple model framework. We present a mechanistically motivated, but simple model framework that is intended to efficiently explore the deeply uncertain sea-level response to anthropogenic climate change. The model consists of 'building blocks' that represent the major components of sea-level response and its uncertainties, including threshold behavior. The framework's simplicity enables the simulation of large ensembles allowing for an efficient exploration of parameter uncertainty and for the simulation of multiple combined adaptation and mitigation strategies. The model framework can skilfully reproduce earlier major sea level assessments, but due to the modular setup it can also be easily utilized to explore high-end scenarios and the effect of competing assumptions and parameterizations.

Using historical and projected future climate model simulations as drivers of agricultural and biological models (Invited)

NASA Astrophysics Data System (ADS)

Stefanova, L. B.

2013-12-01

Climate model evaluation is frequently performed as a first step in analyzing climate change simulations. Atmospheric scientists are accustomed to evaluating climate models through the assessment of model climatology and biases, the models' representation of large-scale modes of variability (such as ENSO, PDO, AMO, etc) and the relationship between these modes and local variability (e.g. the connection between ENSO and the wintertime precipitation in the Southeast US). While these provide valuable information about the fidelity of historical and projected climate model simulations from an atmospheric scientist's point of view, the application of climate model data to fields such as agriculture, ecology and biology may require additional analyses focused on the particular application's requirements and sensitivities. Typically, historical climate simulations are used to determine a mapping between the model and observed climate, either through a simple (additive for temperature or multiplicative for precipitation) or a more sophisticated (such as quantile matching) bias correction on a monthly or seasonal time scale. Plants, animals and humans however are not directly affected by monthly or seasonal means. To assess the impact of projected climate change on living organisms and related industries (e.g. agriculture, forestry, conservation, utilities, etc.), derivative measures such as the heating degree-days (HDD), cooling degree-days (CDD), growing degree-days (GDD), accumulated chill hours (ACH), wet season onset (WSO) and duration (WSD), among others, are frequently useful. We will present a comparison of the projected changes in such derivative measures calculated by applying: (a) the traditional temperature/precipitation bias correction described above versus (b) a bias correction based on the mapping between the historical model and observed derivative measures themselves. In addition, we will present and discuss examples of various application-based climate model evaluations, such as: (a) agricultural crop yield estimates and (b) species population viability estimates modeled using observed climate data vs. historical climate simulations.

Increased evapotranspiration demand in a Mediterranean climate might cause a decline in fungal yields under global warming.

PubMed

Ágreda, Teresa; Águeda, Beatriz; Olano, José M; Vicente-Serrano, Sergio M; Fernández-Toirán, Marina

2015-09-01

Wild fungi play a critical role in forest ecosystems, and its recollection is a relevant economic activity. Understanding fungal response to climate is necessary in order to predict future fungal production in Mediterranean forests under climate change scenarios. We used a 15-year data set to model the relationship between climate and epigeous fungal abundance and productivity, for mycorrhizal and saprotrophic guilds in a Mediterranean pine forest. The obtained models were used to predict fungal productivity for the 2021-2080 period by means of regional climate change models. Simple models based on early spring temperature and summer-autumn rainfall could provide accurate estimates for fungal abundance and productivity. Models including rainfall and climatic water balance showed similar results and explanatory power for the analyzed 15-year period. However, their predictions for the 2021-2080 period diverged. Rainfall-based models predicted a maintenance of fungal yield, whereas water balance-based models predicted a steady decrease of fungal productivity under a global warming scenario. Under Mediterranean conditions fungi responded to weather conditions in two distinct periods: early spring and late summer-autumn, suggesting a bimodal pattern of growth. Saprotrophic and mycorrhizal fungi showed differences in the climatic control. Increased atmospheric evaporative demand due to global warming might lead to a drop in fungal yields during the 21st century. © 2015 John Wiley & Sons Ltd.

Determing Credibility of Regional Simulations of Future Climate

NASA Astrophysics Data System (ADS)

Mearns, L. O.

2009-12-01

Climate models have been evaluated or validated ever since they were first developed. Establishing that a climate model can reproduce (some) aspects of the current climate of the earth on various spatial and temporal scales has long been a standard procedure for providing confidence in the model's ability to simulate future climate. However, direct links between the successes and failures of models in reproducing the current climate with regard to what future climates the models simulate has been largely lacking. This is to say that the model evaluation process has been largely divorced from the projections of future climate that the models produce. This is evidenced in the separation in the Intergovernmental Panel on Climate Change (IPCC) WG1 report of the chapter on evaluation of models from the chapter on future climate projections. There has also been the assumption of 'one model, one vote, that is, that each model projection is given equal weight in any multi-model ensemble presentation of the projections of future climate. There have been various attempts at determing measures of credibility that would avoid the 'ultrademocratic' assumption of the IPCC. Simple distinctions between models were made by research such as in Giorgi and Mearns (2002), Tebaldi et al., (2005), and Greene et al., (2006). But the metrics used were rather simplistic. More ambitous means of discriminating among the quality of model simulations have been made through the production of complex multivariate metrics, but insufficent work has been produced to verify that the metrics successfully discriminate in meaningful ways. Indeed it has been suggested that we really don't know what a model must successfully model to establish confidence in its regional-scale projections (Gleckler et al., 2008). Perhaps a more process oriented regional expert judgment approach is needed to understand which errors in climate models really matter for the model's response to future forcing. Such an approach is being attempted in the North American Climate Change Assessment Program (NARCCAP) whereby multiple global models are used to drive multiple regional models for the current period and the mid-21st century over the continent. Progress in this endeavor will be reported.

Functional approach to exploring climatic and landscape controls of runoff generation: 1. Behavioral constraints on runoff volume

NASA Astrophysics Data System (ADS)

Li, Hong-Yi; Sivapalan, Murugesu; Tian, Fuqiang; Harman, Ciaran

2014-12-01

Inspired by the Dunne diagram, the climatic and landscape controls on the partitioning of annual runoff into its various components (Hortonian and Dunne overland flow and subsurface stormflow) are assessed quantitatively, from a purely theoretical perspective. A simple distributed hydrologic model has been built sufficient to simulate the effects of different combinations of climate, soil, and topography on the runoff generation processes. The model is driven by a sequence of simple hypothetical precipitation events, for a large combination of climate and landscape properties, and hydrologic responses at the catchment scale are obtained through aggregation of grid-scale responses. It is found, first, that the water balance responses, including relative contributions of different runoff generation mechanisms, could be related to a small set of dimensionless similarity parameters. These capture the competition between the wetting, drying, storage, and drainage functions underlying the catchment responses, and in this way, provide a quantitative approximation of the conceptual Dunne diagram. Second, only a subset of all hypothetical catchment/climate combinations is found to be "behavioral," in terms of falling sufficiently close to the Budyko curve, describing mean annual runoff as a function of climate aridity. Furthermore, these behavioral combinations are mostly consistent with the qualitative picture presented in the Dunne diagram, indicating clearly the commonality between the Budyko curve and the Dunne diagram. These analyses also suggest clear interrelationships amongst the "behavioral" climate, soil, and topography parameter combinations, implying these catchment properties may be constrained to be codependent in order to satisfy the Budyko curve.

An ensemble approach to assess hydrological models' contribution to uncertainties in the analysis of climate change impact on water resources

NASA Astrophysics Data System (ADS)

Velázquez, J. A.; Schmid, J.; Ricard, S.; Muerth, M. J.; Gauvin St-Denis, B.; Minville, M.; Chaumont, D.; Caya, D.; Ludwig, R.; Turcotte, R.

2012-06-01

Over the recent years, several research efforts investigated the impact of climate change on water resources for different regions of the world. The projection of future river flows is affected by different sources of uncertainty in the hydro-climatic modelling chain. One of the aims of the QBic3 project (Québec-Bavarian International Collaboration on Climate Change) is to assess the contribution to uncertainty of hydrological models by using an ensemble of hydrological models presenting a diversity of structural complexity (i.e. lumped, semi distributed and distributed models). The study investigates two humid, mid-latitude catchments with natural flow conditions; one located in Southern Québec (Canada) and one in Southern Bavaria (Germany). Daily flow is simulated with four different hydrological models, forced by outputs from regional climate models driven by a given number of GCMs' members over a reference (1971-2000) and a future (2041-2070) periods. The results show that the choice of the hydrological model does strongly affect the climate change response of selected hydrological indicators, especially those related to low flows. Indicators related to high flows seem less sensitive on the choice of the hydrological model. Therefore, the computationally less demanding models (usually simple, lumped and conceptual) give a significant level of trust for high and overall mean flows.

Uncertainty and the Social Cost of Methane Using Bayesian Constrained Climate Models

NASA Astrophysics Data System (ADS)

Errickson, F. C.; Anthoff, D.; Keller, K.

2016-12-01

Social cost estimates of greenhouse gases are important for the design of sound climate policies and are also plagued by uncertainty. One major source of uncertainty stems from the simplified representation of the climate system used in the integrated assessment models that provide these social cost estimates. We explore how uncertainty over the social cost of methane varies with the way physical processes and feedbacks in the methane cycle are modeled by (i) coupling three different methane models to a simple climate model, (ii) using MCMC to perform a Bayesian calibration of the three coupled climate models that simulates direct sampling from the joint posterior probability density function (pdf) of model parameters, and (iii) producing probabilistic climate projections that are then used to calculate the Social Cost of Methane (SCM) with the DICE and FUND integrated assessment models. We find that including a temperature feedback in the methane cycle acts as an additional constraint during the calibration process and results in a correlation between the tropospheric lifetime of methane and several climate model parameters. This correlation is not seen in the models lacking this feedback. Several of the estimated marginal pdfs of the model parameters also exhibit different distributional shapes and expected values depending on the methane model used. As a result, probabilistic projections of the climate system out to the year 2300 exhibit different levels of uncertainty and magnitudes of warming for each of the three models under an RCP8.5 scenario. We find these differences in climate projections result in differences in the distributions and expected values for our estimates of the SCM. We also examine uncertainty about the SCM by performing a Monte Carlo analysis using a distribution for the climate sensitivity while holding all other climate model parameters constant. Our SCM estimates using the Bayesian calibration are lower and exhibit less uncertainty about extremely high values in the right tail of the distribution compared to the Monte Carlo approach. This finding has important climate policy implications and suggests previous work that accounts for climate model uncertainty by only varying the climate sensitivity parameter may overestimate the SCM.

Estimates of runoff using water-balance and atmospheric general circulation models

USGS Publications Warehouse

Wolock, D.M.; McCabe, G.J.

1999-01-01

The effects of potential climate change on mean annual runoff in the conterminous United States (U.S.) are examined using a simple water-balance model and output from two atmospheric general circulation models (GCMs). The two GCMs are from the Canadian Centre for Climate Prediction and Analysis (CCC) and the Hadley Centre for Climate Prediction and Research (HAD). In general, the CCC GCM climate results in decreases in runoff for the conterminous U.S., and the HAD GCM climate produces increases in runoff. These estimated changes in runoff primarily are the result of estimated changes in precipitation. The changes in mean annual runoff, however, mostly are smaller than the decade-to-decade variability in GCM-based mean annual runoff and errors in GCM-based runoff. The differences in simulated runoff between the two GCMs, together with decade-to-decade variability and errors in GCM-based runoff, cause the estimates of changes in runoff to be uncertain and unreliable.

Statistical Approaches for Spatiotemporal Prediction of Low Flows

NASA Astrophysics Data System (ADS)

Fangmann, A.; Haberlandt, U.

2017-12-01

An adequate assessment of regional climate change impacts on streamflow requires the integration of various sources of information and modeling approaches. This study proposes simple statistical tools for inclusion into model ensembles, which are fast and straightforward in their application, yet able to yield accurate streamflow predictions in time and space. Target variables for all approaches are annual low flow indices derived from a data set of 51 records of average daily discharge for northwestern Germany. The models require input of climatic data in the form of meteorological drought indices, derived from observed daily climatic variables, averaged over the streamflow gauges' catchments areas. Four different modeling approaches are analyzed. Basis for all pose multiple linear regression models that estimate low flows as a function of a set of meteorological indices and/or physiographic and climatic catchment descriptors. For the first method, individual regression models are fitted at each station, predicting annual low flow values from a set of annual meteorological indices, which are subsequently regionalized using a set of catchment characteristics. The second method combines temporal and spatial prediction within a single panel data regression model, allowing estimation of annual low flow values from input of both annual meteorological indices and catchment descriptors. The third and fourth methods represent non-stationary low flow frequency analyses and require fitting of regional distribution functions. Method three is subject to a spatiotemporal prediction of an index value, method four to estimation of L-moments that adapt the regional frequency distribution to the at-site conditions. The results show that method two outperforms successive prediction in time and space. Method three also shows a high performance in the near future period, but since it relies on a stationary distribution, its application for prediction of far future changes may be problematic. Spatiotemporal prediction of L-moments appeared highly uncertain for higher-order moments resulting in unrealistic future low flow values. All in all, the results promote an inclusion of simple statistical methods in climate change impact assessment.

Long History of IAM Comparisons

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

Smith, Steven J.; Clarke, Leon E.; Edmonds, James A.

2015-04-23

Correspondence to editor: We agree with the editors that the assumptions behind models of all types, including integrated assessment models (IAMs), should be as transparent as possible. The editors were in error, however, when they implied that the IAM community is just “now emulating the efforts of climate researchers by instigating their own model inter-comparison projects (MIPs).” In fact, model comparisons for integrated assessment and climate models followed a remarkably similar trajectory. Early General Circulation Model (GCM) comparison efforts, evolved to the first Atmospheric Model Inter-comparison Project (AMIP), which was initiated in the early 1990s. Atmospheric models evolved to coupledmore » atmosphere-ocean models (AOGCMs) and results from the first Coupled Model Inter-Comparison Project (CMIP1) become available about a decade later. Results of first energy model comparison exercise, conducted under the auspices of the Stanford Energy Modeling Forum, were published in 1977. A summary of the first comparison focused on climate change was published in 1993. As energy models were coupled to simple economic and climate models to form IAMs, the first comparison exercise for IAMs (EMF-14) was initiated in 1994, and IAM comparison exercises have been on-going since this time.« less

Probabilistic regional climate projection in Japan using a regression model with CMIP5 multi-model ensemble experiments

NASA Astrophysics Data System (ADS)

Ishizaki, N. N.; Dairaku, K.; Ueno, G.

2016-12-01

We have developed a statistical downscaling method for estimating probabilistic climate projection using CMIP5 multi general circulation models (GCMs). A regression model was established so that the combination of weights of GCMs reflects the characteristics of the variation of observations at each grid point. Cross validations were conducted to select GCMs and to evaluate the regression model to avoid multicollinearity. By using spatially high resolution observation system, we conducted statistically downscaled probabilistic climate projections with 20-km horizontal grid spacing. Root mean squared errors for monthly mean air surface temperature and precipitation estimated by the regression method were the smallest compared with the results derived from a simple ensemble mean of GCMs and a cumulative distribution function based bias correction method. Projected changes in the mean temperature and precipitation were basically similar to those of the simple ensemble mean of GCMs. Mean precipitation was generally projected to increase associated with increased temperature and consequent increased moisture content in the air. Weakening of the winter monsoon may affect precipitation decrease in some areas. Temperature increase in excess of 4 K was expected in most areas of Japan in the end of 21st century under RCP8.5 scenario. The estimated probability of monthly precipitation exceeding 300 mm would increase around the Pacific side during the summer and the Japan Sea side during the winter season. This probabilistic climate projection based on the statistical method can be expected to bring useful information to the impact studies and risk assessments.

Cloud Feedbacks in the Climate System: A Critical Review.

NASA Astrophysics Data System (ADS)

Stephens, Graeme L.

2005-01-01

This paper offers a critical review of the topic of cloud-climate feedbacks and exposes some of the underlying reasons for the inherent lack of understanding of these feedbacks and why progress might be expected on this important climate problem in the coming decade. Although many processes and related parameters come under the influence of clouds, it is argued that atmospheric processes fundamentally govern the cloud feedbacks via the relationship between the atmospheric circulations, cloudiness, and the radiative and latent heating of the atmosphere. It is also shown how perturbations to the atmospheric radiation budget that are induced by cloud changes in response to climate forcing dictate the eventual response of the global-mean hydrological cycle of the climate model to climate forcing. This suggests that cloud feedbacks are likely to control the bulk precipitation efficiency and associated responses of the planet's hydrological cycle to climate radiative forcings.The paper provides a brief overview of the effects of clouds on the radiation budget of the earth-atmosphere system and a review of cloud feedbacks as they have been defined in simple systems, one being a system in radiative-convective equilibrium (RCE) and others relating to simple feedback ideas that regulate tropical SSTs. The systems perspective is reviewed as it has served as the basis for most feedback analyses. What emerges is the importance of being clear about the definition of the system. It is shown how different assumptions about the system produce very different conclusions about the magnitude and sign of feedbacks. Much more diligence is called for in terms of defining the system and justifying assumptions. In principle, there is also neither any theoretical basis to justify the system that defines feedbacks in terms of global-time-mean changes in surface temperature nor is there any compelling empirical evidence to do so. The lack of maturity of feedback analysis methods also suggests that progress in understanding climate feedback will require development of alternative methods of analysis.It has been argued that, in view of the complex nature of the climate system, and the cumbersome problems encountered in diagnosing feedbacks, understanding cloud feedback will be gleaned neither from observations nor proved from simple theoretical argument alone. The blueprint for progress must follow a more arduous path that requires a carefully orchestrated and systematic combination of model and observations. Models provide the tool for diagnosing processes and quantifying feedbacks while observations provide the essential test of the model's credibility in representing these processes. While GCM climate and NWP models represent the most complete description of all the interactions between the processes that presumably establish the main cloud feedbacks, the weak link in the use of these models lies in the cloud parameterization imbedded in them. Aspects of these parameterizations remain worrisome, containing levels of empiricism and assumptions that are hard to evaluate with current global observations. Clearly observationally based methods for evaluating cloud parameterizations are an important element in the road map to progress.Although progress in understanding the cloud feedback problem has been slow and confused by past analysis, there are legitimate reasons outlined in the paper that give hope for real progress in the future.

Building Models in the Classroom: Taking Advantage of Sophisticated Geomorphic Numerical Tools Using a Simple Graphical User Interface

NASA Astrophysics Data System (ADS)

Roy, S. G.; Koons, P. O.; Gerbi, C. C.; Capps, D. K.; Tucker, G. E.; Rogers, Z. A.

2014-12-01

Sophisticated numerical tools exist for modeling geomorphic processes and linking them to tectonic and climatic systems, but they are often seen as inaccessible for users with an exploratory level of interest. We have improved the accessibility of landscape evolution models by producing a simple graphics user interface (GUI) that takes advantage of the Channel-Hillslope Integrated Landscape Development (CHILD) model. Model access is flexible: the user can edit values for basic geomorphic, tectonic, and climate parameters, or obtain greater control by defining the spatiotemporal distributions of those parameters. Users can make educated predictions by choosing their own parametric values for the governing equations and interpreting the results immediately through model graphics. This method of modeling allows users to iteratively build their understanding through experimentation. Use of this GUI is intended for inquiry and discovery-based learning activities. We discuss a number of examples of how the GUI can be used at the upper high school, introductory university, and advanced university level. Effective teaching modules initially focus on an inquiry-based example guided by the instructor. As students become familiar with the GUI and the CHILD model, the class can shift to more student-centered exploration and experimentation. To make model interpretations more robust, digital elevation models can be imported and direct comparisons can be made between CHILD model results and natural topography. The GUI is available online through the University of Maine's Earth and Climate Sciences website, through the Community Surface Dynamics Modeling System (CSDMS) model repository, or by contacting the corresponding author.

Uncertainty squared: Choosing among multiple input probability distributions and interpreting multiple output probability distributions in Monte Carlo climate risk models

NASA Astrophysics Data System (ADS)

Baer, P.; Mastrandrea, M.

2006-12-01

Simple probabilistic models which attempt to estimate likely transient temperature change from specified CO2 emissions scenarios must make assumptions about at least six uncertain aspects of the causal chain between emissions and temperature: current radiative forcing (including but not limited to aerosols), current land use emissions, carbon sinks, future non-CO2 forcing, ocean heat uptake, and climate sensitivity. Of these, multiple PDFs (probability density functions) have been published for the climate sensitivity, a couple for current forcing and ocean heat uptake, one for future non-CO2 forcing, and none for current land use emissions or carbon cycle uncertainty (which are interdependent). Different assumptions about these parameters, as well as different model structures, will lead to different estimates of likely temperature increase from the same emissions pathway. Thus policymakers will be faced with a range of temperature probability distributions for the same emissions scenarios, each described by a central tendency and spread. Because our conventional understanding of uncertainty and probability requires that a probabilistically defined variable of interest have only a single mean (or median, or modal) value and a well-defined spread, this "multidimensional" uncertainty defies straightforward utilization in policymaking. We suggest that there are no simple solutions to the questions raised. Crucially, we must dispel the notion that there is a "true" probability probabilities of this type are necessarily subjective, and reasonable people may disagree. Indeed, we suggest that what is at stake is precisely the question, what is it reasonable to believe, and to act as if we believe? As a preliminary suggestion, we demonstrate how the output of a simple probabilistic climate model might be evaluated regarding the reasonableness of the outputs it calculates with different input PDFs. We suggest further that where there is insufficient evidence to clearly favor one range of probabilistic projections over another, that the choice of results on which to base policy must necessarily involve ethical considerations, as they have inevitable consequences for the distribution of risk In particular, the choice to use a more "optimistic" PDF for climate sensitivity (or other components of the causal chain) leads to the allowance of higher emissions consistent with any specified goal for risk reduction, and thus leads to higher climate impacts, in exchange for lower mitigation costs.

Characterizing and Addressing the Need for Statistical Adjustment of Global Climate Model Data

NASA Astrophysics Data System (ADS)

White, K. D.; Baker, B.; Mueller, C.; Villarini, G.; Foley, P.; Friedman, D.

2017-12-01

As part of its mission to research and measure the effects of the changing climate, the U. S. Army Corps of Engineers (USACE) regularly uses the World Climate Research Programme's Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model dataset. However, these data are generated at a global level and are not fine-tuned for specific watersheds. This often causes CMIP5 output to vary from locally observed patterns in the climate. Several downscaling methods have been developed to increase the resolution of the CMIP5 data and decrease systemic differences to support decision-makers as they evaluate results at the watershed scale. Evaluating preliminary comparisons of observed and projected flow frequency curves over the US revealed a simple framework for water resources decision makers to plan and design water resources management measures under changing conditions using standard tools. Using this framework as a basis, USACE has begun to explore to use of statistical adjustment to alter global climate model data to better match the locally observed patterns while preserving the general structure and behavior of the model data. When paired with careful measurement and hypothesis testing, statistical adjustment can be particularly effective at navigating the compromise between the locally observed patterns and the global climate model structures for decision makers.

Change of ocean circulation in the East Asian Marginal Seas under different climate conditions

NASA Astrophysics Data System (ADS)

Min, Hong Sik; Kim, Cheol-Ho; Kim, Young Ho

2010-05-01

Global climate models do not properly resolve an ocean environment in the East Asian Marginal Seas (EAMS), which is mainly due to a poor representation of the topography in continental shelf region and a coarse spatial resolution. To examine a possible change of ocean environment under global warming in the EAMS, therefore we used North Pacific Regional Ocean Model. The regional model was forced by atmospheric conditions extracted from the simulation results of the global climate models for the 21st century projected by the IPCC SRES A1B scenario as well as the 20th century. The North Pacific Regional Ocean model simulated a detailed pattern of temperature change in the EAMS showing locally different rising or falling trend under the future climate condition, while the global climate models simulated a simple pattern like an overall increase. Changes of circulation pattern in the EAMS such as an intrusion of warm water into the Yellow Sea as well as the Kuroshio were also well resolved. Annual variations in volume transports through the Taiwan Strait and the Korea Strait under the future condition were simulated to be different from those under present condition. Relative ratio of volume transport through the Soya Strait to the Tsugaru Strait also responded to the climate condition.

A dynamical stabilizer in the climate system: a mechanism suggested by a simple model

NASA Astrophysics Data System (ADS)

Bates, J. R.

1999-05-01

A simple zonally averaged hemispheric model of the climate system is constructed, based on energy equations for two ocean basins separated at 30° latitude with the surface fluxes calculated explicitly. A combination of empirical input and theoretical calculation is used to determine an annual mean equilibrium climate for the model and to study its stability with respect to small perturbations. The insolation, the mean albedos and the equilibrium temperatures for the two model zones are prescribed from observation. The principal agent of interaction between the zones is the vertically integrated poleward transport of atmospheric angular momentum across their common boundary. This is parameterized using an empirical formula derived from a multiyear atmospheric data set. The surface winds are derived from the angular momentum transport assuming the atmosphere to be in a state of dynamic balance on the climatic timescales of interest. A further assumption that the air sea temperature difference and low level relative humidity remain fixed at their mean observed values then allows the surface fluxes of latent and sensible heat to be calculated. Results from a radiative model, which show a positive lower tropospheric water vapour/infrared radiative feedback on SST perturbations in both zones, are used to calculate the net upward infrared radiative fluxes at the surface. In the model's equilibrium climate, the principal processes balancing the solar radiation absorbed at the surface are evaporation in the tropical zone and net infrared radiation in the extratropical zone. The stability of small perturbations about the equilibrium is studied using a linearized form of the ocean energy equations. Ice-albedo and cloud feedbacks are omitted and attention is focussed on the competing effects of the water vapour/infrared radiative feedback and the turbulent surface flux and oceanic heat transport feedbacks associated with the angular momentum cycle. The perturbation equations involve inter-zone coupling and have coefficients dependent on the values of the equilibrium fluxes and the sensitivity of the angular momentum transport. Analytical solutions for the perturbations are obtained. These provide criteria for the stability of the equilibrium climate. If the evaporative feedback on SST perturbations is omitted, the equilibrium climate is unstable due to the influence of the water vapour/infrared radiative feedback, which dominates over the effects of the sensible heat and ocean heat transport feedbacks. The inclusion of evaporation gives a negative feedback which is of sufficient strength to stabilize the system. The stabilizing mechanism involves wind and humidity factors in the evaporative fluxes that are of comparable magnitude. Both factors involve the angular momentum transport. In including angular momentum and calculating the surface fluxes explicitly, the model presented here differs from the many simple climate models based on the Budyko Sellers formulation. In that formulation, an atmospheric energy balance equation is used to eliminate surface fluxes in favour of top-of-the-atmosphere radiative fluxes and meridional atmospheric energy transports. In the resulting models, infrared radiation appears as a stabilizing influence on SST perturbations and the dynamical stabilizing mechanism found here cannot be identified.

Incorporating water table dynamics in climate modeling: 1. Water table observations and equilibrium water table simulations

NASA Astrophysics Data System (ADS)

Fan, Ying; Miguez-Macho, Gonzalo; Weaver, Christopher P.; Walko, Robert; Robock, Alan

2007-05-01

Soil moisture is a key participant in land-atmosphere interactions and an important determinant of terrestrial climate. In regions where the water table is shallow, soil moisture is coupled to the water table. This paper is the first of a two-part study to quantify this coupling and explore its implications in the context of climate modeling. We examine the observed water table depth in the lower 48 states of the United States in search of salient spatial and temporal features that are relevant to climate dynamics. As a means to interpolate and synthesize the scattered observations, we use a simple two-dimensional groundwater flow model to construct an equilibrium water table as a result of long-term climatic and geologic forcing. Model simulations suggest that the water table depth exhibits spatial organization at watershed, regional, and continental scales, which may have implications for the spatial organization of soil moisture at similar scales. The observations suggest that water table depth varies at diurnal, event, seasonal, and interannual scales, which may have implications for soil moisture memory at these scales.

Quantifying alluvial fan sensitivity to climate in Death Valley, California, from field observations and numerical models

NASA Astrophysics Data System (ADS)

Brooke, Sam; Whittaker, Alexander; Armitage, John; D'Arcy, Mitch; Watkins, Stephen

2017-04-01

A quantitative understanding of landscape sensitivity to climate change remains a key challenge in the Earth Sciences. The stream-flow deposits of coupled catchment-fan systems offer one way to decode past changes in external boundary conditions as they comprise simple, closed systems that can be represented effectively by numerical models. Here we combine the collection and analysis of grain size data on well-dated alluvial fan surfaces in Death Valley, USA, with numerical modelling to address the extent to which sediment routing systems record high-frequency, high-magnitude climate change. We compile a new database of Holocene and Late-Pleistocene grain size trends from 11 alluvial fans in Death Valley, capturing high-resolution grain size data ranging from the Recent to 100 kyr in age. We hypothesise the observed changes in average surface grain size and fining rate over time are a record of landscape response to glacial-interglacial climatic forcing. With this data we are in a unique position to test the predictions of landscape evolution models and evaluate the extent to which climate change has influenced the volume and calibre of sediment deposited on alluvial fans. To gain insight into our field data and study area, we employ an appropriately-scaled catchment-fan model that calculates an eroded volumetric sediment budget to be deposited in a subsiding basin according to mass balance where grain size trends are predicted by a self-similarity fining model. We use the model to compare predicted trends in alluvial fan stratigraphy as a function of boundary condition change for a range of model parameters and input grain size distributions. Subsequently, we perturb our model with a plausible glacial-interglacial magnitude precipitation change to estimate the requisite sediment flux needed to generate observed field grain size trends in Death Valley. Modelled fluxes are then compared with independent measurements of sediment supply over time. Our results constitute one of the first attempts to combine the detailed collection of alluvial fan grain size data in time and space with coupled catchment-fan models, affording us the means to evaluate how well field and model data can be reconciled for simple sediment routing systems.

Solar luminosity variations and the climate of Mars

NASA Technical Reports Server (NTRS)

Toon, O. B.; Gierasch, P. J.; Sagan, C.

1975-01-01

A simple climatological model of Mars indicates that its climate may be more sensitive to luminosity changes than earth's because of strong positive feedback mechanisms at work on Mars. Mariner 9 photographs of Mars show an abundance of large sinuous channels that point to an epoch of higher atmospheric pressures and abundant liquid water. Such an epoch could have been the result of large-scale solar luminosity variations. The climatological model suggests that other less controversial mechanisms, such as obliquity or polar albedo changes, also could have led to such an epoch.

The impacts of changing transport and precipitation on pollutant distributions in a future climate

NASA Astrophysics Data System (ADS)

Fang, Yuanyuan; Fiore, Arlene M.; Horowitz, Larry W.; Gnanadesikan, Anand; Held, Isaac; Chen, Gang; Vecchi, Gabriel; Levy, Hiram

2011-09-01

Air pollution (ozone and particulate matter in surface air) is strongly linked to synoptic weather and thus is likely sensitive to climate change. In order to isolate the responses of air pollutant transport and wet removal to a warming climate, we examine a simple carbon monoxide-like (CO) tracer (COt) and a soluble version (SAt), both with the 2001 CO emissions, in simulations with the Geophysical Fluid Dynamics Laboratory chemistry-climate model (AM3) for present (1981-2000) and future (2081-2100) climates. In 2081-2100, projected reductions in lower-tropospheric ventilation and wet deposition exacerbate surface air pollution as evidenced by higher surface COt and SAt concentrations. However, the average horizontal general circulation patterns in 2081-2100 are similar to 1981-2000, so the spatial distribution of COt changes little. Precipitation is an important factor controlling soluble pollutant wet removal, but the total global precipitation change alone does not necessarily indicate the sign of the soluble pollutant response to climate change. Over certain latitudinal bands, however, the annual wet deposition change can be explained mainly by the simulated changes in large-scale (LS) precipitation. In regions such as North America, differences in the seasonality of LS precipitation and tracer burdens contribute to an apparent inconsistency of changes in annual wet deposition versus annual precipitation. As a step toward an ultimate goal of developing a simple index that can be applied to infer changes in soluble pollutants directly from changes in precipitation fields as projected by physical climate models, we explore here a "Diagnosed Precipitation Impact" (DPI) index. This index captures the sign and magnitude (within 50%) of the relative annual mean changes in the global wet deposition of the soluble pollutant. DPI can only be usefully applied in climate models in which LS precipitation dominates wet deposition and horizontal transport patterns change little as climate warms. Our findings support the need for tighter emission regulations, for both soluble and insoluble pollutants, to obtain a desired level of air quality as climate warms.

Changing climatic response: a conceptual model for glacial cycles and the Mid-Pleistocene Transition

NASA Astrophysics Data System (ADS)

Daruka, I.; Ditlevsen, P. D.

2014-03-01

Milankovitch's astronomical theory of glacial cycles, attributing ice age climate oscillations to orbital changes in Northern Northern-Hemisphere insolation, is challenged by the paleoclimatic record. The climatic response to the variations in insolation is far from trivial. In general the glacial cycles are highly asymmetric in time, with slow cooling from the interglacials to the glacials (inceptions) and very rapid warming from the glacials to the interglacials (terminations). We shall refer to this fast-slow dynamics as the "saw-tooth" shape of the paleoclimatic record. This is non-linearly related to the time-symmetric variations in the orbital forcing. However, the most pronounced challenge to the Milankovitch theory is the Mid-Pleistocene Transition (MPT) occurring about one million years ago. During that event, the prevailing 41 kyr glacial cycles, corresponding to the almost harmonic obliquity cycle were replaced by longer saw-tooth shaped cycles with a time scale around 100 kyr. The MPT must have been driven by internal changes in climate response, since it does not correspond to any apparent changes in the orbital forcing. In order to identify possible mechanisms causing the observed changes in glacial dynamics, it is relevant to study simplified models with the capability of generating temporal behavior similar to the observed records. We present a simple oscillator type model approach, with two variables, a temperature anomaly and an ice volume analogous, climatic memory term. The generalization of the ice albedo feedback is included in terms of an effective multiplicative coupling between this latter climatic memory term (representing the internal degrees of freedom) and the external drive. The simple model reproduces the temporal asymmetry of the late Pleistocene glacial cycles and suggests that the MPT can be explained as a regime shift, aided by climatic noise, from a period 1 frequency locking to the obliquity cycle to a period 2-3 frequency locking to the same obliquity cycle. The change in dynamics has been suggested to be a result of a slow gradual decrease in atmospheric greenhouse gas concentration. The presence of chaos in the (non-autonomous) glacial dynamics and a critical dependence on initial conditions raises fundamental questions about climate predictability.

Performance analysis of the lineal model for estimating the maximum power of a HCPV module in different climate conditions

NASA Astrophysics Data System (ADS)

Fernández, Eduardo F.; Almonacid, Florencia; Sarmah, Nabin; Mallick, Tapas; Sanchez, Iñigo; Cuadra, Juan M.; Soria-Moya, Alberto; Pérez-Higueras, Pedro

2014-09-01

A model based on easily obtained atmospheric parameters and on a simple lineal mathematical expression has been developed at the Centre of Advanced Studies in Energy and Environment in southern Spain. The model predicts the maximum power of a HCPV module as a function of direct normal irradiance, air temperature and air mass. Presently, the proposed model has only been validated in southern Spain and its performance in locations with different atmospheric conditions still remains unknown. In order to address this issue, several HCPV modules have been measured in two different locations with different climate conditions than the south of Spain: the Environment and Sustainability Institute in southern UK and the National Renewable Energy Center in northern Spain. Results show that the model has an adequate match between actual and estimated data with a RMSE lower than 3.9% at locations with different climate conditions.

Regular network model for the sea ice-albedo feedback in the Arctic.

PubMed

Müller-Stoffels, Marc; Wackerbauer, Renate

2011-03-01

The Arctic Ocean and sea ice form a feedback system that plays an important role in the global climate. The complexity of highly parameterized global circulation (climate) models makes it very difficult to assess feedback processes in climate without the concurrent use of simple models where the physics is understood. We introduce a two-dimensional energy-based regular network model to investigate feedback processes in an Arctic ice-ocean layer. The model includes the nonlinear aspect of the ice-water phase transition, a nonlinear diffusive energy transport within a heterogeneous ice-ocean lattice, and spatiotemporal atmospheric and oceanic forcing at the surfaces. First results for a horizontally homogeneous ice-ocean layer show bistability and related hysteresis between perennial ice and perennial open water for varying atmospheric heat influx. Seasonal ice cover exists as a transient phenomenon. We also find that ocean heat fluxes are more efficient than atmospheric heat fluxes to melt Arctic sea ice.

Noise-induced transitions and shifts in a climate-vegetation feedback model.

PubMed

Alexandrov, Dmitri V; Bashkirtseva, Irina A; Ryashko, Lev B

2018-04-01

Motivated by the extremely important role of the Earth's vegetation dynamics in climate changes, we study the stochastic variability of a simple climate-vegetation system. In the case of deterministic dynamics, the system has one stable equilibrium and limit cycle or two stable equilibria corresponding to two opposite (cold and warm) climate-vegetation states. These states are divided by a separatrix going across a point of unstable equilibrium. Some possible stochastic scenarios caused by different externally induced natural and anthropogenic processes inherit properties of deterministic behaviour and drastically change the system dynamics. We demonstrate that the system transitions across its separatrix occur with increasing noise intensity. The climate-vegetation system therewith fluctuates, transits and localizes in the vicinity of its attractor. We show that this phenomenon occurs within some critical range of noise intensities. A noise-induced shift into the range of smaller global average temperatures corresponding to substantial oscillations of the Earth's vegetation cover is revealed. Our analysis demonstrates that the climate-vegetation interactions essentially contribute to climate dynamics and should be taken into account in more precise and complex models of climate variability.

Sustainability Indicators for Coupled Human-Earth Systems

NASA Astrophysics Data System (ADS)

Motesharrei, S.; Rivas, J. R.; Kalnay, E.

2014-12-01

Over the last two centuries, the Human System went from having a small impact on the Earth System (including the Climate System) to becoming dominant, because both population and per capita consumption have grown extremely fast, especially since about 1950. We therefore argue that Human System Models must be included into Earth System Models through bidirectional couplings with feedbacks. In particular, population should be modeled endogenously, rather than exogenously as done currently in most Integrated Assessment Models. The growth of the Human System threatens to overwhelm the Carrying Capacity of the Earth System, and may be leading to catastrophic climate change and collapse. We propose a set of Ecological and Economic "Sustainability Indicators" that can employ large data-sets for developing and assessing effective mitigation and adaptation policies. Using the Human and Nature Dynamical Model (HANDY) and Coupled Human-Climate-Water Model (COWA), we carry out experiments with this set of Sustainability Indicators and show that they are applicable to various coupled systems including Population, Climate, Water, Energy, Agriculture, and Economy. Impact of nonrenewable resources and fossil fuels could also be understood using these indicators. We demonstrate interconnections of Ecological and Economic Indicators. Coupled systems often include feedbacks and can thus display counterintuitive dynamics. This makes it difficult for even experts to see coming catastrophes from just the raw data for different variables. Sustainability Indicators boil down the raw data into a set of simple numbers that cross their sustainability thresholds with a large time-lag before variables enter their catastrophic regimes. Therefore, we argue that Sustainability Indicators constitute a powerful but simple set of tools that could be directly used for making policies for sustainability.

Forward modeling of tree-ring data: a case study with a global network

NASA Astrophysics Data System (ADS)

Breitenmoser, P. D.; Frank, D.; Brönnimann, S.

2012-04-01

Information derived from tree-rings is one of the most powerful tools presently available for studying past climatic variability as well as identifying fundamental relationships between tree-growth and climate. Climate reconstructions are typically performed by extending linear relationships, established during the overlapping period of instrumental and climate proxy archives into the past. Such analyses, however, are limited by methodological assumptions, including stationarity and linearity of the climate-proxy relationship. We investigate climate and tree-ring data using the Vaganov-Shashkin-Lite (VS-Lite) forward model of tree-ring width formation to examine the relations among actual tree growth and climate (as inferred from the simulated chronologies) to reconstruct past climate variability. The VS-lite model has been shown to produce skill comparable to that achieved using classical dendrochronological statistical modeling techniques when applied on simulations of a network of North American tree-ring chronologies. Although the detailed mechanistic processes such as photosynthesis, storage, or cell processes are not modeled directly, the net effect of the dominating nonlinear climatic controls on tree-growth are implemented into the model by the principle of limiting factors and threshold growth response functions. The VS-lite model requires as inputs only latitude, monthly mean temperature and monthly accumulated precipitation. Hence, this simple, process-based model enables ring-width simulation at any location where monthly climate records exist. In this study, we analyse the growth response of simulated tree-rings to monthly climate conditions obtained from the 20th century reanalysis project back to 1871. These simulated tree-ring chronologies are compared to the climate-driven variability in worldwide observed tree-ring chronologies from the International Tree Ring Database. Results point toward the suitability of the relationship among actual tree growth and climate (as inferred from the simulated chronologies) for use in global palaeoclimate reconstructions.

Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs

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

Wood, Andrew W; Leung, Lai R; Sridhar, V

Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregationmore » (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at ½-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.« less

Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing

NASA Astrophysics Data System (ADS)

Vecchi, Gabriel A.; Soden, Brian J.; Wittenberg, Andrew T.; Held, Isaac M.; Leetmaa, Ants; Harrison, Matthew J.

2006-05-01

Since the mid-nineteenth century the Earth's surface has warmed, and models indicate that human activities have caused part of the warming by altering the radiative balance of the atmosphere. Simple theories suggest that global warming will reduce the strength of the mean tropical atmospheric circulation. An important aspect of this tropical circulation is a large-scale zonal (east-west) overturning of air across the equatorial Pacific Ocean-driven by convection to the west and subsidence to the east-known as the Walker circulation. Here we explore changes in tropical Pacific circulation since the mid-nineteenth century using observations and a suite of global climate model experiments. Observed Indo-Pacific sea level pressure reveals a weakening of the Walker circulation. The size of this trend is consistent with theoretical predictions, is accurately reproduced by climate model simulations and, within the climate models, is largely due to anthropogenic forcing. The climate model indicates that the weakened surface winds have altered the thermal structure and circulation of the tropical Pacific Ocean. These results support model projections of further weakening of tropical atmospheric circulation during the twenty-first century.

Web based visualization of large climate data sets

USGS Publications Warehouse

Alder, Jay R.; Hostetler, Steven W.

2015-01-01

We have implemented the USGS National Climate Change Viewer (NCCV), which is an easy-to-use web application that displays future projections from global climate models over the United States at the state, county and watershed scales. We incorporate the NASA NEX-DCP30 statistically downscaled temperature and precipitation for 30 global climate models being used in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), and hydrologic variables we simulated using a simple water-balance model. Our application summarizes very large, complex data sets at scales relevant to resource managers and citizens and makes climate-change projection information accessible to users of varying skill levels. Tens of terabytes of high-resolution climate and water-balance data are distilled to compact binary format summary files that are used in the application. To alleviate slow response times under high loads, we developed a map caching technique that reduces the time it takes to generate maps by several orders of magnitude. The reduced access time scales to >500 concurrent users. We provide code examples that demonstrate key aspects of data processing, data exporting/importing and the caching technique used in the NCCV.

Climate Informatics

NASA Technical Reports Server (NTRS)

Monteleoni, Claire; Schmidt, Gavin A.; Alexander, Francis J.; Niculescu-Mizil, Alexandru; Steinhaeuser, Karsten; Tippett, Michael; Banerjee, Arindam; Blumenthal, M. Benno; Ganguly, Auroop R.; Smerdon, Jason E.;

Flexible Environments for Grand-Challenge Simulation in Climate Science

NASA Astrophysics Data System (ADS)

Pierrehumbert, R.; Tobis, M.; Lin, J.; Dieterich, C.; Caballero, R.

2004-12-01

Current climate models are monolithic codes, generally in Fortran, aimed at high-performance simulation of the modern climate. Though they adequately serve their designated purpose, they present major barriers to application in other problems. Tailoring them to paleoclimate of planetary simulations, for instance, takes months of work. Theoretical studies, where one may want to remove selected processes or break feedback loops, are similarly hindered. Further, current climate models are of little value in education, since the implementation of textbook concepts and equations in the code is obscured by technical detail. The Climate Systems Center at the University of Chicago seeks to overcome these limitations by bringing modern object-oriented design into the business of climate modeling. Our ultimate goal is to produce an end-to-end modeling environment capable of configuring anything from a simple single-column radiative-convective model to a full 3-D coupled climate model using a uniform, flexible interface. Technically, the modeling environment is implemented as a Python-based software component toolkit: key number-crunching procedures are implemented as discrete, compiled-language components 'glued' together and co-ordinated by Python, combining the high performance of compiled languages and the flexibility and extensibility of Python. We are incrementally working towards this final objective following a series of distinct, complementary lines. We will present an overview of these activities, including PyOM, a Python-based finite-difference ocean model allowing run-time selection of different Arakawa grids and physical parameterizations; CliMT, an atmospheric modeling toolkit providing a library of 'legacy' radiative, convective and dynamical modules which can be knitted into dynamical models, and PyCCSM, a version of NCAR's Community Climate System Model in which the coupler and run-control architecture are re-implemented in Python, augmenting its flexibility and adaptability.

Numerical Modeling of Geomorphic Change on Sandy Coasts as a Function of Changing Wave Climate

NASA Astrophysics Data System (ADS)

Adams, P. N.; McNamara, D.; Murray, A. B.; Lovering, J.

2009-12-01

Climate change is expected to affect sandy coast geomorphology through two principal mechanisms: (1) sea level rise, which affects cross-shore sediment transport tending to drive shoreline retreat, and (2) alteration of statistical distributions in ocean storm wave climate (deep water wave height, period, and direction), which affects longshore sediment transport gradients that result in shoreline erosion and accretion. To address potential climate change-driven effects on longshore sediment transport gradients, we are developing techniques to link various numerical models of wave transformation with several different longshore sediment transport formulae in accordance with the Community Surface Dynamics Modeling System (CSDMS) project. Results of the various wave transformation models are compared to field observations of cross-shelf wave transformation along the North Florida Atlantic coast for purposes of model verification and calibration. Initial comparisons between wave-transformation methods (assumption of shore-parallel contours, simple wave ray tracing, and the SWAN spectral wave model) on artificially constructed continental shelves reveal an increasing discrepancy of results for increasing complexity of shelf bathymetry. When the more advanced SWAN spectral wave model is coupled with a simple CERC-type formulation of longshore sediment transport and applied to a real coast with complex offshore shoals (Cape Canaveral region of the North Florida Atlantic Coast), the patterns of erosion and accretion agree with results of the simplest wave-propagation models for some wave conditions, but disagree in others. Model simulations in which wave height and period are held constant show that locations of divergence and convergence of sediment flux shift with deep water wave-approach angle in ways that would not always be predicted using less sophisticated wave propagation models. Thus, predicting long-term local shoreline change on actual coastlines featuring complex bathymetry requires the extra computational effort to run the more advanced model over a wide range of wave conditions.

Geophysical, archaeological and historical evidence support a solar-output model for climate change

USGS Publications Warehouse

Perry, C.A.; Hsu, K.J.

2000-01-01

Although the processes of climate change are not completely understood, an important causal candidate is variation in total solar output. Reported cycles in various climate-proxy data show a tendency to emulate a fundamental harmonic sequence of a basic solar-cycle length (11 years) multiplied by 2(N) (where N equals a positive or negative integer). A simple additive model for total solar-output variations was developed by superimposing a progression of fundamental harmonic cycles with slightly increasing amplitudes. The timeline of the model was calibrated to the Pleistocene/Holocene boundary at 9,000 years before present. The calibrated model was compared with geophysical, archaeological, and historical evidence of warm or cold climates during the Holocene. The evidence of periods of several centuries of cooler climates worldwide called 'little ice ages,' similar to the period anno Domini (A.D.) 1280-1860 and reoccurring approximately every 1,300 years, corresponds well with fluctuations in modeled solar output. A more detailed examination of the climate sensitive history of the last 1,000 years further supports the model. Extrapolation of the model into the future suggests a gradual cooling during the next few centuries with intermittent minor warmups and a return to near little-ice-age conditions within the next 500 years. This cool period then may be followed approximately 1,500 years from now by a return to altithermal conditions similar to the previous Holocene Maximum.

A hydrologic drying bias in water-resource impact analyses of anthropogenic climate change

USGS Publications Warehouse

Milly, Paul; Dunne, Krista A.

2017-01-01

For water-resource planning, sensitivity of freshwater availability to anthropogenic climate change (ACC) often is analyzed with “offline” hydrologic models that use precipitation and potential evapotranspiration (Ep) as inputs. Because Ep is not a climate-model output, an intermediary model of Ep must be introduced to connect the climate model to the hydrologic model. Several Ep methods are used. The suitability of each can be assessed by noting a credible Ep method for offline analyses should be able to reproduce climate models’ ACC-driven changes in actual evapotranspiration in regions and seasons of negligible water stress (Ew). We quantified this ability for seven commonly used Ep methods and for a simple proportionality with available energy (“energy-only” method). With the exception of the energy-only method, all methods tend to overestimate substantially the increase in Ep associated with ACC. In an offline hydrologic model, the Ep-change biases produce excessive increases in actual evapotranspiration (E), whether the system experiences water stress or not, and thence strong negative biases in runoff change, as compared to hydrologic fluxes in the driving climate models. The runoff biases are comparable in magnitude to the ACC-induced runoff changes themselves. These results suggest future hydrologic drying (wetting) trends likely are being systematically and substantially overestimated (underestimated) in many water-resource impact analyses.

Green roofs'retention performances in different climates

NASA Astrophysics Data System (ADS)

Viola, Francesco; Hellies, Matteo; Deidda, Roberto

2017-04-01

The ongoing process of global urbanization contributes to increasing stormwater runoff from impervious surfaces, threatening also water quality. Green roofs have been proved to be an innovative stormwater management tool to partially restore natural state, enhancing interception, infiltration and evapotranspiration fluxes. The amount of water that is retained within green roofs depends mainly on both soil properties and climate. The evaluation of the retained water is not trivial since it depends on the stochastic soil moisture dynamics. The aim of this work is to explore performances of green roofs, in terms of water retention, as a function of their depth considering different climate regimes. The role of climate in driving water retention has been mainly represented by rainfall and potential evapotranspiration dynamics, which are simulated by a simple conceptual weather generator at daily time scale. The model is able to describe seasonal (in-phase and counter-phase) and stationary behaviors of climatic forcings. Model parameters have been estimated on more than 20,000 historical time series retrieved worldwide. Exemplifying cases are discussed for five different climate scenarios, changing the amplitude and/or the phase of daily mean rainfall and evapotranspiration forcings. The first scenario represents stationary climates, in two other cases the daily mean rainfall or the potential evapotranspiration evolve sinusoidally. In the latter two cases, we simulated the in-phase or in counter-phase conditions. Stochastic forcings have been then used as an input to a simple conceptual hydrological model which simulate soil moisture dynamics, evapotranspiration fluxes, runoff and leakage from soil pack at daily time scale. For several combinations of annual rainfall and potential evapotranspiration, the analysis allowed assessing green roofs' retaining capabilities, at annual time scale. Provided abacus allows a first approximation of possible hydrological benefits deriving from the implementation of intensive or extensive green roofs in different world areas, i.e. less input to sewer systems.

Climate system properties determining the social cost of carbon

NASA Astrophysics Data System (ADS)

Otto, Alexander; Todd, Benjamin J.; Bowerman, Niel; Frame, David J.; Allen, Myles R.

2013-06-01

The choice of an appropriate scientific target to guide global mitigation efforts is complicated by uncertainties in the temperature response to greenhouse gas emissions. Much climate policy discourse has been based on the equilibrium global mean temperature increase following a concentration stabilization scenario. This is determined by the equilibrium climate sensitivity (ECS) which, in many studies, shows persistent, fat-tailed uncertainty. However, for many purposes, the equilibrium response is less relevant than the transient response. Here, we show that one prominent policy variable, the social cost of carbon (SCC), is generally better constrained by the transient climate response (TCR) than by the ECS. Simple analytic expressions show the SCC to be directly proportional to the TCR under idealized assumptions when the rate at which we discount future damage equals 2.8%. Using ensemble simulations of a simple climate model we find that knowing the true value of the TCR can reduce the relative uncertainty in the SCC substantially more, up to a factor of 3, than knowing the ECS under typical discounting assumptions. We conclude that the TCR, which is better constrained by observations, less subject to fat-tailed uncertainty and more directly related to the SCC, is generally preferable to the ECS as a single proxy for the climate response in SCC calculations.

Adjusting STEMS growth model for Wisconsin forests.

Treesearch

Margaret R. Holdaway

1985-01-01

Describes a simple procedure for adjusting growth in the STEMS regional tree growth model to compensate for subregional differences. Coefficients are reported to adjust Lake States STEMS to the forests of Northern and Central Wisconsin--an area of essentially uniform climate and similar broad physiographic features. Errors are presented for various combinations of...

A stability theorem for energy-balance climate models

NASA Technical Reports Server (NTRS)

Cahalan, R. F.; North, G. R.

1979-01-01

The paper treats the stability of steady-state solutions of some simple, latitude-dependent, energy-balance climate models. For north-south symmetric solutions of models with an ice-cap-type albedo feedback, and for the sum of horizontal transport and infrared radiation given by a linear operator, it is possible to prove a 'slope stability' theorem, i.e., if the local slope of the steady-state iceline latitude versus solar constant curve is positive (negative) the steady-state solution is stable (unstable). Certain rather weak restrictions on the albedo function and on the heat transport are required for the proof, and their physical basis is discussed.

Test Driven Development of a Parameterized Ice Sheet Component

NASA Astrophysics Data System (ADS)

Clune, T.

2011-12-01

Test driven development (TDD) is a software development methodology that offers many advantages over traditional approaches including reduced development and maintenance costs, improved reliability, and superior design quality. Although TDD is widely accepted in many software communities, the suitability to scientific software is largely undemonstrated and warrants a degree of skepticism. Indeed, numerical algorithms pose several challenges to unit testing in general, and TDD in particular. Among these challenges are the need to have simple, non-redundant closed-form expressions to compare against the results obtained from the implementation as well as realistic error estimates. The necessity for serial and parallel performance raises additional concerns for many scientific applicaitons. In previous work I demonstrated that TDD performed well for the development of a relatively simple numerical model that simulates the growth of snowflakes, but the results were anecdotal and of limited relevance to far more complex software components typical of climate models. This investigation has now been extended by successfully applying TDD to the implementation of a substantial portion of a new parameterized ice sheet component within a full climate model. After a brief introduction to TDD, I will present techniques that address some of the obstacles encountered with numerical algorithms. I will conclude with some quantitative and qualitative comparisons against climate components developed in a more traditional manner.

A Bayesian Approach to Evaluating Consistency between Climate Model Output and Observations

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Cressie, N.; Teixeira, J.

2010-12-01

Like other scientific and engineering problems that involve physical modeling of complex systems, climate models can be evaluated and diagnosed by comparing their output to observations of similar quantities. Though the global remote sensing data record is relatively short by climate research standards, these data offer opportunities to evaluate model predictions in new ways. For example, remote sensing data are spatially and temporally dense enough to provide distributional information that goes beyond simple moments to allow quantification of temporal and spatial dependence structures. In this talk, we propose a new method for exploiting these rich data sets using a Bayesian paradigm. For a collection of climate models, we calculate posterior probabilities its members best represent the physical system each seeks to reproduce. The posterior probability is based on the likelihood that a chosen summary statistic, computed from observations, would be obtained when the model's output is considered as a realization from a stochastic process. By exploring how posterior probabilities change with different statistics, we may paint a more quantitative and complete picture of the strengths and weaknesses of the models relative to the observations. We demonstrate our method using model output from the CMIP archive, and observations from NASA's Atmospheric Infrared Sounder.

Modelling spatial and temporal vegetation variability with the Climate Constrained Vegetation Index: evidence of CO2 fertilisation and of water stress in continental interiors

NASA Astrophysics Data System (ADS)

Los, S. O.

2015-06-01

A model was developed to simulate spatial, seasonal and interannual variations in vegetation in response to temperature, precipitation and atmospheric CO2 concentrations; the model addresses shortcomings in current implementations. The model uses the minimum of 12 temperature and precipitation constraint functions to simulate NDVI. Functions vary based on the Köppen-Trewartha climate classification to take adaptations of vegetation to climate into account. The simulated NDVI, referred to as the climate constrained vegetation index (CCVI), captured the spatial variability (0.82 < r <0.87), seasonal variability (median r = 0.83) and interannual variability (median global r = 0.24) in NDVI. The CCVI simulated the effects of adverse climate on vegetation during the 1984 drought in the Sahel and during dust bowls of the 1930s and 1950s in the Great Plains in North America. A global CO2 fertilisation effect was found in NDVI data, similar in magnitude to that of earlier estimates (8 % for the 20th century). This effect increased linearly with simple ratio, a transformation of the NDVI. Three CCVI scenarios, based on climate simulations using the representative concentration pathway RCP4.5, showed a greater sensitivity of vegetation towards precipitation in Northern Hemisphere mid latitudes than is currently implemented in climate models. This higher sensitivity is of importance to assess the impact of climate variability on vegetation, in particular on agricultural productivity.

CLIMLAB: a Python-based software toolkit for interactive, process-oriented climate modeling

NASA Astrophysics Data System (ADS)

Rose, B. E. J.

2015-12-01

Global climate is a complex emergent property of the rich interactions between simpler components of the climate system. We build scientific understanding of this system by breaking it down into component process models (e.g. radiation, large-scale dynamics, boundary layer turbulence), understanding each components, and putting them back together. Hands-on experience and freedom to tinker with climate models (whether simple or complex) is invaluable for building physical understanding. CLIMLAB is an open-ended software engine for interactive, process-oriented climate modeling. With CLIMLAB you can interactively mix and match model components, or combine simpler process models together into a more comprehensive model. It was created primarily to support classroom activities, using hands-on modeling to teach fundamentals of climate science at both undergraduate and graduate levels. CLIMLAB is written in Python and ties in with the rich ecosystem of open-source scientific Python tools for numerics and graphics. The IPython notebook format provides an elegant medium for distributing interactive example code. I will give an overview of the current capabilities of CLIMLAB, the curriculum we have developed thus far, and plans for the future. Using CLIMLAB requires some basic Python coding skills. We consider this an educational asset, as we are targeting upper-level undergraduates and Python is an increasingly important language in STEM fields. However CLIMLAB is well suited to be deployed as a computational back-end for a graphical gaming environment based on earth-system modeling.

A Process-based, Climate-Sensitive Model to Derive Methane Emissions from Natural Wetlands: Application to 5 Wetland Sites, Sensitivity to Model Parameters and Climate

NASA Technical Reports Server (NTRS)

Walter, Bernadette P.; Heimann, Martin

1999-01-01

Methane emissions from natural wetlands constitutes the largest methane source at present and depends highly on the climate. In order to investigate the response of methane emissions from natural wetlands to climate variations, a 1-dimensional process-based climate-sensitive model to derive methane emissions from natural wetlands is developed. In the model the processes leading to methane emission are simulated within a 1-dimensional soil column and the three different transport mechanisms diffusion, plant-mediated transport and ebullition are modeled explicitly. The model forcing consists of daily values of soil temperature, water table and Net Primary Productivity, and at permafrost sites the thaw depth is included. The methane model is tested using observational data obtained at 5 wetland sites located in North America, Europe and Central America, representing a large variety of environmental conditions. It can be shown that in most cases seasonal variations in methane emissions can be explained by the combined effect of changes in soil temperature and the position of the water table. Our results also show that a process-based approach is needed, because there is no simple relationship between these controlling factors and methane emissions that applies to a variety of wetland sites. The sensitivity of the model to the choice of key model parameters is tested and further sensitivity tests are performed to demonstrate how methane emissions from wetlands respond to climate variations.

A Generalized Simple Formulation of Convective Adjustment ...

EPA Pesticide Factsheets

Convective adjustment timescale (τ) for cumulus clouds is one of the most influential parameters controlling parameterized convective precipitation in climate and weather simulation models at global and regional scales. Due to the complex nature of deep convection, a prescribed value or ad hoc representation of τ is used in most global and regional climate/weather models making it a tunable parameter and yet still resulting in uncertainties in convective precipitation simulations. In this work, a generalized simple formulation of τ for use in any convection parameterization for shallow and deep clouds is developed to reduce convective precipitation biases at different grid spacing. Unlike existing other methods, our new formulation can be used with field campaign measurements to estimate τ as demonstrated by using data from two different special field campaigns. Then, we implemented our formulation into a regional model (WRF) for testing and evaluation. Results indicate that our simple τ formulation can give realistic temporal and spatial variations of τ across continental U.S. as well as grid-scale and subgrid scale precipitation. We also found that as the grid spacing decreases (e.g., from 36 to 4-km grid spacing), grid-scale precipitation dominants over subgrid-scale precipitation. The generalized τ formulation works for various types of atmospheric conditions (e.g., continental clouds due to heating and large-scale forcing over la

The U.S. Geological Survey Climate Geo Data Portal: an integrated broker for climate and geospatial data

USGS Publications Warehouse

Blodgett, David L.

2013-01-01

The increasing availability of downscaled climate projections and other data products that summarize or predict climate conditions, is making climate data use more common in research and management. Scientists and decisionmakers often need to construct ensembles and compare climate hindcasts and future projections for particular spatial areas. These tasks generally require an investigator to procure all datasets of interest en masse, integrate the various data formats and representations into commonly accessible and comparable formats, and then extract the subsets of the datasets that are actually of interest. This process can be challenging and time intensive due to data-transfer, -storage, and(or) -processing limits, or unfamiliarity with methods of accessing climate data. Data management for modeling and assessing the impacts of future climate conditions is also becoming increasingly expensive due to the size of the datasets. The Climate Geo Data Portal (http://cida.usgs.gov/climate/gdp/) addresses these limitations, making access to numerous climate datasets for particular areas of interest a simple and efficient task.

Pleistocene climate, phylogeny, and climate envelope models: an integrative approach to better understand species' response to climate change.

PubMed

Lawing, A Michelle; Polly, P David

2011-01-01

Mean annual temperature reported by the Intergovernmental Panel on Climate Change increases at least 1.1°C to 6.4°C over the next 90 years. In context, a change in climate of 6°C is approximately the difference between the mean annual temperature of the Last Glacial Maximum (LGM) and our current warm interglacial. Species have been responding to changing climate throughout Earth's history and their previous biological responses can inform our expectations for future climate change. Here we synthesize geological evidence in the form of stable oxygen isotopes, general circulation paleoclimate models, species' evolutionary relatedness, and species' geographic distributions. We use the stable oxygen isotope record to develop a series of temporally high-resolution paleoclimate reconstructions spanning the Middle Pleistocene to Recent, which we use to map ancestral climatic envelope reconstructions for North American rattlesnakes. A simple linear interpolation between current climate and a general circulation paleoclimate model of the LGM using stable oxygen isotope ratios provides good estimates of paleoclimate at other time periods. We use geologically informed rates of change derived from these reconstructions to predict magnitudes and rates of change in species' suitable habitat over the next century. Our approach to modeling the past suitable habitat of species is general and can be adopted by others. We use multiple lines of evidence of past climate (isotopes and climate models), phylogenetic topology (to correct the models for long-term changes in the suitable habitat of a species), and the fossil record, however sparse, to cross check the models. Our models indicate the annual rate of displacement in a clade of rattlesnakes over the next century will be 2 to 3 orders of magnitude greater (430-2,420 m/yr) than it has been on average for the past 320 ky (2.3 m/yr).

Pleistocene Climate, Phylogeny, and Climate Envelope Models: An Integrative Approach to Better Understand Species' Response to Climate Change

PubMed Central

Lawing, A. Michelle; Polly, P. David

2011-01-01

Mean annual temperature reported by the Intergovernmental Panel on Climate Change increases at least 1.1°C to 6.4°C over the next 90 years. In context, a change in climate of 6°C is approximately the difference between the mean annual temperature of the Last Glacial Maximum (LGM) and our current warm interglacial. Species have been responding to changing climate throughout Earth's history and their previous biological responses can inform our expectations for future climate change. Here we synthesize geological evidence in the form of stable oxygen isotopes, general circulation paleoclimate models, species' evolutionary relatedness, and species' geographic distributions. We use the stable oxygen isotope record to develop a series of temporally high-resolution paleoclimate reconstructions spanning the Middle Pleistocene to Recent, which we use to map ancestral climatic envelope reconstructions for North American rattlesnakes. A simple linear interpolation between current climate and a general circulation paleoclimate model of the LGM using stable oxygen isotope ratios provides good estimates of paleoclimate at other time periods. We use geologically informed rates of change derived from these reconstructions to predict magnitudes and rates of change in species' suitable habitat over the next century. Our approach to modeling the past suitable habitat of species is general and can be adopted by others. We use multiple lines of evidence of past climate (isotopes and climate models), phylogenetic topology (to correct the models for long-term changes in the suitable habitat of a species), and the fossil record, however sparse, to cross check the models. Our models indicate the annual rate of displacement in a clade of rattlesnakes over the next century will be 2 to 3 orders of magnitude greater (430-2,420 m/yr) than it has been on average for the past 320 ky (2.3 m/yr). PMID:22164305

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.

Liquid water on Mars - an energy balance climate model for CO2/H2O atmospheres

NASA Astrophysics Data System (ADS)

Hoffert, M. I.; Callegari, A. J.; Hsieh, T.; Ziegler, W.

1981-07-01

A simple climatic model is developed for a Mars atmosphere containing CO2 and sufficient liquid water to account for the observed hydrologic surface features by the existence of a CO2/H2O greenhouse effect. A latitude-resolved climate model originally devised for terrestrial climate studies is applied to Martian conditions, with the difference between absorbed solar flux and emitted long-wave flux to space per unit area attributed to the divergence of the meridional heat flux and the poleward heat flux assumed to equal the atmospheric eddy heat flux. The global mean energy balance is calculated as a function of atmospheric pressure to assess the CO2/H2O greenhouse liquid water hypothesis, and some latitude-resolved cases are examined in detail in order to clarify the role of atmospheric transport and temperature-albedo feedback. It is shown that the combined CO2/H2O greenhouse at plausible early surface pressures may account for climates hot enough to support a hydrological cycle and running water at present-day insolation and visible albedo levels.

Liquid water on Mars - An energy balance climate model for CO2/H2O atmospheres

NASA Technical Reports Server (NTRS)

Hoffert, M. I.; Callegari, A. J.; Hsieh, C. T.; Ziegler, W.

1981-01-01

A simple climatic model is developed for a Mars atmosphere containing CO2 and sufficient liquid water to account for the observed hydrologic surface features by the existence of a CO2/H2O greenhouse effect. A latitude-resolved climate model originally devised for terrestrial climate studies is applied to Martian conditions, with the difference between absorbed solar flux and emitted long-wave flux to space per unit area attributed to the divergence of the meridional heat flux and the poleward heat flux assumed to equal the atmospheric eddy heat flux. The global mean energy balance is calculated as a function of atmospheric pressure to assess the CO2/H2O greenhouse liquid water hypothesis, and some latitude-resolved cases are examined in detail in order to clarify the role of atmospheric transport and temperature-albedo feedback. It is shown that the combined CO2/H2O greenhouse at plausible early surface pressures may account for climates hot enough to support a hydrological cycle and running water at present-day insolation and visible albedo levels.

A conceptual model for glacial cycles and the middle Pleistocene transition

NASA Astrophysics Data System (ADS)

Daruka, István; Ditlevsen, Peter D.

2016-01-01

Milankovitch's astronomical theory of glacial cycles, attributing ice age climate oscillations to orbital changes in Northern-Hemisphere insolation, is challenged by the paleoclimatic record. The climatic response to the variations in insolation is far from trivial. In general the glacial cycles are highly asymmetric in time, with slow cooling from the interglacials to the glacials (inceptions) and very rapid warming from the glacials to the interglacials (terminations). We shall refer to this fast-slow dynamics as the "saw-tooth" shape of the paleoclimatic record. This is non-linearly related to the time-symmetric variations in the orbital forcing. However, the most pronounced challenge to the Milankovitch theory is the middle Pleistocene transition (MPT) occurring about one million years ago. During that event, the prevailing 41 kyr glacial cycles, corresponding to the almost harmonic obliquity cycle were replaced by longer saw-tooth shaped cycles with a time-scale around 100 kyr. The MPT must have been driven by internal changes in climate response, since it does not correspond to any apparent changes in the orbital forcing. In order to identify possible mechanisms causing the observed changes in glacial dynamics, it is relevant to study simplified models with the capability of generating temporal behavior similar to the observed records. We present a simple oscillator type model approach, with two variables, a temperature anomaly and a climatic memory term. The generalization of the ice albedo feedback is included in terms of an effective multiplicative coupling between this latter climatic memory term (representing the internal degrees of freedom) and the external drive. The simple model reproduces the temporal asymmetry of the late Pleistocene glacial cycles and suggests that the MPT can be explained as a regime shift, aided by climatic noise, from a period 1 frequency locking to the obliquity cycle to a period 2-3 frequency locking to the same obliquity cycle. The change in dynamics has been suggested to be a result of a slow gradual decrease in atmospheric greenhouse gas concentration. The critical dependence on initial conditions in the (non-autonomous) glacial dynamics raises fundamental questions about climate predictability.

Linking Physical Climate Research and Economic Assessments of Mitigation Policies

NASA Astrophysics Data System (ADS)

Stainforth, David; Calel, Raphael

2017-04-01

Evaluating climate change policies requires economic assessments which balance the costs and benefits of climate action. A certain class of Integrated Assessment Models (IAMS) are widely used for this type of analysis; DICE, PAGE and FUND are three of the most influential. In the economics community there has been much discussion and debate about the economic assumptions implemented within these models. Two aspects in particular have gained much attention: i) the costs of damages resulting from climate change - the so-called damage function, and ii) the choice of discount rate applied to future costs and benefits. There has, however, been rather little attention given to the consequences of the choices made in the physical climate models within these IAMS. Here we discuss the practical aspects of the implementation of the physical models in these IAMS, as well as the implications of choices made in these physical science components for economic assessments[1]. We present a simple breakdown of how these IAMS differently represent the climate system as a consequence of differing underlying physical models, different parametric assumptions (for parameters representing, for instance, feedbacks and ocean heat uptake) and different numerical approaches to solving the models. We present the physical and economic consequences of these differences and reflect on how we might better incorporate the latest physical science understanding in economic models of this type. [1] Calel, R. and Stainforth D.A., "On the Physics of Three Integrated Assessment Models", Bulletin of the American Meteorological Society, in press.

Improved pattern scaling approaches for the use in climate impact studies

NASA Astrophysics Data System (ADS)

Herger, Nadja; Sanderson, Benjamin M.; Knutti, Reto

2015-05-01

Pattern scaling is a simple way to produce climate projections beyond the scenarios run with expensive global climate models (GCMs). The simplest technique has known limitations and assumes that a spatial climate anomaly pattern obtained from a GCM can be scaled by the global mean temperature (GMT) anomaly. We propose alternatives and assess their skills and limitations. One approach which avoids scaling is to consider a period in a different scenario with the same GMT change. It is attractive as it provides patterns of any temporal resolution that are consistent across variables, and it does not distort variability. Second, we extend the traditional approach with a land-sea contrast term, which provides the largest improvements over the traditional technique. When interpolating between known bounding scenarios, the proposed methods significantly improve the accuracy of the pattern scaled scenario with little computational cost. The remaining errors are much smaller than the Coupled Model Intercomparison Project Phase 5 model spread.

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.

Linking climate projections to performance: A yield-based decision scaling assessment of a large urban water resources system

NASA Astrophysics Data System (ADS)

Turner, Sean W. D.; Marlow, David; Ekström, Marie; Rhodes, Bruce G.; Kularathna, Udaya; Jeffrey, Paul J.

2014-04-01

Despite a decade of research into climate change impacts on water resources, the scientific community has delivered relatively few practical methodological developments for integrating uncertainty into water resources system design. This paper presents an application of the "decision scaling" methodology for assessing climate change impacts on water resources system performance and asks how such an approach might inform planning decisions. The decision scaling method reverses the conventional ethos of climate impact assessment by first establishing the climate conditions that would compel planners to intervene. Climate model projections are introduced at the end of the process to characterize climate risk in such a way that avoids the process of propagating those projections through hydrological models. Here we simulated 1000 multisite synthetic monthly streamflow traces in a model of the Melbourne bulk supply system to test the sensitivity of system performance to variations in streamflow statistics. An empirical relation was derived to convert decision-critical flow statistics to climatic units, against which 138 alternative climate projections were plotted and compared. We defined the decision threshold in terms of a system yield metric constrained by multiple performance criteria. Our approach allows for fast and simple incorporation of demand forecast uncertainty and demonstrates the reach of the decision scaling method through successful execution in a large and complex water resources system. Scope for wider application in urban water resources planning is discussed.

The hydrological cycle at European Fluxnet sites: modeling seasonal water and energy budgets at local scale.

NASA Astrophysics Data System (ADS)

Stockli, R.; Vidale, P. L.

2003-04-01

The importance of correctly including land surface processes in climate models has been increasingly recognized in the past years. Even on seasonal to interannual time scales land surface - atmosphere feedbacks can play a substantial role in determining the state of the near-surface climate. The availability of soil moisture for both runoff and evapotranspiration is dependent on biophysical processes occuring in plants and in the soil acting on a wide time-scale from minutes to years. Fluxnet site measurements in various climatic zones are used to drive three generations of LSM's (land surface models) in order to assess the level of complexity needed to represent vegetation processes at the local scale. The three models were the Bucket model (Manabe 1969), BATS 1E (Dickinson 1984) and SiB 2 (Sellers et al. 1996). Evapotranspiration and runoff processes simulated by these models range from simple one-layer soils and no-vegetation parameterizations to complex multilayer soils, including realistic photosynthesis-stomatal conductance models. The latter is driven by satellite remote sensing land surface parameters inheriting the spatiotemporal evolution of vegetation phenology. In addition a simulation with SiB 2 not only including vertical water fluxes but also lateral soil moisture transfers by downslope flow is conducted for a pre-alpine catchment in Switzerland. Preliminary results are presented and show that - depending on the climatic environment and on the season - a realistic representation of evapotranspiration processes including seasonally and interannually-varying state of vegetation is significantly improving the representation of observed latent and sensible heat fluxes on the local scale. Moreover, the interannual evolution of soil moisture availability and runoff is strongly dependent on the chosen model complexity. Biophysical land surface parameters from satellite allow to represent the seasonal changes in vegetation activity, which has great impact on the yearly budget of transpiration fluxes. For some sites, however, the hydrological cycle is simulated reasonably well even with simple land surface representations.

Prescription of land-surface boundary conditions in GISS GCM 2: A simple method based on high-resolution vegetation data bases

NASA Technical Reports Server (NTRS)

Matthews, E.

1984-01-01

A simple method was developed for improved prescription of seasonal surface characteristics and parameterization of land-surface processes in climate models. This method, developed for the Goddard Institute for Space Studies General Circulation Model II (GISS GCM II), maintains the spatial variability of fine-resolution land-cover data while restricting to 8 the number of vegetation types handled in the model. This was achieved by: redefining the large number of vegetation classes in the 1 deg x 1 deg resolution Matthews (1983) vegetation data base as percentages of 8 simple types; deriving roughness length, field capacity, masking depth and seasonal, spectral reflectivity for the 8 types; and aggregating these surface features from the 1 deg x 1 deg resolution to coarser model resolutions, e.g., 8 deg latitude x 10 deg longitude or 4 deg latitude x 5 deg longitude.

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.

Algal wastewater treatment systems for seasonal climates: application of a simple modelling approach to generate local and regional design guidelines.

PubMed

Heaven, Sonia; Salter, Andrew M; Clarke, Derek; Pak, Lyubov N

2012-05-01

Algal waste stabilisation ponds (WSP) provide a means of treating wastewater, and also a potential source of water for re-use in irrigation, aquaculture or algal biomass cultivation. The quantities of treated water available and the periods in which it is suitable for use or discharge are closely linked to climatic factors. This paper describes the application, at a continent-wide scale, of a modelling approach based on the use of readily available climate datasets to provide WSP design and performance guidelines linked to geographical location. Output is presented in regionally-based contour maps covering a wide area of Russia and central Asia and indicating pond area, earliest discharge date, discharge duration, wastewater inflow:outflow ratio and salinity under user-specified conditions. The results confirm that broad-brush discharge guidelines of the type commonly used in North America can safely be applied; but suggest that a more detailed approach is worthwhile to optimise operating regimes for local conditions. The use of long-series climate data can also permit tailoring of designs to specific sites. The work considers a simple 2-pond system, but other configurations and operating regimes should be investigated, especially for the wide range of locations across the world that are intermediate between the 'one short discharge per year' mode and year-round steady-state operation. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

A Unified Approach to Quantifying Feedbacks in Earth System Models

NASA Astrophysics Data System (ADS)

Taylor, K. E.

2008-12-01

In order to speed progress in reducing uncertainty in climate projections, the processes that most strongly influence those projections must be identified. It is of some importance, therefore, to assess the relative strengths of various climate feedbacks and to determine the degree to which various earth system models (ESMs) agree in their simulations of these processes. Climate feedbacks have been traditionally quantified in terms of their impact on the radiative balance of the planet, whereas carbon cycle responses have been assessed in terms of the size of the perturbations to the surface fluxes of carbon dioxide. In this study we introduce a diagnostic strategy for unifying the two approaches, which allows us to directly compare the strength of carbon-climate feedbacks with other conventional climate feedbacks associated with atmospheric and surface changes. Applying this strategy to a highly simplified model of the carbon-climate system demonstrates the viability of the approach. In the simple model we find that even if the strength of the carbon-climate feedbacks is very large, the uncertainty associated with the overall response of the climate system is likely to be dominated by uncertainties in the much larger feedbacks associated with clouds. This does not imply that the carbon cycle itself is unimportant, only that changes in the carbon cycle that are associated with climate change have a relatively small impact on global temperatures. This new, unified diagnostic approach is suitable for assessing feedbacks in even the most sophisticated earth system models. It will be interesting to see whether our preliminary conclusions are confirmed when output from the more realistic models is analyzed. This work was carried out at the University of California Lawrence Livermore National Laboratory under Contract W-7405-Eng-48.

Tree mortality from drought, insects, and their interactions in a changing climate

USGS Publications Warehouse

Anderegg, William R.L.; Hicke, Jeffrey A.; Fisher, Rosie A.; Allen, Craig D.; Aukema, Juliann E.; Bentz, Barbara; Hood, Sharon; Lichstein, Jeremy W.; Macalady, Alison K.; McDowell, Nate G.; Pan, Yude; Raffa, Kenneth; Sala, Anna; Shaw, John D.; Stephenson, Nathan L.; Tague, Christina L.; Zeppel, Melanie

2015-01-01

Climate change is expected to drive increased tree mortality through drought, heat stress, and insect attacks, with manifold impacts on forest ecosystems. Yet, climate-induced tree mortality and biotic disturbance agents are largely absent from process-based ecosystem models. Using data sets from the western USA and associated studies, we present a framework for determining the relative contribution of drought stress, insect attack, and their interactions, which is critical for modeling mortality in future climates. We outline a simple approach that identifies the mechanisms associated with two guilds of insects – bark beetles and defoliators – which are responsible for substantial tree mortality. We then discuss cross-biome patterns of insect-driven tree mortality and draw upon available evidence contrasting the prevalence of insect outbreaks in temperate and tropical regions. We conclude with an overview of tools and promising avenues to address major challenges. Ultimately, a multitrophic approach that captures tree physiology, insect populations, and tree–insect interactions will better inform projections of forest ecosystem responses to climate change.

Boreal forests, aerosols and the impacts on clouds and climate.

PubMed

Spracklen, Dominick V; Bonn, Boris; Carslaw, Kenneth S

2008-12-28

Previous studies have concluded that boreal forests warm the climate because the cooling from storage of carbon in vegetation and soils is cancelled out by the warming due to the absorption of the Sun's heat by the dark forest canopy. However, these studies ignored the impacts of forests on atmospheric aerosol. We use a global atmospheric model to show that, through emission of organic vapours and the resulting condensational growth of newly formed particles, boreal forests double regional cloud condensation nuclei concentrations (from approx. 100 to approx. 200 cm(-3)). Using a simple radiative model, we estimate that the resulting change in cloud albedo causes a radiative forcing of between -1.8 and -6.7 W m(-2) of forest. This forcing may be sufficiently large to result in boreal forests having an overall cooling impact on climate. We propose that the combination of climate forcings related to boreal forests may result in an important global homeostasis. In cold climatic conditions, the snow-vegetation albedo effect dominates and boreal forests warm the climate, whereas in warmer climates they may emit sufficiently large amounts of organic vapour modifying cloud albedo and acting to cool climate.

Promoting Climate Literacy and Conceptual Understanding among In-service Secondary Science Teachers requires an Epistemological Perspective

NASA Astrophysics Data System (ADS)

Bhattacharya, D.; Forbes, C.; Roehrig, G.; Chandler, M. A.

2017-12-01

Promoting climate literacy among in-service science teachers necessitates an understanding of fundamental concepts about the Earth's climate System (USGCRP, 2009). Very few teachers report having any formal instruction in climate science (Plutzer et al., 2016), therefore, rather simple conceptions of climate systems and their variability exist, which has implications for students' science learning (Francies et al., 1993; Libarkin, 2005; Rebich, 2005). This study uses the inferences from a NASA Innovations in Climate Education (NICE) teacher professional development program (CYCLES) to establish the necessity for developing an epistemological perspective among teachers. In CYCLES, 19 middle and high school (male=8, female=11) teachers were assessed for their understanding of global climate change (GCC). A qualitative analysis of their concept maps and an alignment of their conceptions with the Essential Principles of Climate Literacy (NOAA, 2009) demonstrated that participants emphasized on EPCL 1, 3, 6, 7 focusing on the Earth system, atmospheric, social and ecological impacts of GCC. However, EPCL 4 (variability in climate) and 5 (data-based observations and modeling) were least represented and emphasized upon. Thus, participants' descriptions about global climatic patterns were often factual rather than incorporating causation (why the temperatures are increasing) and/or correlation (describing what other factors might influence global temperatures). Therefore, engaging with epistemic dimensions of climate science to understand the processes, tools, and norms through which climate scientists study the Earth's climate system (Huxter et al., 2013) is critical for developing an in-depth conceptual understanding of climate. CLiMES (Climate Modeling and Epistemology of Science), a NSF initiative proposes to use EzGCM (EzGlobal Climate Model) to engage students and teachers in designing and running simulations, performing data processing activities, and analyzing computational models to develop their own evidence-based claims about the Earth's climate system. We describe how epistemological investigations can be conducted using EzGCM to bring the scientific process and authentic climate science practice to middle and high school classrooms.

Simple model to estimate the contribution of atmospheric CO2 to the Earth's greenhouse effect

NASA Astrophysics Data System (ADS)

Wilson, Derrek J.; Gea-Banacloche, Julio

2012-04-01

We show how the CO2 contribution to the Earth's greenhouse effect can be estimated from relatively simple physical considerations and readily available spectroscopic data. In particular, we present a calculation of the "climate sensitivity" (that is, the increase in temperature caused by a doubling of the concentration of CO2) in the absence of feedbacks. Our treatment highlights the important role played by the frequency dependence of the CO2 absorption spectrum. For pedagogical purposes, we provide two simple models to visualize different ways in which the atmosphere might return infrared radiation back to the Earth. The more physically realistic model, based on the Schwarzschild radiative transfer equations, uses as input an approximate form of the atmosphere's temperature profile, and thus includes implicitly the effect of heat transfer mechanisms other than radiation.

Retention performance of green roofs in representative climates worldwide

NASA Astrophysics Data System (ADS)

Viola, F.; Hellies, M.; Deidda, R.

2017-10-01

The ongoing process of global urbanization contributes to an increase in stormwater runoff from impervious surfaces, threatening also water quality. Green roofs have been proved to be innovative stormwater management measures to partially restore natural states, enhancing interception, infiltration and evapotranspiration fluxes. The amount of water that is retained within green roofs depends not only on their depth, but also on the climate, which drives the stochastic soil moisture dynamic. In this context, a simple tool for assessing performance of green roofs worldwide in terms of retained water is still missing and highly desirable for practical assessments. The aim of this work is to explore retention performance of green roofs as a function of their depth and in different climate regimes. Two soil depths are investigated, one representing the intensive configuration and another representing the extensive one. The role of the climate in driving water retention has been represented by rainfall and potential evapotranspiration dynamics. A simple conceptual weather generator has been implemented and used for stochastic simulation of daily rainfall and potential evapotranspiration. Stochastic forcing is used as an input of a simple conceptual hydrological model for estimating long-term water partitioning between rainfall, runoff and actual evapotranspiration. Coupling the stochastic weather generator with the conceptual hydrological model, we assessed the amount of rainfall diverted into evapotranspiration for different combinations of annual rainfall and potential evapotranspiration in five representative climatic regimes. Results quantified the capabilities of green roofs in retaining rainfall and consequently in reducing discharges into sewer systems at an annual time scale. The role of substrate depth has been recognized to be crucial in determining green roofs retention performance, which in general increase from extensive to intensive settings. Looking at the role of climatic conditions, namely annual rainfall, potential evapotranspiration and their seasonality cycles, we found that they drive green roofs retention performance, which are the maxima when rainfall and temperature are in phase. Finally, we provide design charts for a first approximation of possible hydrological benefits deriving from the implementation of intensive or extensive green roofs in different world areas. As an example, 25 big cities have been indicated as benchmark case studies.

Community climate simulations to assess avoided impacts in 1.5 and 2 °C futures

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

Sanderson, Benjamin M.; Xu, Yangyang; Tebaldi, Claudia

The Paris Agreement of December 2015 stated a goal to pursue efforts to keep global temperatures below 1.5 °C above preindustrial levels and well below 2 °C. The IPCC was charged with assessing climate impacts at these temperature levels, but fully coupled equilibrium climate simulations do not currently exist to inform such assessments. Here, we produce a set of scenarios using a simple model designed to achieve long-term 1.5 and 2 °C temperatures in a stable climate. These scenarios are then used to produce century-scale ensemble simulations using the Community Earth System Model, providing impact-relevant long-term climate data for stabilization pathways at 1.5 andmore » 2 °C levels and an overshoot 1.5 °C case, which are realized (for the 21st century) in the coupled model and are freely available to the community. We also describe the design of the simulations and a brief overview of their impact-relevant climate response. Exceedance of historical record temperature occurs with 60 % greater frequency in the 2 °C climate than in a 1.5 °C climate aggregated globally, and with twice the frequency in equatorial and arid regions. Extreme precipitation intensity is statistically significantly higher in a 2.0 °C climate than a 1.5 °C climate in some specific regions (but not all). The model exhibits large differences in the Arctic, which is ice-free with a frequency of 1 in 3 years in the 2.0 °C scenario, and 1 in 40 years in the 1.5 °C scenario. Significance of impact differences with respect to multi-model variability is not assessed.« less

Community climate simulations to assess avoided impacts in 1.5 and 2 °C futures

DOE PAGES

Sanderson, Benjamin M.; Xu, Yangyang; Tebaldi, Claudia; ...

2017-09-19

The Paris Agreement of December 2015 stated a goal to pursue efforts to keep global temperatures below 1.5 °C above preindustrial levels and well below 2 °C. The IPCC was charged with assessing climate impacts at these temperature levels, but fully coupled equilibrium climate simulations do not currently exist to inform such assessments. Here, we produce a set of scenarios using a simple model designed to achieve long-term 1.5 and 2 °C temperatures in a stable climate. These scenarios are then used to produce century-scale ensemble simulations using the Community Earth System Model, providing impact-relevant long-term climate data for stabilization pathways at 1.5 andmore » 2 °C levels and an overshoot 1.5 °C case, which are realized (for the 21st century) in the coupled model and are freely available to the community. We also describe the design of the simulations and a brief overview of their impact-relevant climate response. Exceedance of historical record temperature occurs with 60 % greater frequency in the 2 °C climate than in a 1.5 °C climate aggregated globally, and with twice the frequency in equatorial and arid regions. Extreme precipitation intensity is statistically significantly higher in a 2.0 °C climate than a 1.5 °C climate in some specific regions (but not all). The model exhibits large differences in the Arctic, which is ice-free with a frequency of 1 in 3 years in the 2.0 °C scenario, and 1 in 40 years in the 1.5 °C scenario. Significance of impact differences with respect to multi-model variability is not assessed.« less

A Generalized Simple Formulation of Convective Adjustment Timescale for Cumulus Convection Parameterizations

EPA Science Inventory

Convective adjustment timescale (τ) for cumulus clouds is one of the most influential parameters controlling parameterized convective precipitation in climate and weather simulation models at global and regional scales. Due to the complex nature of deep convection, a pres...

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.

Geophysical, archaeological, and historical evidence support a solar-output model for climate change

PubMed Central

Perry, Charles A.; Hsu, Kenneth J.

2000-01-01

Although the processes of climate change are not completely understood, an important causal candidate is variation in total solar output. Reported cycles in various climate-proxy data show a tendency to emulate a fundamental harmonic sequence of a basic solar-cycle length (11 years) multiplied by 2N (where N equals a positive or negative integer). A simple additive model for total solar-output variations was developed by superimposing a progression of fundamental harmonic cycles with slightly increasing amplitudes. The timeline of the model was calibrated to the Pleistocene/Holocene boundary at 9,000 years before present. The calibrated model was compared with geophysical, archaeological, and historical evidence of warm or cold climates during the Holocene. The evidence of periods of several centuries of cooler climates worldwide called “little ice ages,” similar to the period anno Domini (A.D.) 1280–1860 and reoccurring approximately every 1,300 years, corresponds well with fluctuations in modeled solar output. A more detailed examination of the climate sensitive history of the last 1,000 years further supports the model. Extrapolation of the model into the future suggests a gradual cooling during the next few centuries with intermittent minor warmups and a return to near little-ice-age conditions within the next 500 years. This cool period then may be followed approximately 1,500 years from now by a return to altithermal conditions similar to the previous Holocene Maximum. PMID:11050181

The Importance of Considering the Temporal Distribution of Climate Variables for Ecological-Economic Modeling to Calculate the Consequences of Climate Change for Agriculture

NASA Astrophysics Data System (ADS)

Plegnière, Sabrina; Casper, Markus; Hecker, Benjamin; Müller-Fürstenberger, Georg

2014-05-01

The basis of many models to calculate and assess climate change and its consequences are annual means of temperature and precipitation. This method leads to many uncertainties especially at the regional or local level: the results are not realistic or too coarse. Particularly in agriculture, single events and the distribution of precipitation and temperature during the growing season have enormous influences on plant growth. Therefore, the temporal distribution of climate variables should not be ignored. To reach this goal, a high-resolution ecological-economic model was developed which combines a complex plant growth model (STICS) and an economic model. In this context, input data of the plant growth model are daily climate values for a specific climate station calculated by the statistical climate model (WETTREG). The economic model is deduced from the results of the plant growth model STICS. The chosen plant is corn because corn is often cultivated and used in many different ways. First of all, a sensitivity analysis showed that the plant growth model STICS is suitable to calculate the influences of different cultivation methods and climate on plant growth or yield as well as on soil fertility, e.g. by nitrate leaching, in a realistic way. Additional simulations helped to assess a production function that is the key element of the economic model. Thereby the problems when using mean values of temperature and precipitation in order to compute a production function by linear regression are pointed out. Several examples show why a linear regression to assess a production function based on mean climate values or smoothed natural distribution leads to imperfect results and why it is not possible to deduce a unique climate factor in the production function. One solution for this problem is the additional consideration of stress indices that show the impairment of plants by water or nitrate shortage. Thus, the resulting model takes into account not only the ecological factors (e.g. the plant growth) or the economical factors as a simple monetary calculation, but also their mutual influences. Finally, the ecological-economic model enables us to make a risk assessment or evaluate adaptation strategies.

Replumbing of the Biological Pump caused by Millennial Climate Variability

NASA Astrophysics Data System (ADS)

Galbraith, E.; Sarmiento, J.

2008-12-01

It has been hypothesized that millennial-timescale variability in the biological pump was a critical instigator of glacial-interglacial cycles. However, even in the absence of changes in ecosystem function (e.g. due to iron fertilization), determining the mechanisms by which physical climate variability alters the biological pump is not simple. Changes in upper ocean circulation and deep water formation have previously been shown to alter both the downward flux of organic matter and the mass of respired carbon in the ocean interior, often in non- intuitive ways. For example, a reduced upward flux of nutrients at the global scale will decrease the global rate of export production, but it could either increase or decrease the respired carbon content of the ocean interior, depending on where the reduced upward flux of nutrients occurs. Furthermore, viable candidates for physical climate forcing are numerous, including changes in the westerly winds, changes in the depth of the thermocline, and changes in the formation rate of North Atlantic Deep Water, among others. We use a simple, prognostic, light-and temperature-dependent model of biogeochemical cycling within a state-of-the- art global coupled ocean-atmosphere model to examine the response of the biological pump to changes in the coupled Earth system over multiple centuries. The biogeochemical model explicitly distinguishes respired carbon from preformed and saturation carbon, allowing the activity of the biological pump to be clearly quantified. Changes are forced in the model by altering the background climate state, and by manipulating the flux of freshwater to the North Atlantic region. We show how these changes in the physical state of the coupled ocean-atmosphere system impact the distribution and mass of respired carbon in the ocean interior, and the relationship these changes bear to global patterns of export production via the redistribution of nutrients.

Identifying and Evaluating the Relationships that Control a Land Surface Model's Hydrological Behavior

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Mahanama, Sarith P.

2012-01-01

The inherent soil moisture-evaporation relationships used in today 's land surface models (LSMs) arguably reflect a lot of guesswork given the lack of contemporaneous evaporation and soil moisture observations at the spatial scales represented by regional and global models. The inherent soil moisture-runoff relationships used in the LSMs are also of uncertain accuracy. Evaluating these relationships is difficult but crucial given that they have a major impact on how the land component contributes to hydrological and meteorological variability within the climate system. The relationships, it turns out, can be examined efficiently and effectively with a simple water balance model framework. The simple water balance model, driven with multi-decadal observations covering the conterminous United States, shows how different prescribed relationships lead to different manifestations of hydrological variability, some of which can be compared directly to observations. Through the testing of a wide suite of relationships, the simple model provides estimates for the underlying relationships that operate in nature and that should be operating in LSMs. We examine the relationships currently used in a number of different LSMs in the context of the simple water balance model results and make recommendations for potential first-order improvements to these LSMs.

A simple, single-substrate model to interpret intra-annual stable isotope signals in tree-ring cellulose

NASA Astrophysics Data System (ADS)

Ogée, J.; Barbour, M. M.; Wingate, L.; Bert, D.; Bosc, A.; Stievenard, M.; Lambrot, C.; Pierre, M.; Bariac, T.; Dewar, R. C.

2009-04-01

High-resolution intra-annual measurements of the carbon and oxygen stable isotope composition of cellulose in annual tree rings (δ13Ccellulose and δ18Ocellulose, respectively) reveal well-defined seasonal patterns that could contain valuable records of past climate and tree function. Interpreting these signals is nonetheless complex because they not only record the signature of current assimilates, but also depend on carbon allocation dynamics within the trees. Here, we present a simple, single-substrate model for wood growth containing only 12 main parameters. The model is used to interpret an isotopic intra-annual chronology collected in an even-aged maritime pine plantation growing in the South-West of France, where climate, soil and flux variables were also monitored. The empirical δ13Ccellulose and δ18Ocellulose exhibit dynamic seasonal patterns, with clear differences between years and individuals, that are mostly captured by the model. In particular, the amplitude of both signals is reproduced satisfactorily as well as the sharp 18O enrichment at the beginning of 1997 and the less pronounced 13C and 18O depletion observed at the end of the latewood. Our results suggest that the single-substrate hypothesis is a good approximation for tree ring studies on Pinus pinaster, at least for the environmental conditions covered by this study. A sensitivity analysis revealed that, in the early wood, the model was particularly sensitive to the date when cell wall thickening begins (twt). We therefore propose to use the model to reconstruct time series of twt and explore how climate influences this key parameter of xylogenesis.

Analysis and Experimental Investigation of Optimum Design of Thermoelectric Cooling/Heating System for Car Seat Climate Control (CSCC)

NASA Astrophysics Data System (ADS)

Elarusi, Abdulmunaem; Attar, Alaa; Lee, HoSung

2018-02-01

The optimum design of a thermoelectric system for application in car seat climate control has been modeled and its performance evaluated experimentally. The optimum design of the thermoelectric device combining two heat exchangers was obtained by using a newly developed optimization method based on the dimensional technique. Based on the analytical optimum design results, commercial thermoelectric cooler and heat sinks were selected to design and construct the climate control heat pump. This work focuses on testing the system performance in both cooling and heating modes to ensure accurate analytical modeling. Although the analytical performance was calculated using the simple ideal thermoelectric equations with effective thermoelectric material properties, it showed very good agreement with experiment for most operating conditions.

The climatic effect of explosive volcanic activity: Analysis of the historical data

NASA Technical Reports Server (NTRS)

Bryson, R. A.; Goodman, B. M.

1982-01-01

By using the most complete available records of direct beam radiation and volcanic eruptions, an historical analysis of the role of the latter in modulating the former was made. A very simple fallout and dispersion model was applied to the historical chronology of explosive eruptions. The resulting time series explains about 77 percent of the radiation variance, as well as suggests that tropical and subpolar eruptions are more important than mid-latitude eruptions in their impact on the stratospheric aerosol optical depth. The simpler climatic models indicate that past hemispheric temperature can be stimulated very well with volcanic and CO2 inputs and suggest that climate forecasting will also require volcano forecasting. There is some evidence that this is possible some years in advance.

Clouds and the Earth's Radiant Energy System (CERES) Data Products for Climate Research

NASA Technical Reports Server (NTRS)

Kato, Seiji; Loeb, Norman G.; Rutan, David A.; Rose, Fred G.

2015-01-01

NASA's Clouds and the Earth's Radiant Energy System (CERES) project integrates CERES, Moderate Resolution Imaging Spectroradiometer (MODIS), and geostationary satellite observations to provide top-of-atmosphere (TOA) irradiances derived from broadband radiance observations by CERES instruments. It also uses snow cover and sea ice extent retrieved from microwave instruments as well as thermodynamic variables from reanalysis. In addition, these variables are used for surface and atmospheric irradiance computations. The CERES project provides TOA, surface, and atmospheric irradiances in various spatial and temporal resolutions. These data sets are for climate research and evaluation of climate models. Long-term observations are required to understand how the Earth system responds to radiative forcing. A simple model is used to estimate the time to detect trends in TOA reflected shortwave and emitted longwave irradiances.

Scenario Analysis With Economic-Energy Systems Models Coupled to Simple Climate Models

NASA Astrophysics Data System (ADS)

Hanson, D. A.; Kotamarthi, V. R.; Foster, I. T.; Franklin, M.; Zhu, E.; Patel, D. M.

2008-12-01

Here, we compare two scenarios based on Stanford University's Energy Modeling Forum Study 22 on global cooperative and non-cooperative climate policies. In the former, efficient transition paths are implemented including technology Research and Development effort, energy conservation programs, and price signals for greenhouse gas (GHG) emissions. In the non-cooperative case, some countries try to relax their regulations and be free riders. Total emissions and costs are higher in the non-cooperative scenario. The simulations, including climate impacts, run to the year 2100. We use the Argonne AMIGA-MARS economic-energy systems model, the Texas AM University's Forest and Agricultural Sector Optimization Model (FASOM), and the University of Illinois's Integrated Science Assessment Model (ISAM), with offline coupling between the FASOM and AMIGA-MARS and an online coupling between AMIGA-MARS and ISAM. This set of models captures the interaction of terrestrial systems, land use, crops and forests, climate change, human activity, and energy systems. Our scenario simulations represent dynamic paths over which all the climate, terrestrial, economic, and energy technology equations are solved simultaneously Special attention is paid to biofuels and how they interact with conventional gasoline/diesel fuel markets. Possible low-carbon penetration paths are based on estimated costs for new technologies, including cellulosic biomass, coal-to-liquids, plug-in electric vehicles, solar and nuclear energy. We explicitly explore key uncertainties that affect mitigation and adaptation scenarios.

From climate model ensembles to climate change impacts and adaptation: A case study of water resource management in the southwest of England

NASA Astrophysics Data System (ADS)

Lopez, Ana; Fung, Fai; New, Mark; Watts, Glenn; Weston, Alan; Wilby, Robert L.

2009-08-01

The majority of climate change impacts and adaptation studies so far have been based on at most a few deterministic realizations of future climate, usually representing different emissions scenarios. Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. Because of the novelty of this ensemble information, there is little previous experience of practical applications or of the added value of this information for impacts and adaptation decision making. This paper evaluates the value of perturbed physics ensembles of climate models for understanding and planning public water supply under climate change. We deliberately select water resource models that are already used by water supply companies and regulators on the assumption that uptake of information from large ensembles of climate models will be more likely if it does not involve significant investment in new modeling tools and methods. We illustrate the methods with a case study on the Wimbleball water resource zone in the southwest of England. This zone is sufficiently simple to demonstrate the utility of the approach but with enough complexity to allow a variety of different decisions to be made. Our research shows that the additional information contained in the climate model ensemble provides a better understanding of the possible ranges of future conditions, compared to the use of single-model scenarios. Furthermore, with careful presentation, decision makers will find the results from large ensembles of models more accessible and be able to more easily compare the merits of different management options and the timing of different adaptation. The overhead in additional time and expertise for carrying out the impacts analysis will be justified by the increased quality of the decision-making process. We remark that even though we have focused our study on a water resource system in the United Kingdom, our conclusions about the added value of climate model ensembles in guiding adaptation decisions can be generalized to other sectors and geographical regions.

A simple model that identifies potential effects of sea-level rise on estuarine and estuary-ecotone habitat locations for salmonids in Oregon, USA.

PubMed

Flitcroft, Rebecca; Burnett, Kelly; Christiansen, Kelly

2013-07-01

Diadromous aquatic species that cross a diverse range of habitats (including marine, estuarine, and freshwater) face different effects of climate change in each environment. One such group of species is the anadromous Pacific salmon (Oncorhynchus spp.). Studies of the potential effects of climate change on salmonids have focused on both marine and freshwater environments. Access to a variety of estuarine habitat has been shown to enhance juvenile life-history diversity, thereby contributing to the resilience of many salmonid species. Our study is focused on the effect of sea-level rise on the availability, complexity, and distribution of estuarine, and low-freshwater habitat for Chinook salmon (Oncorhynchus tshawytscha), steelhead (anadromous O. mykiss), and coho salmon (O. kisutch) along the Oregon Coast under future climate change scenarios. Using LiDAR, we modeled the geomorphologies of five Oregon estuaries and estimated a contour associated with the current mean high tide. Contour intervals at 1- and 2-m increments above the current mean high tide were generated, and changes in the estuary morphology were assessed. Because our analysis relied on digital data, we compared three types of digital data in one estuary to assess the utility of different data sets in predicting the changes in estuary shape. For each salmonid species, changes in the amount and complexity of estuarine edge habitats varied by estuary. The simple modeling approach we applied can also be used to identify areas that may be most amenable to pre-emptive restoration actions to mitigate or enhance salmonid habitat under future climatic conditions.

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.

An Overview of the Future Development of Climate and Earth System Models for Scientific and Policy Use (Invited)

NASA Astrophysics Data System (ADS)

Washington, W. M.

2010-12-01

The development of climate and earth system models has been regarded primarily as the making of scientific tools to study the complex nature of the Earth’s climate. These models have a long history starting with very simple physical models based on fundamental physics in the 1960s and over time they have become much more complex with atmospheric, ocean, sea ice, land/vegetation, biogeochemical, glacial and ecological components. The policy use aspects of these models did not start in the 1960s and 1970s as decision making tools but were used to answer fundamental scientific questions such as what happens when the atmospheric carbon dioxide concentration increases or is doubled. They gave insights into the various interactions and were extensively compared with observations. It was realized that models of the earlier time periods could only give first order answers to many of the fundamental policy questions. As societal concerns about climate change rose, the policy questions of anthropogenic climate change became better defined; they were mostly concerned with the climate impacts of increasing greenhouse gases, aerosols, and land cover change. In the late 1980s, the United Nations set up the Intergovernmental Panel on Climate Change to perform assessments of the published literature. Thus, the development of climate and Earth system models became intimately linked to the need to not only improve our scientific understanding but also answering fundamental policy questions. In order to meet this challenge, the models became more complex and realistic so that they could address these policy oriented science questions such as rising sea level. The presentation will discuss the past and future development of global climate and earth system models for science and policy purposes. Also to be discussed is their interactions with economic integrated assessment models, regional and specialized models such as river transport or ecological components. As an example of one development pathway, the NSF/Department of Energy supported Community Climate System and Earth System Models will be featured in the presentation. Computational challenges will also part of the discussion.

Effect of land model ensemble versus coupled model ensemble on the simulation of precipitation climatology and variability

NASA Astrophysics Data System (ADS)

Wei, Jiangfeng; Dirmeyer, Paul A.; Yang, Zong-Liang; Chen, Haishan

2017-10-01

Through a series of model simulations with an atmospheric general circulation model coupled to three different land surface models, this study investigates the impacts of land model ensembles and coupled model ensemble on precipitation simulation. It is found that coupling an ensemble of land models to an atmospheric model has a very minor impact on the improvement of precipitation climatology and variability, but a simple ensemble average of the precipitation from three individually coupled land-atmosphere models produces better results, especially for precipitation variability. The generally weak impact of land processes on precipitation should be the main reason that the land model ensembles do not improve precipitation simulation. However, if there are big biases in the land surface model or land surface data set, correcting them could improve the simulated climate, especially for well-constrained regional climate simulations.

Convective Detrainment and Control of the Tropical Water Vapor Distribution

NASA Astrophysics Data System (ADS)

Kursinski, E. R.; Rind, D.

2006-12-01

Sherwood et al. (2006) developed a simple power law model describing the relative humidity distribution in the tropical free troposphere where the power law exponent is the ratio of a drying time scale (tied to subsidence rates) and a moistening time which is the average time between convective moistening events whose temporal distribution is described as a Poisson distribution. Sherwood et al. showed that the relative humidity distribution observed by GPS occultations and MLS is indeed close to a power law, approximately consistent with the simple model's prediction. Here we modify this simple model to be in terms of vertical length scales rather than time scales in a manner that we think more correctly matches the model predictions to the observations. The subsidence is now in terms of the vertical distance the air mass has descended since it last detrained from a convective plume. The moisture source term becomes a profile of convective detrainment flux versus altitude. The vertical profile of the convective detrainment flux is deduced from the observed distribution of the specific humidity at each altitude combined with sinking rates estimated from radiative cooling. The resulting free tropospheric detrainment profile increases with altitude above 3 km somewhat like an exponential profile which explains the approximate power law behavior observed by Sherwood et al. The observations also reveal a seasonal variation in the detrainment profile reflecting changes in the convective behavior expected by some based on observed seasonal changes in the vertical structure of convective regions. The simple model results will be compared with the moisture control mechanisms in a GCM with many additional mechanisms, the GISS climate model, as described in Rind (2006). References Rind. D., 2006: Water-vapor feedback. In Frontiers of Climate Modeling, J. T. Kiehl and V. Ramanathan (eds), Cambridge University Press [ISBN-13 978-0-521- 79132-8], 251-284. Sherwood, S., E. R. Kursinski and W. Read, A distribution law for free-tropospheric relative humidity, J. Clim. In press. 2006

Means and extremes: building variability into community-level climate change experiments.

PubMed

Thompson, Ross M; Beardall, John; Beringer, Jason; Grace, Mike; Sardina, Paula

2013-06-01

Experimental studies assessing climatic effects on ecological communities have typically applied static warming treatments. Although these studies have been informative, they have usually failed to incorporate either current or predicted future, patterns of variability. Future climates are likely to include extreme events which have greater impacts on ecological systems than changes in means alone. Here, we review the studies which have used experiments to assess impacts of temperature on marine, freshwater and terrestrial communities, and classify them into a set of 'generations' based on how they incorporate variability. The majority of studies have failed to incorporate extreme events. In terrestrial ecosystems in particular, experimental treatments have reduced temperature variability, when most climate models predict increased variability. Marine studies have tended to not concentrate on changes in variability, likely in part because the thermal mass of oceans will moderate variation. In freshwaters, climate change experiments have a much shorter history than in the other ecosystems, and have tended to take a relatively simple approach. We propose a new 'generation' of climate change experiments using down-scaled climate models which incorporate predicted changes in climatic variability, and describe a process for generating data which can be applied as experimental climate change treatments. © 2013 John Wiley & Sons Ltd/CNRS.

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.

A GRASS GIS module to obtain an estimation of glacier behavior under climate change: A pilot study on Italian glacier

NASA Astrophysics Data System (ADS)

Strigaro, Daniele; Moretti, Massimiliano; Mattavelli, Matteo; Frigerio, Ivan; Amicis, Mattia De; Maggi, Valter

2016-09-01

The aim of this work is to integrate the Minimal Glacier Model in a Geographic Information System Python module in order to obtain spatial simulations of glacier retreat and to assess the future scenarios with a spatial representation. The Minimal Glacier Models are a simple yet effective way of estimating glacier response to climate fluctuations. This module can be useful for the scientific and glaciological community in order to evaluate glacier behavior, driven by climate forcing. The module, called r.glacio.model, is developed in a GRASS GIS (GRASS Development Team, 2016) environment using Python programming language combined with different libraries as GDAL, OGR, CSV, math, etc. The module is applied and validated on the Rutor glacier, a glacier in the south-western region of the Italian Alps. This glacier is very large in size and features rather regular and lively dynamics. The simulation is calibrated by reconstructing the 3-dimensional dynamics flow line and analyzing the difference between the simulated flow line length variations and the observed glacier fronts coming from ortophotos and DEMs. These simulations are driven by the past mass balance record. Afterwards, the future assessment is estimated by using climatic drivers provided by a set of General Circulation Models participating in the Climate Model Inter-comparison Project 5 effort. The approach devised in r.glacio.model can be applied to most alpine glaciers to obtain a first-order spatial representation of glacier behavior under climate change.

Probabilistic hindcasts and projections of the coupled climate, carbon cycle and Atlantic meridional overturning circulation system: a Bayesian fusion of century-scale observations with a simple model

NASA Astrophysics Data System (ADS)

Urban, Nathan M.; Keller, Klaus

2010-10-01

How has the Atlantic Meridional Overturning Circulation (AMOC) varied over the past centuries and what is the risk of an anthropogenic AMOC collapse? We report probabilistic projections of the future climate which improve on previous AMOC projection studies by (i) greatly expanding the considered observational constraints and (ii) carefully sampling the tail areas of the parameter probability distribution function (pdf). We use a Bayesian inversion to constrain a simple model of the coupled climate, carbon cycle and AMOC systems using observations to derive multicentury hindcasts and projections. Our hindcasts show considerable skill in representing the observational constraints. We show that robust AMOC risk estimates can require carefully sampling the parameter pdfs. We find a low probability of experiencing an AMOC collapse within the 21st century for a business-as-usual emissions scenario. The probability of experiencing an AMOC collapse within two centuries is 1/10. The probability of crossing a forcing threshold and triggering a future AMOC collapse (by 2300) is approximately 1/30 in the 21st century and over 1/3 in the 22nd. Given the simplicity of the model structure and uncertainty in the forcing assumptions, our analysis should be considered a proof of concept and the quantitative conclusions subject to severe caveats.

Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

NASA Astrophysics Data System (ADS)

Williams, Paul; Howe, Nicola; Gregory, Jonathan; Smith, Robin; Joshi, Manoj

2016-04-01

In climate simulations, the impacts of the sub-grid scales on the resolved scales are conventionally represented using deterministic closure schemes, which assume that the impacts are uniquely determined by the resolved scales. Stochastic parameterization relaxes this assumption, by sampling the sub-grid variability in a computationally inexpensive manner. This presentation shows that the simulated climatological state of the ocean is improved in many respects by implementing a simple stochastic parameterization of ocean eddies into a coupled atmosphere-ocean general circulation model. Simulations from a high-resolution, eddy-permitting ocean model are used to calculate the eddy statistics needed to inject realistic stochastic noise into a low-resolution, non-eddy-permitting version of the same model. A suite of four stochastic experiments is then run to test the sensitivity of the simulated climate to the noise definition, by varying the noise amplitude and decorrelation time within reasonable limits. The addition of zero-mean noise to the ocean temperature tendency is found to have a non-zero effect on the mean climate. Specifically, in terms of the ocean temperature and salinity fields both at the surface and at depth, the noise reduces many of the biases in the low-resolution model and causes it to more closely resemble the high-resolution model. The variability of the strength of the global ocean thermohaline circulation is also improved. It is concluded that stochastic ocean perturbations can yield reductions in climate model error that are comparable to those obtained by refining the resolution, but without the increased computational cost. Therefore, stochastic parameterizations of ocean eddies have the potential to significantly improve climate simulations. Reference PD Williams, NJ Howe, JM Gregory, RS Smith, and MM Joshi (2016) Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies. Journal of Climate, under revision.

Software Testing and Verification in Climate Model Development

NASA Technical Reports Server (NTRS)

Clune, Thomas L.; Rood, RIchard B.

2011-01-01

Over the past 30 years most climate models have grown from relatively simple representations of a few atmospheric processes to a complex multi-disciplinary system. Computer infrastructure over that period has gone from punch card mainframes to modem parallel clusters. Model implementations have become complex, brittle, and increasingly difficult to extend and maintain. Existing verification processes for model implementations rely almost exclusively upon some combination of detailed analysis of output from full climate simulations and system-level regression tests. In additional to being quite costly in terms of developer time and computing resources, these testing methodologies are limited in terms of the types of defects that can be detected, isolated and diagnosed. Mitigating these weaknesses of coarse-grained testing with finer-grained "unit" tests has been perceived as cumbersome and counter-productive. In the commercial software sector, recent advances in tools and methodology have led to a renaissance for systematic fine-grained testing. We discuss the availability of analogous tools for scientific software and examine benefits that similar testing methodologies could bring to climate modeling software. We describe the unique challenges faced when testing complex numerical algorithms and suggest techniques to minimize and/or eliminate the difficulties.

Groundwater dynamics mediate low-flow response to global warming in snow-dominated alpine regions

Treesearch

Christina Tague; Gordon E. Grant

2009-01-01

In mountain environments, spatial and temporal patterns of snow accumulation and melt are dominant controls on hydrologic responses to climate change. In this paper, we develop a simple conceptual model that links the timing of peak snowmelt with geologically mediated differences in rate of streamflow recession. This model demonstrates that within the western United...

Climate-change-driven accelerated sea-level rise detected in the altimeter era.

PubMed

Nerem, R S; Beckley, B D; Fasullo, J T; Hamlington, B D; Masters, D; Mitchum, G T

2018-02-27

Using a 25-y time series of precision satellite altimeter data from TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3, we estimate the climate-change-driven acceleration of global mean sea level over the last 25 y to be 0.084 ± 0.025 mm/y 2 Coupled with the average climate-change-driven rate of sea level rise over these same 25 y of 2.9 mm/y, simple extrapolation of the quadratic implies global mean sea level could rise 65 ± 12 cm by 2100 compared with 2005, roughly in agreement with the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5) model projections. Copyright © 2018 the Author(s). Published by PNAS.

Identifying a key physical factor sensitive to the performance of Madden-Julian oscillation simulation in climate models

NASA Astrophysics Data System (ADS)

Kim, Go-Un; Seo, Kyong-Hwan

2018-01-01

A key physical factor in regulating the performance of Madden-Julian oscillation (MJO) simulation is examined by using 26 climate model simulations from the World Meteorological Organization's Working Group for Numerical Experimentation/Global Energy and Water Cycle Experiment Atmospheric System Study (WGNE and MJO-Task Force/GASS) global model comparison project. For this, intraseasonal moisture budget equation is analyzed and a simple, efficient physical quantity is developed. The result shows that MJO skill is most sensitive to vertically integrated intraseasonal zonal wind convergence (ZC). In particular, a specific threshold value of the strength of the ZC can be used as distinguishing between good and poor models. An additional finding is that good models exhibit the correct simultaneous convection and large-scale circulation phase relationship. In poor models, however, the peak circulation response appears 3 days after peak rainfall, suggesting unfavorable coupling between convection and circulation. For an improving simulation of the MJO in climate models, we propose that this delay of circulation in response to convection needs to be corrected in the cumulus parameterization scheme.

A simple framework to analyze water constraints on seasonal transpiration in rubber tree (Hevea brasiliensis) plantations

PubMed Central

Sopharat, Jessada; Gay, Frederic; Thaler, Philippe; Sdoodee, Sayan; Isarangkool Na Ayutthaya, Supat; Tanavud, Charlchai; Hammecker, Claude; Do, Frederic C.

2015-01-01

Climate change and fast extension in climatically suboptimal areas threaten the sustainability of rubber tree cultivation. A simple framework based on reduction factors of potential transpiration was tested to evaluate the water constraints on seasonal transpiration in tropical sub-humid climates, according pedoclimatic conditions. We selected a representative, mature stand in a drought-prone area. Tree transpiration, evaporative demand and soil water availability were measured every day over 15 months. The results showed that basic relationships with evaporative demand, leaf area index and soil water availability were globally supported. However, the implementation of a regulation of transpiration at high evaporative demand whatever soil water availability was necessary to avoid large overestimates of transpiration. The details of regulation were confirmed by the analysis of canopy conductance response to vapor pressure deficit. The final objective of providing hierarchy between the main regulation factors of seasonal and annual transpiration was achieved. In the tested environmental conditions, the impact of atmospheric drought appeared larger importance than soil drought contrary to expectations. Our results support the interest in simple models to provide a first diagnosis of water constraints on transpiration with limited data, and to help decision making toward more sustainable rubber plantations. PMID:25610443

Aerosol Complexity and Implications for Predictability and Short-Term Forecasting

NASA Technical Reports Server (NTRS)

Colarco, Peter

2016-01-01

There are clear NWP and climate impacts from including aerosol radiative and cloud interactions. Changes in dynamics and cloud fields affect aerosol lifecycle, plume height, long-range transport, overall forcing of the climate system, etc. Inclusion of aerosols in NWP systems has benefit to surface field biases (e.g., T2m, U10m). Including aerosol affects has impact on analysis increments and can have statistically significant impacts on, e.g., tropical cyclogenesis. Above points are made especially with respect to aerosol radiative interactions, but aerosol-cloud interaction is a bigger signal on the global system. Many of these impacts are realized even in models with relatively simple (bulk) aerosol schemes (approx.10 -20 tracers). Simple schemes though imply simple representation of aerosol absorption and importantly for aerosol-cloud interaction particle-size distribution. Even so, more complex schemes exhibit a lot of diversity between different models, with issues such as size selection both for emitted particles and for modes. Prospects for complex sectional schemes to tune modal (and even bulk) schemes toward better selection of size representation. I think this is a ripe topic for more research -Systematic documentation of benefits of no vs. climatological vs. interactive (direct and then direct+indirect) aerosols. Document aerosol impact on analysis increments, inclusion in NWP data assimilation operator -Further refinement of baseline assumptions in model design (e.g., absorption, particle size distribution). Did not get into model resolution and interplay of other physical processes with aerosols (e.g., moist physics, obviously important), chemistry

Global and Arctic climate engineering: numerical model studies.

PubMed

Caldeira, Ken; Wood, Lowell

2008-11-13

We perform numerical simulations of the atmosphere, sea ice and upper ocean to examine possible effects of diminishing incoming solar radiation, insolation, on the climate system. We simulate both global and Arctic climate engineering in idealized scenarios in which insolation is diminished above the top of the atmosphere. We consider the Arctic scenarios because climate change is manifesting most strongly there. Our results indicate that, while such simple insolation modulation is unlikely to perfectly reverse the effects of greenhouse gas warming, over a broad range of measures considering both temperature and water, an engineered high CO2 climate can be made much more similar to the low CO2 climate than would be a high CO2 climate in the absence of such engineering. At high latitudes, there is less sunlight deflected per unit albedo change but climate system feedbacks operate more powerfully there. These two effects largely cancel each other, making the global mean temperature response per unit top-of-atmosphere albedo change relatively insensitive to latitude. Implementing insolation modulation appears to be feasible.

Evaporation and transpiration

Treesearch

Robert R. Ziemer

1979-01-01

For years, the principal objective of evapotranspiration research has been to calculate the loss of water under varying conditions of climate, soil, and vegetation. The early simple empirical methods have generally been replaced by more detailed models which more closely represent the physical and biological processes involved. Monteith's modification of the...

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

Climate science and famine early warning

USGS Publications Warehouse

Verdin, James P.; Funk, Chris; Senay, Gabriel B.; Choularton, R.

2005-01-01

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Impact of climate change on soil thermal and moisture regimes in Serbia: An analysis with data from regional climate simulations under SRES-A1B.

PubMed

Mihailović, D T; Drešković, N; Arsenić, I; Ćirić, V; Djurdjević, V; Mimić, G; Pap, I; Balaž, I

2016-11-15

We considered temporal and spatial variations to the thermal and moisture regimes of the most common RSGs (Reference Soil Groups) in Serbia under the A1B scenario for the 2021-2050 and 2071-2100 periods, with respect to the 1961-1990 period. We utilized dynamically downscaled global climate simulations from the ECHAM5 model using the coupled regional climate model EBU-POM (Eta Belgrade University-Princeton Ocean Model). We analysed the soil temperature and moisture time series using simple statistics and a Kolmogorov complexity (KC) analysis. The corresponding metrics were calculated for 150 sites. In the future, warmer and drier regimes can be expected for all RSGs in Serbia. The calculated soil temperature and moisture variations include increases in the mean annual soil temperature (up to 3.8°C) and decreases in the mean annual soil moisture (up to 11.3%). Based on the KC values, the soils in Serbia are classified with respect to climate change impacts as (1) less sensitive (Vertisols, Umbrisols and Dystric Cambisols) or (2) more sensitive (Chernozems, Eutric Cambisols and Planosols). Copyright © 2016 Elsevier B.V. All rights reserved.

Climate science and famine early warning.

PubMed

Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard

2005-11-29

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Climate science and famine early warning

PubMed Central

Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard

2005-01-01

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised. PMID:16433101

Predicting Dengue Fever Outbreaks in French Guiana Using Climate Indicators.

PubMed

Adde, Antoine; Roucou, Pascal; Mangeas, Morgan; Ardillon, Vanessa; Desenclos, Jean-Claude; Rousset, Dominique; Girod, Romain; Briolant, Sébastien; Quenel, Philippe; Flamand, Claude

2016-04-01

Dengue fever epidemic dynamics are driven by complex interactions between hosts, vectors and viruses. Associations between climate and dengue have been studied around the world, but the results have shown that the impact of the climate can vary widely from one study site to another. In French Guiana, climate-based models are not available to assist in developing an early warning system. This study aims to evaluate the potential of using oceanic and atmospheric conditions to help predict dengue fever outbreaks in French Guiana. Lagged correlations and composite analyses were performed to identify the climatic conditions that characterized a typical epidemic year and to define the best indices for predicting dengue fever outbreaks during the period 1991-2013. A logistic regression was then performed to build a forecast model. We demonstrate that a model based on summer Equatorial Pacific Ocean sea surface temperatures and Azores High sea-level pressure had predictive value and was able to predict 80% of the outbreaks while incorrectly predicting only 15% of the non-epidemic years. Predictions for 2014-2015 were consistent with the observed non-epidemic conditions, and an outbreak in early 2016 was predicted. These findings indicate that outbreak resurgence can be modeled using a simple combination of climate indicators. This might be useful for anticipating public health actions to mitigate the effects of major outbreaks, particularly in areas where resources are limited and medical infrastructures are generally insufficient.

Do planetary seasons play a role in attaining stable climates?

NASA Astrophysics Data System (ADS)

Olsen, Kasper Wibeck; Bohr, Jakob

2018-05-01

A simple phenomenological account for planetary climate instabilities is presented. The description is based on the standard model where the balance of incoming stellar radiation and outward thermal radiation is described by the effective planet temperature. Often, it is found to have three different points, or temperatures, where the influx of radiation is balanced with the out-flux, even with conserved boundary conditions. Two of these points are relatively long-term stable, namely the point corresponding to a cold climate and the point corresponding to a hot climate. In a classical sense these points are equilibrium balance points. The hypothesis promoted in this paper is the possibility that the intermediate third point can become long-term stable by being driven dynamically. The initially unstable point is made relatively stable over a long period by the presence of seasonal climate variations.

Geospatial modeling of plant stable isotope ratios - the development of isoscapes

NASA Astrophysics Data System (ADS)

West, J. B.; Ehleringer, J. R.; Hurley, J. M.; Cerling, T. E.

2007-12-01

Large-scale spatial variation in stable isotope ratios can yield critical insights into the spatio-temporal dynamics of biogeochemical cycles, animal movements, and shifts in climate, as well as anthropogenic activities such as commerce, resource utilization, and forensic investigation. Interpreting these signals requires that we understand and model the variation. We report progress in our development of plant stable isotope ratio landscapes (isoscapes). Our approach utilizes a GIS, gridded datasets, a range of modeling approaches, and spatially distributed observations. We synthesize findings from four studies to illustrate the general utility of the approach, its ability to represent observed spatio-temporal variability in plant stable isotope ratios, and also outline some specific areas of uncertainty. We also address two basic, but critical questions central to our ability to model plant stable isotope ratios using this approach: 1. Do the continuous precipitation isotope ratio grids represent reasonable proxies for plant source water?, and 2. Do continuous climate grids (as is or modified) represent a reasonable proxy for the climate experienced by plants? Plant components modeled include leaf water, grape water (extracted from wine), bulk leaf material ( Cannabis sativa; marijuana), and seed oil ( Ricinus communis; castor bean). Our approaches to modeling the isotope ratios of these components varied from highly sophisticated process models to simple one-step fractionation models to regression approaches. The leaf water isosocapes were produced using steady-state models of enrichment and continuous grids of annual average precipitation isotope ratios and climate. These were compared to other modeling efforts, as well as a relatively sparse, but geographically distributed dataset from the literature. The latitudinal distributions and global averages compared favorably to other modeling efforts and the observational data compared well to model predictions. These results yield confidence in the precipitation isoscapes used to represent plant source water, the modified climate grids used to represent leaf climate, and the efficacy of this approach to modeling. Further work confirmed these observations. The seed oil isoscape was produced using a simple model of lipid fractionation driven with the precipitation grid, and compared well to widely distributed observations of castor bean oil, again suggesting that the precipitation grids were reasonable proxies for plant source water. The marijuana leaf δ2H observations distributed across the continental United States were regressed against the precipitation δ2H grids and yielded a strong relationship between them, again suggesting that plant source water was reasonably well represented by the precipitation grid. Finally, the wine water δ18O isoscape was developed from regressions that related precipitation isotope ratios and climate to observations from a single vintage. Favorable comparisons between year-specific wine water isoscapes and inter-annual variations in previous vintages yielded confidence in the climate grids. Clearly significant residual variability remains to be explained in all of these cases and uncertainties vary depending on the component modeled, but we conclude from this synthesis that isoscapes are capable of representing real spatial and temporal variability in plant stable isotope ratios.

A conceptual model of oceanic heat transport in the Snowball Earth scenario

NASA Astrophysics Data System (ADS)

Comeau, Darin; Kurtze, Douglas A.; Restrepo, Juan M.

2016-12-01

Geologic evidence suggests that the Earth may have been completely covered in ice in the distant past, a state known as Snowball Earth. This is still the subject of controversy, and has been the focus of modeling work from low-dimensional models up to state-of-the-art general circulation models. In our present global climate, the ocean plays a large role in redistributing heat from the equatorial regions to high latitudes, and as an important part of the global heat budget, its role in the initiation a Snowball Earth, and the subsequent climate, is of great interest. To better understand the role of oceanic heat transport in the initiation of Snowball Earth, and the resulting global ice covered climate state, the goal of this inquiry is twofold: we wish to propose the least complex model that can capture the Snowball Earth scenario as well as the present-day climate with partial ice cover, and we want to determine the relative importance of oceanic heat transport. To do this, we develop a simple model, incorporating thermohaline dynamics from traditional box ocean models, a radiative balance from energy balance models, and the more contemporary "sea glacier" model to account for viscous flow effects of extremely thick sea ice. The resulting model, consisting of dynamic ocean and ice components, is able to reproduce both Snowball Earth and present-day conditions through reasonable changes in forcing parameters. We find that including or neglecting oceanic heat transport may lead to vastly different global climate states, and also that the parameterization of under-ice heat transfer in the ice-ocean coupling plays a key role in the resulting global climate state, demonstrating the regulatory effect of dynamic ocean heat transport.

Assessing the Impact of Retreat Mechanisms in a Simple Antarctic Ice Sheet Model Using Bayesian Calibration.

PubMed

Ruckert, Kelsey L; Shaffer, Gary; Pollard, David; Guan, Yawen; Wong, Tony E; Forest, Chris E; Keller, Klaus

2017-01-01

The response of the Antarctic ice sheet (AIS) to changing climate forcings is an important driver of sea-level changes. Anthropogenic climate change may drive a sizeable AIS tipping point response with subsequent increases in coastal flooding risks. Many studies analyzing flood risks use simple models to project the future responses of AIS and its sea-level contributions. These analyses have provided important new insights, but they are often silent on the effects of potentially important processes such as Marine Ice Sheet Instability (MISI) or Marine Ice Cliff Instability (MICI). These approximations can be well justified and result in more parsimonious and transparent model structures. This raises the question of how this approximation impacts hindcasts and projections. Here, we calibrate a previously published and relatively simple AIS model, which neglects the effects of MICI and regional characteristics, using a combination of observational constraints and a Bayesian inversion method. Specifically, we approximate the effects of missing MICI by comparing our results to those from expert assessments with more realistic models and quantify the bias during the last interglacial when MICI may have been triggered. Our results suggest that the model can approximate the process of MISI and reproduce the projected median melt from some previous expert assessments in the year 2100. Yet, our mean hindcast is roughly 3/4 of the observed data during the last interglacial period and our mean projection is roughly 1/6 and 1/10 of the mean from a model accounting for MICI in the year 2100. These results suggest that missing MICI and/or regional characteristics can lead to a low-bias during warming period AIS melting and hence a potential low-bias in projected sea levels and flood risks.

A simple next-best alternative to seasonal predictions in Europe

NASA Astrophysics Data System (ADS)

Buontempo, Carlo; De Felice, Matteo

2016-04-01

In order to build a climate proof society, we need to learn how to best use the climate information we have. Having spent time and resources in developing complex numerical models has often blinded us on the value some of this information really has in the eyes of a decision maker. An effective way to assess this is to check the quality of the forecast (and its cost) to the quality of the forecast from a prediction system based on simpler assumption (and thus cheaper to run). Such a practice is common in marketing analysis where it is often referred to as the next-best alternative. As a way to facilitate such an analysis, climate service providers should always provide alongside the predictions a set of skill scores. These are usually based on climatological means, anomaly persistence or more recently multiple linear regressions. We here present an equally simple benchmark based on a Markov chain process locally trained at a monthly or seasonal time-scale. We demonstrate that in spite of its simplicity the model easily outperforms not only the standard benchmark but also most of the seasonal predictions system at least in EUROPE. We suggest that a benchmark of this kind could represent a useful next-best alternative for a number of users.

Climate change impact assessment on food security in Indonesia

NASA Astrophysics Data System (ADS)

Ettema, Janneke; Aldrian, Edvin; de Bie, Kees; Jetten, Victor; Mannaerts, Chris

2013-04-01

As Indonesia is the world's fourth most populous country, food security is a persistent challenge. The potential impact of future climate change on the agricultural sector needs to be addressed in order to allow early implementation of mitigation strategies. The complex island topography and local sea-land-air interactions cannot adequately be represented in large scale General Climate Models (GCMs) nor visualized by TRMM. Downscaling is needed. Using meteorological observations and a simple statistical downscaling tool, local future projections are derived from state-of-the-art, large-scale GCM scenarios, provided by the CMIP5 project. To support the agriculture sector, providing information on especially rainfall and temperature variability is essential. Agricultural production forecast is influenced by several rain and temperature factors, such as rainy and dry season onset, offset and length, but also by daily and monthly minimum and maximum temperatures and its rainfall amount. A simple and advanced crop model will be used to address the sensitivity of different crops to temperature and rainfall variability, present-day and future. As case study area, Java Island is chosen as it is fourth largest island in Indonesia but contains more than half of the nation's population and dominates it politically and economically. The objective is to identify regions at agricultural risk due to changing patterns in precipitation and temperature.

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

NASA Astrophysics Data System (ADS)

Forsythe, N.; Blenkinsop, S.; Fowler, H. J.

2015-05-01

A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.

A Simple Model of Global Aerosol Indirect Effects

NASA Technical Reports Server (NTRS)

Ghan, Steven J.; Smith, Steven J.; Wang, Minghuai; Zhang, Kai; Pringle, Kirsty; Carslaw, Kenneth; Pierce, Jeffrey; Bauer, Susanne; Adams, Peter

2013-01-01

Most estimates of the global mean indirect effect of anthropogenic aerosol on the Earth's energy balance are from simulations by global models of the aerosol lifecycle coupled with global models of clouds and the hydrologic cycle. Extremely simple models have been developed for integrated assessment models, but lack the flexibility to distinguish between primary and secondary sources of aerosol. Here a simple but more physically based model expresses the aerosol indirect effect (AIE) using analytic representations of cloud and aerosol distributions and processes. Although the simple model is able to produce estimates of AIEs that are comparable to those from some global aerosol models using the same global mean aerosol properties, the estimates by the simple model are sensitive to preindustrial cloud condensation nuclei concentration, preindustrial accumulation mode radius, width of the accumulation mode, size of primary particles, cloud thickness, primary and secondary anthropogenic emissions, the fraction of the secondary anthropogenic emissions that accumulates on the coarse mode, the fraction of the secondary mass that forms new particles, and the sensitivity of liquid water path to droplet number concentration. Estimates of present-day AIEs as low as 5 W/sq m and as high as 0.3 W/sq m are obtained for plausible sets of parameter values. Estimates are surprisingly linear in emissions. The estimates depend on parameter values in ways that are consistent with results from detailed global aerosol-climate simulation models, which adds to understanding of the dependence on AIE uncertainty on uncertainty in parameter values.

Time variation of effective climate sensitivity in GCMs

NASA Astrophysics Data System (ADS)

Williams, K. D.; Ingram, W. J.; Gregory, J. M.

2009-04-01

Effective climate sensitivity is often assumed to be constant (if uncertain), but some previous studies of General Circulation Model (GCM) simulations have found it varying as the simulation progresses. This complicates the fitting of simple models to such simulations, as well as having implications for the estimation of climate sensitivity from observations. This study examines the evolution of the feedbacks determining the climate sensitivity in GCMs submitted to the Coupled Model Intercomparison Project. Apparent centennial-timescale variations of effective climate sensitivity during stabilisation to a forcing can be considered an artefact of using conventional forcings which only allow for instantaneous effects and stratospheric adjustment. If the forcing is adjusted for processes occurring on timescales which are short compared to the climate stabilisation timescale then there is little centennial timescale evolution of effective climate sensitivity in any of the GCMs. We suggest that much of the apparent variation in effective climate sensitivity identified in previous studies is actually due to the comparatively fast forcing adjustment. Persistent differences are found in the strength of the feedbacks between the coupled atmosphere - ocean (AO) versions and their atmosphere - mixed-layer ocean (AML) counterparts, (the latter are often assumed to give the equilibrium climate sensitivity of the AOGCM). The AML model can typically only estimate the equilibrium climate sensitivity of the parallel AO version to within about 0.5K. The adjustment to the forcing to account for comparatively fast processes varies in magnitude and sign between GCMs, as well as differing between AO and AML versions of the same model. There is evidence from one AOGCM that the forcing adjustment may take a couple of decades, with implications for observationally based estimates of equilibrium climate sensitivity. We suggest that at least some of the spread in 21st century global temperature predictions between GCMs is due to differing adjustment processes, hence work to understand these differences should be a priority.

Using Impact-Relevant Sensitivities to Efficiently Evaluate and Select Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Vano, J. A.; Kim, J. B.; Rupp, D. E.; Mote, P.

2014-12-01

We outline an efficient approach to help researchers and natural resource managers more effectively use global climate model information in their long-term planning. The approach provides an estimate of the magnitude of change of a particular impact (e.g., summertime streamflow) from a large ensemble of climate change projections prior to detailed analysis. These estimates provide both qualitative information as an end unto itself (e.g., the distribution of future changes between emissions scenarios for the specific impact) and a judicious, defensible evaluation structure that can be used to qualitatively select a sub-set of climate models for further analysis. More specifically, the evaluation identifies global climate model scenarios that both (1) span the range of possible futures for the variable/s most important to the impact under investigation, and (2) come from global climate models that adequately simulate historical climate, providing plausible results for the future climate in the region of interest. To identify how an ecosystem process responds to projected future changes, we methodically sample, using a simple sensitivity analysis, how an impact variable (e.g., streamflow magnitude, vegetation carbon) responds locally to projected regional temperature and precipitation changes. We demonstrate our technique over the Pacific Northwest, focusing on two types of impacts each in three distinct geographic settings: (a) changes in streamflow magnitudes in critical seasons for water management in the Willamette, Yakima, and Upper Columbia River basins; and (b) changes in annual vegetation carbon in the Oregon and Washington Coast Ranges, Western Cascades, and Columbia Basin ecoregions.

Climate Change and Projected Impacts in Agriculture: an Example on Mediterranean Crops

NASA Astrophysics Data System (ADS)

Ferrise, R.; Moriondo, M.; Bindi, M.

2009-04-01

Recently, the availability of multi-model ensemble prediction methods has permitted the assignment of likelihoods to future climate projections. This allowed moving from the scenario-based approach to the risk-based approach in assessing the effects of climate change, thus providing more useful information for decision-makers that, as reported by Schneider (2001), need probability estimates to assess the seriousness of the projected impacts. The probabilistic approach to evaluate crop response to climate change mainly consists in applying an impact model (such as crop growth model) to a very large number of climate projections so to provide a probabilistic distribution of the variable selected to evaluate the impact. By comparing the outputs of the multi-simulation with a critical threshold (such as minimum yield below which it is not admissible to fall), it is possible to evaluate the risk related to future climate conditions. Unfortunately, such an approach is a time-consuming process due to the large number of model runs needed for such a procedure. An alternative method relies on the set up of impact response surfaces (RS) with respect to key climatic variables on which a probabilistic representation of projected changes in the same climatic variables may be overlaid (Fronzek et al. 2008). This approach was exploited within the ENSEMBLES EU Project aiming at assessing climate change impact on typical Mediterranean crops. This work presents the results of the project with a particular concerning about the assessment of risk, of durum wheat (T. turgidum L. subsp. durum (Desf.) Husn) and grapevine (Vitis vinifera L.) yield falling below fixed thresholds, using probabilistic information about future climate. Methodology The simple mechanistic crop growth models, SIRIUS Quality (Jamieson et al., 1998) and VITE-model (Bindi et al., 1997a,b), were selected to respectively simulate durum wheat and grapevine yields in present and future scenarios. SIRIUS Quality is a wheat simulation model that calculates biomass production from photosynthetically active radiation and grain growth from simple partition rules. VITE-model is a model that uses a simplified mechanistic approach based on the accumulated degree days, the radiation use efficiency and the fruit biomass index to simulate the main processes regulating grapevine development, growth and yield. The selected crop growth models were adopted to create yield RSs of both crops over the suitable cultivated area in the Mediterranean Basin. Yield RSs were calculated performing a scenario sensitivity analysis by altering the baseline climate with respect to temperature and precipitation changes. The baseline climate consisted of 30 years (1975-2005) of daily minimum and maximum temperatures, rainfall and global radiation. Meteorological data were extracted from the MARS JRC Archive and are referred to a grid with a spatial resolution of 50 Km x 50 Km covering the whole European area. The sensitivity analysis was performed for precipitation changes (from -40% to 20%) and temperature changes (from 0°C to +8°C), uniformly applied across all the year. To take in account for the effect of rising CO2, the yield RSs for future periods, were produced considering CO2 air concentration level according to the A1B SRES emission scenario. For each rainfall and temperature combination the average yield over the 30-years period was calculated. The probabilistic distribution of future yields was estimated by applying a bilinear interpolative method to overlap, onto the RSs, the data from perturbed physics experiment of Hadley Centre for future scenarios (joint distribution of annual temperature and rainfall changes). Critical thresholds of impact were determined by calculating, for each grid cell, the distribution of the 30-years average yield according to the joint distribution data for present period (1990-2010) and selecting the values that correspond to the 20th percentile of the cumulative distribution. Finally, future yields were compared with yield threshold to assess the risk of yield shortfall that, in each time period, was defined as the percentage of projected yields that not overcome the selected threshold. Results Maps of durum wheat and grapevine low productivity risk were generated for the next century over the Mediterranean Basin. For durum wheat, with the exception of Portugal and Southern Spain, in the next 30 years risk of low crop productivity shows an overall reduction, due to the fertilizing effect of CO2 increase that counterbalances for the negative impact of rising temperature and reducing rainfall. Thereafter, these latter negative effects become greater and the risk progressively increases starting from lower latitudes. Maximum risk was estimated in 2060 when strong reductions in yield were accounted all over the study area. The smaller reductions in risk, estimated for the end of the next century, may be explained by the greater uncertainty in climate projections. South Portugal, South Spain and Peloponnesus resulted the most vulnerable areas showing increase in risk probability up to 50%, while risk in Galicia, Slovenia, Croatia and central-southern France always resulted lower then present time. As regard grapevine, in the great part of the case study area, the yield seems to have beneficial effect from future climate change. In Central-Western Europe and at lower latitudes the projected yields never fall below the risk threshold, indicating a prevailing effect of CO2 fertilisation. By the other hand, Central-Northern Italy and North of Greece result the most vulnerable areas. In these regions the likelihood of reduced yields quickly rises and remains very high (>50%) until the end of the century, denoting a greater negative effect of temperature and rainfall. Conclusions From these results it may be argued that the impact of future climate change on crop yields is the resultant of the contrasting effects of changes in temperature and precipitation, CO2 increase and uncertainty in climate projections. The intensity of these effects is very site and crop dependent and may vary with time, differently affecting the assessment of risk. As a consequence, the patterns of risk of low crop productivity will change depending on which of these effects will prevail. References Bindi M. et al., 1997a "A simple model for simulation of growth and development in grapevine (Vitis vinifera L.). I. Model description". Vitis 36:67-71 Bindi M. et al., 1997b "A simple model for simulation of growth and development in grapevine (Vitis vinifera L.). II. Model validation". Vitis 36:73-76 Carter T. et al., 2006 "". Fronzek S. et al 2008 "Applying probabilistic projections of climate change with impact models: a case study for sub-arctic palsa mires in Fennoscandia". Climatic Change (submitted) Jamieson et al., 1998 "Sirius: a mechanistic model of wheat response to environmental variation". Eur. J. Agron. 8:161-179. Schneider S. 2001 "What is ‘dangerous' climate change?". Nature 411:17-19

Identification of the prediction model for dengue incidence in Can Tho city, a Mekong Delta area in Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Manh, Cuong Do

2015-01-01

The Mekong Delta is highly vulnerable to climate change and a dengue endemic area in Vietnam. This study aims to examine the association between climate factors and dengue incidence and to identify the best climate prediction model for dengue incidence in Can Tho city, the Mekong Delta area in Vietnam. We used three different regression models comprising: standard multiple regression model (SMR), seasonal autoregressive integrated moving average model (SARIMA), and Poisson distributed lag model (PDLM) to examine the association between climate factors and dengue incidence over the period 2003-2010. We validated the models by forecasting dengue cases for the period of January-December, 2011 using the mean absolute percentage error (MAPE). Receiver operating characteristics curves were used to analyze the sensitivity of the forecast of a dengue outbreak. The results indicate that temperature and relative humidity are significantly associated with changes in dengue incidence consistently across the model methods used, but not cumulative rainfall. The Poisson distributed lag model (PDLM) performs the best prediction of dengue incidence for a 6, 9, and 12-month period and diagnosis of an outbreak however the SARIMA model performs a better prediction of dengue incidence for a 3-month period. The simple or standard multiple regression performed highly imprecise prediction of dengue incidence. We recommend a follow-up study to validate the model on a larger scale in the Mekong Delta region and to analyze the possibility of incorporating a climate-based dengue early warning method into the national dengue surveillance system. Copyright © 2014 Elsevier B.V. All rights reserved.

Modeling the effects of weather and climate change on malaria transmission.

PubMed

Parham, Paul Edward; Michael, Edwin

2010-05-01

In recent years, the impact of climate change on human health has attracted considerable attention; the effects on malaria have been of particular interest because of its disease burden and its transmission sensitivity to environmental conditions. We investigated and illustrated the role that dynamic process-based mathematical models can play in providing strategic insights into the effects of climate change on malaria transmission. We evaluated a relatively simple model that permitted valuable and novel insights into the simultaneous effects of rainfall and temperature on mosquito population dynamics, malaria invasion, persistence and local seasonal extinction, and the impact of seasonality on transmission. We illustrated how large-scale climate simulations and infectious disease systems may be modeled and analyzed and how these methods may be applied to predicting changes in the basic reproduction number of malaria across Tanzania. We found extinction to be more strongly dependent on rainfall than on temperature and identified a temperature window of around 32-33 degrees C where endemic transmission and the rate of spread in disease-free regions is optimized. This window was the same for Plasmodium falciparum and P. vivax, but mosquito density played a stronger role in driving the rate of malaria spread than did the Plasmodium species. The results improved our understanding of how temperature shifts affect the global distribution of at-risk regions, as well as how rapidly malaria outbreaks take off within vulnerable populations. Disease emergence, extinction, and transmission all depend strongly on climate. Mathematical models offer powerful tools for understanding geographic shifts in incidence as climate changes. Nonlinear dependences of transmission on climate necessitates consideration of both changing climate trends and variability across time scales of interest.

Moving toward climate-informed agricultural decision support - can we use PRISM data for more than just monthly averages?

USDA-ARS?s Scientific Manuscript database

Decision support systems/models for agriculture are varied in target application and complexity, ranging from simple worksheets to near real-time forecast systems requiring significant computational and manpower resources. Until recently, most such decision support systems have been constructed with...

THE WATER BALANCE OF THE SUSQUEHANNA RIVER BASIN AND ITS RESPONSE TO CLIMATE CHANGE. (R824995)

EPA Science Inventory

Abstract

Historical precipitation, temperature and streamflow data for the Susquehanna River Basin (SRB) are analyzed with the objective of developing simple statistical and water balance models of streamflow at the watershed's outlet. Annual streamflow is highly corre...

A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions

NASA Astrophysics Data System (ADS)

Millar, Richard J.; Nicholls, Zebedee R.; Friedlingstein, Pierre; Allen, Myles R.

2017-06-01

Projections of the response to anthropogenic emission scenarios, evaluation of some greenhouse gas metrics, and estimates of the social cost of carbon often require a simple model that links emissions of carbon dioxide (CO2) to atmospheric concentrations and global temperature changes. An essential requirement of such a model is to reproduce typical global surface temperature and atmospheric CO2 responses displayed by more complex Earth system models (ESMs) under a range of emission scenarios, as well as an ability to sample the range of ESM response in a transparent, accessible and reproducible form. Here we adapt the simple model of the Intergovernmental Panel on Climate Change 5th Assessment Report (IPCC AR5) to explicitly represent the state dependence of the CO2 airborne fraction. Our adapted model (FAIR) reproduces the range of behaviour shown in full and intermediate complexity ESMs under several idealised carbon pulse and exponential concentration increase experiments. We find that the inclusion of a linear increase in 100-year integrated airborne fraction with cumulative carbon uptake and global temperature change substantially improves the representation of the response of the climate system to CO2 on a range of timescales and under a range of experimental designs.

A new statistical approach to climate change detection and attribution

NASA Astrophysics Data System (ADS)

Ribes, Aurélien; Zwiers, Francis W.; Azaïs, Jean-Marc; Naveau, Philippe

2017-01-01

We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the " models are statistically indistinguishable from the truth" paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951-2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 ± 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings (-0.01± 0.02 K).

1/f model for long-time memory of the ocean surface temperature

NASA Astrophysics Data System (ADS)

Fraedrich, Klaus; Luksch, Ute; Blender, Richard

2004-09-01

The 1/f spectrum of the ocean surface temperature in the Atlantic and Pacific midlatitudes is explained by a simple vertical diffusion model with a shallow mixed layer on top of a deep ocean. The model is forced at the air-sea interface with the total surface heat flux from a 1000 year climate simulation. The analysis reveals the role of ocean advection and substantiates estimates of internal thermal diffusivities.

Modeling contemporary climate profiles of whitebark pine (Pinus albicaulis) and predicting responses to global warming

Treesearch

Marcus V. Warwell; Gerald E. Rehfeldt; Nicholas L. Crookston

2006-01-01

The Random Forests multiple regression tree was used to develop an empirically-based bioclimate model for the distribution of Pinus albicaulis (whitebark pine) in western North America, latitudes 31° to 51° N and longitudes 102° to 125° W. Independent variables included 35 simple expressions of temperature and precipitation and their interactions....

The treatment of climate science in Integrated Assessment Modelling: integration of climate step function response in an energy system integrated assessment model.

NASA Astrophysics Data System (ADS)

Dessens, Olivier

2016-04-01

Integrated Assessment Models (IAMs) are used as crucial inputs to policy-making on climate change. These models simulate aspect of the economy and climate system to deliver future projections and to explore the impact of mitigation and adaptation policies. The IAMs' climate representation is extremely important as it can have great influence on future political action. The step-function-response is a simple climate model recently developed by the UK Met Office and is an alternate method of estimating the climate response to an emission trajectory directly from global climate model step simulations. Good et al., (2013) have formulated a method of reconstructing general circulation models (GCMs) climate response to emission trajectories through an idealized experiment. This method is called the "step-response approach" after and is based on an idealized abrupt CO2 step experiment results. TIAM-UCL is a technology-rich model that belongs to the family of, partial-equilibrium, bottom-up models, developed at University College London to represent a wide spectrum of energy systems in 16 regions of the globe (Anandarajah et al. 2011). The model uses optimisation functions to obtain cost-efficient solutions, in meeting an exogenously defined set of energy-service demands, given certain technological and environmental constraints. Furthermore, it employs linear programming techniques making the step function representation of the climate change response adapted to the model mathematical formulation. For the first time, we have introduced the "step-response approach" method developed at the UK Met Office in an IAM, the TIAM-UCL energy system, and we investigate the main consequences of this modification on the results of the model in term of climate and energy system responses. The main advantage of this approach (apart from the low computational cost it entails) is that its results are directly traceable to the GCM involved and closely connected to well-known methods of analysing GCMs with the step-experiments. Acknowledgments: This work is supported by the FP7 HELIX project (www.helixclimate.eu) References: Anandarajah, G., Pye, S., Usher, W., Kesicki, F., & Mcglade, C. (2011). TIAM-UCL Global model documentation. https://www.ucl.ac.uk/energy-models/models/tiam-ucl/tiam-ucl-manual Good, P., Gregory, J. M., Lowe, J. A., & Andrews, T. (2013). Abrupt CO2 experiments as tools for predicting and understanding CMIP5 representative concentration pathway projections. Climate Dynamics, 40(3-4), 1041-1053.

Beyond equilibrium climate sensitivity

NASA Astrophysics Data System (ADS)

Knutti, Reto; Rugenstein, Maria A. A.; Hegerl, Gabriele C.

2017-10-01

Equilibrium climate sensitivity characterizes the Earth's long-term global temperature response to increased atmospheric CO2 concentration. It has reached almost iconic status as the single number that describes how severe climate change will be. The consensus on the 'likely' range for climate sensitivity of 1.5 °C to 4.5 °C today is the same as given by Jule Charney in 1979, but now it is based on quantitative evidence from across the climate system and throughout climate history. The quest to constrain climate sensitivity has revealed important insights into the timescales of the climate system response, natural variability and limitations in observations and climate models, but also concerns about the simple concepts underlying climate sensitivity and radiative forcing, which opens avenues to better understand and constrain the climate response to forcing. Estimates of the transient climate response are better constrained by observed warming and are more relevant for predicting warming over the next decades. Newer metrics relating global warming directly to the total emitted CO2 show that in order to keep warming to within 2 °C, future CO2 emissions have to remain strongly limited, irrespective of climate sensitivity being at the high or low end.

Tree mortality from drought, insects, and their interactions in a changing climate.

PubMed

Anderegg, William R L; Hicke, Jeffrey A; Fisher, Rosie A; Allen, Craig D; Aukema, Juliann; Bentz, Barbara; Hood, Sharon; Lichstein, Jeremy W; Macalady, Alison K; McDowell, Nate; Pan, Yude; Raffa, Kenneth; Sala, Anna; Shaw, John D; Stephenson, Nathan L; Tague, Christina; Zeppel, Melanie

2015-11-01

Climate change is expected to drive increased tree mortality through drought, heat stress, and insect attacks, with manifold impacts on forest ecosystems. Yet, climate-induced tree mortality and biotic disturbance agents are largely absent from process-based ecosystem models. Using data sets from the western USA and associated studies, we present a framework for determining the relative contribution of drought stress, insect attack, and their interactions, which is critical for modeling mortality in future climates. We outline a simple approach that identifies the mechanisms associated with two guilds of insects - bark beetles and defoliators - which are responsible for substantial tree mortality. We then discuss cross-biome patterns of insect-driven tree mortality and draw upon available evidence contrasting the prevalence of insect outbreaks in temperate and tropical regions. We conclude with an overview of tools and promising avenues to address major challenges. Ultimately, a multitrophic approach that captures tree physiology, insect populations, and tree-insect interactions will better inform projections of forest ecosystem responses to climate change. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

The regrets of procrastination in climate policy

NASA Astrophysics Data System (ADS)

Keller, Klaus; Robinson, Alexander; Bradford, David F.; Oppenheimer, Michael

2007-04-01

Anthropogenic carbon dioxide (CO2) emissions are projected to impose economic costs due to the associated climate change impacts. Climate change impacts can be reduced by abating CO2 emissions. What would be an economically optimal investment in abating CO2 emissions? Economic models typically suggest that reducing CO2 emissions by roughly ten to twenty per cent relative to business-as-usual would be an economically optimal strategy. The currently implemented CO2 abatement of a few per cent falls short of this benchmark. Hence, the global community may be procrastinating in implementing an economically optimal strategy. Here we use a simple economic model to estimate the regrets of this procrastination—the economic costs due to the suboptimal strategy choice. The regrets of procrastination can range from billions to trillions of US dollars. The regrets increase with increasing procrastination period and with decreasing limits on global mean temperature increase. Extended procrastination may close the window of opportunity to avoid crossing temperature limits interpreted by some as 'dangerous anthropogenic interference with the climate system' in the sense of Article 2 of the United Nations Framework Convention on Global Climate Change.

New Gravity Wave Treatments for GISS Climate Models

NASA Technical Reports Server (NTRS)

Geller, Marvin A.; Zhou, Tiehan; Ruedy, Reto; Aleinov, Igor; Nazarenko, Larissa; Tausnev, Nikolai L.; Sun, Shan; Kelley, Maxwell; Cheng, Ye

2011-01-01

Previous versions of GISS climate models have either used formulations of Rayleigh drag to represent unresolved gravity wave interactions with the model-resolved flow or have included a rather complicated treatment of unresolved gravity waves that, while being climate interactive, involved the specification of a relatively large number of parameters that were not well constrained by observations and also was computationally very expensive. Here, the authors introduce a relatively simple and computationally efficient specification of unresolved orographic and nonorographic gravity waves and their interaction with the resolved flow. Comparisons of the GISS model winds and temperatures with no gravity wave parameterization; with only orographic gravity wave parameterization; and with both orographic and nonorographic gravity wave parameterizations are shown to illustrate how the zonal mean winds and temperatures converge toward observations. The authors also show that the specifications of orographic and nonorographic gravity waves must be different in the Northern and Southern Hemispheres. Then results are presented where the nonorographic gravity wave sources are specified to represent sources from convection in the intertropical convergence zone and spontaneous emission from jet imbalances. Finally, a strategy to include these effects in a climate-dependent manner is suggested.

New Gravity Wave Treatments for GISS Climate Models

NASA Technical Reports Server (NTRS)

Geller, Marvin A.; Zhou, Tiehan; Ruedy, Reto; Aleinov, Igor; Nazarenko, Larissa; Tausnev, Nikolai L.; Sun, Shan; Kelley, Maxwell; Cheng, Ye

2010-01-01

Previous versions of GISS climate models have either used formulations of Rayleigh drag to represent unresolved gravity wave interactions with the model resolved flow or have included a rather complicated treatment of unresolved gravity waves that, while being climate interactive, involved the specification of a relatively large number of parameters that were not well constrained by observations and also was computationally very expensive. Here, we introduce a relatively simple and computationally efficient specification of unresolved orographic and non-orographic gravity waves and their interaction with the resolved flow. We show comparisons of the GISS model winds and temperatures with no gravity wave parametrization; with only orographic gravity wave parameterization; and with both orographic and non-orographic gravity wave parameterizations to illustrate how the zonal mean winds and temperatures converge toward observations. We also show that the specifications of orographic and nonorographic gravity waves must be different in the Northern and Southern Hemispheres. We then show results where the non-orographic gravity wave sources are specified to represent sources from convection in the Intertropical Convergence Zone and spontaneous emission from jet imbalances. Finally, we suggest a strategy to include these effects in a climate dependent manner.

Food Prices and Climate Extremes: A Model of Global Grain Price Variability with Storage

NASA Astrophysics Data System (ADS)

Otto, C.; Schewe, J.; Frieler, K.

2015-12-01

Extreme climate events such as droughts, floods, or heat waves affect agricultural production in major cropping regions and therefore impact the world market prices of staple crops. In the last decade, crop prices exhibited two very prominent price peaks in 2007-2008 and 2010-2011, threatening food security especially for poorer countries that are net importers of grain. There is evidence that these spikes in grain prices were at least partly triggered by actual supply shortages and the expectation of bad harvests. However, the response of the market to supply shocks is nonlinear and depends on complex and interlinked processes such as warehousing, speculation, and trade policies. Quantifying the contributions of such different factors to short-term price variability remains difficult, not least because many existing models ignore the role of storage which becomes important on short timescales. This in turn impedes the assessment of future climate change impacts on food prices. Here, we present a simple model of annual world grain prices that integrates grain stocks into the supply and demand functions. This firstly allows us to model explicitly the effect of storage strategies on world market price, and thus, for the first time, to quantify the potential contribution of trade policies to price variability in a simple global framework. Driven only by reported production and by long--term demand trends of the past ca. 40 years, the model reproduces observed variations in both the global storage volume and price of wheat. We demonstrate how recent price peaks can be reproduced by accounting for documented changes in storage strategies and trade policies, contrasting and complementing previous explanations based on different mechanisms such as speculation. Secondly, we show how the integration of storage allows long-term projections of grain price variability under climate change, based on existing crop yield scenarios.

The Paleoclimate Uncertainty Cascade: Tracking Proxy Errors Via Proxy System Models.

NASA Astrophysics Data System (ADS)

Emile-Geay, J.; Dee, S. G.; Evans, M. N.; Adkins, J. F.

2014-12-01

Paleoclimatic observations are, by nature, imperfect recorders of climate variables. Empirical approaches to their calibration are challenged by the presence of multiple sources of uncertainty, which may confound the interpretation of signals and the identifiability of the noise. In this talk, I will demonstrate the utility of proxy system models (PSMs, Evans et al, 2013, 10.1016/j.quascirev.2013.05.024) to quantify the impact of all known sources of uncertainty. PSMs explicitly encode the mechanistic knowledge of the physical, chemical, biological and geological processes from which paleoclimatic observations arise. PSMs may be divided into sensor, archive and observation components, all of which may conspire to obscure climate signals in actual paleo-observations. As an example, we couple a PSM for the δ18O of speleothem calcite to an isotope-enabled climate model (Dee et al, submitted) to analyze the potential of this measurement as a proxy for precipitation amount. A simple soil/karst model (Partin et al, 2013, 10.1130/G34718.1) is used as sensor model, while a hiatus-permitting chronological model (Haslett & Parnell, 2008, 10.1111/j.1467-9876.2008.00623.x) is used as part of the observation model. This subdivision allows us to explicitly model the transformation from precipitation amount to speleothem calcite δ18O as a multi-stage process via a physical and chemical sensor model, and a stochastic archive model. By illustrating the PSM's behavior within the context of the climate simulations, we show how estimates of climate variability may be affected by each submodel's transformation of the signal. By specifying idealized climate signals(periodic vs. episodic, slow vs. fast) to the PSM, we investigate how frequency and amplitude patterns are modulated by sensor and archive submodels. To the extent that the PSM and the climate models are representative of real world processes, then the results may help us more accurately interpret existing paleodata, characterize their uncertainties, and design sampling strategies that exploit their strengths while mitigating their weaknesses.

The essential interactions between understanding climate variability and climate change

NASA Astrophysics Data System (ADS)

Neelin, J. D.

2017-12-01

Global change is sometimes perceived as a field separate from other aspects of atmospheric and oceanic sciences. Despite the long history of communication between the scientific communities studying global change and those studying interannual variability and weather, increasing specialization and conflicting societal demands on the fields can put these interactions at risk. At the same time, current trajectories for greenhouse gas emissions imply substantial adaptation to climate change will be necessary. Instead of simply projecting effects to be avoided, the field is increasingly being asked to provide regional-level information for specific adaptation strategies—with associated requirements for increased precision on projections. For extreme events, challenges include validating models for rare events, especially for events that are unprecedented in the historical record. These factors will be illustrated with examples of information transfer to climate change from work on fundamental climate processes aimed originally at timescales from hours to interannual. Work to understand the effects that control probability distributions of moisture, temperature and precipitation in historical weather can yield new factors to examine for the changes in the extremes of these distributions under climate change. Surprisingly simple process models can give insights into the behavior of vastly more complex climate models. Observation systems and model ensembles aimed at weather and interannual variations prove valuable for global change and vice versa. Work on teleconnections in the climate system, such as the remote impacts of El Niño, is informing analysis of projected regional rainfall change over California. Young scientists need to prepare to work across the full spectrum of climate variability and change, and to communicate their findings, as they and our society head for future that is more interesting than optimal.

Dam Construction in Lancang-Mekong River Basin Could Mitigate Future Flood Risk From Warming-Induced Intensified Rainfall: Dam Mitigate Flood Risk in Mekong

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

Wang, Wei; Lu, Hui; Ruby Leung, L.

Water resources management, in particular flood control, in the Mekong River Basin (MRB) faces two key challenges in the 21st century: climate change and dam construction. A large scale distributed Geomorphology-Based Hydrological Model coupled with a simple reservoir regulation model (GBHM-MK-SOP) is used to investigate the relative effects of climate change and dam construction on the flood characteristics in the MRB. Results suggest an increase in both flood magnitude and frequency under climate change, which is more severe in the upstream basin and increases over time. However, dam construction and stream regulation reduce flood risk consistently throughout this century, withmore » more obvious effects in the upstream basin where larger reservoirs will be located. The flood mitigation effect of dam regulation dominates over the flood intensification effect of climate change before 2060, but the latter emerges more prominently after 2060 and dominates the flood risk especially in the lower basin.« less

Dam Construction in Lancang-Mekong River Basin Could Mitigate Future Flood Risk From Warming-Induced Intensified Rainfall

NASA Astrophysics Data System (ADS)

Wang, Wei; Lu, Hui; Ruby Leung, L.; Li, Hong-Yi; Zhao, Jianshi; Tian, Fuqiang; Yang, Kun; Sothea, Khem

2017-10-01

Water resources management, in particular flood control, in the Lancang-Mekong River Basin (LMRB) faces two key challenges in the 21st century: climate change and dam construction. A large-scale distributed Geomorphology-Based Hydrological Model coupled with a simple reservoir regulation model (GBHM-LMK-SOP) is used to investigate the relative effects of climate change and dam construction on the flood characteristics in the LMRB. Results suggest an increase in both flood magnitude and frequency under climate change, which is more severe in the upstream basin and increases over time. However, stream regulation by dam reduces flood risk consistently throughout this century, with more obvious effects in the upstream basin where larger reservoirs will be located. The flood mitigation effect of dam regulation dominates over the flood intensification effect of climate change before 2060, but the latter emerges more prominently after 2060 and dominates the flood risk especially in the lower basin.

Multi-objective optimization for generating a weighted multi-model ensemble

NASA Astrophysics Data System (ADS)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic ensemble mean and may provide reliable future projections.

Decision-relevant evaluation of climate models: A case study of chill hours in California

NASA Astrophysics Data System (ADS)

Jagannathan, K. A.; Jones, A. D.; Kerr, A. C.

2017-12-01

The past decade has seen a proliferation of different climate datasets with over 60 climate models currently in use. Comparative evaluation and validation of models can assist practitioners chose the most appropriate models for adaptation planning. However, such assessments are usually conducted for `climate metrics' such as seasonal temperature, while sectoral decisions are often based on `decision-relevant outcome metrics' such as growing degree days or chill hours. Since climate models predict different metrics with varying skill, the goal of this research is to conduct a bottom-up evaluation of model skill for `outcome-based' metrics. Using chill hours (number of hours in winter months where temperature is lesser than 45 deg F) in Fresno, CA as a case, we assess how well different GCMs predict the historical mean and slope of chill hours, and whether and to what extent projections differ based on model selection. We then compare our results with other climate-based evaluations of the region, to identify similarities and differences. For the model skill evaluation, historically observed chill hours were compared with simulations from 27 GCMs (and multiple ensembles). Model skill scores were generated based on a statistical hypothesis test of the comparative assessment. Future projections from RCP 8.5 runs were evaluated, and a simple bias correction was also conducted. Our analysis indicates that model skill in predicting chill hour slope is dependent on its skill in predicting mean chill hours, which results from the non-linear nature of the chill metric. However, there was no clear relationship between the models that performed well for the chill hour metric and those that performed well in other temperature-based evaluations (such winter minimum temperature or diurnal temperature range). Further, contrary to conclusions from other studies, we also found that the multi-model mean or large ensemble mean results may not always be most appropriate for this outcome metric. Our assessment sheds light on key differences between global versus local skill, and broad versus specific skill of climate models, highlighting that decision-relevant model evaluation may be crucial for providing practitioners with the best available climate information for their specific needs.

Spatial interactions in a modified Daisyworld model: Heat diffusivity and greenhouse effects

NASA Astrophysics Data System (ADS)

Alberti, T.; Primavera, L.; Vecchio, A.; Lepreti, F.; Carbone, V.

2015-11-01

In this work we investigate a modified version of the Daisyworld model, originally introduced by Lovelock and Watson to describe in a simple way the interactions between an Earth-like planet, its biosphere, and the incoming solar radiation. Here a spatial dependency on latitude is included, and both a variable heat diffusivity along latitudes and a simple greenhouse effect description are introduced in the model. We show that the spatial interactions between the variables of the system can locally stabilize the coexistence of the two vegetation types. The feedback on albedo is able to generate equilibrium solutions which can efficiently self-regulate the planet climate, even for values of the solar luminosity relatively far from the current Earth conditions.

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.

Keeping Pace with Climate Change: Habitat Protection in the Face of Uncertainty

NASA Astrophysics Data System (ADS)

Flitcroft, R. L.; Burnett, K.; Giannico, G.

2014-12-01

Estuaries provide critical habitat for many economically and culturally important species. In the Pacific Northwest, intertidal and subtidal areas provide critical habitat for production of native and commercial oysters (Olympia oyster Ostrea lurida and Pacific oyster Crassostrea gigas, respectively) that in turn provide refuge and rearing habitat for Dungeness Crab, Metacarcinus magister. Environments ranging from subtidal through freshwater zones provide nursery areas for juvenile salmonids at different development stages in their life history. Most Oregon estuaries have been significantly altered by humans over the past century, reducing the quantity and diversity of available habitats. Management agencies have responded with projects to restore and enhance estuarine habitats. Unfortunately, future climate change and sea-level rise could render many current restoration projects ineffective over time. Planning for habitat restoration that keeps pace with climate change will be critical to the sustainable production of seafood and maintenance of ecosystem function. However, land managers and citizens lack the spatially-explicit data needed to incorporate the potential effects of climate change and sea-level rise into planning for habitat improvement projects in estuarine areas. To meet this need, we developed simple models using LiDAR to characterize the geomorphologies of multiple Oregon estuaries. We were able to map the margin of current mean high tide, and contour intervals associated with different potential increases in mean high tide. Because our analysis relied on digital data, we compared three types of digital data in one estuary to assess the utility of different data sets in predicting changes in estuary shape. For each estuary, we assessed changes in the amount and complexity of edge habitats. The simple modeling approach we applied can also be used to identify areas that may be most amenable to pre-emptive restoration actions to mitigate or enhance habitats under future climatic conditions.

Trends in Global Vegetation Activity and Climatic Drivers Indicate a Decoupled Response to Climate Change.

PubMed

Schut, Antonius G T; Ivits, Eva; Conijn, Jacob G; Ten Brink, Ben; Fensholt, Rasmus

2015-01-01

Detailed understanding of a possible decoupling between climatic drivers of plant productivity and the response of ecosystems vegetation is required. We compared trends in six NDVI metrics (1982-2010) derived from the GIMMS3g dataset with modelled biomass productivity and assessed uncertainty in trend estimates. Annual total biomass weight (TBW) was calculated with the LINPAC model. Trends were determined using a simple linear regression, a Thiel-Sen medium slope and a piecewise regression (PWR) with two segments. Values of NDVI metrics were related to Net Primary Production (MODIS-NPP) and TBW per biome and land-use type. The simple linear and Thiel-Sen trends did not differ much whereas PWR increased the fraction of explained variation, depending on the NDVI metric considered. A positive trend in TBW indicating more favorable climatic conditions was found for 24% of pixels on land, and for 5% a negative trend. A decoupled trend, indicating positive TBW trends and monotonic negative or segmented and negative NDVI trends, was observed for 17-36% of all productive areas depending on the NDVI metric used. For only 1-2% of all pixels in productive areas, a diverging and greening trend was found despite a strong negative trend in TBW. The choice of NDVI metric used strongly affected outcomes on regional scales and differences in the fraction of explained variation in MODIS-NPP between biomes were large, and a combination of NDVI metrics is recommended for global studies. We have found an increasing difference between trends in climatic drivers and observed NDVI for large parts of the globe. Our findings suggest that future scenarios must consider impacts of constraints on plant growth such as extremes in weather and nutrient availability to predict changes in NPP and CO2 sequestration capacity.

Impact of different climatic flows on typhoon tracks

NASA Astrophysics Data System (ADS)

Qian, Wei-hong; Huang, Jing

2018-04-01

A tropical cyclone (TC) vortex is considered to be embedded in and steered by a large-scale environmental flow. The environmental flow can be decomposed into two parts: temporal climatic flow and anomaly. The former is defined according to the calendar climatology with a diurnal cycle and a seasonal cycle. Thus, the temporal climatic flow of the atmosphere, which can be estimated using reanalysis data, varies with regions, altitudes, and hours. The impact of different climatic flows on TC tracks in the Northwest Pacific is examined using a simple generalized beta-advection model. Results show that the predicted tracks of two TC cases have large deviations from their best tracks in the following 1-2 days if the temporal climatic wind is replaced by other hourly climatic winds on the same calendar day or by a several-day-mean climatic wind. The track deviation is more significant when the climatic wind difference is larger than 2 m s-1. This experiment reconfirms that a TC track is influenced by temporal climatic flow and interaction with other disturbances in the vicinity.

Geomorphic and climate influences on soil organic carbon concentration at large catchment scales

NASA Astrophysics Data System (ADS)

Hancock, G. R.; Martinez, C.; Wells, T.; Dever, C.; Willgoose, G. R.; Bissett, A.

2013-12-01

Soils represent the largest terrestrial sink of carbon on Earth. Managing the soil organic carbon (SOC) pool is becoming increasingly important in light of growing concerns over global food security and the climatic effects of anthropogenic CO2 emissions. The development of accurate predictive SOC models are an important step for both land resource managers and policy makers alike. Presently, a number of SOC models are available which incorporate environmental data to produce SOC estimates. The accuracy of these models varies significantly over a range of landscapes due to the highly complex nature of SOC dynamics. Fundamental gaps exist in our understanding of SOC controls. To date, studies of SOC controls, and the subsequent models derived from their findings have focussed mainly on North American and European landscapes. Additionally, SOC studies often focus on the paddock to small catchment scale. Consequently, information about SOC in Australian landscapes and at the larger scale is limited. This study examines controls over SOC across a large catchment of approximately 600 km2 in the Upper Hunter Valley, New South Wales, Australia. The aim was to develop a predictive model for use across a range of catchment sizes and climate. Here it was found that elevation (derived from DEMs) and vegetation (above ground biomass quantified by remote sensing were the primary controls of SOC. SOC was seen to increase with elevation and NDVI. This relationship is believed to be a reflection of rainfall patterns across the study area and plant growth potential. Further, a relationship was observed between SOC and the environmental tracer 137Cs which suggests that SOC and 137Cs move through catchment via similar sediment transport mechanisms. Therefore loss of SOC by erosion and gain by deposition may be necessary to be accounted for in any SOC budget. Model validation indicated that the use of simple linear relationships could predict SOC based on rainfall and vegetation (above ground biomass as quantified by remote sensing). The results suggest that simple landscape and climate models have the potential to predict the spatial distribution of SOC. The findings of this study emphasise the importance of tailoring SOC models to the appropriate scale.

The utility of the historical record in assessing future carbon budgets

NASA Astrophysics Data System (ADS)

Millar, R.; Friedlingstein, P.; Allen, M. R.

2017-12-01

It has long been known that the cumulative emissions of carbon dioxide (CO2) is the most physically relevant determiner of long-lived anthropogenic climate change, with an approximately linear relationship between CO2-induced global mean surface warming and cumulative emissions. The historical observational record offers a way to constrain the relationship between cumulative carbon dioxide emission and global mean warming using observations to date. Here we show that simple regression analysis indicates that the 1.5°C carbon budget would be exhausted after nearly three decades of current emissions, substantially in excess of many estimates from Earth System Models. However, there are many reasons to be cautious about carbon budget assessments from the historical record alone. Accounting for the uncertainty in non-CO2 radiative forcing using a simple climate model and a standard optimal fingerprinting detection attribution technique gives substantial uncertainty in the contribution of CO2 warming to date, and hence the transient climate response to cumulative emissions. Additionally, the existing balance between CO2 and non-CO2 forcing may change in the future under ambitious mitigation scenarios as non-CO2 emissions become more (or less) important to global mean temperature changes. Natural unforced variability can also have a substantial impact on estimates of remaining carbon budgets. By examining all warmings of a given magnitude in both the historical record and past and future ESM simulations we quantify the impact unforced climate variability may have on estimates of remaining carbon budgets, derived as a function of estimated non-CO2 warming and future emission scenario. In summary, whilst the historical record can act as a useful test of climate models, uncertainties in the response to future cumulative emissions remain large and extrapolations of future carbon budgets from the historical record alone should be treated with caution.

Medium term hurricane catastrophe models: a validation experiment

NASA Astrophysics Data System (ADS)

Bonazzi, Alessandro; Turner, Jessica; Dobbin, Alison; Wilson, Paul; Mitas, Christos; Bellone, Enrica

2013-04-01

Climate variability is a major source of uncertainty for the insurance industry underwriting hurricane risk. Catastrophe models provide their users with a stochastic set of events that expands the scope of the historical catalogue by including synthetic events that are likely to happen in a defined time-frame. The use of these catastrophe models is widespread in the insurance industry but it is only in recent years that climate variability has been explicitly accounted for. In the insurance parlance "medium term catastrophe model" refers to products that provide an adjusted view of risk that is meant to represent hurricane activity on a 1 to 5 year horizon, as opposed to long term models that integrate across the climate variability of the longest available time series of observations. In this presentation we discuss how a simple reinsurance program can be used to assess the value of medium term catastrophe models. We elaborate on similar concepts as discussed in "Potential Economic Value of Seasonal Hurricane Forecasts" by Emanuel et al. (2012, WCAS) and provide an example based on 24 years of historical data of the Chicago Mercantile Hurricane Index (CHI), an insured loss proxy. Profit and loss volatility of a hypothetical primary insurer are used to score medium term models versus their long term counterpart. Results show that medium term catastrophe models could help a hypothetical primary insurer to improve their financial resiliency to varying climate conditions.

The Power of the Spectrum: Combining Numerical Proxy System Models with Analytical Error Spectra to Better Understand Timescale Dependent Proxy Uncertainty

NASA Astrophysics Data System (ADS)

Dolman, A. M.; Laepple, T.; Kunz, T.

2017-12-01

Understanding the uncertainties associated with proxy-based reconstructions of past climate is critical if they are to be used to validate climate models and contribute to a comprehensive understanding of the climate system. Here we present two related and complementary approaches to quantifying proxy uncertainty. The proxy forward model (PFM) "sedproxy" bitbucket.org/ecus/sedproxy numerically simulates the creation, archiving and observation of marine sediment archived proxies such as Mg/Ca in foraminiferal shells and the alkenone unsaturation index UK'37. It includes the effects of bioturbation, bias due to seasonality in the rate of proxy creation, aliasing of the seasonal temperature cycle into lower frequencies, and error due to cleaning, processing and measurement of samples. Numerical PFMs have the advantage of being very flexible, allowing many processes to be modelled and assessed for their importance. However, as more and more proxy-climate data become available, their use in advanced data products necessitates rapid estimates of uncertainties for both the raw reconstructions, and their smoothed/derived products, where individual measurements have been aggregated to coarser time scales or time-slices. To address this, we derive closed-form expressions for power spectral density of the various error sources. The power spectra describe both the magnitude and autocorrelation structure of the error, allowing timescale dependent proxy uncertainty to be estimated from a small number of parameters describing the nature of the proxy, and some simple assumptions about the variance of the true climate signal. We demonstrate and compare both approaches for time-series of the last millennia, Holocene, and the deglaciation. While the numerical forward model can create pseudoproxy records driven by climate model simulations, the analytical model of proxy error allows for a comprehensive exploration of parameter space and mapping of climate signal re-constructability, conditional on the climate and sampling conditions.

Adapting regional watershed management to climate change in Bavaria and Québec

NASA Astrophysics Data System (ADS)

Ludwig, Ralf; Muerth, Markus; Schmid, Josef; Jobst, Andreas; Caya, Daniel; Gauvin St-Denis, Blaise; Chaumont, Diane; Velazquez, Juan-Alberto; Turcotte, Richard; Ricard, Simon

2013-04-01

The international research project QBic3 (Quebec-Bavarian Collaboration on Climate Change) aims at investigating the potential impacts of climate change on the hydrology of regional scale catchments in Southern Quebec (Canada) and Bavaria (Germany). For this purpose, a hydro-meteorological modeling chain has been established, applying climatic forcing from both dynamical and statistical climate model data to an ensemble of hydrological models of varying complexity. The selection of input data, process descriptions and scenarios allows for the inter-comparison of the uncertainty ranges on selected runoff indicators; a methodology to display the relative importance of each source of uncertainty is developed and results for past runoff (1971-2000) and potential future changes (2041-2070) are obtained. Finally, the impact of hydrological changes on the operational management of dams, reservoirs and transfer systems is investigated and shown for the Bavarian case studies, namely the potential change in i) hydro-power production for the Upper Isar watershed and ii) low flow augmentation and water transfer rates at the Donau-Main transfer system in Central Franconia. Two overall findings will be presented and discussed in detail: a) the climate change response of selected hydrological indicators, especially those related to low flows, is strongly affected by the choice of the hydrological model. It can be shown that an assessment of the changes in the hydrological cycle is best represented by a complex physically based hydrological model, computationally less demanding models (usually simple, lumped and conceptual) can give a significant level of trust for selected indicators. b) the major differences in the projected climate forcing stemming from the ensemble of dynamic climate models (GCM/RCM) versus the statistical-stochastical WETTREG2010 approach. While the dynamic ensemble reveals a moderate modification of the hydrological processes in the investigated catchments, the WETTREG2010 driven runs show a severe detraction for all water operations, mainly related to a strong decline in projected precipitation in all seasons (except winter).

Climate Change Impacts at Department of Defense

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

Kotamarthi, Rao; Wang, Jiali; Zoebel, Zach

This project is aimed at providing the U.S. Department of Defense (DoD) with a comprehensive analysis of the uncertainty associated with generating climate projections at the regional scale that can be used by stakeholders and decision makers to quantify and plan for the impacts of future climate change at specific locations. The merits and limitations of commonly used downscaling models, ranging from simple to complex, are compared, and their appropriateness for application at installation scales is evaluated. Downscaled climate projections are generated at selected DoD installations using dynamic and statistical methods with an emphasis on generating probability distributions of climatemore » variables and their associated uncertainties. The sites selection and selection of variables and parameters for downscaling was based on a comprehensive understanding of the current and projected roles that weather and climate play in operating, maintaining, and planning DoD facilities and installations.« less

A climate-associated multispecies cryptic cline in the northwest Atlantic

PubMed Central

DiBacco, Claudio; Lowen, Ben; Beiko, Robert G.; Bentzen, Paul; Brickman, David; Johnson, Catherine; Wang, Zeliang; Wringe, Brendan F.; Bradbury, Ian R.

2018-01-01

The spatial genetic structure of most species in the open marine environment remains largely unresolved. This information gap creates uncertainty in the sustainable management, recovery, and associated resilience of marine communities and our capacity to extrapolate beyond the few species for which such information exists. We document a previously unidentified multispecies biogeographic break aligned with a steep climatic gradient and driven by seasonal temperature minima in the northwest Atlantic. The coherence of this genetic break across our five study species with contrasting life histories suggests a pervasive macroecological phenomenon. The integration of this genetic structure with habitat suitability models and climate forecasts predicts significant variation in northward distributional shifts among populations and availability of suitable habitat in future oceans. The results of our integrated approach provide new perspective on how cryptic intraspecific diversity associated with climatic variation influences species and community response to climate change beyond simple poleward shifts. PMID:29600272

Cloud Macroscopic Organization: Order Emerging from Randomness

NASA Technical Reports Server (NTRS)

Yuan, Tianle

2011-01-01

Clouds play a central role in many aspects of the climate system and their forms and shapes are remarkably diverse. Appropriate representation of clouds in climate models is a major challenge because cloud processes span at least eight orders of magnitude in spatial scales. Here we show that there exists order in cloud size distribution of low-level clouds, and that it follows a power-law distribution with exponent gamma close to 2. gamma is insensitive to yearly variations in environmental conditions, but has regional variations and land-ocean contrasts. More importantly, we demonstrate this self-organizing behavior of clouds emerges naturally from a complex network model with simple, physical organizing principles: random clumping and merging. We also demonstrate symmetry between clear and cloudy skies in terms of macroscopic organization because of similar fundamental underlying organizing principles. The order in the apparently complex cloud-clear field thus has its root in random local interactions. Studying cloud organization with complex network models is an attractive new approach that has wide applications in climate science. We also propose a concept of cloud statistic mechanics approach. This approach is fully complementary to deterministic models, and the two approaches provide a powerful framework to meet the challenge of representing clouds in our climate models when working in tandem.

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.

Complex systems approach to fire dynamics and climate change impacts

NASA Astrophysics Data System (ADS)

Pueyo, S.

2012-04-01

I present some recent advances in complex systems theory as a contribution to understanding fire regimes and forecasting their response to a changing climate, qualitatively and quantitatively. In many regions of the world, fire sizes have been found to follow, approximately, a power-law frequency distribution. As noted by several authors, this distribution also arises in the "forest fire" model used by physicists to study mechanisms that give rise to scale invariance (the power law is a scale-invariant distribution). However, this model does not give and does not pretend to give a realistic description of fire dynamics. For example, it gives no role to weather and climate. Pueyo (2007) developed a variant of the "forest fire" model that is also simple but attempts to be more realistic. It also results into a power law, but the parameters of this distribution change through time as a function of weather and climate. Pueyo (2007) observed similar patterns of response to weather in data from boreal forest fires, and used the fitted response functions to forecast fire size distributions in a possible climate change scenario, including the upper extreme of the distribution. For some parameter values, the model in Pueyo (2007) displays a qualitatively different behavior, consisting of simple percolation. In this case, fire is virtually absent, but megafires sweep through the ecosystem a soon as environmental forcings exceed a critical threshold. Evidence gathered by Pueyo et al. (2010) suggests that this is realistic for tropical rainforests (specifically, well-conserved upland rainforests). Some climate models suggest that major tropical rainforest regions are going to become hotter and drier if climate change goes ahead unchecked, which could cause such abrupt shifts. Not all fire regimes are well described by this model. Using data from a tropical savanna region, Pueyo et al. (2010) found that the dynamics in this area do not match its assumptions, even though fire sizes are also well fitted by a power law. A possible interpretation is that the spatial structure of fire in savannas is strongly constrained by the spatial structure of their environment. Instead of resulting from ecosystem self-organization as in the model, in this case the scale invariance in fire events would be just a reflection of scale invariance in the environment in which the ecosystem lives. These results suggest at least three major types of fire dynamics: endogenous scaling, percolating, and exogenous scaling, in addition to intermediate options. The world's biomes can be classified based on the type of dynamics that is most likely to apply in each of them, and forecasts can be carried out with the tools developed for each of these types.

Climate response of the soil nitrogen cycle in three forest types of a headwater Mediterranean catchment

NASA Astrophysics Data System (ADS)

Lupon, Anna; Gerber, Stefan; Sabater, Francesc; Bernal, Susana

2015-05-01

Future changes in climate may affect soil nitrogen (N) transformations, and consequently, plant nutrition and N losses from terrestrial to stream ecosystems. We investigated the response of soil N cycling to changes in soil moisture, soil temperature, and precipitation across three Mediterranean forest types (evergreen oak, beech, and riparian) by fusing a simple process-based model (which included climate modifiers for key soil N processes) with measurements of soil organic N content, mineralization, nitrification, and concentration of ammonium and nitrate. The model describes sources (atmospheric deposition and net N mineralization) and sinks (plant uptake and hydrological losses) of inorganic N from and to the 0-10 cm soil pool as well as net nitrification. For the three forest types, the model successfully recreated the magnitude and temporal pattern of soil N processes and N concentrations (Nash-Sutcliffe coefficient = 0.49-0.96). Changes in soil water availability drove net N mineralization and net nitrification at the oak and beech forests, while temperature and precipitation were the strongest climatic factors for riparian soil N processes. In most cases, net N mineralization and net nitrification showed a different sensitivity to climatic drivers (temperature, soil moisture, and precipitation). Our model suggests that future climate change may have a minimal effect on the soil N cycle of these forests (<10% change in mean annual rates) because positive warming and negative drying effects on the soil N cycle may counterbalance each other.

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.

Fire and climate suitability for woody vegetation communities in the south central United States

USGS Publications Warehouse

Stroh, Esther; Struckhoff, Matthew; Stambaugh, Michael C.; Guyette, Richard P.

2018-01-01

using a physical chemistry fire frequency model. We then used the fire probability data with additional climate parameters to construct maximum entropy environmental suitability models for three south central US vegetation communities. The modeled communities included an oak type (dominated by post oak, Quercus stellata Wangenh., and blackjack oak, Q. marilandica Münchh.), a mesquite type (dominated by honey mesquite, Prosopis glandulosa Torr., and velvet mesquite, P. velutina Wooton), and a pinyon−juniper type (dominated by pinyon pine, Pinus edulis Engelm., and Utah juniper, Juniperus osteosperma [Torr.] Little). We mapped baseline and future mean fire-climate suitability using data from three global climate models for 2040 to 2069 and 2070 to 2099; we also mapped future locations of threshold conditions for which all three models agreed on suitability for each community. Future projections included northward, southward, and eastward shifts in suitable conditions for the oaks along a broad path of fire-climate stability; an overall reduction in suitable area for historic mesquite communities coupled with potential expansion to new areas; and constriction and isolation of suitable conditions for pinyon−juniper communities. The inclusion of fire probability adds an important driver of vegetation distribution to climate envelope modeling. The simple models showed good fit, but future projections failed to account for future management activities or land use changes. Results provided information on potential future de-coupling and spatial re-arrangement of environmental conditions under which these communities have historically persisted and been managed. In particular, consensus threshold maps can inform long-term planning for maintenance or restoration of these communities, and they can be used as a potential tool for other communities in fire-prone environments within the study area and beyond its borders.

Can metric-based approaches really improve multi-model climate projections? A perfect model framework applied to summer temperature change in France.

NASA Astrophysics Data System (ADS)

Boé, Julien; Terray, Laurent

2014-05-01

Ensemble approaches for climate change projections have become ubiquitous. Because of large model-to-model variations and, generally, lack of rationale for the choice of a particular climate model against others, it is widely accepted that future climate change and its impacts should not be estimated based on a single climate model. Generally, as a default approach, the multi-model ensemble mean (MMEM) is considered to provide the best estimate of climate change signals. The MMEM approach is based on the implicit hypothesis that all the models provide equally credible projections of future climate change. This hypothesis is unlikely to be true and ideally one would want to give more weight to more realistic models. A major issue with this alternative approach lies in the assessment of the relative credibility of future climate projections from different climate models, as they can only be evaluated against present-day observations: which present-day metric(s) should be used to decide which models are "good" and which models are "bad" in the future climate? Once a supposedly informative metric has been found, other issues arise. What is the best statistical method to combine multiple models results taking into account their relative credibility measured by a given metric? How to be sure in the end that the metric-based estimate of future climate change is not in fact less realistic than the MMEM? It is impossible to provide strict answers to those questions in the climate change context. Yet, in this presentation, we propose a methodological approach based on a perfect model framework that could bring some useful elements of answer to the questions previously mentioned. The basic idea is to take a random climate model in the ensemble and treat it as if it were the truth (results of this model, in both past and future climate, are called "synthetic observations"). Then, all the other members from the multi-model ensemble are used to derive thanks to a metric-based approach a posterior estimate of climate change, based on the synthetic observation of the metric. Finally, it is possible to compare the posterior estimate to the synthetic observation of future climate change to evaluate the skill of the method. The main objective of this presentation is to describe and apply this perfect model framework to test different methodological issues associated with non-uniform model weighting and similar metric-based approaches. The methodology presented is general, but will be applied to the specific case of summer temperature change in France, for which previous works have suggested potentially useful metrics associated with soil-atmosphere and cloud-temperature interactions. The relative performances of different simple statistical approaches to combine multiple model results based on metrics will be tested. The impact of ensemble size, observational errors, internal variability, and model similarity will be characterized. The potential improvements associated with metric-based approaches compared to the MMEM is terms of errors and uncertainties will be quantified.

The response of fabric variations to simple shear and migration recrystallization

DOE PAGES

Kennedy, Joseph H.; Pettit, Erin C.

2015-06-01

The observable microstructures in ice are the result of many dynamic and competing processes. These processes are influenced by climate variables in the firn. Layers deposited in different climate regimes may show variations in fabric which can persist deep into the ice sheet; fabric may 'remember' these past climate regimes. In this paper, we model the evolution of fabric variations below the firn–ice transition and show that the addition of shear to compressive-stress regimes preserves the modeled fabric variations longer than compression-only regimes, because shear drives a positive feedback between crystal rotation and deformation. Even without shear, the modeled icemore » retains memory of the fabric variation for ~200 ka in typical polar ice-sheet conditions. Our model shows that temperature affects how long the fabric variation is preserved, but only affects the strain-integrated fabric evolution profile when comparing results straddling the thermal-activation-energy threshold (~–10°C). Even at high temperatures, migration recrystallization does not eliminate the modeled fabric's memory under most conditions. High levels of nearest-neighbor interactions will, however, eliminate the modeled fabric's memory more quickly than low levels of nearest-neighbor interactions. Finally, our model predicts that fabrics will retain memory of past climatic variations when subject to a wide variety of conditions found in polar ice sheets.« less

Global Potential Net Prmary Production Predicted from Vegetation Class, Precipitation, and Temperature

USDA-ARS?s Scientific Manuscript database

Net Primary Production (NPP), the difference between CO2 fixed by photosynthesis and CO2 lost to autotrophic respiration, is one of the most important components of the carbon cycle. Our goal was to develop a simple regression model to estimate global NPP using climate and land cover data. Approxima...

Climate analyses to assess risks from invasive forest insects: Simple matching to advanced models

Treesearch

Robert C. Venette

2017-01-01

Purpose of Review. The number of invasive alien insects that adversely affect trees and forests continues to increase as do associated ecological, economic, and sociological impacts. Prevention strategies remain the most cost-effective approach to address the issue, but risk management decisions, particularly those affecting international trade,...

Design tools

Treesearch

Anton TenWolde; Mark T. Bomberg

2009-01-01

Overall, despite the lack of exact input data, the use of design tools, including models, is much superior to the simple following of rules of thumbs, and a moisture analysis should be standard procedure for any building envelope design. Exceptions can only be made for buildings in the same climate, similar occupancy, and similar envelope construction. This chapter...

A Community Perspective on the Effects of Climate Change on Species Distributions in the Boreal Forest of the Northeastern United States

NASA Astrophysics Data System (ADS)

Morelli, T. L.; DeLuca, W. V.; Duclos, T. R.; Foster, J. R.; Siren, A. P.

2016-12-01

The way that climate change will impact species ranges through habitat change and modify species interactions is not well enough understood. We took a community view of the climate-vulnerable, biologically-important spruce-fir forest ecosystem of the northeastern U.S., examining if and how species are responding to warming and changing precipitation patterns. We examined how fluctuations in temperature and snowpack influence distributional shifts along elevational and latitudinal gradients; for example, milder winter conditions may allow generalist carnivores such as bobcats to access boreal forest habitat, increasing direct and indirect competition with Canada lynx and American marten for prey. In another example of climate-driven predation shifts, upslope shifts of American red squirrels may increase predation rates on vulnerable montane songbirds. We combined data from weather stations with model-based high resolution data to obtain information on historical and present climate variables. We forecasted spruce-fir forest extent using landscape and ecosystem models under a combination of global circulation model projections and representative concentration pathways for the northern Appalachians. Presence and abundance data from animal surveys were used to build occupancy models to assess the habitat, climate, and species relationships. Species responded individually with geographic variation in response within and across species. Some species closely tracked climate changes, whereas others showed no response, or even responses such as shifts southward that were counter to what would be expected. For example, although low elevation boreal bird species showed evidence of expanding upslope, most high elevation species expanded downslope. This work highlights the need to take a mechanistic perspective of species responses to climate change and avoid generalizations of simple shifts northward and upward. Understanding how climate change affects community dynamics will improve predictions of how individual species will respond to climate change. These predictions then provide information about how distributional shifts will occur in a biologically critical ecosystem and if there will be climate change refugia they can target for management.

Climatic controls on ecosystem resilience: Postfire regeneration in the Cape Floristic Region of South Africa.

PubMed

Wilson, Adam M; Latimer, Andrew M; Silander, John A

2015-07-21

Conservation of biodiversity and natural resources in a changing climate requires understanding what controls ecosystem resilience to disturbance. This understanding is especially important in the fire-prone Mediterranean systems of the world. The fire frequency in these systems is sensitive to climate, and recent climate change has resulted in more frequent fires over the last few decades. However, the sensitivity of postfire recovery and biomass/fuel load accumulation to climate is less well understood than fire frequency despite its importance in driving the fire regime. In this study, we develop a hierarchical statistical framework to model postfire ecosystem recovery using satellite-derived observations of vegetation as a function of stand age, topography, and climate. In the Cape Floristic Region (CFR) of South Africa, a fire-prone biodiversity hotspot, we found strong postfire recovery gradients associated with climate resulting in faster recovery in regions with higher soil fertility, minimum July (winter) temperature, and mean January (summer) precipitation. Projections using an ensemble of 11 downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) suggest that warmer winter temperatures in 2080-2100 will encourage faster postfire recovery across the region, which could further increase fire frequency due to faster fuel accumulation. However, some models project decreasing precipitation in the western CFR, which would slow recovery rates there, likely reducing fire frequency through lack of fuel and potentially driving local biome shifts from fynbos shrubland to nonburning semidesert vegetation. This simple yet powerful approach to making inferences from large, remotely sensed datasets has potential for wide application to modeling ecosystem resilience in disturbance-prone ecosystems globally.

Climatic controls on ecosystem resilience: Postfire regeneration in the Cape Floristic Region of South Africa

PubMed Central

Wilson, Adam M.; Latimer, Andrew M.; Silander, John A.

2015-01-01

Conservation of biodiversity and natural resources in a changing climate requires understanding what controls ecosystem resilience to disturbance. This understanding is especially important in the fire-prone Mediterranean systems of the world. The fire frequency in these systems is sensitive to climate, and recent climate change has resulted in more frequent fires over the last few decades. However, the sensitivity of postfire recovery and biomass/fuel load accumulation to climate is less well understood than fire frequency despite its importance in driving the fire regime. In this study, we develop a hierarchical statistical framework to model postfire ecosystem recovery using satellite-derived observations of vegetation as a function of stand age, topography, and climate. In the Cape Floristic Region (CFR) of South Africa, a fire-prone biodiversity hotspot, we found strong postfire recovery gradients associated with climate resulting in faster recovery in regions with higher soil fertility, minimum July (winter) temperature, and mean January (summer) precipitation. Projections using an ensemble of 11 downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) suggest that warmer winter temperatures in 2080–2100 will encourage faster postfire recovery across the region, which could further increase fire frequency due to faster fuel accumulation. However, some models project decreasing precipitation in the western CFR, which would slow recovery rates there, likely reducing fire frequency through lack of fuel and potentially driving local biome shifts from fynbos shrubland to nonburning semidesert vegetation. This simple yet powerful approach to making inferences from large, remotely sensed datasets has potential for wide application to modeling ecosystem resilience in disturbance-prone ecosystems globally. PMID:26150521

Analysis of changes in tornadogenesis conditions over Northern Eurasia based on a simple index of atmospheric convective instability

NASA Astrophysics Data System (ADS)

Chernokulsky, A. V.; Kurgansky, M. V.; Mokhov, I. I.

2017-12-01

A simple index of convective instability (3D-index) is used for analysis of weather and climate processes that favor to the occurrence of severe convective events including tornadoes. The index is based on information on the surface air temperature and humidity. The prognostic ability of the index to reproduce severe convective events (thunderstorms, showers, tornadoes) is analyzed. It is shown that most tornadoes in North Eurasia are characterized by high values of the 3D-index; furthermore, the 3D-index is significantly correlated with the available convective potential energy. Reanalysis data (for recent decades) and global climate model simulations (for the 21st century) show an increase in the frequency of occurrence of favorable for tornado formation meteorological conditions in the regions of Northern Eurasia. The most significant increase is found on the Black Sea coast and in the south of the Far East.

The Impact of Long-Term Climate Change on Nitrogen Runoff at the Watershed Scale.

NASA Astrophysics Data System (ADS)

Dorley, J.; Duffy, C.; Arenas Amado, A.

2017-12-01

The impact of agricultural runoff is a major concern for water quality of mid-western streams. This concern is largely due to excessive use of agricultural fertilizer, a major source of nutrients in many Midwestern watersheds. In order to improve water quality in these watersheds, understanding the long-term trends in nutrient concentration and discharge is an important water quality problem. This study attempts to analyze the role of long-term temperature and precipitation on nitrate runoff in an agriculturally dominated watershed in Iowa. The approach attempts to establish the concentration-discharge (C-Q) signature for the watershed using time series analysis, frequency analysis and model simulation. The climate data is from the Intergovernmental Panel on Climate Change (IPCC), model GFDL-CM3 (Geophysical Fluid Dynamic Laboratory Coupled Model 3). The historical water quality data was made available by the IIHR-Hydroscience & Engineering at the University of Iowa for the clear creek watershed (CCW). The CCW is located in east-central Iowa. The CCW is representative of many Midwestern watersheds with humid-continental climate with predominantly agricultural land use. The study shows how long-term climate changes in temperature and precipitation affects the C-Q dynamics and how a relatively simple approach to data analysis and model projections can be applied to best management practices at the site.

Easy Volcanic Aerosol (EVA v1.0): an idealized forcing generator for climate simulations

NASA Astrophysics Data System (ADS)

Toohey, Matthew; Stevens, Bjorn; Schmidt, Hauke; Timmreck, Claudia

2016-11-01

Stratospheric sulfate aerosols from volcanic eruptions have a significant impact on the Earth's climate. To include the effects of volcanic eruptions in climate model simulations, the Easy Volcanic Aerosol (EVA) forcing generator provides stratospheric aerosol optical properties as a function of time, latitude, height, and wavelength for a given input list of volcanic eruption attributes. EVA is based on a parameterized three-box model of stratospheric transport and simple scaling relationships used to derive mid-visible (550 nm) aerosol optical depth and aerosol effective radius from stratospheric sulfate mass. Precalculated look-up tables computed from Mie theory are used to produce wavelength-dependent aerosol extinction, single scattering albedo, and scattering asymmetry factor values. The structural form of EVA and the tuning of its parameters are chosen to produce best agreement with the satellite-based reconstruction of stratospheric aerosol properties following the 1991 Pinatubo eruption, and with prior millennial-timescale forcing reconstructions, including the 1815 eruption of Tambora. EVA can be used to produce volcanic forcing for climate models which is based on recent observations and physical understanding but internally self-consistent over any timescale of choice. In addition, EVA is constructed so as to allow for easy modification of different aspects of aerosol properties, in order to be used in model experiments to help advance understanding of what aspects of the volcanic aerosol are important for the climate system.

Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

NASA Astrophysics Data System (ADS)

Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, P.; Williams, C.; Ardö, J.; Boucher, M.; Cappelaere, B.; de Grandcourt, A.; Nickless, A.; Nouvellon, Y.; Scholes, R.; Kutsch, W.

2013-03-01

Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET), driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at National Weather Service Land Surface Model (GNOAH) to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.

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

Modelling climate change and malaria transmission.

PubMed

Parham, Paul E; Michael, Edwin

2010-01-01

The impact of climate change on human health has received increasing attention in recent years, with potential impacts due to vector-borne diseases only now beginning to be understood. As the most severe vector-borne disease, with one million deaths globally in 2006, malaria is thought most likely to be affected by changes in climate variables due to the sensitivity of its transmission dynamics to environmental conditions. While considerable research has been carried out using statistical models to better assess the relationship between changes in environmental variables and malaria incidence, less progress has been made on developing process-based climate-driven mathematical models with greater explanatory power. Here, we develop a simple model of malaria transmission linked to climate which permits useful insights into the sensitivity of disease transmission to changes in rainfall and temperature variables. Both the impact of changes in the mean values of these key external variables and importantly temporal variation in these values are explored. We show that the development and analysis of such dynamic climate-driven transmission models will be crucial to understanding the rate at which P. falciparum and P. vivax may either infect, expand into or go extinct in populations as local environmental conditions change. Malaria becomes endemic in a population when the basic reproduction number R0 is greater than unity and we identify an optimum climate-driven transmission window for the disease, thus providing a useful indicator for determing how transmission risk may change as climate changes. Overall, our results indicate that considerable work is required to better understand ways in which global malaria incidence and distribution may alter with climate change. In particular, we show that the roles of seasonality, stochasticity and variability in environmental variables, as well as ultimately anthropogenic effects, require further study. The work presented here offers a theoretical framework upon which this future research may be developed.

Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

NASA Astrophysics Data System (ADS)

Williams, Paul; Howe, Nicola; Gregory, Jonathan; Smith, Robin; Joshi, Manoj

2017-04-01

In climate simulations, the impacts of the subgrid scales on the resolved scales are conventionally represented using deterministic closure schemes, which assume that the impacts are uniquely determined by the resolved scales. Stochastic parameterization relaxes this assumption, by sampling the subgrid variability in a computationally inexpensive manner. This study shows that the simulated climatological state of the ocean is improved in many respects by implementing a simple stochastic parameterization of ocean eddies into a coupled atmosphere-ocean general circulation model. Simulations from a high-resolution, eddy-permitting ocean model are used to calculate the eddy statistics needed to inject realistic stochastic noise into a low-resolution, non-eddy-permitting version of the same model. A suite of four stochastic experiments is then run to test the sensitivity of the simulated climate to the noise definition by varying the noise amplitude and decorrelation time within reasonable limits. The addition of zero-mean noise to the ocean temperature tendency is found to have a nonzero effect on the mean climate. Specifically, in terms of the ocean temperature and salinity fields both at the surface and at depth, the noise reduces many of the biases in the low-resolution model and causes it to more closely resemble the high-resolution model. The variability of the strength of the global ocean thermohaline circulation is also improved. It is concluded that stochastic ocean perturbations can yield reductions in climate model error that are comparable to those obtained by refining the resolution, but without the increased computational cost. Therefore, stochastic parameterizations of ocean eddies have the potential to significantly improve climate simulations. Reference Williams PD, Howe NJ, Gregory JM, Smith RS, and Joshi MM (2016) Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies. Journal of Climate, 29, 8763-8781. http://dx.doi.org/10.1175/JCLI-D-15-0746.1

Challenges in global modeling of wetland extent and wetland methane dynamics

NASA Astrophysics Data System (ADS)

Spahni, R.; Melton, J. R.; Wania, R.; Stocker, B. D.; Zürcher, S.; Joos, F.

2012-12-01

Global wetlands are known to be climate sensitive, and are the largest natural emitters of methane (CH4). Increased wetland CH4 emissions could act as a positive feedback to future warming. Modelling of global wetland extent and wetland CH4 dynamics remains a challenge. Here we present results from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) that investigated our present ability to simulate large scale wetland characteristics (e.g. wetland type, water table, carbon cycling, gas transport, etc.) and corresponding CH4 emissions. Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The WETCHIMP experiments showed that while models disagree in spatial and temporal patterns of simulated CH4 emissions and wetland areal extent, they all do agree on a strong positive response to increased carbon dioxide concentrations. WETCHIMP made clear that we currently lack observation data sets that are adequate to evaluate model CH4 soil-atmosphere fluxes at a spatial scale comparable to model grid cells. Thus there are substantial parameter and structural uncertainties in large-scale CH4 emission models. As an illustration of the implications of CH4 emissions on climate we show results of the LPX-Bern model, as one of the models participating in WETCHIMP. LPX-Bern is forced with observed 20th century climate and climate output from an ensemble of five comprehensive climate models for a low and a high emission scenario till 2100 AD. In the high emission scenario increased substrate availability for methanogenesis due to a strong stimulation of net primary productivity, and faster soil turnover leads to an amplification of CH4 emissions with the sharpest increase in peatlands (+180% compared to present). Combined with prescribed anthropogenic CH4 emissions, simulated atmospheric CH4 concentration reaches ~4500 ppbv by 2100 AD, about 800 ppbv more than in standard IPCC scenarios. This represents a significant contribution to radiative forcing of global climate.

Progress Towards Achieving the Challenge of Indian Summer Monsoon Climate Simulation in a Coupled Ocean-Atmosphere Model

NASA Astrophysics Data System (ADS)

Hazra, Anupam; Chaudhari, Hemantkumar S.; Saha, Subodh Kumar; Pokhrel, Samir; Goswami, B. N.

2017-10-01

Simulation of the spatial and temporal structure of the monsoon intraseasonal oscillations (MISOs), which have effects on the seasonal mean and annual cycle of Indian summer monsoon (ISM) rainfall, remains a grand challenge for the state-of-the-art global coupled models. Biases in simulation of the amplitude and northward propagation of MISOs and related dry rainfall bias over ISM region in climate models are limiting the current skill of monsoon prediction. Recent observations indicate that the convective microphysics of clouds may be critical in simulating the observed MISOs. The hypothesis is strongly supported by high fidelity in simulation of the amplitude and space-time spectra of MISO by a coupled climate model, when our physically based modified cloud microphysics scheme is implemented in conjunction with a modified new Simple Arakawa Schubert (nSAS) convective parameterization scheme. Improved simulation of MISOs appears to have been aided by much improved simulation of the observed high cloud fraction and convective to stratiform rain fractions and resulted into a much improved simulation of the ISM rainfall, monsoon onset, and the annual cycle.

Named Data Networking in Climate Research and HEP Applications

NASA Astrophysics Data System (ADS)

Shannigrahi, Susmit; Papadopoulos, Christos; Yeh, Edmund; Newman, Harvey; Jerzy Barczyk, Artur; Liu, Ran; Sim, Alex; Mughal, Azher; Monga, Inder; Vlimant, Jean-Roch; Wu, John

2015-12-01

The Computing Models of the LHC experiments continue to evolve from the simple hierarchical MONARC[2] model towards more agile models where data is exchanged among many Tier2 and Tier3 sites, relying on both large scale file transfers with strategic data placement, and an increased use of remote access to object collections with caching through CMS's AAA, ATLAS' FAX and ALICE's AliEn projects, for example. The challenges presented by expanding needs for CPU, storage and network capacity as well as rapid handling of large datasets of file and object collections have pointed the way towards future more agile pervasive models that make best use of highly distributed heterogeneous resources. In this paper, we explore the use of Named Data Networking (NDN), a new Internet architecture focusing on content rather than the location of the data collections. As NDN has shown considerable promise in another data intensive field, Climate Science, we discuss the similarities and differences between the Climate and HEP use cases, along with specific issues HEP faces and will face during LHC Run2 and beyond, which NDN could address.

Constraints and Opportunities in GCM Model Development

NASA Technical Reports Server (NTRS)

Schmidt, Gavin; Clune, Thomas

2010-01-01

Over the past 30 years climate models have evolved from relatively simple representations of a few atmospheric processes to complex multi-disciplinary system models which incorporate physics from bottom of the ocean to the mesopause and are used for seasonal to multi-million year timescales. Computer infrastructure over that period has gone from punchcard mainframes to modern parallel clusters. Constraints of working within an ever evolving research code mean that most software changes must be incremental so as not to disrupt scientific throughput. Unfortunately, programming methodologies have generally not kept pace with these challenges, and existing implementations now present a heavy and growing burden on further model development as well as limiting flexibility and reliability. Opportunely, advances in software engineering from other disciplines (e.g. the commercial software industry) as well as new generations of powerful development tools can be incorporated by the model developers to incrementally and systematically improve underlying implementations and reverse the long term trend of increasing development overhead. However, these methodologies cannot be applied blindly, but rather must be carefully tailored to the unique characteristics of scientific software development. We will discuss the need for close integration of software engineers and climate scientists to find the optimal processes for climate modeling.

The epistemological status of general circulation models

NASA Astrophysics Data System (ADS)

Loehle, Craig

2018-03-01

Forecasts of both likely anthropogenic effects on climate and consequent effects on nature and society are based on large, complex software tools called general circulation models (GCMs). Forecasts generated by GCMs have been used extensively in policy decisions related to climate change. However, the relation between underlying physical theories and results produced by GCMs is unclear. In the case of GCMs, many discretizations and approximations are made, and simulating Earth system processes is far from simple and currently leads to some results with unknown energy balance implications. Statistical testing of GCM forecasts for degree of agreement with data would facilitate assessment of fitness for use. If model results need to be put on an anomaly basis due to model bias, then both visual and quantitative measures of model fit depend strongly on the reference period used for normalization, making testing problematic. Epistemology is here applied to problems of statistical inference during testing, the relationship between the underlying physics and the models, the epistemic meaning of ensemble statistics, problems of spatial and temporal scale, the existence or not of an unforced null for climate fluctuations, the meaning of existing uncertainty estimates, and other issues. Rigorous reasoning entails carefully quantifying levels of uncertainty.

Improved Decadal Climate Prediction in the North Atlantic using EnOI-Assimilated Initial Condition

NASA Astrophysics Data System (ADS)

Li, Q.; Xin, X.; Wei, M.; Zhou, W.

2017-12-01

Decadal prediction experiments of Beijing Climate Center climate system model version 1.1(BCC-CSM1.1) participated in Coupled Model Intercomparison Project Phase 5 (CMIP5) had poor skill in extratropics of the North Atlantic, the initialization of which was done by relaxing modeled ocean temperature to the Simple Ocean Data Assimilation (SODA) reanalysis data. This study aims to improve the prediction skill of this model by using the assimilation technique in the initialization. New ocean data are firstly generated by assimilating the sea surface temperature (SST) of the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset to the ocean model of BCC-CSM1.1 via Ensemble Optimum Interpolation (EnOI). Then a suite of decadal re-forecasts launched annually over the period 1961-2005 is carried out with simulated ocean temperature restored to the assimilated ocean data. Comparisons between the re-forecasts and previous CMIP5 forecasts show that the re-forecasts are more skillful in mid-to-high latitude SST of the North Atlantic. Improved prediction skill is also found for the Atlantic multi-decadal Oscillation (AMO), which is consistent with the better skill of Atlantic meridional overturning circulation (AMOC) predicted by the re-forecasts. We conclude that the EnOI assimilation generates better ocean data than the SODA reanalysis for initializing decadal climate prediction of BCC-CSM1.1 model.

Interactive coupling of regional climate and sulfate aerosol models over eastern Asia

NASA Astrophysics Data System (ADS)

Qian, Yun; Giorgi, Filippo

1999-03-01

The NCAR regional climate model (RegCM) is interactively coupled to a simple radiatively active sulfate aerosol model over eastern Asia. Both direct and indirect aerosol effects are represented. The coupled model system is tested for two simulation periods, November 1994 and July 1995, with aerosol sources representative of present-day anthropogenic sulfur emissions. The model sensitivity to the intensity of the aerosol source is also studied. The main conclusions from our work are as follows: (1) The aerosol distribution and cycling processes show substantial regional spatial variability, and temporal variability varying on a range of scales, from the diurnal scale of boundary layer and cumulus cloud evolution to the 3-10 day scale of synoptic scale events and the interseasonal scale of general circulation features; (2) both direct and indirect aerosol forcings have regional effects on surface climate; (3) the regional climate response to the aerosol forcing is highly nonlinear, especially during the summer, due to the interactions with cloud and precipitation processes; (4) in our simulations the role of the aerosol indirect effects is dominant over that of direct effects; (5) aerosol-induced feedback processes can affect the aerosol burdens at the subregional scale. This work constitutes the first step in a long term research project aimed at coupling a hierarchy of chemistry/aerosol models to the RegCM over the eastern Asia region.

Improved climate model evaluation using a new, 750-year Antarctic-wide snow accumulation product

NASA Astrophysics Data System (ADS)

Medley, B.; Thomas, E. R.

2017-12-01

Snow that accumulates over the cold, dry grounded ice of Antarctica is an important component of its mass balance, mitigating the ice sheet's contribution to sea level. Secular trends in accumulation not only result trends in the mass balance of the Antarctic Ice Sheet, but also directly and indirectly impact surface height changes. Long-term and spatiotemporally complete records of snow accumulation are needed to understand part and present Antarctic-wide mass balance, to convert from altimetry derived volume change to mass change, and to evaluate the ability of climate models to reproduce the observed climate change. We need measurements in both time and space, yet they typically sample one dimension at the expense of the other. Here, we develop a spatially complete, annually resolved snow accumulation product for the Antarctic Ice Sheet over the past 750 years by combining a newly compiled database of ice core accumulation records with climate model output. We mainly focus on climate model evaluation. Because the product spans several centuries, we can evaluate model ability in representing the preindustrial as well as present day accumulation change. Significant long-term trends in snow accumulation are found over the Ross and Bellingshausen Sea sectors of West Antarctica, the Antarctic Peninsula, and several sectors in East Antarctica. These results suggest that change is more complex over the Antarctic Ice Sheet than a simple uniform change (i.e., more snowfall in a warming world), which highlights the importance of atmospheric circulation as a major driver of change. By evaluating several climate models' ability to reproduce the observed trends, we can deduce whether their projections are reasonable or potentially biased where the latter would result in a misrepresentation of the Antarctic contribution to sea level.

Java Climate Model: a tool for interaction between science, policy and citizens, to avoid dangerous anthropogenic interference in the climate system

NASA Astrophysics Data System (ADS)

Matthews, B.

2003-04-01

To reach an effective global agreement to help avoid "dangerous anthropogenic interference in the climate system" (UNFCCC article 2) we must balance many complex interacting issues, and also inspire the active engagement of citizens around the world. So we have to transfer understanding back from computers and experts, into the ultimate "integrated assessment model" which remains the global network of human heads. The Java Climate Model (JCM) tries to help provide a quantitative framework for this global dialogue, by enabling anybody to explore many mitigation policy options and scientific uncertainties simply by adjusting parameter controls with a mouse in a web browser. The instant response on linked plots helps to demonstrate cause and effect, and the sensitivity to various assumptions, risk and value judgements. JCM implements the same simple models and formulae as used by IPCC for the TAR projections, in efficient modular structure, including carbon cycle and atmospheric chemistry, radiative forcing, changes in temperature and sealevel, including some feedbacks. As well as explore the SRES scenarios, the user can create a wide variety of inverse scenarios for stabilising CO2, forcing, or temperature. People ask how local emissions which they can control, may influence the vast global natural and human systems, and change local climate impacts which affect them directly. JCM includes regional emissions and socioeconomic data, and scaled climate impact maps. However to complete this loop in a fast interactive model is a challenge! For transparency and accessibility, pop-up information is provided in ten languages, and documentation ranges from key cross-cutting questions, to them details of the model formulae, including all source code.

The sensitivity of the Arctic sea ice to orbitally induced insolation changes: a study of the mid-Holocene Paleoclimate Modelling Intercomparison Project 2 and 3 simulations

NASA Astrophysics Data System (ADS)

Berger, M.; Brandefelt, J.; Nilsson, J.

2013-04-01

In the present work the Arctic sea ice in the mid-Holocene and the pre-industrial climates are analysed and compared on the basis of climate-model results from the Paleoclimate Modelling Intercomparison Project phase 2 (PMIP2) and phase 3 (PMIP3). The PMIP3 models generally simulate smaller and thinner sea-ice extents than the PMIP2 models both for the pre-industrial and the mid-Holocene climate. Further, the PMIP2 and PMIP3 models all simulate a smaller and thinner Arctic summer sea-ice cover in the mid-Holocene than in the pre-industrial control climate. The PMIP3 models also simulate thinner winter sea ice than the PMIP2 models. The winter sea-ice extent response, i.e. the difference between the mid-Holocene and the pre-industrial climate, varies among both PMIP2 and PMIP3 models. Approximately one half of the models simulate a decrease in winter sea-ice extent and one half simulates an increase. The model-mean summer sea-ice extent is 11 % (21 %) smaller in the mid-Holocene than in the pre-industrial climate simulations in the PMIP2 (PMIP3). In accordance with the simple model of Thorndike (1992), the sea-ice thickness response to the insolation change from the pre-industrial to the mid-Holocene is stronger in models with thicker ice in the pre-industrial climate simulation. Further, the analyses show that climate models for which the Arctic sea-ice responses to increasing atmospheric CO2 concentrations are similar may simulate rather different sea-ice responses to the change in solar forcing between the mid-Holocene and the pre-industrial. For two specific models, which are analysed in detail, this difference is found to be associated with differences in the simulated cloud fractions in the summer Arctic; in the model with a larger cloud fraction the effect of insolation change is muted. A sub-set of the mid-Holocene simulations in the PMIP ensemble exhibit open water off the north-eastern coast of Greenland in summer, which can provide a fetch for surface waves. This is in broad agreement with recent analyses of sea-ice proxies, indicating that beach-ridges formed on the north-eastern coast of Greenland during the early- to mid-Holocene.

Mass Balance of Multiyear Sea Ice in the Southern Beaufort Sea

DTIC Science & Technology

2012-09-30

datasets. Table 1 lists the primary data sources to be used. To determine sources and sinks of MY ice, we use a simple model of MY ice circulation, which is...shown in Figure 1. In this model , we consider the Beaufort Sea to consist of four zones defined by mean drift of sea ice in summer and winter, such...Healy/Louis S. St. Laurant cruises 1 Seasonal Ice Zone Observing Network 2 Polar Airborne Measurements and Arctic Regional Climate Model

A New Framework for Cumulus Parametrization - A CPT in action

NASA Astrophysics Data System (ADS)

Jakob, C.; Peters, K.; Protat, A.; Kumar, V.

2016-12-01

The representation of convection in climate model remains a major Achilles Heel in our pursuit of better predictions of global and regional climate. The basic principle underpinning the parametrisation of tropical convection in global weather and climate models is that there exist discernible interactions between the resolved model scale and the parametrised cumulus scale. Furthermore, there must be at least some predictive power in the larger scales for the statistical behaviour on small scales for us to be able to formally close the parametrised equations. The presentation will discuss a new framework for cumulus parametrisation based on the idea of separating the prediction of cloud area from that of velocity. This idea is put into practice by combining an existing multi-scale stochastic cloud model with observations to arrive at the prediction of the area fraction for deep precipitating convection. Using mid-tropospheric humidity and vertical motion as predictors, the model is shown to reproduce the observed behaviour of both mean and variability of deep convective area fraction well. The framework allows for the inclusion of convective organisation and can - in principle - be made resolution-aware or resolution-independent. When combined with simple assumptions about cloud-base vertical motion the model can be used as a closure assumption in any existing cumulus parametrisation. Results of applying this idea in the the ECHAM model indicate significant improvements in the simulation of tropical variability, including but not limited to the MJO. This presentation will highlight how the close collaboration of the observational, theoretical and model development community in the spirit of the climate process teams can lead to significant progress in long-standing issues in climate modelling while preserving the freedom of individual groups in pursuing their specific implementation of an agreed framework.

Vulnerability assessment and risk level of ecosystem services for climate change impacts and adaptation in the High-Atlas mountain of Morocco

NASA Astrophysics Data System (ADS)

Messouli, Mohammed; Bounoua, Lahouari; Babqiqi, Abdelaziz; Ben Salem, Abdelkrim; Yacoubi-Khebiza, Mohammed

2010-05-01

Moroccan mountain biomes are considered endangered due to climate change that affects directly or indirectly different key features (biodiversity, snow cover, run-off processes, and water availability). The present article describes the strategy for achieving collaboration between natural and social scientists, stakeholders, decision-makers, and other societal groups, in order to carry out an integrated assessment of climate change in the High-Atlas Mountains of Morocco, with an emphasis on vulnerability and adaptation. We will use a robust statistical technique to dynamically downscale outputs from the IPCC climates models to the regional study area. Statistical downscaling provides a powerful method for deriving local-to-regional scale information on climate variables from large-scale climate model outputs. The SDSM will be used to produce the high resolution climate change scenarios from climate model outputs at low resolution. These data will be combined with socio-economic attributes such as the amount of water used for irrigation of agricultural lands, agricultural practices and phenology, cost of water delivery and non-market values of produced goods and services. This study, also analyzed spatial and temporal in land use/land cover changes (LUCC) in a typical watershed covering an area of 203 km2 by comparing classified satellite images from 1976, 1989 and 2000 coupled by GIS analyses and also investigated changes in the shape of land use patches over the period. The GIS-platform, which compiles gridded spatial and temporal information of environmental, socio-economic and biophysical data is used to map vulnerability assessment and risk levels over a wide region of Southern High-Atlas. For each scenario, we will derive and analyze near future (10-15 years) key climate indicators strongly related to sustainable management of ecosystem goods and services. Forest cover declined at an average rate of 0.35 ha per year due to timber extraction, cultivation, grazing, and urbanization processes. Historically, cultivation has resulted in such a high loss of plant communities in lowlands that regional diversity has been threatened. Grazing has increased due to low labor costs and economic policies that provide incentives for cattle production in Morocco. Finally to address the interaction among ecosystem services principles, we will use the Integrated Valuation of Ecosystem Services and Tradeoffs tool (InVEST) recently developed by the Natural Capital Project. The "Tier 1" modes are theoretically grounded but simple, and are designed for areas where few data are available. The most useful applications of the simple, Tier 1 models are to identify areas of high and low ecosystem service production and biodiversity across the Mountain and illuminate the tradeoffs and synergies among services under current or future conditions. While some Tier 1 models give outputs in absolute terms, others return relative indices of importance.

Analytically based forward and inverse models of fluvial landscape evolution during temporally continuous climatic and tectonic variations

NASA Astrophysics Data System (ADS)

Goren, Liran; Petit, Carole

2017-04-01

Fluvial channels respond to changing tectonic and climatic conditions by adjusting their patterns of erosion and relief. It is therefore expected that by examining these patterns, we can infer the tectonic and climatic conditions that shaped the channels. However, the potential interference between climatic and tectonic signals complicates this inference. Within the framework of the stream power model that describes incision rate of mountainous bedrock rivers, climate variability has two effects: it influences the erosive power of the river, causing local slope change, and it changes the fluvial response time that controls the rate at which tectonically and climatically induced slope breaks are communicated upstream. Because of this dual role, the fluvial response time during continuous climate change has so far been elusive, which hinders our understanding of environmental signal propagation and preservation in the fluvial topography. An analytic solution of the stream power model during general tectonic and climatic histories gives rise to a new definition of the fluvial response time. The analytic solution offers accurate predictions for landscape evolution that are hard to achieve with classical numerical schemes and thus can be used to validate and evaluate the accuracy of numerical landscape evolution models. The analytic solution together with the new definition of the fluvial response time allow inferring either the tectonic history or the climatic history from river long profiles by using simple linear inversion schemes. Analytic study of landscape evolution during periodic climate change reveals that high frequency (10-100 kyr) climatic oscillations with respect to the response time, such as Milankovitch cycles, are not expected to leave significant fingerprints in the upstream reaches of fluvial channels. Linear inversion schemes are applied to the Tinee river tributaries in the southern French Alps, where tributary long profiles are used to recover the incision rate history of the Tinee main trunk. Inversion results show periodic, high incision rate pulses, which are correlated with interglacial episodes. Similar incision rate histories are recovered for the past 100 kyr when assuming constant climatic conditions or periodic climatic oscillations, in agreement with theoretical predictions.

Modeling Methods

USGS Publications Warehouse

Healy, Richard W.; Scanlon, Bridget R.

2010-01-01

Simulation models are widely used in all types of hydrologic studies, and many of these models can be used to estimate recharge. Models can provide important insight into the functioning of hydrologic systems by identifying factors that influence recharge. The predictive capability of models can be used to evaluate how changes in climate, water use, land use, and other factors may affect recharge rates. Most hydrological simulation models, including watershed models and groundwater-flow models, are based on some form of water-budget equation, so the material in this chapter is closely linked to that in Chapter 2. Empirical models that are not based on a water-budget equation have also been used for estimating recharge; these models generally take the form of simple estimation equations that define annual recharge as a function of precipitation and possibly other climatic data or watershed characteristics.Model complexity varies greatly. Some models are simple accounting models; others attempt to accurately represent the physics of water movement through each compartment of the hydrologic system. Some models provide estimates of recharge explicitly; for example, a model based on the Richards equation can simulate water movement from the soil surface through the unsaturated zone to the water table. Recharge estimates can be obtained indirectly from other models. For example, recharge is a parameter in groundwater-flow models that solve for hydraulic head (i.e. groundwater level). Recharge estimates can be obtained through a model calibration process in which recharge and other model parameter values are adjusted so that simulated water levels agree with measured water levels. The simulation that provides the closest agreement is called the best fit, and the recharge value used in that simulation is the model-generated estimate of recharge.

The use of Meteonorm weather generator for climate change studies

NASA Astrophysics Data System (ADS)

Remund, J.; Müller, S. C.; Schilter, C.; Rihm, B.

2010-09-01

The global climatological database Meteonorm (www.meteonorm.com) is widely used as meteorological input for simulation of solar applications and buildings. It's a combination of a climate database, a spatial interpolation tool and a stochastic weather generator. Like this typical years with hourly or minute time resolution can be calculated for any site. The input of Meteonorm for global radiation is the Global Energy Balance Archive (GEBA, http://proto-geba.ethz.ch). All other meteorological parameters are taken from databases of WMO and NCDC (periods 1961-90 and 1996-2005). The stochastic generation of global radiation is based on a Markov chain model for daily values and an autoregressive model for hourly and minute values (Aguiar and Collares-Pereira, 1988 and 1992). The generation of temperature is based on global radiation and measured distribution of daily temperature values of approx. 5000 sites. Meteonorm generates also additional parameters like precipitation, wind speed or radiation parameters like diffuse and direct normal irradiance. Meteonorm can also be used for climate change studies. Instead of climate values, the results of IPCC AR4 results are used as input. From all 18 public models an average has been made at a resolution of 1°. The anomalies of the parameters temperature, precipitation and global radiation and the three scenarios B1, A1B and A2 have been included. With the combination of Meteonorm's current database 1961-90, the interpolation algorithms and the stochastic generation typical years can be calculated for any site, for different scenarios and for any period between 2010 and 2200. From the analysis of variations of year to year and month to month variations of temperature, precipitation and global radiation of the past ten years as well of climate model forecasts (from project prudence, http://prudence.dmi.dk) a simple autoregressive model has been formed which is used to generate realistic monthly time series of future periods. Meteonorm can therefore be used as a relatively simple method to enhance the spatial and temporal resolution instead of using complicated and time consuming downscaling methods based on regional climate models. The combination of Meteonorm, gridded historical (based on work of Luterbach et al.) and IPCC results has been used for studies of vegetation simulation between 1660 and 2600 (publication of first version based on IS92a scenario and limited time period 1950 - 2100: http://www.pbl.nl/images/H5_Part2_van%20CCE_opmaak%28def%29_tcm61-46625.pdf). It's also applicable for other adaptation studies for e.g. road surfaces or building simulation. In Meteonorm 6.1 one scenario (IS92a) and one climate model has been included (Hadley CM3). In the new Meteonorm 7 (coming spring 2011) the model averages of the three above mentioned scenarios of the IPCC AR4 will be included.

WEPP FuME Analysis for a North Idaho Site

Treesearch

William Elliot; Ina Sue Miller; David Hall

2007-01-01

A computer interface has been developed to assist with analyzing soil erosion rates associated with fuel management activities. This interface uses the Water Erosion Prediction Project (WEPP) model to predict sediment yields from hillslopes and road segments to the stream network. The simple interface has a large database of climates, vegetation files and forest soil...

Models and the paleo record of biome responses to glacial climate and CO2

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

Prentice; Colin, I.; Haxeltine

1995-06-01

Continental-scale reconstructions of the distribution of biomes at the last glacial maximum (LGM) indicate big changes, which can primarily be explained by climate. The climate was different from today mainly because of a combination of low concentrations of CO{sub 2} and other greenhouse gases and the presence of large continental ice sheets. Atmospheric general circulation model (AGCM) simulations, driven by these factors and linked to simple biome models in {open_quotes}diagnostic{close_quotes} mode, account for the broad outlines of the changes in vegetation patterns, including encroachment of C4 grasslands and savannas on what are now tropical forests. Physiological effects of low CO{submore » 2} might also have played a role by altering the partitioning of precipitation to evapotranspiration and runoff, and altering the competitive balance of C3 and C4 plants. Such effects have not been quantified until recently, with the development of integrated biome/biochemistry models like those used in the VEMAP project. In these models, vegetation composition affects the coupled C and H{sub 2}O fluxes, which in turn influence the competitive balance of the constituent plant types. The relative importance of climatic and physiological effects of CO{sub 2} on biome distributions is a key issue for the future. This is gives added impetus to research that aims to exploit the potential of palaeo, data, through global data synthesis projects like BIOME 6000, to provide objective benchmarks against which to test models of the biosphere and climate.« less

Global Climatic Controls On Leaf Size

NASA Astrophysics Data System (ADS)

Wright, I. J.; Prentice, I. C.; Dong, N.; Maire, V.

2015-12-01

Since the 1890s it's been known that the wet tropics harbour plants with exceptionally large leaves. Yet the observed latitudinal gradient of leaf size has never been fully explained: it is still unclear which aspects of climate are most important for understanding geographic trends in leaf size, a trait that varies many thousand-fold among species. The key is the leaf-to-air temperature difference, which depends on the balance of energy inputs (irradiance) and outputs (transpirational cooling, losses to the night sky). Smaller leaves track air temperatures more closely than larger leaves. Widely cited optimality-based theories predict an advantage for smaller leaves in dry environments, where transpiration is restricted, but are silent on the latitudinal gradient. We aimed to characterize and explain the worldwide pattern of leaf size. Across 7900 species from 651 sites, here we show that: large-leaved species predominate in wet, hot, sunny environments; smaller-leaved species typify hot, sunny environments only when arid; small leaves are required to avoid freezing in high latitudes and at high elevation, and to avoid overheating in dry environments. This simple pattern was unclear in earlier, more limited analyses. We present a simple but robust, fresh approach to energy-balance modelling for both day-time and night-time leaf-to-air temperature differences, and thus risk of overheating and of frost damage. Our analysis shows night-chilling is important as well as day-heating, and simplifies leaf temperature modelling. It provides both a framework for modelling leaf size constraints, and a solution to one of the oldest conundrums in ecology. Although the path forward is not yet fully clear, because of its role in controlling leaf temperatures we suggest that climate-related leaf size constraints could usefully feature in the next generation of land ecosystem models.

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.

Estimating global distribution of boreal, temperate, and tropical tree plant functional types using clustering techniques

NASA Astrophysics Data System (ADS)

Wang, Audrey; Price, David T.

2007-03-01

A simple integrated algorithm was developed to relate global climatology to distributions of tree plant functional types (PFT). Multivariate cluster analysis was performed to analyze the statistical homogeneity of the climate space occupied by individual tree PFTs. Forested regions identified from the satellite-based GLC2000 classification were separated into tropical, temperate, and boreal sub-PFTs for use in the Canadian Terrestrial Ecosystem Model (CTEM). Global data sets of monthly minimum temperature, growing degree days, an index of climatic moisture, and estimated PFT cover fractions were then used as variables in the cluster analysis. The statistical results for individual PFT clusters were found consistent with other global-scale classifications of dominant vegetation. As an improvement of the quantification of the climatic limitations on PFT distributions, the results also demonstrated overlapping of PFT cluster boundaries that reflected vegetation transitions, for example, between tropical and temperate biomes. The resulting global database should provide a better basis for simulating the interaction of climate change and terrestrial ecosystem dynamics using global vegetation models.

Multiple abiotic stimuli are integrated in the regulation of rice gene expression under field conditions.

PubMed

Plessis, Anne; Hafemeister, Christoph; Wilkins, Olivia; Gonzaga, Zennia Jean; Meyer, Rachel Sarah; Pires, Inês; Müller, Christian; Septiningsih, Endang M; Bonneau, Richard; Purugganan, Michael

2015-11-26

Plants rely on transcriptional dynamics to respond to multiple climatic fluctuations and contexts in nature. We analyzed the genome-wide gene expression patterns of rice (Oryza sativa) growing in rainfed and irrigated fields during two distinct tropical seasons and determined simple linear models that relate transcriptomic variation to climatic fluctuations. These models combine multiple environmental parameters to account for patterns of expression in the field of co-expressed gene clusters. We examined the similarities of our environmental models between tropical and temperate field conditions, using previously published data. We found that field type and macroclimate had broad impacts on transcriptional responses to environmental fluctuations, especially for genes involved in photosynthesis and development. Nevertheless, variation in solar radiation and temperature at the timescale of hours had reproducible effects across environmental contexts. These results provide a basis for broad-based predictive modeling of plant gene expression in the field.

The fate of threatened coastal dune habitats in Italy under climate change scenarios.

PubMed

Prisco, Irene; Carboni, Marta; Acosta, Alicia T R

2013-01-01

Coastal dunes worldwide harbor threatened habitats characterized by high diversity in terms of plant communities. In Italy, recent assessments have highlighted the insufficient state of conservation of these habitats as defined by the EU Habitats Directive. The effects of predicted climate change could have dramatic consequences for coastal environments in the near future. An assessment of the efficacy of protection measures under climate change is thus a priority. Here, we have developed environmental envelope models for the most widespread dune habitats in Italy, following two complementary approaches: an "indirect" plant-species-based one and a simple "direct" one. We analyzed how habitats distribution will be altered under the effects of two climate change scenarios and evaluated if the current Italian network of protected areas will be effective in the future after distribution shifts. While modeling dune habitats with the "direct" approach was unsatisfactory, "indirect" models had a good predictive performance, highlighting the importance of using species' responses to climate change for modeling these habitats. The results showed that habitats closer to the sea may even increase their geographical distribution in the near future. The transition dune habitat is projected to remain stable, although mobile and fixed dune habitats are projected to lose most of their actual geographical distribution, the latter being more sensitive to climate change effects. Gap analysis highlighted that the habitats' distribution is currently adequately covered by protected areas, achieving the conservation target. However, according to predictions, protection level for mobile and fixed dune habitats is predicted to drop drastically under the climate change scenarios which we examined. Our results provide useful insights for setting management priorities and better addressing conservation efforts to preserve these threatened habitats in future.

The Fate of Threatened Coastal Dune Habitats in Italy under Climate Change Scenarios

PubMed Central

Prisco, Irene; Carboni, Marta; Acosta, Alicia T. R.

2013-01-01

Coastal dunes worldwide harbor threatened habitats characterized by high diversity in terms of plant communities. In Italy, recent assessments have highlighted the insufficient state of conservation of these habitats as defined by the EU Habitats Directive. The effects of predicted climate change could have dramatic consequences for coastal environments in the near future. An assessment of the efficacy of protection measures under climate change is thus a priority. Here, we have developed environmental envelope models for the most widespread dune habitats in Italy, following two complementary approaches: an “indirect” plant-species-based one and a simple “direct” one. We analyzed how habitats distribution will be altered under the effects of two climate change scenarios and evaluated if the current Italian network of protected areas will be effective in the future after distribution shifts. While modeling dune habitats with the “direct” approach was unsatisfactory, “indirect” models had a good predictive performance, highlighting the importance of using species’ responses to climate change for modeling these habitats. The results showed that habitats closer to the sea may even increase their geographical distribution in the near future. The transition dune habitat is projected to remain stable, although mobile and fixed dune habitats are projected to lose most of their actual geographical distribution, the latter being more sensitive to climate change effects. Gap analysis highlighted that the habitats’ distribution is currently adequately covered by protected areas, achieving the conservation target. However, according to predictions, protection level for mobile and fixed dune habitats is predicted to drop drastically under the climate change scenarios which we examined. Our results provide useful insights for setting management priorities and better addressing conservation efforts to preserve these threatened habitats in future. PMID:23874787

The Amazon rainforest, climate change, and drought: How will what is below the surface affect the climate of tropical South America?

NASA Astrophysics Data System (ADS)

Harper, A.; Denning, A. S.; Baker, I.; Randall, D.; Dazlich, D.

2008-12-01

Several climate models have predicted an increase in long-term droughts in tropical South America due to increased greenhouse gases in the atmosphere. Although the Amazon rainforest is resilient to seasonal drought, multi-year droughts pose a definite problem for the ecosystem's health. Furthermore, drought- stressed vegetation participates in feedbacks with the atmosphere that can exacerbate drought. Namely, reduced evapotranspiration further dries out the atmosphere and affects the regional climate. Trees in the rainforest survive seasonal drought by using deep roots to access adequate stores of soil moisture. We investigate the climatic impacts of deep roots and soil moisture by coupling the Simple Biosphere (SiB3) model to Colorado State University's general circulation model (BUGS5). We compare two versions of SiB3 in the GCM during years with anomalously low rainfall. The first has strong vegetative stress due to soil moisture limitations. The second experiences less stress and has more realistic representations of surface biophysics. In the model, basin-wide reductions in soil moisture stress result in increased evapotranspiration, precipitation, and moisture recycling in the Amazon basin. In the savannah region of southeastern Brazil, the unstressed version of SiB3 produces decreased precipitation and weaker moisture flux, which is more in-line with observations. The improved simulation of precipitation and evaporation also produces a more realistic Bolivian high and Nordeste low. These changes highlight the importance of subsurface biophysics for the Amazonian climate. The presence of deep roots and soil moisture will become even more important if climate change brings more frequent droughts to this region in the future.

Influence of external forcings on abrupt millennial-scale climate changes: a statistical modelling study

NASA Astrophysics Data System (ADS)

Mitsui, Takahito; Crucifix, Michel

2017-04-01

The last glacial period was punctuated by a series of abrupt climate shifts, the so-called Dansgaard-Oeschger (DO) events. The frequency of DO events varied in time, supposedly because of changes in background climate conditions. Here, the influence of external forcings on DO events is investigated with statistical modelling. We assume two types of simple stochastic dynamical systems models (double-well potential-type and oscillator-type), forced by the northern hemisphere summer insolation change and/or the global ice volume change. The model parameters are estimated by using the maximum likelihood method with the NGRIP Ca^{2+} record. The stochastic oscillator model with at least the ice volume forcing reproduces well the sample autocorrelation function of the record and the frequency changes of warming transitions in the last glacial period across MISs 2, 3, and 4. The model performance is improved with the additional insolation forcing. The BIC scores also suggest that the ice volume forcing is relatively more important than the insolation forcing, though the strength of evidence depends on the model assumption. Finally, we simulate the average number of warming transitions in the past four glacial periods, assuming the model can be extended beyond the last glacial, and compare the result with an Iberian margin sea-surface temperature (SST) record (Martrat et al. in Science 317(5837): 502-507, 2007). The simulation result supports the previous observation that abrupt millennial-scale climate changes in the penultimate glacial (MIS 6) are less frequent than in the last glacial (MISs 2-4). On the other hand, it suggests that the number of abrupt millennial-scale climate changes in older glacial periods (MISs 6, 8, and 10) might be larger than inferred from the SST record.

Prehistoric land use and Neolithisation in Europe in the context of regional climate events

NASA Astrophysics Data System (ADS)

Lemmen, C.; Wirtz, K. W.; Gronenborn, D.

2009-04-01

We present a simple, adaptation-driven, spatially explicit model of pre-Bronze age socio-technological change, called the Global Land Use and Technological Evolution Simulator (GLUES). The socio-technological realm is described by three characteristic traits: available technology, subsistence style ratio, and economic diversity. Human population and culture develop in the context of global paleoclimate and regional paleoclimate events. Global paleoclimate is derived from CLIMBER-2 Earth System Model anomalies superimposed on the IIASA temperature and precipitation database. Regional a forcing is provided by abrupt climate deteriorations from a compilation of 138 long-term high-resolution climate proxy time series from mostly terrestrial and near-shore archives. The GLUES simulator provides for a novel way to explore the interplay between climate, climate change, and cultural evolution both on the Holocene timescale as well as for short-term extreme event periods. We sucessfully simulate the migration of people and the diffusion of Neolithic technology from the Near East into Europe in the period 12000-4000 a BP. We find good agreement with recent archeological compilations of Western Eurasian Neolithic sites. No causal relationship between climate events and cultural evolution could be identified, but the speed of cultural development is found to be modulated by the frequency of climate events. From the demographic evolution and regional ressource consumption, we estimate regional land use change and prehistoric greenhouse gas emissions.

Using Extreme Tropical Precipitation Statistics to Constrain Future Climate States

NASA Astrophysics Data System (ADS)

Igel, M.; Biello, J. A.

2017-12-01

Tropical precipitation is characterized by a rapid growth in mean intensity as the column humidity increases. This behavior is examined in both a cloud resolving model and with high-resolution observations of precipitation and column humidity from CloudSat and AIRS, respectively. The model and the observations exhibit remarkable consistency and suggest a new paradigm for extreme precipitation. We show that the total precipitation can be decomposed into a product of contributions from a mean intensity, a probability of precipitation, and a global PDF of column humidity values. We use the modeling and observational results to suggest simple, analytic forms for each of these functions. The analytic representations are then used to construct a simple expression for the global accumulated precipitation as a function of the parameters of each of the component functions. As the climate warms, extreme precipitation intensity and global precipitation are expected to increase, though at different rates. When these predictions are incorporated into the new analytic expression for total precipitation, predictions for changes due to global warming to the probability of precipitation and the PDF of column humidity can be made. We show that strong constraints can be imposed on the future shape of the PDF of column humidity but that only weak constraints can be set on the probability of precipitation. These are largely imposed by the intensification of extreme precipitation. This result suggests that understanding precisely how extreme precipitation responds to climate warming is critical to predicting other impactful properties of global hydrology. The new framework can also be used to confirm and discount existing theories for shifting precipitation.

A Web-Based Polar Firn Model to Motivate Interest in Climate Change

NASA Astrophysics Data System (ADS)

Harris, P. D.; Lundin, J.; Stevens, C.; Leahy, W.; Waddington, E. D.

2013-12-01

How long would you have to dig straight down in Greenland before you reached solid ice? This is one of many questions that could be answered by a typical high school student using our online firn model. Firn is fallen snow that compacts under its own weight and eventually turns into glacial ice. The Herron and Langway (1980) firn model describes this process. An important component of predicting future climate change is researching past climate change. Some details of our past climate are discovered by analyzing polar ice and the firn process. Firn research can also be useful for understanding how changes in ice surface levels reflect changes in the ice mass. We have produced an online version of the Herron and Langway model that provides a simple way for students to learn how polar snow turns into ice. As a user, you can enter some climatic conditions (accumulation rate, temperature, and surface density) into our graphical user interface and press 'Submit'. We take the numbers you enter in your internet browser, send them to the model written in Python that is running on our server, and provide links to your results, all within seconds. The model produces firn depth, density, and age data. The results appear on the webpage in both text and graphical format. We have developed an example lesson plan appropriate for a high-school physics or environmental science class. The online model offers students an opportunity to apply their scientific knowledge in order to understand real-world physical processes. Additionally, students learn about scientific research and the tools scientists use to conduct it. The model can be used as a standalone lesson or as a part of a larger climate-science unit. The online model was created with funding from the Washington NASA Space Grant Consortium and the National Science Foundation's Partnerships for International Research and Education program.

Projections of Ocean Acidification Under the U.N. Framework Convention of Climate Change Using a Reduced-Form Climate Carbon-Cycle Model

NASA Astrophysics Data System (ADS)

Hartin, C.

2016-02-01

Ocean chemistry is quickly changing in response to continued anthropogenic emissions of carbon to the atmosphere. Mean surface ocean pH has already decreased by 0.1 units relative to the preindustrial era. We use an open-source, simple climate and carbon cycle model ("Hector") to investigate future changes in ocean acidification (pH and calcium carbonate saturations) under the climate agreement from the United Nations Convention on Climate Change Conference (UNFCCC) of Parties in Paris 2015 (COP 21). Hector is a reduced-form, very fast-executing model that can emulate the global mean climate of the CMIP5 models, as well as the inorganic carbon cycle in the upper ocean, allowing us to investigate future changes in ocean acidification. We ran Hector under three different emissions trajectories, using a sensitivity analysis approach to quantify model uncertainty and capture a range of possible ocean acidification changes. The first trajectory is a business-as-usual scenario comparable to a Representative Concentration Pathway (RCP) 8.5, the second a scenario with the COP 21 commitments enacted, and the third an idealized scenario keeping global temperature change to 2°C, comparable to a RCP 2.6. Preliminary results suggest that under the COP 21 agreements ocean pH at 2100 will decrease by 0.2 units and surface saturations of aragonite (calcite) will decrease by 0.9 (1.4) units relative to 1850. Under the COP 21 agreement the world's oceans will be committed to a degree of ocean acidification, however, these changes may be within the range of natural variability evident in some paleo records.

Maximum warming occurs about one decade after carbon dioxide emission

NASA Astrophysics Data System (ADS)

Ricke, K.; Caldeira, K.

2014-12-01

There has been a long tradition of estimating the amount of climate change that would result from various carbon dioxide emission or concentration scenarios but there has been relatively little quantitative analysis of how long it takes to feel the consequences of an individual carbon dioxide emission. Using conjoined results of recent carbon-cycle and physical-climate model intercomparison projects, we find the median time between an emission and maximum warming is 10.1 years, with a 90% probability range of 6.6 to 30.7 years. We evaluate uncertainties in timing and amount of warming, partitioning them into three contributing factors: carbon cycle, climate sensitivity and ocean thermal inertia. To characterize the carbon cycle uncertainty associated with the global temperature response to a carbon dioxide emission today, we use fits to the time series of carbon dioxide concentrations from a CO2-impulse response function model intercomparison project's 15 ensemble members (1). To characterize both the uncertainty in climate sensitivity and in the thermal inertia of the climate system, we use fits to the time series of global temperature change from the Coupled Model Intercomparison Project phase 5 (CMIP5; 2) abrupt4xco2 experiment's 20 ensemble's members separating the effects of each uncertainty factors using one of two simple physical models for each CMIP5 climate model. This yields 6,000 possible combinations of these three factors using a standard convolution integral approach. Our results indicate that benefits of avoided climate damage from avoided CO2 emissions will be manifested within the lifetimes of people who acted to avoid that emission. While the relevant time lags imposed by the climate system are substantially shorter than a human lifetime, they are substantially longer than the typical political election cycle, making the delay and its associated uncertainties both economically and politically significant. References: 1. Joos F et al. (2013) Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics: a multi-model analysis. Atmos Chem Phys 13:2793-2825. 2. Taylor KE, Stouffer RJ, Meehl GA (2011) An Overview of CMIP5 and the Experiment Design. Bull Am Meteorol Soc 93:485-498.

A first-order global model of Late Cenozoic climatic change: Orbital forcing as a pacemaker of the ice ages

NASA Technical Reports Server (NTRS)

Saltzman, Barry

1992-01-01

The development of a theory of the evolution of the climate of the earth over millions of years can be subdivided into three fundamental, nested, problems: (1) to establish by equilibrium climate models (e.g., general circulation models) the diagnostic relations, valid at any time, between the fast-response climate variables (i.e., the 'weather statistics') and both the prescribed external radiative forcing and the prescribed distribution of the slow response variables (e.g., the ice sheets and shelves, the deep ocean state, and the atmospheric CO2 concentration); (2) to construct, by an essentially inductive process, a model of the time-dependent evolution of the slow-response climatic variables over time scales longer than the damping times of these variables but shorter than the time scale of tectonic changes in the boundary conditions (e.g., altered geography and elevation of the continents, slow outgassing, and weathering) and ultra-slow astronomical changes such as in the solar radiative output; and (3) to determine the nature of these ultra-slow processes and their effects on the evolution of the equilibrium state of the climatic system about which the above time-dependent variations occur. All three problems are discussed in the context of the theory of the Quaternary climate, which will be incomplete unless it is embedded in a more general theory for the fuller Cenozoic that can accommodate the onset of the ice-age fluctuations. We construct a simple mathematical model for the Late Cenozoic climatic changes based on the hypothesis that forced and free variations of the concentration of atmospheric greenhouse gases (notably CO2), coupled with changes in the deep ocean state and ice mass, under the additional 'pacemaking' influence of earth-orbital forcing, are primary determinants of the climate state over this period. Our goal is to illustrate how a single model governing both very long term variations and higher frequency oscillatory variations in the Pleistocene can be formulated with relatively few adjustable parameters.

Climate change streamflow scenarios designed for critical period water resources planning studies

NASA Astrophysics Data System (ADS)

Hamlet, A. F.; Snover, A. K.; Lettenmaier, D. P.

2003-04-01

Long-range water planning in the United States is usually conducted by individual water management agencies using a critical period planning exercise based on a particular period of the observed streamflow record and a suite of internally-developed simulation tools representing the water system. In the context of planning for climate change, such an approach is flawed in that it assumes that the future climate will be like the historic record. Although more sophisticated planning methods will probably be required as time goes on, a short term strategy for incorporating climate uncertainty into long-range water planning as soon as possible is to create alternate inputs to existing planning methods that account for climate uncertainty as it affects both supply and demand. We describe a straight-forward technique for constructing streamflow scenarios based on the historic record that include the broad-based effects of changed regional climate simulated by several global climate models (GCMs). The streamflow scenarios are based on hydrologic simulations driven by historic climate data perturbed according to regional climate signals from four GCMs using the simple "delta" method. Further data processing then removes systematic hydrologic model bias using a quantile-based bias correction scheme, and lastly, the effects of random errors in the raw hydrologic simulations are removed. These techniques produce streamflow scenarios that are consistent in time and space with the historic streamflow record while incorporating fundamental changes in temperature and precipitation from the GCM scenarios. Planning model simulations based on these climate change streamflow scenarios can therefore be compared directly to planning model simulations based on the historic record of streamflows to help planners understand the potential impacts of climate uncertainty. The methods are currently being tested and refined in two large-scale planning exercises currently being conducted in the Pacific Northwest (PNW) region of the US, and the resulting streamflow scenarios will be made freely available on the internet for a large number of sites in the PNW to help defray the costs of including climate change information in other studies.

The American Climate Prospectus: a risk-centered analysis of the economic impacts of climate change

NASA Astrophysics Data System (ADS)

Jina, A.; Houser, T.; Hsiang, S. M.; Kopp, R. E., III; Delgado, M.; Larsen, K.; Mohan, S.; Rasmussen, D.; Rising, J.; Wilson, P. S.; Muir-Wood, R.

2014-12-01

The American Climate Prospectus (ACP), the analysis underlying the Risky Business project, quantitatively assessed the climate risks posed to the United States' economy in six sectors - crop yields, energy demand, coastal property, crime, labor productivity, and mortality [1]. The ACP is unique in its characterization of the full probability distribution of economic impacts of climate change throughout the 21st century, making it an extremely useful basis for risk assessments. Three key innovations allow for this characterization. First, climate projections from CMIP5 models are scaled to a temperature probability distribution derived from a coarser climate model (MAGICC). This allows a more accurate representation of the whole distribution of future climates (in particular the tails) than a simple ensemble average. These are downscaled both temporally and spatially. Second, a set of local sea level rise and tropical cyclone projections are used in conjunction with the most detailed dataset of coastal property in the US in order to capture the risks of rising seas and storm surge. Third, we base many of our sectors on empirically-derived responses to temperature and precipitation. Each of these dose-response functions is resampled many times to populate a statistical distribution. Combining these with uncertainty in emissions scenario, climate model, and weather, we create the full probability distribution of climate impacts from county up to national levels, as well as model the effects upon the economy as a whole. Results are presented as likelihood ranges, as well as changes to return intervals of extreme events. The ACP analysis allows us to compare between sectors to understand the magnitude of required policy responses, and also to identify risks through time. Many sectors displaying large impacts at the end of the century, like those of mortality, have smaller changes in the near-term, due to non-linearities in the response functions. Other sectors, like coastal damages, have monotonically increasing costs throughout the 21st century. Taken together, the results from the ACP presents a unique and novel view of the short-, medium-, and long-term economic risks of climate change in the US. References: [1] T. Houser et al (2014), American Climate Prospectus, www.climateprospectus.org.

Clime: analyzing and producing climate data in GIS environment

NASA Astrophysics Data System (ADS)

Cattaneo, Luigi; Rillo, Valeria; Mercogliano, Paola

2014-05-01

In the last years, Impacts on Soil and Coasts Division (ISC) of CMCC (Euro-Mediterranean Center on Climate Change) had several collaboration experiences with impact communities, including IS-ENES (FP7-INF) and SafeLand (FP7-ENV) projects, which involved a study of landslide risk in Europe, and is currently active in GEMINA (FIRB) and ORIENTGATE (SEE Transnational Cooperation Programme) research projects. As a result, it has brought research activities about different impact of climate changes as flood and landslide hazards, based on climate simulation obtained from the high resolution regional climate models COSMO CLM, developed at CMCC as member of the consortium CLM Assembly. ISC-Capua also collaborates with local institutions interested in atmospherical climate change and also of their impacts on the soil, such as river basin authorities in the Campania region, ARPA Emilia Romagna and ARPA Calabria. Impact models (e.g. hydraulic or stability models) are usually developed in a GIS environment, since they need an accurate territory description, so Clime has been designed to bridge the usually existing gap between climate data - both observed and simulated - gathered from different sources, and impact communities. The main goal of Clime, special purpose Geographic Information System (GIS) software integrated in ESRI ArcGIS Desktop 10, is to easily evaluate multiple climate features and study climate changes over specific geographical domains with their related effects on environment, including impacts on soil. Developed as an add-in tool, this software has been conceived for research activities of ISC Division in order to provide a substantial contribution during post-processing and validation phase. Therefore, it is possible to analyze and compare multiple datasets (observations, climate simulations, etc.) through processes involving statistical functions, percentiles, trends test and evaluation of extreme events with a flexible system of temporal and spatial filtering, and to represent results as maps, temporal and statistic plots (time series, seasonal cycles, PDFs, scatter plots, Taylor diagrams) or Excel tables; in addition, it features bias correction techniques for climate model results. Summarizing, Clime is able to provide users a simple and fast way to retrieve analysis over simulated climate data and observations within any geographical site of interest (provinces, regions, countries, etc.).

The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

NASA Technical Reports Server (NTRS)

Shukla, Sonali P.; Ruane, Alexander Clark

2014-01-01

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, and water (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models' responses to CTW changes (Rotter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012). To fulfill this need, the Coordinated Climate-Crop Modeling Project (C3MP) (Ruane et al., 2014) was initiated within the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013). The submitted results from C3MP Phase 1 (February 15, 2013-December 31, 2013) are currently being analyzed. This chapter serves to present and update the C3MP protocols, discuss the initial participation and general findings, comment on needed adjustments, and describe continued and future development. AgMIP aims to improve substantially the climate, crop, and economic simulation tools that are used to characterize the agricultural sector, to assess future world food security under changing climate conditions, and to enhance adaptation capacity both globally and regionally. To understand better and improve the modeled crop responses, AgMIP has conducted detailed crop model intercomparisons at closely observed field sites for wheat (Asseng et al., 2013), rice (Li et al., in review), maize (Bassu et al., 2014), and sugarcane (Singels et al., 2013). A coordinated modeling exercise was one of the original motivations for AgMIP, and C3MP provides rapid estimation of crop responses to CO2, water, and temperature (CTW) changes, adding dimension and insight into the crop model intercomparisons, while facilitating interactions within the global community of modelers. C3MP also contributes a fast-track, multi-model climate sensitivity assessment for the AgMIP climate and crop modeling teams on Research Track 2 (Fig. 1), which seeks to understand the impact of projected climatic changes on crop production and food security (Rosenzweig et al., 2013; Ruane et al., 2014).

A simple daily soil-water balance model for estimating the spatial and temporal distribution of groundwater recharge in temperate humid areas

USGS Publications Warehouse

Dripps, W.R.; Bradbury, K.R.

2007-01-01

Quantifying the spatial and temporal distribution of natural groundwater recharge is usually a prerequisite for effective groundwater modeling and management. As flow models become increasingly utilized for management decisions, there is an increased need for simple, practical methods to delineate recharge zones and quantify recharge rates. Existing models for estimating recharge distributions are data intensive, require extensive parameterization, and take a significant investment of time in order to establish. The Wisconsin Geological and Natural History Survey (WGNHS) has developed a simple daily soil-water balance (SWB) model that uses readily available soil, land cover, topographic, and climatic data in conjunction with a geographic information system (GIS) to estimate the temporal and spatial distribution of groundwater recharge at the watershed scale for temperate humid areas. To demonstrate the methodology and the applicability and performance of the model, two case studies are presented: one for the forested Trout Lake watershed of north central Wisconsin, USA and the other for the urban-agricultural Pheasant Branch Creek watershed of south central Wisconsin, USA. Overall, the SWB model performs well and presents modelers and planners with a practical tool for providing recharge estimates for modeling and water resource planning purposes in humid areas. ?? Springer-Verlag 2007.

Linking the M&Rfi Weather Generator with Agrometeorological Models

NASA Astrophysics Data System (ADS)

Dubrovsky, Martin; Trnka, Miroslav

2015-04-01

Realistic meteorological inputs (representing the present and/or future climates) for the agrometeorological model simulations are often produced by stochastic weather generators (WGs). This contribution presents some methodological issues and results obtained in our recent experiments. We also address selected questions raised in the synopsis of this session. The input meteorological time series for our experiments are produced by the parametric single site weather generator (WG) Marfi, which is calibrated from the available observational data (or interpolated from surrounding stations). To produce meteorological series representing the future climate, the WG parameters are modified by climate change scenarios, which are prepared by the pattern scaling method: the standardised scenarios derived from Global or Regional Climate Models are multiplied by the change in global mean temperature (ΔTG) determined by the simple climate model MAGICC. The presentation will address following questions: (i) The dependence of the quality of the synthetic weather series and impact results on the WG settings. An emphasis will be put on an effect of conditioning the daily WG on monthly WG (presently being one of our hot topics), which aims at improvement of the reproduction of the low-frequency weather variability. Comparison of results obtained with various WG settings is made in terms of climatic and agroclimatic indices (including extreme temperature and precipitation characteristics and drought indices). (ii) Our methodology accounts for the uncertainties coming from various sources. We will show how the climate change impact results are affected by 1. uncertainty in climate modelling, 2. uncertainty in ΔTG, and 3. uncertainty related to the complexity of the climate change scenario (focusing on an effect of inclusion of changes in variability into the climate change scenarios). Acknowledgements: This study was funded by project "Building up a multidisciplinary scientific team focused on drought" No. CZ.1.07/2.3.00/20.0248. The weather generator is being developed within the frame of WG4VALUE project (LD12029), which is supported by Ministry of Education, Youth and Sports and linked to the COST action ES1102 VALUE.

Precipitation Organization in a Warmer Climate

NASA Astrophysics Data System (ADS)

Rickenbach, T. M.; Nieto Ferreira, R.; Nissenbaum, M.

2014-12-01

This study will investigate changes in precipitation organization in a warmer climate using the Weather Research and Forecasting (WRF) model and CMIP-5 ensemble climate simulations. This work builds from an existing four-year NEXRAD radar-based precipitation climatology over the southeastern U.S. that uses a simple two-category framework of precipitation organization based on instantaneous precipitating feature size. The first category - mesoscale precipitation features (MPF) - dominates winter precipitation and is linked to the more predictable large-scale forcing provided by the extratropical cyclones. In contrast, the second category - isolated precipitation - dominates the summer season precipitation in the southern coastal and inland regions but is linked to less predictable mesoscale circulations and to local thermodynamics more crudely represented in climate models. Most climate modeling studies suggest that an accelerated water cycle in a warmer world will lead to an overall increase in precipitation, but few studies have addressed how precipitation organization may change regionally. To address this, WRF will simulate representative wintertime and summertime precipitation events in the Southeast US under the current and future climate. These events will be simulated in an environment resembling the future climate of the 2090s using the pseudo-global warming (PGW) approach based on an ensemble of temperature projections. The working hypothesis is that the higher water vapor content in the future simulation will result in an increase in the number of isolated convective systems, while MPFs will be more intense and longer-lasting. In the context of the seasonal climatology of MPF and isolated precipitation, these results have implications for assessing the predictability of future regional precipitation in the southeastern U.S.

A Comparison of Climate Feedback Strength between CO2 Doubling and LGM Experiments

NASA Astrophysics Data System (ADS)

Yoshimori, M.; Yokohata, T.; Abe-Ouchi, A.

2008-12-01

Studies of past climate potentially provide a constraint on the uncertainty of climate sensitivity, but previous studies warn against a simple scaling to the future. The climate sensitivity is determined by various feedback processes and they may vary with climate states and forcings. In this study, we investigate similarities and differences of feedbacks for a CO2 doubling, a last glacial maximum (LGM), and LGM greenhouse gas (GHG) forcing experiments, using an atmospheric general circulation model coupled to a slab ocean model. After computing the radiative forcing, the individual feedback strengths: water vapor, lapse rate, albedo, and cloud feedbacks, are evaluated explicitly. For this particular model, the difference in the climate sensitivity among experiments is attributed to the shortwave cloud feedback in which there is a tendency that it becomes weaker or even negative in the cooling experiments. No significant difference is found in the water vapor feedback between warming and cooling experiments by GHGs despite the nonlinear dependence of the Clausius-Clapeyron relation on temperature. The weaker water vapor feedback in the LGM experiment due to a relatively weaker tropical forcing is compensated by the stronger lapse rate feedback due to a relatively stronger extratropical forcing. A hypothesis is proposed which explains the asymmetric cloud response between warming and cooling experiments associated with a displacement of the region of mixed- phase clouds. The difference in the total feedback strength between experiments is, however, relatively small compared to the current intermodel spread, and does not necessarily preclude the use of LGM climate as a future constraint.

Climate Envelope Modeling and Dispersal Simulations Show Little Risk of Range Extension of the Shipworm, Teredo navalis (L.), in the Baltic Sea

PubMed Central

Appelqvist, Christin; Al-Hamdani, Zyad K.; Jonsson, Per R.; Havenhand, Jon N.

2015-01-01

The shipworm, Teredo navalis, is absent from most of the Baltic Sea. In the last 20 years, increased frequency of T. navalis has been reported along the southern Baltic Sea coasts of Denmark, Germany, and Sweden, indicating possible range-extensions into previously unoccupied areas. We evaluated the effects of historical and projected near-future changes in salinity, temperature, and oxygen on the risk of spread of T. navalis in the Baltic. Specifically, we developed a simple, GIS-based, mechanistic climate envelope model to predict the spatial distribution of favourable conditions for adult reproduction and larval metamorphosis of T. navalis, based on published environmental tolerances to these factors. In addition, we used a high-resolution three-dimensional hydrographic model to simulate the probability of spread of T. navalis larvae within the study area. Climate envelope modeling showed that projected near-future climate change is not likely to change the overall distribution of T. navalis in the region, but will prolong the breeding season and increase the risk of shipworm establishment at the margins of the current range. Dispersal simulations indicated that the majority of larvae were philopatric, but those that spread over a wider area typically spread to areas unfavourable for their survival. Overall, therefore, we found no substantive evidence for climate-change related shifts in the distribution of T. navalis in the Baltic Sea, and no evidence for increased risk of spread in the near-future. PMID:25768305

Lags in the response of mountain plant communities to climate change

PubMed Central

Alexander, Jake M.; Chalmandrier, Loïc; Lenoir, Jonathan; Burgess, Treena I.; Essl, Franz; Haider, Sylvia; Kueffer, Christoph; McDougall, Keith; Milbau, Ann; Nuñez, Martin A.; Pauchard, Aníbal; Rabitsch, Wolfgang; Rew, Lisa J.; Sanders, Nathan J.; Pellissier, Loïc

2018-01-01

Rapid climatic changes and increasing human influence at high elevations around the world will have profound impacts on mountain biodiversity. However, forecasts from statistical models (e.g. species distribution models) rarely consider that plant community changes could substantially lag behind climatic changes, hindering our ability to make temporally realistic projections for the coming century. Indeed, the magnitudes of lags, and the relative importance of the different factors giving rise to them, remain poorly understood. We review evidence for three types of lag: “dispersal lags” affecting plant species’ spread along elevational gradients, “establishment lags” following their arrival in recipient communities, and “extinction lags” of resident species. Variation in lags is explained by variation among species in physiological and demographic responses, by effects of altered biotic interactions, and by aspects of the physical environment. Of these, altered biotic interactions could contribute substantially to establishment and extinction lags, yet impacts of biotic interactions on range dynamics are poorly understood. We develop a mechanistic community model to illustrate how species turnover in future communities might lag behind simple expectations based on species’ range shifts with unlimited dispersal. The model shows a combined contribution of altered biotic interactions and dispersal lags to plant community turnover along an elevational gradient following climate warming. Our review and simulation support the view that accounting for disequilibrium range dynamics will be essential for realistic forecasts of patterns of biodiversity under climate change, with implications for the conservation of mountain species and the ecosystem functions they provide. PMID:29112781

Understanding the Impacts of Climate Change and Land Use Dynamics Using a Fully Coupled Hydrologic Feedback Model between Surface and Subsurface Systems

NASA Astrophysics Data System (ADS)

Park, C.; Lee, J.; Koo, M.

2011-12-01

Climate is the most critical driving force of the hydrologic system of the Earth. Since the industrial revolution, the impacts of anthropogenic activities to the Earth environment have been expanded and accelerated. Especially, the global emission of carbon dioxide into the atmosphere is known to have significantly increased temperature and affected the hydrologic system. Many hydrologists have contributed to the studies regarding the climate change on the hydrologic system since the Intergovernmental Panel on Climate Change (IPCC) was created in 1988. Among many components in the hydrologic system groundwater and its response to the climate change and anthropogenic activities are not fully understood due to the complexity of subsurface conditions between the surface and the groundwater table. A new spatio-temporal hydrologic model has been developed to estimate the impacts of climate change and land use dynamics on the groundwater. The model consists of two sub-models: a surface model and a subsurface model. The surface model involves three surface processes: interception, runoff, and evapotranspiration, and the subsurface model does also three subsurface processes: soil moisture balance, recharge, and groundwater flow. The surface model requires various input data including land use, soil types, vegetation types, topographical elevations, and meteorological data. The surface model simulates daily hydrological processes for rainfall interception, surface runoff varied by land use change and crop growth, and evapotranspiration controlled by soil moisture balance. The daily soil moisture balance is a key element to link two sub-models as it calculates infiltration and groundwater recharge by considering a time delay routing through a vadose zone down to the groundwater table. MODFLOW is adopted to simulate groundwater flow and interaction with surface water components as well. The model is technically flexible to add new model or modify existing model as it is developed with an object-oriented language - Python. The model also can easily be localized by simple modification of soil and crop properties. The actual application of the model after calibration was successful and results showed reliable water balance and interaction between the surface and subsurface hydrologic systems.

Future nutrient load scenarios for the Baltic Sea due to climate and lifestyle changes.

PubMed

Hägg, Hanna Eriksson; Lyon, Steve W; Wällstedt, Teresia; Mörth, Carl-Magnus; Claremar, Björn; Humborg, Christoph

2014-04-01

Dynamic model simulations of the future climate and projections of future lifestyles within the Baltic Sea Drainage Basin (BSDB) were considered in this study to estimate potential trends in future nutrient loads to the Baltic Sea. Total nitrogen and total phosphorus loads were estimated using a simple proxy based only on human population (to account for nutrient sources) and stream discharges (to account for nutrient transport). This population-discharge proxy provided a good estimate for nutrient loads across the seven sub-basins of the BSDB considered. All climate scenarios considered here produced increased nutrient loads to the Baltic Sea over the next 100 years. There was variation between the climate scenarios such that sub-basin and regional differences were seen in future nutrient runoff depending on the climate model and scenario considered. Regardless, the results of this study indicate that changes in lifestyle brought about through shifts in consumption and population potentially overshadow the climate effects on future nutrient runoff for the entire BSDB. Regionally, however, lifestyle changes appear relatively more important in the southern regions of the BSDB while climatic changes appear more important in the northern regions with regards to future increases in nutrient loads. From a whole-ecosystem management perspective of the BSDB, this implies that implementation of improved and targeted management practices can still bring about improved conditions in the Baltic Sea in the face of a warmer and wetter future climate.

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.

Sensitivity of regional forest carbon budgets to continuous and stochastic climate change pressures

NASA Astrophysics Data System (ADS)

Sulman, B. N.; Desai, A. R.; Scheller, R. M.

2010-12-01

Climate change is expected to impact forest-atmosphere carbon budgets through three processes: 1. Increased disturbance rates, including fires, mortality due to pest outbreaks, and severe storms 2. Changes in patterns of inter-annual variability, related to increased incidence of severe droughts and defoliating insect outbreaks 3. Continuous changes in forest productivity and respiration, related to increases in mean temperature, growing season length, and CO2 fertilization While the importance of these climate change effects in future regional carbon budgets has been established, quantitative characterization of the relative sensitivity of forested landscapes to these different types of pressures is needed. We present a model- and- data-based approach to understanding the sensitivity of forested landscapes to climate change pressures. Eddy-covariance and biometric measurements from forests in the northern United States were used to constrain two forest landscape models. The first, LandNEP, uses a prescribed functional form for the evolution of net ecosystem productivity (NEP) over the age of a forested grid cell, which is reset following a disturbance event. This model was used for investigating the basic statistical properties of a simple landscape’s responses to climate change pressures. The second model, LANDIS-II, includes different tree species and models forest biomass accumulation and succession, allowing us to investigate the effects of more complex forest processes such as species change and carbon pool accumulation on landscape responses to climate change effects. We tested the sensitivity of forested landscapes to these three types of climate change pressures by applying ensemble perturbations of random disturbance rates, distribution functions of inter-annual variability, and maximum potential carbon uptake rates, in the two models. We find that landscape-scale net carbon exchange responds linearly to continuous changes in potential carbon uptake and inter-annual variability, while responses to stochastic changes are non-linear and become more important at shorter mean disturbance intervals. These results provide insight on how to better parameterize coupled carbon-climate models to more realistically simulate feedbacks between forests and the atmosphere.

Climate change and potato cropping in the Peruvian Altiplano

NASA Astrophysics Data System (ADS)

Sanabria, J.; Lhomme, J. P.

2013-05-01

The potential impacts of climate change on potatoes cropping in the Peruvian highlands (Altiplano) is assessed using climate projections for 2071-2100, obtained from the HadRM3P regional atmospheric model of the Hadley Centre. The atmospheric model is run under two different special report on emission scenarios: high CO2 concentration (A2) and moderate CO2 concentration (B2) for four locations situated in the surroundings of Lake Titicaca. The two main varieties of potato cultivated in the area are studied: the Andean potato ( Solanum tuberosum) and the bitter potato ( Solanum juzepczukii). A simple process-oriented model is used to quantify the climatic impacts on crops cycles and yields by combining the effects of temperature on phenology, of radiation and CO2 on maximum yield and of water balance on yield deficit. In future climates, air temperature systematically increases, precipitation tends to increase at the beginning of the rainy season and slightly decreases during the rest of the season. The direct effects of these climatic changes are earlier planting dates, less planting failures and shorter crop cycles in all the four locations and for both scenarios. Consequently, the harvesting dates occur systematically earlier: roughly in January for the Andean potato instead of March in the current situation and in February for the bitter potato instead of April. Overall, yield deficits will be higher under climate change than in the current climate. There will be a strong negative impact on yields for S. tuberosum (stronger under A2 scenario than under B2); the impact on S. juzepczukii yields, however, appears to be relatively mixed and not so negative.

A Semi-empirical Model of the Stratosphere in the Climate System

NASA Astrophysics Data System (ADS)

Sodergren, A. H.; Bodeker, G. E.; Kremser, S.; Meinshausen, M.; McDonald, A.

2014-12-01

Chemistry climate models (CCMs) currently used to project changes in Antarctic ozone are extremely computationally demanding. CCM projections are uncertain due to lack of knowledge of future emissions of greenhouse gases (GHGs) and ozone depleting substances (ODSs), as well as parameterizations within the CCMs that have weakly constrained tuning parameters. While projections should be based on an ensemble of simulations, this is not currently possible due to the complexity of the CCMs. An inexpensive but realistic approach to simulate changes in stratospheric ozone, and its coupling to the climate system, is needed as a complement to CCMs. A simple climate model (SCM) can be used as a fast emulator of complex atmospheric-ocean climate models. If such an SCM includes a representation of stratospheric ozone, the evolution of the global ozone layer can be simulated for a wide range of GHG and ODS emissions scenarios. MAGICC is an SCM used in previous IPCC reports. In the current version of the MAGICC SCM, stratospheric ozone changes depend only on equivalent effective stratospheric chlorine (EESC). In this work, MAGICC is extended to include an interactive stratospheric ozone layer using a semi-empirical model of ozone responses to CO2and EESC, with changes in ozone affecting the radiative forcing in the SCM. To demonstrate the ability of our new, extended SCM to generate projections of global changes in ozone, tuning parameters from 19 coupled atmosphere-ocean general circulation models (AOGCMs) and 10 carbon cycle models (to create an ensemble of 190 simulations) have been used to generate probability density functions of the dates of return of stratospheric column ozone to 1960 and 1980 levels for different latitudes.

Climate models with delay differential equations

NASA Astrophysics Data System (ADS)

Keane, Andrew; Krauskopf, Bernd; Postlethwaite, Claire M.

2017-11-01

A fundamental challenge in mathematical modelling is to find a model that embodies the essential underlying physics of a system, while at the same time being simple enough to allow for mathematical analysis. Delay differential equations (DDEs) can often assist in this goal because, in some cases, only the delayed effects of complex processes need to be described and not the processes themselves. This is true for some climate systems, whose dynamics are driven in part by delayed feedback loops associated with transport times of mass or energy from one location of the globe to another. The infinite-dimensional nature of DDEs allows them to be sufficiently complex to reproduce realistic dynamics accurately with a small number of variables and parameters. In this paper, we review how DDEs have been used to model climate systems at a conceptual level. Most studies of DDE climate models have focused on gaining insights into either the global energy balance or the fundamental workings of the El Niño Southern Oscillation (ENSO) system. For example, studies of DDEs have led to proposed mechanisms for the interannual oscillations in sea-surface temperature that is characteristic of ENSO, the irregular behaviour that makes ENSO difficult to forecast and the tendency of El Niño events to occur near Christmas. We also discuss the tools used to analyse such DDE models. In particular, the recent development of continuation software for DDEs makes it possible to explore large regions of parameter space in an efficient manner in order to provide a "global picture" of the possible dynamics. We also point out some directions for future research, including the incorporation of non-constant delays, which we believe could improve the descriptive power of DDE climate models.

Climate models with delay differential equations.

PubMed

Keane, Andrew; Krauskopf, Bernd; Postlethwaite, Claire M

2017-11-01

A fundamental challenge in mathematical modelling is to find a model that embodies the essential underlying physics of a system, while at the same time being simple enough to allow for mathematical analysis. Delay differential equations (DDEs) can often assist in this goal because, in some cases, only the delayed effects of complex processes need to be described and not the processes themselves. This is true for some climate systems, whose dynamics are driven in part by delayed feedback loops associated with transport times of mass or energy from one location of the globe to another. The infinite-dimensional nature of DDEs allows them to be sufficiently complex to reproduce realistic dynamics accurately with a small number of variables and parameters. In this paper, we review how DDEs have been used to model climate systems at a conceptual level. Most studies of DDE climate models have focused on gaining insights into either the global energy balance or the fundamental workings of the El Niño Southern Oscillation (ENSO) system. For example, studies of DDEs have led to proposed mechanisms for the interannual oscillations in sea-surface temperature that is characteristic of ENSO, the irregular behaviour that makes ENSO difficult to forecast and the tendency of El Niño events to occur near Christmas. We also discuss the tools used to analyse such DDE models. In particular, the recent development of continuation software for DDEs makes it possible to explore large regions of parameter space in an efficient manner in order to provide a "global picture" of the possible dynamics. We also point out some directions for future research, including the incorporation of non-constant delays, which we believe could improve the descriptive power of DDE climate models.

Real versus Artificial Variation in the Thermal Sensitivity of Biological Traits.

PubMed

Pawar, Samraat; Dell, Anthony I; Savage, Van M; Knies, Jennifer L

2016-02-01

Whether the thermal sensitivity of an organism's traits follows the simple Boltzmann-Arrhenius model remains a contentious issue that centers around consideration of its operational temperature range and whether the sensitivity corresponds to one or a few underlying rate-limiting enzymes. Resolving this issue is crucial, because mechanistic models for temperature dependence of traits are required to predict the biological effects of climate change. Here, by combining theory with data on 1,085 thermal responses from a wide range of traits and organisms, we show that substantial variation in thermal sensitivity (activation energy) estimates can arise simply because of variation in the range of measured temperatures. Furthermore, when thermal responses deviate systematically from the Boltzmann-Arrhenius model, variation in measured temperature ranges across studies can bias estimated activation energy distributions toward higher mean, median, variance, and skewness. Remarkably, this bias alone can yield activation energies that encompass the range expected from biochemical reactions (from ~0.2 to 1.2 eV), making it difficult to establish whether a single activation energy appropriately captures thermal sensitivity. We provide guidelines and a simple equation for partially correcting for such artifacts. Our results have important implications for understanding the mechanistic basis of thermal responses of biological traits and for accurately modeling effects of variation in thermal sensitivity on responses of individuals, populations, and ecological communities to changing climatic temperatures.

Modelling technical snow production for skiing areas in the Austrian Alps with the physically based snow model AMUNDSEN

NASA Astrophysics Data System (ADS)

Hanzer, F.; Marke, T.; Steiger, R.; Strasser, U.

2012-04-01

Tourism and particularly winter tourism is a key factor for the Austrian economy. Judging from currently available climate simulations, the Austrian Alps show a particularly high vulnerability to climatic changes. To reduce the exposure of ski areas towards changes in natural snow conditions as well as to generally enhance snow conditions at skiing sites, technical snowmaking is widely utilized across Austrian ski areas. While such measures result in better snow conditions at the skiing sites and are important for the local skiing industry, its economic efficiency has also to be taken into account. The current work emerges from the project CC-Snow II, where improved future climate scenario simulations are used to determine future natural and artificial snow conditions and their effects on tourism and economy in the Austrian Alps. In a first step, a simple technical snowmaking approach is incorporated into the process based snow model AMUNDSEN, which operates at a spatial resolution of 10-50 m and a temporal resolution of 1-3 hours. Locations of skiing slopes within a ski area in Styria, Austria, were digitized and imported into the model environment. During a predefined time frame in the beginning of the ski season, the model produces a maximum possible amount of technical snow and distributes the associated snow on the slopes, whereas afterwards, until to the end of the ski season, the model tries to maintain a certain snow depth threshold value on the slopes. Due to only few required input parameters, this approach is easily transferable to other ski areas. In our poster contribution, we present first results of this snowmaking approach and give an overview of the data and methodology applied. In a further step in CC-Snow, this simple bulk approach will be extended to consider actual snow cannon locations and technical specifications, which will allow a more detailed description of technical snow production as well as cannon-based recordings of water and energy consumption.

Predictability of Seasonal Rainfall over the Greater Horn of Africa

NASA Astrophysics Data System (ADS)

Ngaina, J. N.

2016-12-01

The El Nino-Southern Oscillation (ENSO) is a primary mode of climate variability in the Greater of Africa (GHA). The expected impacts of climate variability and change on water, agriculture, and food resources in GHA underscore the importance of reliable and accurate seasonal climate predictions. The study evaluated different model selection criteria which included the Coefficient of determination (R2), Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Fisher information approximation (FIA). A forecast scheme based on the optimal model was developed to predict the October-November-December (OND) and March-April-May (MAM) rainfall. The predictability of GHA rainfall based on ENSO was quantified based on composite analysis, correlations and contingency tables. A test for field-significance considering the properties of finiteness and interdependence of the spatial grid was applied to avoid correlations by chance. The study identified FIA as the optimal model selection criterion. However, complex model selection criteria (FIA followed by BIC) performed better compared to simple approach (R2 and AIC). Notably, operational seasonal rainfall predictions over the GHA makes of simple model selection procedures e.g. R2. Rainfall is modestly predictable based on ENSO during OND and MAM seasons. El Nino typically leads to wetter conditions during OND and drier conditions during MAM. The correlations of ENSO indices with rainfall are statistically significant for OND and MAM seasons. Analysis based on contingency tables shows higher predictability of OND rainfall with the use of ENSO indices derived from the Pacific and Indian Oceans sea surfaces showing significant improvement during OND season. The predictability based on ENSO for OND rainfall is robust on a decadal scale compared to MAM. An ENSO-based scheme based on an optimal model selection criterion can thus provide skillful rainfall predictions over GHA. This study concludes that the negative phase of ENSO (La Niña) leads to dry conditions while the positive phase of ENSO (El Niño) anticipates enhanced wet conditions

Science support for the Earth radiation budget sensor on the Nimbus-7 spacecraft

NASA Technical Reports Server (NTRS)

Ingersoll, A. P.

1982-01-01

Experimental data supporting the Earth radiation budget sensor on the Nimbus 7 Satellite is given. The data deals with the empirical relations between radiative flux, cloudiness, and other meteorological parameters; response of a zonal climate ice sheet model to the orbital perturbations during the quaternary ice ages; and a simple parameterization for ice sheet ablation rate.

X-DRAIN and XDS: a simplified road erosion prediction method

Treesearch

William J. Elliot; David E. Hall; S. R. Graves

1998-01-01

To develop a simple road sediment delivery tool, the WEPP program modeled sedimentation from forest roads for more than 50,000 combinations of distance between cross drains, road gradient, soil texture, distance from stream, steepness of the buffer between the road and the stream, and climate. The sediment yield prediction from each of these runs was stored in a data...

The importance of planetary rotation period for ocean heat transport.

PubMed

Cullum, J; Stevens, D; Joshi, M

2014-08-01

The climate and, hence, potential habitability of a planet crucially depends on how its atmospheric and ocean circulation transports heat from warmer to cooler regions. However, previous studies of planetary climate have concentrated on modeling the dynamics of atmospheres, while dramatically simplifying the treatment of oceans, which neglects or misrepresents the effect of the ocean in the total heat transport. Even the majority of studies with a dynamic ocean have used a simple so-called aquaplanet that has no continental barriers, which is a configuration that dramatically changes the ocean dynamics. Here, the significance of the response of poleward ocean heat transport to planetary rotation period is shown with a simple meridional barrier--the simplest representation of any continental configuration. The poleward ocean heat transport increases significantly as the planetary rotation period is increased. The peak heat transport more than doubles when the rotation period is increased by a factor of ten. There are also significant changes to ocean temperature at depth, with implications for the carbon cycle. There is strong agreement between the model results and a scale analysis of the governing equations. This result highlights the importance of both planetary rotation period and the ocean circulation when considering planetary habitability.

Summer Rains and Dry Seasons in the Upper Blue Nile Basin: The Predictability of Half a Century of Past and Future Spatiotemporal Patterns

PubMed Central

Mellander, Per-Erik; Gebrehiwot, Solomon G.; Gärdenäs, Annemieke I.; Bewket, Woldeamlak; Bishop, Kevin

2013-01-01

During the last 100 years the Ethiopian upper Blue Nile Basin (BNB) has undergone major changes in land use, and is now potentially facing changes in climate. Rainfall over BNB supplies over two-thirds of the water to the Nile and supports a large local population living mainly on subsistence agriculture. Regional food security is sensitive to both the amount and timing of rain and is already an important political challenge that will be further complicated if scenarios of climate change are realized. In this study a simple spatial model of the timing and duration of summer rains (Kiremt) and dry season (Bega), and annual rain over the upper BNB was established from observed data between 1952 and 2004. The model was used to explore potential impacts of climate change on these rains, using a down-scaled ECHAM5/MP1-OM scenario between 2050 and 2100. Over the observed period the amount, onset and duration of Kiremt rains and rain-free Bega days have exhibited a consistent spatial pattern. The spatially averaged annual rainfall was 1490 mm of which 93% was Kiremt rain. The average Kiremt rain and number of rainy days was higher in the southwest (322 days) and decreased towards the north (136 days). Under the 2050–2100 scenario, the annual mean rainfall is predicted to increase by 6% and maintain the same spatial pattern as in the past. A larger change in annual rainfall is expected in the southwest (ca. +130 mm) with a gradually smaller change towards the north (ca. +70 mm). Results highlight the need to account for the characteristic spatiotemporal zonation when planning water management and climate adaptation within the upper BNB. The presented simple spatial resolved models of the presence of Kiremt and annual total rainfall could be used as a baseline for such long-term planning. PMID:23869219

Indian Ocean zonal mode activity in 20th century observations and simulations

NASA Astrophysics Data System (ADS)

Sendelbeck, Anja; Mölg, Thomas

2016-04-01

The Indian Ocean zonal mode (IOZM) is a coupled ocean-atmosphere system with anomalous cooling in the east, warming in the west and easterly wind anomalies, resulting in a complete reversal of the climatological zonal sea surface temperature (SST) gradient. The IOZM has a strong influence on East African climate by causing anomalously strong October - December (OND) precipitation. Using observational data and historical CMIP5 (Coupled Model Intercomparison Project phase 5) model output, the September - November (SON) dipole mode index (DMI), OND East African precipitation and SON zonal wind index (ZWI) are calculated. We pay particular attention to detrending SSTs for calculating the DMI, which seems to have been neglected in some published research. The ZWI is defined as the area-averaged zonal wind component at 850 hPa over the central Indian Ocean. Regression analysis is used to evaluate the models' capability to represent the IOZM and its impact on east African climate between 1948 and 2005. Simple correlations are calculated between SST, zonal wind and precipitation to show their interdependence. High correlation in models implies a good representation of the influence of IOZM on East African climate variability and our goal is to detect the models with the highest correlation coefficients. In future research, these model data might be used to investigate the impact of IOZM on the East African climate variability in the late 20's century with regard to anthropogenic causes and internal variability.

Stochastic Parametrisations and Regime Behaviour of Atmospheric Models

NASA Astrophysics Data System (ADS)

Arnold, Hannah; Moroz, Irene; Palmer, Tim

2013-04-01

The presence of regimes is a characteristic of non-linear, chaotic systems (Lorenz, 2006). In the atmosphere, regimes emerge as familiar circulation patterns such as the El-Nino Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) and Scandinavian Blocking events. In recent years there has been much interest in the problem of identifying and studying atmospheric regimes (Solomon et al, 2007). In particular, how do these regimes respond to an external forcing such as anthropogenic greenhouse gas emissions? The importance of regimes in observed trends over the past 50-100 years indicates that in order to predict anthropogenic climate change, our climate models must be able to represent accurately natural circulation regimes, their statistics and variability. It is well established that representing model uncertainty as well as initial condition uncertainty is important for reliable weather forecasts (Palmer, 2001). In particular, stochastic parametrisation schemes have been shown to improve the skill of weather forecast models (e.g. Berner et al., 2009; Frenkel et al., 2012; Palmer et al., 2009). It is possible that including stochastic physics as a representation of model uncertainty could also be beneficial in climate modelling, enabling the simulator to explore larger regions of the climate attractor including other flow regimes. An alternative representation of model uncertainty is a perturbed parameter scheme, whereby physical parameters in subgrid parametrisation schemes are perturbed about their optimal value. Perturbing parameters gives a greater control over the ensemble than multi-model or multiparametrisation ensembles, and has been used as a representation of model uncertainty in climate prediction (Stainforth et al., 2005; Rougier et al., 2009). We investigate the effect of including representations of model uncertainty on the regime behaviour of a simulator. A simple chaotic model of the atmosphere, the Lorenz '96 system, is used to study the predictability of regime changes (Lorenz 1996, 2006). Three types of models are considered: a deterministic parametrisation scheme, stochastic parametrisation schemes with additive or multiplicative noise, and a perturbed parameter ensemble. Each forecasting scheme was tested on its ability to reproduce the attractor of the full system, defined in a reduced space based on EOF decomposition. None of the forecast models accurately capture the less common regime, though a significant improvement is observed over the deterministic parametrisation when a temporally correlated stochastic parametrisation is used. The attractor for the perturbed parameter ensemble improves on that forecast by the deterministic or white additive schemes, showing a distinct peak in the attractor corresponding to the less common regime. However, the 40 constituent members of the perturbed parameter ensemble each differ greatly from the true attractor, with many only showing one dominant regime with very rare transitions. These results indicate that perturbed parameter ensembles must be carefully analysed as individual members may have very different characteristics to the ensemble mean and to the true system being modelled. On the other hand, the stochastic parametrisation schemes tested performed well, improving the simulated climate, and motivating the development of a stochastic earth-system simulator for use in climate prediction. J. Berner, G. J. Shutts, M. Leutbecher, and T. N. Palmer. A spectral stochastic kinetic energy backscatter scheme and its impact on flow dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci., 66(3):603-626, 2009. Y. Frenkel, A. J. Majda, and B. Khouider. Using the stochastic multicloud model to improve tropical convective parametrisation: A paradigm example. J. Atmos. Sci., 69(3):1080-1105, 2012. E. N. Lorenz. Predictability: a problem partly solved. In Proceedings, Seminar on Predictability, 4-8 September 1995, volume 1, pages 1-18, Shinfield Park, Reading, 1996. ECMWF. E. N. Lorenz. Regimes in simple systems. J. Atmos. Sci., 63(8):2056-2073, 2006. T. N Palmer. A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parametrisation in weather and climate prediction models. Q. J. Roy. Meteor. Soc., 127(572):279-304, 2001. T. N. Palmer, R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher, G. J. Shutts, M. Steinheimer, and A. Weisheimer. Stochastic parametrization and model uncertainty. Technical Report 598, European Centre for Medium-Range Weather Forecasts, 2009. J. Rougier, D. M. H. Sexton, J. M. Murphy, and D. Stainforth. Analyzing the climate sensitivity of the HadSM3 climate model using ensembles from different but related experiments. J. Climate, 22:3540-3557, 2009. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, Tignor M., and H. L. Miller. Climate models and their evaluation. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, United Kingdom and New York, NY, USA, 2007. Cambridge University Press. D. A Stainforth, T. Aina, C. Christensen, M. Collins, N. Faull, D. J. Frame, J. A. Kettleborough, S. Knight, A. Martin, J. M. Murphy, C. Piani, D. Sexton, L. A. Smith, R. A Spicer, A. J. Thorpe, and M. R Allen. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature, 433(7024):403-406, 2005.

Trends in Global Vegetation Activity and Climatic Drivers Indicate a Decoupled Response to Climate Change

PubMed Central

Schut, Antonius G. T.; Ivits, Eva; Conijn, Jacob G.; ten Brink, Ben; Fensholt, Rasmus

2015-01-01

Detailed understanding of a possible decoupling between climatic drivers of plant productivity and the response of ecosystems vegetation is required. We compared trends in six NDVI metrics (1982–2010) derived from the GIMMS3g dataset with modelled biomass productivity and assessed uncertainty in trend estimates. Annual total biomass weight (TBW) was calculated with the LINPAC model. Trends were determined using a simple linear regression, a Thiel-Sen medium slope and a piecewise regression (PWR) with two segments. Values of NDVI metrics were related to Net Primary Production (MODIS-NPP) and TBW per biome and land-use type. The simple linear and Thiel-Sen trends did not differ much whereas PWR increased the fraction of explained variation, depending on the NDVI metric considered. A positive trend in TBW indicating more favorable climatic conditions was found for 24% of pixels on land, and for 5% a negative trend. A decoupled trend, indicating positive TBW trends and monotonic negative or segmented and negative NDVI trends, was observed for 17–36% of all productive areas depending on the NDVI metric used. For only 1–2% of all pixels in productive areas, a diverging and greening trend was found despite a strong negative trend in TBW. The choice of NDVI metric used strongly affected outcomes on regional scales and differences in the fraction of explained variation in MODIS-NPP between biomes were large, and a combination of NDVI metrics is recommended for global studies. We have found an increasing difference between trends in climatic drivers and observed NDVI for large parts of the globe. Our findings suggest that future scenarios must consider impacts of constraints on plant growth such as extremes in weather and nutrient availability to predict changes in NPP and CO2 sequestration capacity. PMID:26466347

The Role of Forcing and Internal Dynamics in explaining the 'Medieval Climate Anomaly'

NASA Technical Reports Server (NTRS)

Goossee, Hugues; Crespin, Elisabeth; Dubinkina, Svetlana; Loutre, Marie-France; Mann, Michael E.; Renssen, Hans; Shindell, Drew

2012-01-01

Proxy reconstructions suggest that peak global temperature during the past warm interval known as the Medieval Climate Anomaly (MCA, roughly 950-1250 AD) has been exceeded only during the most recent decades. To better understand the origin of this warm period, we use model simulations constrained by data assimilation establishing the spatial pattern of temperature changes that is most consistent with forcing estimates, model physics and the empirical information contained in paleoclimate proxy records. These numerical experiments demonstrate that the reconstructed spatial temperature pattern of the MCA can be explained by a simple thermodynamical response of the climate system to relatively weak changes in radiative forcing combined with a modification of the atmospheric circulation, displaying some similarities with the positive phase of the so-called Arctic Oscillation, and with northward shifts in the position of the Gulf Stream and Kuroshio currents. The mechanisms underlying the MCA are thus quite different from anthropogenic mechanisms responsible for modern global warming.

Manifestation of remote response over the equatorial Pacific in a climate model

NASA Astrophysics Data System (ADS)

Misra, Vasubandhu; Marx, L.

2007-10-01

In this paper we examine the simulations over the tropical Pacific Ocean from long-term simulations of two different versions of the Center for Ocean-Land-Atmosphere Studies (COLA) coupled climate model that have a different global distribution of the inversion clouds. We find that subtle changes made to the numerics of an empirical parameterization of the inversion clouds can result in a significant change in the coupled climate of the equatorial Pacific Ocean. In one coupled simulation of this study we enforce a simple linear spatial filtering of the diagnostic inversion clouds to ameliorate its spatial incoherency (as a result of the Gibbs effect) while in the other we conduct no such filtering. It is found from the comparison of these two simulations that changing the distribution of the shallow inversion clouds prevalent in the subsidence region of the subtropical high over the eastern oceans in this manner has a direct bearing on the surface wind stress through surface pressure modifications. The SST in the warm pool region responds to this modulation of the wind stress, thus affecting the convective activity over the warm pool region and also the large-scale Walker and Hadley circulation. The interannual variability of SST in the eastern equatorial Pacific Ocean is also modulated by this change to the inversion clouds. Consequently, this sensitivity has a bearing on the midlatitude height response. The same set of two experiments were conducted with the respective versions of the atmosphere general circulation model uncoupled to the ocean general circulation model but forced with observed SST to demonstrate that this sensitivity of the mean climate of the equatorial Pacific Ocean is unique to the coupled climate model where atmosphere, ocean and land interact. Therefore a strong case is made for adopting coupled ocean-land-atmosphere framework to develop climate models as against the usual practice of developing component models independent of each other.

Forecasting malaria cases using climatic factors in delhi, India: a time series analysis.

PubMed

Kumar, Varun; Mangal, Abha; Panesar, Sanjeet; Yadav, Geeta; Talwar, Richa; Raut, Deepak; Singh, Saudan

2014-01-01

Background. Malaria still remains a public health problem in developing countries and changing environmental and climatic factors pose the biggest challenge in fighting against the scourge of malaria. Therefore, the study was designed to forecast malaria cases using climatic factors as predictors in Delhi, India. Methods. The total number of monthly cases of malaria slide positives occurring from January 2006 to December 2013 was taken from the register maintained at the malaria clinic at Rural Health Training Centre (RHTC), Najafgarh, Delhi. Climatic data of monthly mean rainfall, relative humidity, and mean maximum temperature were taken from Regional Meteorological Centre, Delhi. Expert modeler of SPSS ver. 21 was used for analyzing the time series data. Results. Autoregressive integrated moving average, ARIMA (0,1,1) (0,1,0)(12), was the best fit model and it could explain 72.5% variability in the time series data. Rainfall (P value = 0.004) and relative humidity (P value = 0.001) were found to be significant predictors for malaria transmission in the study area. Seasonal adjusted factor (SAF) for malaria cases shows peak during the months of August and September. Conclusion. ARIMA models of time series analysis is a simple and reliable tool for producing reliable forecasts for malaria in Delhi, India.

Mechanisms of shrub encroachment into Northern Chihuahuan Desert grasslands and impacts of climate change investigated using a cellular automata model

NASA Astrophysics Data System (ADS)

Caracciolo, Domenico; Istanbulluoglu, Erkan; Noto, Leonardo Valerio; Collins, Scott L.

2016-05-01

Arid and semiarid grasslands of southwestern North America have changed dramatically over the last 150 years as a result of woody plant encroachment. Overgrazing, reduced fire frequency, and climate change are known drivers of woody plant encroachment into grasslands. In this study, relatively simple algorithms for encroachment factors (i.e., grazing, grassland fires, and seed dispersal by grazers) are proposed and implemented in the ecohydrological Cellular-Automata Tree Grass Shrub Simulator (CATGraSS). CATGraSS is used in a 7.3 km2 rectangular domain located in central New Mexico along a zone of grassland to shrubland transition, where shrub encroachment is currently active. CATGraSS is calibrated and used to investigate the relative contributions of grazing, fire frequency, seed dispersal by herbivores and climate change on shrub abundance over a 150-year period of historical shrub encroachment. The impact of future climate change is examined using a model output that realistically represents current vegetation cover as initial condition, in a series of stochastic CATGraSS future climate simulations. Model simulations are found to be highly sensitive to the initial distribution of shrub cover. Encroachment factors more actively lead to shrub propagation within the domain when the model starts with randomly distributed individual shrubs. However, when shrubs are naturally evolved into clusters, the model response to encroachment factors is muted unless the effect of seed dispersal by herbivores is amplified. The relative contribution of different drivers on modeled shrub encroachment varied based on the initial shrub cover condition used in the model. When historical weather data is used, CATGraSS predicted loss of shrub and grass cover during the 1950 s drought. While future climate change is found to amplify shrub encroachment (∼13% more shrub cover by 2100), grazing remains the dominant factor promoting shrub encroachment. When we modeled future climate change, however, encroachment still occurred at a reduced rate in the absence of grazing along with pre-grazing fire frequency because of lower shrub water stress leading to reduced shrub mortality which increases the probability of shrub establishment.

Simulating Pacific Northwest Forest Response to Climate Change: How We Made Model Results Useful for Vulnerability Assessments

NASA Astrophysics Data System (ADS)

Kim, J. B.; Kerns, B. K.; Halofsky, J.

2014-12-01

GCM-based climate projections and downscaled climate data proliferate, and there are many climate-aware vegetation models in use by researchers. Yet application of fine-scale DGVM based simulation output in national forest vulnerability assessments is not common, because there are technical, administrative and social barriers for their use by managers and policy makers. As part of a science-management climate change adaptation partnership, we performed simulations of vegetation response to climate change for four national forests in the Blue Mountains of Oregon using the MC2 dynamic global vegetation model (DGVM) for use in vulnerability assessments. Our simulation results under business-as-usual scenarios suggest a starkly different future forest conditions for three out of the four national forests in the study area, making their adoption by forest managers a potential challenge. However, using DGVM output to structure discussion of potential vegetation changes provides a suitable framework to discuss the dynamic nature of vegetation change compared to using more commonly available model output (e.g. species distribution models). From the onset, we planned and coordinated our work with national forest managers to maximize the utility and the consideration of the simulation results in planning. Key lessons from this collaboration were: (1) structured and strategic selection of a small number climate change scenarios that capture the range of variability in future conditions simplified results; (2) collecting and integrating data from managers for use in simulations increased support and interest in applying output; (3) a structured, regionally focused, and hierarchical calibration of the DGVM produced well-validated results; (4) simple approaches to quantifying uncertainty in simulation results facilitated communication; and (5) interpretation of model results in a holistic context in relation to multiple lines of evidence produced balanced guidance. This latest point demonstrates the importance of using model out as a forum for discussion along with other information, rather than using model output in an inappropriately predictive sense. These lessons are being applied currently to other national forests in the Pacific Northwest to contribute in vulnerability assessments.

Theoretical electron scattering amplitudes and spin polarizations. Electron energies 100 to 1500 eV Part II. Be, N, O, Al, Cl, V, Co, Cu, As, Nb, Ag, Sn, Sb, I, and Ta targets

NASA Astrophysics Data System (ADS)

Wildhaber, M. L.; Wikle, C. K.; Anderson, C. J.; Franz, K. J.; Moran, E. H.; Dey, R.

2012-12-01

Recent decades have brought substantive changes in land use and climate across the earth, prompting a need to think of population and community ecology not as a static entity, but as a dynamic process. Increasingly there is evidence of ecological changes due to climate change. Although much of this evidence comes from ground-truth observations of biogeographic data, there is increasing reliance on models that relate climate variables to biological systems. Such models can then be used to explore potential changes to population and community level ecological systems in response to climate scenarios as obtained from global climate models (GCMs). A key issue associated with modeling ecosystem response to climate is GCM downscaling to regional and local ecological/biological response models that can be used in vulnerability and risk assessments of the potential effects of climate change. The need is for an explicit means for scaling results up or down multiple hierarchical levels and an effective assessment of the level of uncertainty surrounding current knowledge, data, and data collection methods with these goals identified as in need of acceleration in the U.S. Climate Change Science Program FY2009 Implementation Priorities. In the end, such work should provide the information needed to develop adaptation and mitigation methodologies to minimize the effects of directional and nonlinear climate change on the Nation's land, water, ecosystems, and biological populations. We are working to develop an approach that includes multi-scale and hierarchical Bayesian modeling of Missouri River sturgeon population dynamics. Statistical linkages are defined to quantify implications of climate on fish populations of the Missouri River ecosystem. This approach is a hybrid between physical (deterministic) downscaling and statistical downscaling, recognizing that there is uncertainty in both. The model must include linkages between climate and habitat, and between habitat and population. A key advantage of the hierarchical approach used in this study is that it incorporates various sources of observations and includes established scientific knowledge, and associated uncertainties. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. The predictive modeling system being developed will be a powerful tool for evaluating management options for coping with global change consequences and assessing uncertainty of those evaluations. Specifically for the endangered pallid sturgeon (Scaphirhynchus albus), we are already able to assess potential effects of any climate scenario on growth and population size distribution. Future models will incorporate survival and reproduction. Ultimately, these models provide guidance for successful recovery and conservation of the pallid sturgeon. Here we present a basic outline of the approach we are developing and a simple pallid sturgeon example to demonstrate how multiple scales and parameter uncertainty are incorporated.

The impact of forest structure and light utilization on carbon cycling in tropical forests

NASA Astrophysics Data System (ADS)

Morton, D. C.; Longo, M.; Leitold, V.; Keller, M. M.

2015-12-01

Light competition is a fundamental organizing principle of forest ecosystems, and interactions between forest structure and light availability provide an important constraint on forest productivity. Tropical forests maintain a dense, multi-layered canopy, based in part on abundant diffuse light reaching the forest understory. Climate-driven changes in light availability, such as more direct illumination during drought conditions, therefore alter the potential productivity of forest ecosystems during such events. Here, we used multi-temporal airborne lidar data over a range of Amazon forest conditions to explore the influence of forest structure on gross primary productivity (GPP). Our analysis combined lidar-based observations of canopy illumination and turnover in the Ecosystem Demography model (ED, version 2.2). The ED model was updated to specifically account for regional differences in canopy and understory illumination using lidar-derived measures of canopy light environments. Model simulations considered the influence of forest structure on GPP over seasonal to decadal time scales, including feedbacks from differential productivity between illuminated and shaded canopy trees on mortality rates and forest composition. Finally, we constructed simple scenarios with varying diffuse and direct illumination to evaluate the potential for novel plant-climate interactions under scenarios of climate change. Collectively, the lidar observations and model simulations underscore the need to account for spatial heterogeneity in the vertical structure of tropical forests to constrain estimates of tropical forest productivity under current and future climate conditions.

Methodology to assess and map the potential development of forest ecosystems exposed to climate change and atmospheric nitrogen deposition: A pilot study in Germany.

PubMed

Schröder, Winfried; Nickel, Stefan; Jenssen, Martin; Riediger, Jan

2015-07-15

A methodology for mapping ecosystems and their potential development under climate change and atmospheric nitrogen deposition was developed using examples from Germany. The methodology integrated data on vegetation, soil, climate change and atmospheric nitrogen deposition. These data were used to classify ecosystem types regarding six ecological functions and interrelated structures. Respective data covering 1961-1990 were used for reference. The assessment of functional and structural integrity relies on comparing a current or future state with an ecosystem type-specific reference. While current functions and structures of ecosystems were quantified by measurements, potential future developments were projected by geochemical soil modelling and data from a regional climate change model. The ecosystem types referenced the potential natural vegetation and were mapped using data on current tree species coverage and land use. In this manner, current ecosystem types were derived, which were related to data on elevation, soil texture, and climate for the years 1961-1990. These relations were quantified by Classification and Regression Trees, which were used to map the spatial patterns of ecosystem type clusters for 1961-1990. The climate data for these years were subsequently replaced by the results of a regional climate model for 1991-2010, 2011-2040, and 2041-2070. For each of these periods, one map of ecosystem type clusters was produced and evaluated with regard to the development of areal coverage of ecosystem type clusters over time. This evaluation of the structural aspects of ecological integrity at the national level was added by projecting potential future values of indicators for ecological functions at the site level by using the Very Simple Dynamic soil modelling technique based on climate data and two scenarios of nitrogen deposition as input. The results were compared to the reference and enabled an evaluation of site-specific ecosystem changes over time which proved to be both, positive and negative. Copyright © 2015 Elsevier B.V. All rights reserved.

The CSIRO Mk3L climate system model v1.0 coupled to the CABLE land surface scheme v1.4b: evaluation of the control climatology

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

Mao, Jiafu; Phipps, S.J.; Pitman, A.J.

The CSIRO Mk3L climate system model, a reduced-resolution coupled general circulation model, has previously been described in this journal. The model is configured for millennium scale or multiple century scale simulations. This paper reports the impact of replacing the relatively simple land surface scheme that is the default parameterisation in Mk3L with a sophisticated land surface model that simulates the terrestrial energy, water and carbon balance in a physically and biologically consistent way. An evaluation of the new model s near-surface climatology highlights strengths and weaknesses, but overall the atmospheric variables, including the near-surface air temperature and precipitation, are simulatedmore » well. The impact of the more sophisticated land surface model on existing variables is relatively small, but generally positive. More significantly, the new land surface scheme allows an examination of surface carbon-related quantities including net primary productivity which adds significantly to the capacity of Mk3L. Overall, results demonstrate that this reduced-resolution climate model is a good foundation for exploring long time scale phenomena. The addition of the more sophisticated land surface model enables an exploration of important Earth System questions including land cover change and abrupt changes in terrestrial carbon storage.« less

Terrestrial biosphere changes over the last 120 kyr and their impact on ocean δ 13C

NASA Astrophysics Data System (ADS)

Hoogakker, B. A. A.; Smith, R. S.; Singarayer, J. S.; Marchant, R.; Prentice, I. C.; Allen, J. R. M.; Anderson, R. S.; Bhagwat, S. A.; Behling, H.; Borisova, O.; Bush, M.; Correa-Metrio, A.; de Vernal, A.; Finch, J. M.; Fréchette, B.; Lozano-Garcia, S.; Gosling, W. D.; Granoszewski, W.; Grimm, E. C.; Grüger, E.; Hanselman, J.; Harrison, S. P.; Hill, T. R.; Huntley, B.; Jiménez-Moreno, G.; Kershaw, P.; Ledru, M.-P.; Magri, D.; McKenzie, M.; Müller, U.; Nakagawa, T.; Novenko, E.; Penny, D.; Sadori, L.; Scott, L.; Stevenson, J.; Valdes, P. J.; Vandergoes, M.; Velichko, A.; Whitlock, C.; Tzedakis, C.

2015-03-01

A new global synthesis and biomization of long (>40 kyr) pollen-data records is presented, and used with simulations from the HadCM3 and FAMOUS climate models to analyse the dynamics of the global terrestrial biosphere and carbon storage over the last glacial-interglacial cycle. Global modelled (BIOME4) biome distributions over time generally agree well with those inferred from pollen data. The two climate models show good agreement in global net primary productivity (NPP). NPP is strongly influenced by atmospheric carbon dioxide (CO2) concentrations through CO2 fertilization. The combined effects of modelled changes in vegetation and (via a simple model) soil carbon result in a global terrestrial carbon storage at the Last Glacial Maximum that is 210-470 Pg C less than in pre-industrial time. Without the contribution from exposed glacial continental shelves the reduction would be larger, 330-960 Pg C. Other intervals of low terrestrial carbon storage include stadial intervals at 108 and 85 ka BP, and between 60 and 65 ka BP during Marine Isotope Stage 4. Terrestrial carbon storage, determined by the balance of global NPP and decomposition, influences the stable carbon isotope composition (δ13C) of seawater because terrestrial organic carbon is depleted in 13C. Using a simple carbon-isotope mass balance equation we find agreement in trends between modelled ocean δ13C based on modelled land carbon storage, and palaeo-archives of ocean δ13C, confirming that terrestrial carbon storage variations may be important drivers of ocean δ13C changes.

Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations

NASA Astrophysics Data System (ADS)

Schneider, Tapio; Lan, Shiwei; Stuart, Andrew; Teixeira, João.

2017-12-01

Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both and quantifies uncertainties. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.

Dynamics of the Coupled Human-climate System Resulting from Closed-loop Control of Solar Geoengineering

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

MacMartin, Douglas; Kravitz, Benjamin S.; Keith, David

2014-07-08

If solar radiation management (SRM) were ever implemented, feedback of the observed climate state might be used to adjust the radiative forcing of SRM, in order to compensate for uncertainty in either the forcing or the climate response; this would also compensate for unexpected changes in the system, e.g. a nonlinear change in climate sensitivity. This feedback creates an emergent coupled human-climate system, with entirely new dynamics. In addition to the intended response to greenhouse-gas induced changes, the use of feedback would also result in a geoengineering response to natural climate variability. We use a simple box-diffusion dynamic model tomore » understand how changing feedback-control parameters and time delay affect the behavior of this coupled natural-human system, and verify these predictions using the HadCM3L general circulation model. In particular, some amplification of natural variability is unavoidable; any time delay (e.g., to average out natural variability, or due to decision-making) exacerbates this amplification, with oscillatory behavior possible if there is a desire for rapid correction (high feedback gain), but a delayed response needed for decision making. Conversely, the need for feedback to compensate for uncertainty, combined with a desire to avoid excessive amplification, results in a limit on how rapidly SRM could respond to uncertain changes.« less

Martian climate - An empirical test of possible gross variations

NASA Technical Reports Server (NTRS)

Owen, T.

1974-01-01

There appears to be evidence for a cyclic behavior of the Martian climate in which the surface pressure periodically reaches values compatible with the flow of water in equatorial regions on the planet. A relatively simple test of such hypotheses is pointed out. The premise on which cyclic models are based is that a substantial reservoir of volatils exist in frozen form at one or both poles. The proposed test involves a determination of the relative abundances of neon and argon isotopes. The required measurements may be made after the soft landing next February of Soviet spacecraft presently en route to the planet.

Global sea level linked to global temperature

PubMed Central

Vermeer, Martin; Rahmstorf, Stefan

2009-01-01

We propose a simple relationship linking global sea-level variations on time scales of decades to centuries to global mean temperature. This relationship is tested on synthetic data from a global climate model for the past millennium and the next century. When applied to observed data of sea level and temperature for 1880–2000, and taking into account known anthropogenic hydrologic contributions to sea level, the correlation is >0.99, explaining 98% of the variance. For future global temperature scenarios of the Intergovernmental Panel on Climate Change's Fourth Assessment Report, the relationship projects a sea-level rise ranging from 75 to 190 cm for the period 1990–2100. PMID:19995972

A Summary of the Naval Postgraduate School Research Program.

DTIC Science & Technology

1981-04-01

1467-1489. K. M. Lau," Climatic Feedback Mechanisms in the Tropical Pacific,"Ocean Modeling , 28. K. M. Lau,"Oscillation in a Simple Equatorial...and 74 R. H. Shudde Support J. K. Arima Research in Officer Manpower and 75 P. R. Milch Personnel Planning R. A. Weitzman D. R. Barr C3 Technology...Procedures D. P. Gaver Modeling and Influence of Informa- 83 tion on the Progress of Conflict or Combat by Mathematical and Compu- tational Methods D. P. Gaver

Terraforming planet Dune: Climate-vegetation interactions on a sandy planet

NASA Astrophysics Data System (ADS)

Cresto Aleina, F.; Baudena, M.; D'Andrea, F.; Provenzale, A.

2012-04-01

The climate and the biosphere of planet Earth interact in multiple, complicated ways and on many spatial and temporal scales. Some of these processes can be studied with the help of simple mathematical models, as done for the effects of vegetation on albedo in desert areas and for the mechanisms by which terrestrial vegetation affects water fluxes in arid environments. Conceptual models of this kind do not attempt at providing quantitative descriptions of the climate-biosphere interaction, but rather to explore avenues and mechanisms which can play a role in the real system, providing inspiration for further research. In this work, we develop a simple conceptual box model in the spirit illustrated above, to explore whether and how vegetation affects the planetary hydrologic cycle. We imagine a planet with no oceans and whose surface is entirely covered with sand, quite similar to planet Dune of the science-fiction series by Frank Herbert (1965). We suppose that water is entirely in the sand, below the surface. Without vegetation, only evaporation takes place, affecting the upper sand layer for a maximum depth of a few cm. The amount of water that is evaporated in the atmosphere is relatively small, and not sufficient to trigger a full hydrologic cycle. The question is what happens to this planet when vegetation is introduced: the root depth can reach a meter or more, and plant transpiration can then transfer a much larger amount of water to the atmosphere. One may wonder whether the presence of vegetation is sufficient to trigger a hydrologic cycle with enough precipitation to sustain the vegetation itself and, if the answer is positive, what is the minimum vegetation cover that is required to maintain the cycle active. In more precise terms, we want to know whether the introduction of vegetation and of the evapotranspiration feedback allows for the existence of multiple equilibria (or solutions) in the soil-vegetation-atmosphere system. Although the box model introduced here is best formulated in terms of a hypothetical sandy planet, the results can be used to study the hydrologic cycle on wide continental regions of the Earth. On the other hand, our findings show how the definition of a habitable climate may also depend on surface characteristics, and in particular on biosphere and climate interactions.

Sensitivity of the Eocene climate to CO2 and orbital variability

NASA Astrophysics Data System (ADS)

Keery, John S.; Holden, Philip B.; Edwards, Neil R.

2018-02-01

The early Eocene, from about 56 Ma, with high atmospheric CO2 levels, offers an analogue for the response of the Earth's climate system to anthropogenic fossil fuel burning. In this study, we present an ensemble of 50 Earth system model runs with an early Eocene palaeogeography and variation in the forcing values of atmospheric CO2 and the Earth's orbital parameters. Relationships between simple summary metrics of model outputs and the forcing parameters are identified by linear modelling, providing estimates of the relative magnitudes of the effects of atmospheric CO2 and each of the orbital parameters on important climatic features, including tropical-polar temperature difference, ocean-land temperature contrast, Asian, African and South (S.) American monsoon rains, and climate sensitivity. Our results indicate that although CO2 exerts a dominant control on most of the climatic features examined in this study, the orbital parameters also strongly influence important components of the ocean-atmosphere system in a greenhouse Earth. In our ensemble, atmospheric CO2 spans the range 280-3000 ppm, and this variation accounts for over 90 % of the effects on mean air temperature, southern winter high-latitude ocean-land temperature contrast and northern winter tropical-polar temperature difference. However, the variation of precession accounts for over 80 % of the influence of the forcing parameters on the Asian and African monsoon rainfall, and obliquity variation accounts for over 65 % of the effects on winter ocean-land temperature contrast in high northern latitudes and northern summer tropical-polar temperature difference. Our results indicate a bimodal climate sensitivity, with values of 4.36 and 2.54 °C, dependent on low or high states of atmospheric CO2 concentration, respectively, with a threshold at approximately 1000 ppm in this model, and due to a saturated vegetation-albedo feedback. Our method gives a quantitative ranking of the influence of each of the forcing parameters on key climatic model outputs, with additional spatial information from singular value decomposition providing insights into likely physical mechanisms. The results demonstrate the importance of orbital variation as an agent of change in climates of the past, and we demonstrate that emulators derived from our modelling output can be used as rapid and efficient surrogates of the full complexity model to provide estimates of climate conditions from any set of forcing parameters.

Incorporating climate change and morphological uncertainty into coastal change hazard assessments

USGS Publications Warehouse

Baron, Heather M.; Ruggiero, Peter; Wood, Nathan J.; Harris, Erica L.; Allan, Jonathan; Komar, Paul D.; Corcoran, Patrick

2015-01-01

Documented and forecasted trends in rising sea levels and changes in storminess patterns have the potential to increase the frequency, magnitude, and spatial extent of coastal change hazards. To develop realistic adaptation strategies, coastal planners need information about coastal change hazards that recognizes the dynamic temporal and spatial scales of beach morphology, the climate controls on coastal change hazards, and the uncertainties surrounding the drivers and impacts of climate change. We present a probabilistic approach for quantifying and mapping coastal change hazards that incorporates the uncertainty associated with both climate change and morphological variability. To demonstrate the approach, coastal change hazard zones of arbitrary confidence levels are developed for the Tillamook County (State of Oregon, USA) coastline using a suite of simple models and a range of possible climate futures related to wave climate, sea-level rise projections, and the frequency of major El Niño events. Extreme total water levels are more influenced by wave height variability, whereas the magnitude of erosion is more influenced by sea-level rise scenarios. Morphological variability has a stronger influence on the width of coastal hazard zones than the uncertainty associated with the range of climate change scenarios.

Incorporating Student Activities into Climate Change Education

NASA Astrophysics Data System (ADS)

Steele, H.; Kelly, K.; Klein, D.; Cadavid, A. C.

2013-12-01

Under a NASA grant, Mathematical and Geospatial Pathways to Climate Change Education, students at California State University, Northridge integrated Geographic Information Systems (GIS), remote sensing, satellite data technologies, and climate modelling into the study of global climate change under a Pathway for studying the Mathematics of Climate Change (PMCC). The PMCC, which is an interdisciplinary option within the BS in Applied Mathematical Sciences, consists of courses offered by the departments of Mathematics, Physics, and Geography and is designed to prepare students for careers and Ph.D. programs in technical fields relevant to global climate change. Under this option students are exposed to the science, mathematics, and applications of climate change science through a variety of methods including hands-on experience with computer modeling and image processing software. In the Geography component of the program, ESRI's ArcGIS and ERDAS Imagine mapping, spatial analysis and image processing software were used to explore NASA satellite data to examine the earth's atmosphere, hydrosphere and biosphere in areas that are affected by climate change or affect climate. These technology tools were incorporated into climate change and remote sensing courses to enhance students' knowledge and understanding of climate change through hands-on application of image processing techniques to NASA data. Several sets of exercises were developed with specific learning objectives in mind. These were (1) to increase student understanding of climate change and climate change processes; (2) to develop student skills in understanding, downloading and processing satellite data; (3) to teach remote sensing technology and GIS through applications to climate change; (4) to expose students to climate data and methods they can apply to solve real world problems and incorporate in future research projects. In the Math and Physics components of the course, students learned about atmospheric circulation with applications of the Lorenz model, explored the land-sea breeze problem with the Dynamics and Thermodynamics Circulation Model (DTDM), and developed simple radiative transfer models. Class projects explored the effects of varying the content of CO2 and CH4 in the atmosphere, as well as the properties of paleoclimates in atmospheric simulations using EdGCM. Initial assessment of student knowledge, attitudes, and behaviors associated with these activities, particularly about climate change, was measured. Pre- and post-course surveys provided student perspectives about the courses and their learning about remote sensing and climate change concepts. Student performance on the tutorials and course projects evaluated students' ability to learn and apply their knowledge about climate change and skills with remote sensing to assigned problems or proposed projects of their choice. Survey and performance data illustrated that the exercises were successful in meeting their intended learning objectives as well as opportunities for further refinement and expansion.

Stochastic Modeling of Soil Salinity

NASA Astrophysics Data System (ADS)

Suweis, Samir; Rinaldo, Andrea; van der Zee, Sjoerd E. A. T. M.; Maritan, Amos; Porporato, Amilcare

2010-05-01

Large areas of cultivated land worldwide are affected by soil salinity. Estimates report that 10% of arable land in over 100 countries, and nine million km2 are salt affected, especially in arid and semi-arid regions. High salinity causes both ion specific and osmotic stress effects, with important consequences for plant production and quality. Salt accumulation in the root zone may be due to natural factors (primary salinization) or due to irrigation (secondary salinization). Simple (e.g., vertically averaged over the soil depth) coupled soil moisture and salt balance equations have been used in the past. Despite their approximations, these models have the advantage of parsimony, thus allowing a direct analysis of the interplay of the main processes. They also provide the ideal starting point to include external, random hydro-climatic fluctuations in the analysis of long-term salinization trends. We propose a minimalist stochastic model of primary soil salinity, in which the rate of soil salinization is determined by the balance between dry and wet salt deposition and the intermittent leaching events caused by rainfall events. The long term probability density functions of salt mass and concentration are found by reducing the coupled soil moisture and salt mass balance equation to a stochastic differential equation driven by multiplicative Poisson noise. The novel analytical solutions provide insight on the interplay of the main soil, plant and climate parameters responsible for long-term soil salinization. In fact, soil salinity statistics are obtained as a function of climate, soil and vegetation parameters. These, in turn, can be combined with soil moisture statistics to obtain a full characterization of soil salt concentrations and the ensuing risk of primary salinization. In particular, the solutions show the existence of two quite distinct regimes, the first one where the mean salt mass remains nearly constant with increasing rainfall frequency, and the second one where mean salt content increases markedly with increasing rainfall frequency. As a result, relatively small reductions of rainfall in drier climates may entail dramatic shifts in long-term soil salinization trends, with significant consequences e.g. for climate change impacts on rain-fed agriculture. The analytical nature of the solution allows direct estimation of the impact of changes in the climatic drivers on soil salinity and makes it suitable for computations of salinity risk at the global scale as a function of simple parameters. Moreover it facilitates their coupling with other models of long-term soil-plant biogeochemistry.

Exploring Water Management Options with SIWA: A Simple, Coupled Human-Water-Climate Model

NASA Astrophysics Data System (ADS)

Motesharrei, S.; Gustafson, K. C.; Zhao, F.; Rivas, J.; Zeng, N.; Miralles-Wilhelm, F.; Kalnay, E.

2013-12-01

Water is, and has always been, a critical resource for survival of civilizations and a key to prosperity of societies. Over the past several decades, demand for freshwater has increased significantly due to growth of both population and consumption. Such soaring demands have put serious strain on freshwater sources at many regions of the world, and climate change can only worsen the uncertainty in availability of needed freshwater. Therefore, it is essential to study the water system in conjunction with the Earth system and the Human system. Most importantly, we need to understand effectiveness of various managerial decisions on the water system, since efficient policy making is the only viable solution for sustaining water sources and supply (reservoir) at any water-scarce region of the world. We have developed a SImple WAter model (SIWA) that is integrated with the human system and the earth system through bidirectional feedbacks. Policies are introduced as drivers of the model so that the effect of each policy on the system can be measured as we change its level. We have applied our model to two data-rich watersheds in the United States: Phoenix AMA watershed and the Potomac River Basin. The latter receives plenty of precipitation while the former is rather dry. Model is trained with the data from 1900-2010, and then projections are made for the next several decades. Historical data were recovered from the records at the US National Archives. We have also used remotely sensed satellite data in conjunction with data from local municipalities. Response of the system to six different short and long term policies are presented under three different climate scenarios. We show that it is possible to guarantee the freshwater supply and sustain the freshwater sources through a proper set of policy choices for any specific region.

Taking the Pulse of PyroCumulus Clouds

NASA Technical Reports Server (NTRS)

Gatebe, C. K.; Varnai, T.; Poudyal, R.; Ichoku, C.; King, M. D.

2012-01-01

Forest fires can burn large areas, but can also inject smoke into the upper troposphere/lower stratosphere (UT/LS), where stakes are even higher for climate, because emissions tend to have a longer lifetime, and can produce significant regional and even global climate effects, as is the case with some volcanoes. Large forest fires are now believed to be more common in summer, especially in the boreal regions, where pyrocumulus (pyroCu), and occasionally pyrocumuionimbus (pyroCb) clouds are formed, which can transport emissions into the UT/LS. A major difficulty in developing realistic fire plume models is the lack of observational data within fire plumes that resolves structure at a few 100 m scales, which can be used to validate these models. Here, we report detailed airborne radiation measurements within strong pyroCu taken over boreal forest fires in Saskatchewan, Canada during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) summer field campaign in 2008. We find that the angular distribution of radiance within the pyroCu is closely related to the diffusion domain in water clouds and can be described by very similar simple cosine functions. We demonstrate with Monte Carlo simulations that radiation transport in pyroCu is inherently a 3D phenomenon and must account for particle absorption. However, the simple cosine function promises to offer an easy solution for climate models. The presence of a prominent smoke core, defined by strong extinction in the UV, VIS and NIR, suggests that the core might be an important pathway for emission transport to the upper troposphere and lower stratosphere. We speculate that this plume injection core is generated and sustained by complex processes not yet well understood, but not necessarily related directly to the intense fires that originally initiated the plume rise.

A comprehensive surface-groundwater flow model

NASA Astrophysics Data System (ADS)

Arnold, Jeffrey G.; Allen, Peter M.; Bernhardt, Gilbert

1993-02-01

In this study, a simple groundwater flow and height model was added to an existing basin-scale surface water model. The linked model is: (1) watershed scale, allowing the basin to be subdivided; (2) designed to accept readily available inputs to allow general use over large regions; (3) continuous in time to allow simulation of land management, including such factors as climate and vegetation changes, pond and reservoir management, groundwater withdrawals, and stream and reservoir withdrawals. The model is described, and is validated on a 471 km 2 watershed near Waco, Texas. This linked model should provide a comprehensive tool for water resource managers in development and planning.

Dynamic reserve design in the face of climate change and urbanization

USGS Publications Warehouse

Romañach, Stephanie; Johnson, Fred A.; Stith, Bradley M.; Bonneau, Mathieu

2015-01-01

Reserve design is a process that must address many ecological, social, and political factors to successfully identify parcels of land in need of protection to sustain wildlife populations and other natural resources. Making land acquisition choices for a large, terrestrial protected area is difficult because it occurs over a long timeframe and may involve consideration future conditions such as climate and urbanization changes. Decision makers need to consider factors including: order of parcel purchasing given budget constraints, future uncertainty, potential future landscape‐scale changes from urbanization and climate. In central Florida, two new refuges and the expansion of a third refuge are in various stages of USFWS planning. The Everglades Headwaters National Wildlife Refuge (EHNWR) has recently been established, is at the top of the Presidential Administration’s priority conservation areas, and is cited by the Secretary of DOI routinely in the context of conservation. The new refuges were strategically located for both for species adaptation from climate change impacts as well as currently being host to a number of important threatened and endangered species and habitats. We plan to combine a structured decision making framework, optimal solution theory, and output from ecological and sociological models (these modeling efforts were previously funded by DOI partners) that incorporate climate change to provide guidance for EHNWR reserve design. Utilizing a SDM approach and optimal solution theory, decision support tools will be developed that will incorporate stakeholder and agency objectives into targeting conservation lands both through fee simple purchase and other incentives such as easements based on ecological and socioeconomic modeling outputs driven by climate change.

A (very) Simple Model for the Aspect Ratio of High-Order River Basins

NASA Astrophysics Data System (ADS)

Shelef, E.

2017-12-01

The structure of river networks dictates the distribution of elevation, water, and sediments across Earth's surface. Despite its intricate shape, the structure of high-order river networks displays some surprising regularities such as the consistent aspect ratio (i.e., basin's width over length) of river basins along linear mountain fronts. This ratio controls the spacing between high-order channels as well as the spacing between the depositional bodies they form. It is generally independent of tectonic and climatic conditions and is often attributed to the initial topography over which the network was formed. This study shows that a simple, cross-like channel model explains this ratio via a requirement for equal elevation gain between the outlets and drainage-divides of adjacent channels at topographic steady state. This model also explains the dependence of aspect ratio on channel concavity and the location of the widest point on a drainage divide.

Remote tropical and sub-tropical responses to Amazon deforestation

NASA Astrophysics Data System (ADS)

Badger, Andrew M.; Dirmeyer, Paul A.

2016-05-01

Replacing natural vegetation with realistic tropical crops over the Amazon region in a global Earth system model impacts vertical transport of heat and moisture, modifying the interaction between the atmospheric boundary layer and the free atmosphere. Vertical velocity is decreased over a majority of the Amazon region, shifting the ascending branch and modifying the seasonality of the Hadley circulation over the Atlantic and eastern Pacific oceans. Using a simple model that relates circulation changes to heating anomalies and generalizing the upper-atmosphere temperature response to deforestation, agreement is found between the response in the fully-coupled model and the simple solution. These changes to the large-scale dynamics significantly impact precipitation in several remote regions, namely sub-Saharan Africa, Mexico, the southwestern United States and extratropical South America, suggesting non-local climate repercussions for large-scale land use changes in the tropics are possible.

Changing skewness: an early warning signal of regime shifts in ecosystems.

PubMed

Guttal, Vishwesha; Jayaprakash, Ciriyam

2008-05-01

Empirical evidence for large-scale abrupt changes in ecosystems such as lakes and vegetation of semi-arid regions is growing. Such changes, called regime shifts, can lead to degradation of ecological services. We study simple ecological models that show a catastrophic transition as a control parameter is varied and propose a novel early warning signal that exploits two ubiquitous features of ecological systems: nonlinearity and large external fluctuations. Either reduced resilience or increased external fluctuations can tip ecosystems to an alternative stable state. It is shown that changes in asymmetry in the distribution of time series data, quantified by changing skewness, is a model-independent and reliable early warning signal for both routes to regime shifts. Furthermore, using model simulations that mimic field measurements and a simple analysis of real data from abrupt climate change in the Sahara, we study the feasibility of skewness calculations using data available from routine monitoring.

Quantitative Modeling of Earth Surface Processes

NASA Astrophysics Data System (ADS)

Pelletier, Jon D.

This textbook describes some of the most effective and straightforward quantitative techniques for modeling Earth surface processes. By emphasizing a core set of equations and solution techniques, the book presents state-of-the-art models currently employed in Earth surface process research, as well as a set of simple but practical research tools. Detailed case studies demonstrate application of the methods to a wide variety of processes including hillslope, fluvial, aeolian, glacial, tectonic, and climatic systems. Exercises at the end of each chapter begin with simple calculations and then progress to more sophisticated problems that require computer programming. All the necessary computer codes are available online at www.cambridge.org/9780521855976. Assuming some knowledge of calculus and basic programming experience, this quantitative textbook is designed for advanced geomorphology courses and as a reference book for professional researchers in Earth and planetary science looking for a quantitative approach to Earth surface processes.

Long-term simulations of dissolved oxygen concentrations in Lake Trout lakes

NASA Astrophysics Data System (ADS)

Jabbari, A.; Boegman, L.; MacKay, M.; Hadley, K.; Paterson, A.; Jeziorski, A.; Nelligan, C.; Smol, J. P.

2016-02-01

Lake Trout are a rare and valuable natural resource that are threatened by multiple environmental stressors. With the added threat of climate warming, there is growing concern among resource managers that increased thermal stratification will reduce the habitat quality of deep-water Lake Trout lakes through enhanced oxygen depletion. To address this issue, a three-part study is underway, which aims to: analyze sediment cores to understand the past, develop empirical formulae to model the present and apply computational models to forecast the future. This presentation reports on the computational modeling efforts. To this end, a simple dissolved oxygen sub-model has been embedded in the one-dimensional bulk mixed-layer thermodynamic Canadian Small Lake Model (CSLM). This model is currently being incorporated into the Canadian Land Surface Scheme (CLASS), the primary land surface component of Environment Canada's global and regional climate modelling systems. The oxygen model was calibrated and validated by hind-casting temperature and dissolved oxygen profiles from two Lake Trout lakes on the Canadian Shield. These data sets include 5 years of high-frequency (10 s to 10 min) data from Eagle Lake and 30 years of bi-weekly data from Harp Lake. Initial results show temperature and dissolved oxygen was predicted with root mean square error <1.5 °C and <3 mgL-1, respectively. Ongoing work is validating the model, over climate-change relevant timescales, against dissolved oxygen reconstructions from the sediment cores and predicting future deep-water temperature and dissolved oxygen concentrations in Canadian Lake Trout lakes under future climate change scenarios. This model will provide a useful tool for managers to ensure sustainable fishery resources for future generations.

Hydraulic redistribution of soil water in two old-growth coniferous forests: quantifying patterns and controls.

Treesearch

J.M. Warren; F.C. Meinzer; J.R. Brooks; J.-C. Domec; R. Coulombe

2006-01-01

We incorporated soil/plant biophysical properties into a simple model to predict seasonal trajectories of hydraulic redistribution (HR). We measured soil water content, water potential root conductivity, and climate across multiple years in two old-growth coniferous forests. The HR variability within sites (0 to 0.5 mm/d) was linked to spatial patterns of roots, soil...

Climate effects caused by land plant invasion in the Devonian

NASA Astrophysics Data System (ADS)

Hir guillaume, Le; yannick, Donnadieu; yves, Goddéris; brigitte, Meyer-Berthaud; gilles, Ramstein

2017-04-01

Land plants invaded continents during the Mid-Paleozoic. Their spreading and diversification have been compared to the Cambrian explosion in terms of intensity and impact on the diversification of life on Earth. Whereas prior studies were focused on the evolution of the root system and its weathering contribution, here we investigated the biophysical impacts of plant colonization on the surface climate through changes in continental albedo, roughness, thermal properties, and potential evaporation using a 3D-climate model coupled to a global biogeochemical cycles associated to a simple model for vegetation dynamics adapted to Devonian conditions. From the Early to the Late Devonian, we show that continental surface changes induced by land plants and tectonic drift have produced a large CO2 drawdown without being associated to a global cooling, because the cooling trend is counteracted by a warming trend resulting from the surface albedo reduction. If CO2 is consensually assumed as the main driver of the Phanerozoic climate, during land-plant invasion, the modifications of soil properties could have played in the opposite direction of the carbon dioxide fall, hence maintaining warm temperatures during part of the Devonian.

Global optimum vegetation rain water use is determined by aridity

NASA Astrophysics Data System (ADS)

Good, S. P.; Wang, L.; Caylor, K. K.

2015-12-01

The amount of rainwater that vegetation is able to transpire directly determines the total productivity of ecosystems, yet broad-scale trends in this sub-component of total evapotranspiration remain unclear. Since development in the 1970's, the Budyko framework has provided a simple, first-order, approach to partitioning total rainfall into runoff and evapotranspiration across climates. However, this classic paradigm provides little insight into the strength of biological mediation (i.e. transpiration flux) of the hydrologic cycle. Through a minimalist stochastic hydrology model we analytically extend the classical Budyko framework to predict the magnitude of transpiration relative to total rainfall as a function of ecosystem aridity. Consistent with a synthesis of experimental partitioning studies across climates, this model suggests a peak in the biological contribution to the hydrologic cycle at intermediate moisture values, with both arid and wet climates seeing decreased transpiration:precipitation ratios. To best match observed transpiration:precipitation ratios requires incorporation of elevated evaporation at lower canopy covers due to greater energy availability at the soil surface and elevated evaporation at higher canopy covers due to greater interception amounts. This new approach provides a connection between current and future climate regimes, hydrologic flux partitioning, and macro-system ecology.

Precipitation-centered Conceptual Model for Sub-humid Uplands in Lampasas Cut Plains, TX

NASA Astrophysics Data System (ADS)

Potter, S. R.; Tu, M.; Wilcox, B. P.

2011-12-01

Conceptual understandings of dominant hydrological processes, system interactions and feedbacks, and external forcings operating within catchments often defy simple definition and explanation, especially catchments encompassing transition zones, degraded landscapes, rapid development, and where climate forcings exhibit large variations across time and space. However, it is precisely those areas for which understanding and knowledge are most needed to innovate sustainable management strategies and counter past management blunders and failed restoration efforts. The cut plain of central Texas is one such area. Complex geographic and climatic factors lead to spatially and temporally variable precipitation having frequent dry periods interrupted by intense high-volume precipitation. Fort Hood, an army post located in the southeast cut plain contains landscapes ranging from highly degraded to nearly pristine with a topography mainly comprised of flat-topped mesas separated by broad u-shaped valleys. To understand the hydrology of the area and responses to wet-dry cycles we analyzed 4-years of streamflow and rainfall from 8 catchments, sized between 1819 and 16,000 ha. Since aquifer recharge/discharge and surface stream-groundwater interactions are unimportant, we hypothesized a simple conceptual model driven by precipitation and radiative forcings and having stormflow, baseflow, ET, and two hypothetical storage components. The key storage component was conceptualized as a buffer that was highly integrated with the ET component and exerted controls on baseflow. Radiative energy controlled flux from the buffer to ET. We used the conceptual model in making a bimonthly hydrologic budget, which included buffer volumes and a deficit-surplus indicator. Through the analysis, we were led to speculate that buffer capacity plays key roles in these landscapes and even relatively minor changes in capacity, due to soil compaction for example, might lead to ecological shifts. The model led us to other hypotheses concerning stormflow mechanisms and controls on baseflow, which we then tested against observations. It was instructive that such a simple model could lead to interesting new theories.

Lags in the response of mountain plant communities to climate change.

PubMed

Alexander, Jake M; Chalmandrier, Loïc; Lenoir, Jonathan; Burgess, Treena I; Essl, Franz; Haider, Sylvia; Kueffer, Christoph; McDougall, Keith; Milbau, Ann; Nuñez, Martin A; Pauchard, Aníbal; Rabitsch, Wolfgang; Rew, Lisa J; Sanders, Nathan J; Pellissier, Loïc

2018-02-01

Rapid climatic changes and increasing human influence at high elevations around the world will have profound impacts on mountain biodiversity. However, forecasts from statistical models (e.g. species distribution models) rarely consider that plant community changes could substantially lag behind climatic changes, hindering our ability to make temporally realistic projections for the coming century. Indeed, the magnitudes of lags, and the relative importance of the different factors giving rise to them, remain poorly understood. We review evidence for three types of lag: "dispersal lags" affecting plant species' spread along elevational gradients, "establishment lags" following their arrival in recipient communities, and "extinction lags" of resident species. Variation in lags is explained by variation among species in physiological and demographic responses, by effects of altered biotic interactions, and by aspects of the physical environment. Of these, altered biotic interactions could contribute substantially to establishment and extinction lags, yet impacts of biotic interactions on range dynamics are poorly understood. We develop a mechanistic community model to illustrate how species turnover in future communities might lag behind simple expectations based on species' range shifts with unlimited dispersal. The model shows a combined contribution of altered biotic interactions and dispersal lags to plant community turnover along an elevational gradient following climate warming. Our review and simulation support the view that accounting for disequilibrium range dynamics will be essential for realistic forecasts of patterns of biodiversity under climate change, with implications for the conservation of mountain species and the ecosystem functions they provide. © 2017 John Wiley & Sons Ltd.

The Effects of Anthropogenic Land Cover Change on Global and Regional Climate in the Preindustrial Holocene: A Review

NASA Astrophysics Data System (ADS)

Kaplan, J. O.

2014-12-01

The recent development of anthropogenic land cover change (ALCC) scenarios that cover all or part of the preindustrial Holocene (11,700 BP to ~AD 1850) has led to a number of modelling studies on the impacts of land cover change on climate, using both GCMs and regional climate models. Because most ALCC scenarios arrive at similar estimates of anthropogenic deforestation by the late preindustrial, most models agree that the net biogeophysical effect of ALCC by AD 1850 is regional cooling at mid- to high-latitudes and warming and drying over the tropics and subtropics. In particular, tropical deforestation appears to lead to local amplification of externally forced drought cycles, e.g., from ENSO. The spatial extent of these climate changes varies between models because the choice of ALCC scenario leads to large differences in the initial forcing. Those model studies that considered biogeochemical feedbacks show that the importance of preindustrial CO2 emissions ranges from being insignificant to larger than the global biogeophysical feedback, depending on assumptions made about potential natural atmospheric CO2 at the beginning of the Industrial Revolution. While the net magnitude of deforestation is similar among ALCC scenarios at AD 1850, the timing of deforestation varies widely, which, in addition to affecting the inferred importance of biogeochemical feedbacks, leads to large differences in the estimated importance of ALCC on climate earlier in the Holocene. For example, modelling experiments performed on Europe and the Mediterranean representing conditions at the peak of the Roman Empire or in Mesoamerica for the Classic Maya period show large differences in the estimated importance of the biogeophysical feedback to regional climate depending on the ALCC scenario used. The wide variety of results gained so far from ALCC and climate modelling experiments shows that the question of "how much did humans influence the state of the Earth System before the Industrial Revolution?" is far from being resolved. Future improvements to ALCC scenarios that improve thematic resolution to go beyond simple deforestation are essential, for example to include locally important types of historical land use such as irrigation and forest pasture, and Earth System models should move towards coupling between ALCC and climate.

Research on Climate and Dengue in Malaysia: A Systematic Review.

PubMed

Hii, Yien Ling; Zaki, Rafdzah Ahmad; Aghamohammadi, Nasrin; Rocklöv, Joacim

2016-03-01

Dengue is a climate-sensitive infectious disease. Climate-based dengue early warning may be a simple, low-cost, and effective tool for enhancing surveillance and control. Scientific studies on climate and dengue in local context form the basis for advancing the development of a climate-based early warning system. This study aims to review the current status of scientific studies in climate and dengue and the prospect or challenges of such research on a climate-based dengue early warning system in a dengue-endemic country, taking Malaysia as a case study. We reviewed the relationship between climate and dengue derived from statistical modeling, laboratory tests, and field studies. We searched electronic databases including PubMed, Scopus, EBSCO (MEDLINE), Web of Science, and the World Health Organization publications, and assessed climate factors and their influence on dengue cases, mosquitoes, and virus and recent development in the field of climate and dengue. Few studies in Malaysia have emphasized the relationship between climate and dengue. Climatic factors such as temperature, rainfall, and humidity are associated with dengue; however, these relationships were not consistent. Climate change projections for Malaysia show a mounting risk for dengue in the future. Scientific studies on climate and dengue enhance dengue surveillance in the long run. It is essential for institutions in Malaysia to promote research on climate and vector-borne diseases to advance the development of climate-based early warning systems. Together, effective strategies that improve existing research capacity, maximize the use of limited resources, and promote local-international partnership are crucial for sustaining research on climate and health.

Attribution of glacier fluctuations to climate change

NASA Astrophysics Data System (ADS)

Oerlemans, J.

2012-04-01

Glacier retreat is a worlwide phenomenon, which started around the middle of the 19th century. During the period 1800-1850 the number of retreating and advancing glaciers was roughly equal (based on 42 records from different continents). During the period 1850-1900 about 92% of all mountain glaciers became shorter (based on 65 records). After this, the percentage of shrinking glaciers has been around 90% until the present time. The glacier signal is rather coherent over the globe, especially when surging and calving glaciers are not considered (for such glaciers the response to climate change is often masked by length changes related to internal dynamics). From theoretical studies as well as extensive meteorological work on glaciers, the processes that control the response of glaciers to climate change are now basically understood. It is useful to make a difference between geometric factors (e.g. slope, altitudinal range, hypsometry) and climatic setting (e.g. seasonal cycle, precipitation). The most sensitive glaciers appear to be flat glaciers in a maritime climate. Characterizing the dynamic properties of a glacier requires at least two quantities: the climate sensitivity, expressing how the equilibrium glacier state depends on the climatic conditions, and the response time, indicating how fast a glacier approaches a new equilibrium state after a stepwise change in the climatic forcing. These quantities can be estimated from relatively simple theory, showing that differences among glaciers are substantial. For larger glaciers, climate sensitivities (in terms of glacier length) vary from 1 to 8 km per 100 m change in the equilibrium-line altitude. Response times are mainly in the range of 20 to 200 years, with most values between 30 and 80 years. Changes in the equilibrium-line altitude or net mass balance of a glacier are mainly driven by fluctuations in air temperature, precipitation, and global radiation. Energy-balance modelling for many glaciers shows that, globally speaking, a 1 K temperature increase has the same effect as a ~25% decrease in precipitation, or a ~15% increase in global radiation. However, the relative importance of these drivers depends significantly on the climatic setting (notably continentality). In this contribution I will give a brief survey of glacier fluctuations over the past few centuries, and provide arguments that on the worldwide scale air temperature must have been the main driver of these fluctuations. A history of global mean temperature that explains the observed glacier fluctuations best will be discussed. On smaller spatial (regional) and temporal (decades) scales, changes in precipitation become important. Both with respect to the attribution problem (what caused the glacier fluctuations in the past?) and the projection issue (what will happen in the next 100 years?), it is important that many more glaciers are explicitly studied with numerical models. I will argue that for non-calving glaciers these models can be relatively simple.

Climate Change Impact Uncertainties for Maize in Panama: Farm Information, Climate Projections, and Yield Sensitivities

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Cecil, L. Dewayne; Horton, Radley M.; Gordon, Roman; McCollum, Raymond (Brown, Douglas); Brown, Douglas; Killough, Brian; Goldberg, Richard; Greeley, Adam P.; Rosenzweig, Cynthia

2011-01-01

We present results from a pilot project to characterize and bound multi-disciplinary uncertainties around the assessment of maize (Zea mays) production impacts using the CERES-Maize crop model in a climate-sensitive region with a variety of farming systems (Panama). Segunda coa (autumn) maize yield in Panama currently suffers occasionally from high water stress at the end of the growing season, however under future climate conditions warmer temperatures accelerate crop maturation and elevated CO (sub 2) concentrations improve water retention. This combination reduces end-of-season water stresses and eventually leads to small mean yield gains according to median projections, although accelerated maturation reduces yields in seasons with low water stresses. Calibrations of cultivar traits, soil profile, and fertilizer amounts are most important for representing baseline yields, however sensitivity to all management factors is reduced in an assessment of future yield changes (most dramatically for fertilizers), suggesting that yield changes may be more generalizable than absolute yields. Uncertainty around General Circulation Model (GCM)s' projected changes in rainfall gain in importance throughout the century, with yield changes strongly correlated with growing season rainfall totals. Climate changes are expected to be obscured by the large inter-annual variations in Panamanian climate that will continue to be the dominant influence on seasonal maize yield into the coming decades. The relatively high (A2) and low (B1) emissions scenarios show little difference in their impact on future maize yields until the end of the century. Uncertainties related to the sensitivity of CERES-Maize to carbon dioxide concentrations have a substantial influence on projected changes, and remain a significant obstacle to climate change impacts assessment. Finally, an investigation into the potential of simple statistical yield emulators based upon key climate variables characterizes the important uncertainties behind the selection of climate change metrics and their performance against more complex process-based crop model simulations, revealing a danger in relying only on long-term mean quantities for crop impact assessment.

Do we know what difference a delay makes?

NASA Astrophysics Data System (ADS)

Risbey, James S.; Handel, Mark David; Stone, Peter H.

In our original comment [Risbey et al.., 1991] we argued that the work of Schlesinger and Jiang [1991a] is too limited to determine whether or not (as they put it) “the penalty is small for a 10-year delay in initiating the transition to a regime in which greenhouse-gas emissions are reduced.” In their reply, Schlesinger and Jiang [1991b] (hereafter S&J) presented their reasons for concluding definitively that the penalty is small. However S&J's discussion of the evidence and literature on climate change and greenhouse warming contains significant omissions and mis-statements.In dismissing our concern that their model was too simple to evaluate the possibility of abrupt climate change, S&J rely on results from coupled ocean-atmosphere general circulation models (GCMs), in particular the work of Cubasch et al.. [1991]. Here S&J make two claims, one of which is incorrect and the other questionable. First, they claim that “the coupled atmosphere-ocean model of Cubasch et al. [1991] does allow the nonlinearities that Risbey et al.. [1991] criticize our simple model for not including.” In fact we explicitly mentioned changes in polar ice caps [Oerlemans and van der Veen, 1984] and release of methane from clathrates [MacDonald, 1990; Bell, 1982], neither of which are included in the model of Cubasch et al.. [1991]. Indeed, none of the published simulations of global warming using coupled ocean-atmosphere GCMs include these effects. Nor do these models yet include in their enhanced greenhouse simulations many of the possible feedbacks involving the carbon cycle and biosphere [Lashof, 1989; Bacastow and Maier-Reimer, 1990; Sellers, 1987] that could significantly alter greenhouse gas concentrations and surface properties. The published simulations with these models do allow for some changes in deep ocean circulation and cloud behavior, but there is controversy over whether they correctly represent these processes [Marotzke, 1991; Mitchell, 1989; Cess, 1990]. In addition the coupled models must be arbitrarily tuned (requiring substantial artificial fluxes of heat and moisture) to get the current climate right [Manabe et al.., 1991; Cubasch et al.., 1991]. Their greenhouse change simulations are at least partly constrained by these flux adjustments.

The climate response to five trillion tonnes of carbon

NASA Astrophysics Data System (ADS)

Tokarska, Katarzyna B.; Gillett, Nathan P.; Weaver, Andrew J.; Arora, Vivek K.; Eby, Michael

2016-09-01

Concrete actions to curtail greenhouse gas emissions have so far been limited on a global scale, and therefore the ultimate magnitude of climate change in the absence of further mitigation is an important consideration for climate policy. Estimates of fossil fuel reserves and resources are highly uncertain, and the amount used under a business-as-usual scenario would depend on prevailing economic and technological conditions. In the absence of global mitigation actions, five trillion tonnes of carbon (5 EgC), corresponding to the lower end of the range of estimates of the total fossil fuel resource, is often cited as an estimate of total cumulative emissions. An approximately linear relationship between global warming and cumulative CO2 emissions is known to hold up to 2 EgC emissions on decadal to centennial timescales; however, in some simple climate models the predicted warming at higher cumulative emissions is less than that predicted by such a linear relationship. Here, using simulations from four comprehensive Earth system models, we demonstrate that CO2-attributable warming continues to increase approximately linearly up to 5 EgC emissions. These models simulate, in response to 5 EgC of CO2 emissions, global mean warming of 6.4-9.5 °C, mean Arctic warming of 14.7-19.5 °C, and mean regional precipitation increases by more than a factor of four. These results indicate that the unregulated exploitation of the fossil fuel resource could ultimately result in considerably more profound climate changes than previously suggested.

Exploring global carbon turnover and radiocarbon cycling in terrestrial biosphere models

NASA Astrophysics Data System (ADS)

Graven, H. D.; Warren, H.

2017-12-01

The uptake of carbon into terrestrial ecosystems through net primary productivity (NPP) and the turnover of that carbon through various pathways are the fundamental drivers of changing carbon stocks on land, in addition to human-induced and natural disturbances. Terrestrial biosphere models use different formulations for carbon uptake and release, resulting in a range of values in NPP of 40-70 PgC/yr and biomass turnover times of about 25-40 years for the preindustrial period in current-generation models from CMIP5. Biases in carbon uptake and turnover impact simulated carbon uptake and storage in the historical period and later in the century under changing climate and CO2 concentration, however evaluating global-scale NPP and carbon turnover is challenging. Scaling up of plot-scale measurements involves uncertainty due to the large heterogeneity across ecosystems and biomass types, some of which are not well-observed. We are developing the modelling of radiocarbon in terrestrial biosphere models, with a particular focus on decadal 14C dynamics after the nuclear weapons testing in the 1950s-60s, including the impact of carbon flux trends and variability on 14C cycling. We use an estimate of the total inventory of excess 14C in the biosphere constructed by Naegler and Levin (2009) using a 14C budget approach incorporating estimates of total 14C produced by the weapons tests and atmospheric and oceanic 14C observations. By simulating radiocarbon in simple biosphere box models using carbon fluxes from the CMIP5 models, we find that carbon turnover is too rapid in many of the simple models - the models appear to take up too much 14C and release it too quickly. Therefore many CMIP5 models may also simulate carbon turnover that is too rapid. A caveat is that the simple box models we use may not adequately represent carbon dynamics in the full-scale models. Explicit simulation of radiocarbon in terrestrial biosphere models would allow more robust evaluation of biosphere models and the investigation of climate-carbon cycle feedbacks on various timescales. Explicit simulation of radiocarbon and carbon-13 in terrestrial biosphere models of Earth System Models, as well as in ocean models, is recommended by CMIP6 and supported by CMIP6 protocols and forcing datasets.

A simple model of the effect of ocean ventilation on ocean heat uptake

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

Nadiga, Balasubramanya T.; Urban, Nathan Mark

Presentation includes slides on Earth System Models vs. Simple Climate Models; A Popular SCM: Energy Balance Model of Anomalies; On calibrating against one ESM experiment, the SCM correctly captures that ESM's surface warming response with other forcings; Multi-Model Analysis: Multiple ESMs, Single SCM; Posterior Distributions of ECS; However In Excess of 90% of TOA Energy Imbalance is Sequestered in the World Oceans; Heat Storage in the Two Layer Model; Heat Storage in the Two Layer Model; Including TOA Rad. Imbalance and Ocean Heat in Calibration Improves Repr., but Significant Errors Persist; Improved Vertical Resolution Does Not Fix Problem; A Seriesmore » of Expts. Confirms That Anomaly-Diffusing Models Cannot Properly Represent Ocean Heat Uptake; Physics of the Thermocline; Outcropping Isopycnals and Horizontally-Averaged Layers; Local interactions between outcropping isopycnals leads to non-local interactions between horizontally-averaged layers; Both Surface Warming and Ocean Heat are Well Represented With Just 4 Layers; A Series of Expts. Confirms That When Non-Local Interactions are Allowed, the SCMs Can Represent Both Surface Warming and Ocean Heat Uptake; and Summary and Conclusions.« less

Rising tides, cumulative impacts and cascading changes to estuarine ecosystem functions.

PubMed

O'Meara, Theresa A; Hillman, Jenny R; Thrush, Simon F

2017-08-31

In coastal ecosystems, climate change affects multiple environmental factors, yet most predictive models are based on simple cause-and-effect relationships. Multiple stressor scenarios are difficult to predict because they can create a ripple effect through networked ecosystem functions. Estuarine ecosystem function relies on an interconnected network of physical and biological processes. Estuarine habitats play critical roles in service provision and represent global hotspots for organic matter processing, nutrient cycling and primary production. Within these systems, we predicted functional changes in the impacts of land-based stressors, mediated by changing light climate and sediment permeability. Our in-situ field experiment manipulated sea level, nutrient supply, and mud content. We used these stressors to determine how interacting environmental stressors influence ecosystem function and compared results with data collected along elevation gradients to substitute space for time. We show non-linear, multi-stressor effects deconstruct networks governing ecosystem function. Sea level rise altered nutrient processing and impacted broader estuarine services ameliorating nutrient and sediment pollution. Our experiment demonstrates how the relationships between nutrient processing and biological/physical controls degrade with environmental stress. Our results emphasise the importance of moving beyond simple physically-forced relationships to assess consequences of climate change in the context of ecosystem interactions and multiple stressors.

The Promise and Limitations of Using Analogies to Improve Decision-Relevant Understanding of Climate Change.

PubMed

Raimi, Kaitlin T; Stern, Paul C; Maki, Alexander

2017-01-01

To make informed choices about how to address climate change, members of the public must develop ways to consider established facts of climate science and the uncertainties about its future trajectories, in addition to the risks attendant to various responses, including non-response, to climate change. One method suggested for educating the public about these issues is the use of simple mental models, or analogies comparing climate change to familiar domains such as medical decision making, disaster preparedness, or courtroom trials. Two studies were conducted using online participants in the U.S.A. to test the use of analogies to highlight seven key decision-relevant elements of climate change, including uncertainties about when and where serious damage may occur, its unprecedented and progressive nature, and tradeoffs in limiting climate change. An internal meta-analysis was then conducted to estimate overall effect sizes across the two studies. Analogies were not found to inform knowledge about climate literacy facts. However, results suggested that people found the medical analogy helpful and that it led people-especially political conservatives-to better recognize several decision-relevant attributes of climate change. These effects were weak, perhaps reflecting a well-documented and overwhelming effect of political ideology on climate change communication and education efforts in the U.S.A. The potential of analogies and similar education tools to improve understanding and communication in a polarized political environment are discussed.

Scalariform-to-simple transition in vessel perforation plates triggered by differences in climate during the evolution of Adoxaceae

PubMed Central

Lens, Frederic; Vos, Rutger A.; Charrier, Guillaume; van der Niet, Timo; Merckx, Vincent; Baas, Pieter; Aguirre Gutierrez, Jesus; Jacobs, Bart; Chacon Dória, Larissa; Smets, Erik; Delzon, Sylvain; Janssens, Steven B.

2016-01-01

Background and Aims Angiosperms with simple vessel perforations have evolved many times independently of species having scalariform perforations, but detailed studies to understand why these transitions in wood evolution have happened are lacking. We focus on the striking difference in wood anatomy between two closely related genera of Adoxaceae, Viburnum and Sambucus, and link the anatomical divergence with climatic and physiological insights. Methods After performing wood anatomical observations, we used a molecular phylogenetic framework to estimate divergence times for 127 Adoxaceae species. The conditions under which the genera diversified were estimated using ancestral area reconstruction and optimization of ancestral climates, and xylem-specific conductivity measurements were performed. Key Results Viburnum, characterized by scalariform vessel perforations (ancestral), diversified earlier than Sambucus, having simple perforations (derived). Ancestral climate reconstruction analyses point to cold temperate preference for Viburnum and warm temperate for Sambucus. This is reflected in the xylem-specific conductivity rates of the co-occurring species investigated, showing that Viburnum lantana has rates much lower than Sambucus nigra. Conclusions The lack of selective pressure for high conductive efficiency during early diversification of Viburnum and the potentially adaptive value of scalariform perforations in frost-prone cold temperate climates have led to retention of the ancestral vessel perforation type, while higher temperatures during early diversification of Sambucus have triggered the evolution of simple vessel perforations, allowing more efficient long-distance water transport. PMID:27498812

Multiple abiotic stimuli are integrated in the regulation of rice gene expression under field conditions

PubMed Central

Plessis, Anne; Hafemeister, Christoph; Wilkins, Olivia; Gonzaga, Zennia Jean; Meyer, Rachel Sarah; Pires, Inês; Müller, Christian; Septiningsih, Endang M; Bonneau, Richard; Purugganan, Michael

2015-01-01

Plants rely on transcriptional dynamics to respond to multiple climatic fluctuations and contexts in nature. We analyzed the genome-wide gene expression patterns of rice (Oryza sativa) growing in rainfed and irrigated fields during two distinct tropical seasons and determined simple linear models that relate transcriptomic variation to climatic fluctuations. These models combine multiple environmental parameters to account for patterns of expression in the field of co-expressed gene clusters. We examined the similarities of our environmental models between tropical and temperate field conditions, using previously published data. We found that field type and macroclimate had broad impacts on transcriptional responses to environmental fluctuations, especially for genes involved in photosynthesis and development. Nevertheless, variation in solar radiation and temperature at the timescale of hours had reproducible effects across environmental contexts. These results provide a basis for broad-based predictive modeling of plant gene expression in the field. DOI: http://dx.doi.org/10.7554/eLife.08411.001 PMID:26609814

Climate reconstruction from borehole temperatures influenced by groundwater flow

NASA Astrophysics Data System (ADS)

Kurylyk, B.; Irvine, D. J.; Tang, W.; Carey, S. K.; Ferguson, G. A. G.; Beltrami, H.; Bense, V.; McKenzie, J. M.; Taniguchi, M.

2017-12-01

Borehole climatology offers advantages over other climate reconstruction methods because further calibration steps are not required and heat is a ubiquitous subsurface property that can be measured from terrestrial boreholes. The basic theory underlying borehole climatology is that past surface air temperature signals are reflected in the ground surface temperature history and archived in subsurface temperature-depth profiles. High frequency surface temperature signals are attenuated in the shallow subsurface, whereas low frequency signals can be propagated to great depths. A limitation of analytical techniques to reconstruct climate signals from temperature profiles is that they generally require that heat flow be limited to conduction. Advection due to groundwater flow can thermally `contaminate' boreholes and result in temperature profiles being rejected for regional climate reconstructions. Although groundwater flow and climate change can result in contrasting or superimposed thermal disturbances, groundwater flow will not typically remove climate change signals in a subsurface thermal profile. Thus, climate reconstruction is still possible in the presence of groundwater flow if heat advection is accommodated in the conceptual and mathematical models. In this study, we derive a new analytical solution for reconstructing surface temperature history from borehole thermal profiles influenced by vertical groundwater flow. The boundary condition for the solution is composed of any number of sequential `ramps', i.e. periods with linear warming or cooling rates, during the instrumented and pre-observational periods. The boundary condition generation and analytical temperature modeling is conducted in a simple computer program. The method is applied to reconstruct climate in Winnipeg, Canada and Tokyo, Japan using temperature profiles recorded in hydrogeologically active environments. The results demonstrate that thermal disturbances due to groundwater flow and climate change must be considered in a holistic manner as opposed to isolating either perturbation as was done in prior analytical studies.

Simulating Freshwater Availability under Future Climate Conditions

NASA Astrophysics Data System (ADS)

Zhao, F.; Zeng, N.; Motesharrei, S.; Gustafson, K. C.; Rivas, J.; Miralles-Wilhelm, F.; Kalnay, E.

2013-12-01

Freshwater availability is a key factor for regional development. Precipitation, evaporation, river inflow and outflow are the major terms in the estimate of regional water supply. In this study, we aim to obtain a realistic estimate for these variables from 1901 to 2100. First we calculated the ensemble mean precipitation using the 2011-2100 RCP4.5 output (re-sampled to half-degree spatial resolution) from 16 General Circulation Models (GCMs) participating the Coupled Model Intercomparison Project Phase 5 (CMIP5). The projections are then combined with the half-degree 1901-2010 Climate Research Unit (CRU) TS3.2 dataset after bias correction. We then used the combined data to drive our UMD Earth System Model (ESM), in order to generate evaporation and runoff. We also developed a River-Routing Scheme based on the idea of Taikan Oki, as part of the ESM. It is capable of calculating river inflow and outflow for any region, driven by the gridded runoff output. River direction and slope information from Global Dominant River Tracing (DRT) dataset are included in our scheme. The effects of reservoirs/dams are parameterized based on a few simple factors such as soil moisture, population density and geographic regions. Simulated river flow is validated with river gauge measurements for the world's major rivers. We have applied our river flow calculation to two data-rich watersheds in the United States: Phoenix AMA watershed and the Potomac River Basin. The results are used in our SImple WAter model (SIWA) to explore water management options.

The potential effects of climate change on malaria in tropical Africa using regionalised climate projections

NASA Astrophysics Data System (ADS)

Ermert, V.; Fink, A. H.; Paeth, H.; Morse, A. P.

2012-04-01

The projected climate change will probably alter the range and transmission potential of malaria in Africa. The potential impacts of climate change on the malaria distribution is assessed for tropical Africa. Bias-corrected regional climate projections with a horizontal resolution of 0.5° are used from the Regional Model (REMO), which include land use and land cover changes. The malaria models employed are the 2010 version of the Liverpool Malaria Model (LMM2010), the Garki model, the Plasmodium falciparum infection model from Smith et al. (2005) (S2005), and the Malaria Seasonality Model (MSM) from the Mapping Malaria Risk in Africa project. The results of the models are compared with data from the Malaria Atlas Project (MAP) and novel validation procedures for the LMM2010 and MSM lend more credence to their results. For climate scenarios A1B and B1 and for 2001-2050, REMO projects an overall drying and warming trend in the African malaria belt, that is largely imposed by the man-made degradation of vegetation. As a result, the malaria projections show a decreased malaria spread in West Africa. The northern Sahel is no more suitable for malaria in the projections. More unstable malaria transmission and shorter malaria seasons are expected for various areas farther south. An increase in the malaria epidemic risk is found for more densely populated areas in the southern part of the Sahel. In East Africa, higher temperatures and nearly unchanged precipitation patterns lead to longer transmission seasons and an increase in the area of highland malaria. For altitudes up to 2000 m the malaria transmission stabilises and the epidemic risk is reduced but for higher altitudes the risk of malaria epidemics is increased. The results of the more complex and simple malaria models are similar to each other. However, a different response to the warming of highlands is found for the LMM2010 and MSM. This shows the requirement of a multi model uncertainty analysis for the projection of the future malaria spread.

A model for evaluating effects of climate, water availability, and water management on wetland impoundments--a case study on Bowdoin, Long Lake, and Sand Lake National Wildlife Refuges

USGS Publications Warehouse

Tangen, Brian A.; Gleason, Robert A.; Stamm, John F.

2013-01-01

Many wetland impoundments managed by the U.S. Fish and Wildlife Service (USFWS) National Wildlife Refuge System throughout the northern Great Plains rely on rivers as a primary water source. A large number of these impoundments currently are being stressed from changes in water supplies and quality, and these problems are forecast to worsen because of projected changes to climate and land use. For example, many managed wetlands in arid regions have become degraded owing to the long-term accumulation of salts and increased salinity associated with evapotranspiration. A primary goal of the USFWS is to provide aquatic habitats for a diversity of waterbirds; thus, wetland managers would benefit from a tool that facilitates evaluation of wetland habitat quality in response to current and anticipated impacts of altered hydrology and salt balances caused by factors such as climate change, water availability, and management actions. A spreadsheet model that simulates the overall water and salinity balance (WSB model) of managed wetland impoundments is presented. The WSB model depicts various habitat metrics, such as water depth, salinity, and surface areas (inundated, dry), which can be used to evaluate alternative management actions under various water-availability and climate scenarios. The WSB model uses widely available spreadsheet software, is relatively simple to use, relies on widely available inputs, and is readily adaptable to specific locations. The WSB model was validated using data from three National Wildlife Refuges with direct and indirect connections to water resources associated with rivers, and common data limitations are highlighted. The WSB model also was used to conduct simulations based on hypothetical climate and management scenarios to demonstrate the utility of the model for evaluating alternative management strategies and climate futures. The WSB model worked well across a range of National Wildlife Refuges and could be a valuable tool for USFWS staff when evaluating system state and management alternatives and establishing long-term goals and objectives.

The Climate Impact of the Household Sector in China

NASA Astrophysics Data System (ADS)

Aunan, K.; Berntsen, T. K.; Rypdal, K.; Streets, D. G.; Woo, J.; Smith, K. R.

2005-05-01

If it ever enters into force the impact of the Kyoto Protocol on climate change is likely to be small. The USA and Australia have not ratified the Protocol and the initial emission reduction target was only 5.2 per cent. There is an increasing call for post-Kyoto climate treaties, whether they be global or regional, to widen the scope to take into account the impacts that air pollutants as tropospheric ozone and aerosols may have on climate. There are two main reasons for this. First and foremost, there is increasing evidence that these air pollutants play an important role in the climate system. Secondly, it is suggested that including radiative forcing components that also have adverse impacts on human health and environment may increase participation, which will be a prerequisite for future treaties to be effective. China's approval of the Kyoto Protocol in 2002 suggests that it is considering a more active role in the global effort to mitigate global warming. Given its many other priorities, however, China needs to understand what national policies would reduce its contribution to global warming in the most cost-efficient way and at the same time contribute the most to economic and social development in the country. The objective of the present study is to contribute knowledge that is helpful to Chinese policy makers dealing with this question. We do this by addressing emissions that according to the World Health Organisation are among the leading health risks to people in the developing world, China included, i.e. smoke from solid fuels burned in peoples' homes. In China, about 72 per cent of the population lives in rural or peri-urban areas where use of simple, low-efficiency household stoves for coal or biomass is common. Even though the residential sector stands for no more than 11 per cent of the primary energy consumption (biomass included), the sector contributes to, e.g., more than 70 per cent of Chinese emissions of black carbon, about a third of its methane emissions, and more than 40 per cent of the nmVOC emissions (which contributes to global warming through tropospheric ozone production). Thus, policies addressing these sources may be important in the context of global warming in addition to substantially improving living conditions for many people. The question we ask in the present paper is how important are they? Two global models are applied to estimate the climate impact on a global scale of emissions from the Chinese residential sector. To estimate the impact on the development of the global climate in terms of radiative forcing and global mean temperature of a possible reduction in these emissions we use a simple climate model. A global, three-dimensional photochemical tracer/transport model of the troposphere is used to model the changes in concentration of air pollutants that have a radiative forcing. Estimates for Chinese household sector emissions are taken from previous work on emission inventories in Asia.

An approach for assessing the sensitivity of floods to regional climate change

NASA Astrophysics Data System (ADS)

Hughes, James P.; Lettenmaier, Dennis P.; Wood, Eric F.

1992-06-01

A high visibility afforded climate change issues is recent years has led to conflicts between and among decision makers and scientists. Decision makers inevitably feel pressure to assess the effect of climate change on the public welfare, while most climate modelers are, to a greater or lesser degree, concerned about the extent to which known inaccuracies in their models limit or preclude the use of modeling results for policy making. The water resources sector affords a good example of the limitations of the use of alternative climate scenarios derived from GCMs for decision making. GCM simulations of precipitation agree poorly between GCMs, and GCM predictions of runoff and evapotranspiration are even more uncertain. Further, water resources managers must be concerned about hydrologic extremes (floods and droughts) which are much more difficult to predict than ``average'' conditions. Most studies of the sensitivity of water resource systems and operating policies to climate change to data have been based on simple perturbations of historic hydroclimatological time series to reflect the difference between large area GCM simulations for an altered climate (e.g., CO2 doubling) and a GCM simulation of present climate. Such approaches are especially limited for assessment of the sensitivity of water resources systems under extreme conditions, conditions, since the distribution of storm inter-arrival times, for instance, is kept identical to that observed in the historic past. Further, such approaches have generally been based on the difference between the GCM altered and present climates for a single grid cell, primarily because the GCM spatial scale is often much larger than the scale at which climate interpretations are desired. The use of single grid cell GCM results is considered inadvisable by many GCM modelers, who feel the spatial scale for which interpretation of GCM results is most reasonable is on the order of several grid cells. In this paper, we demonstrate an alternative approach to assessing the implications of altered climates as predicted by GCMs for extreme (flooding) conditions. The approach is based on the characterization of regional atmospheric circulation patterns through a weather typing procedure, from which a stochastic model of the weather class occurrences is formulated. Weather types are identified through a CART (Classification and Regression Tree) approach. Precipitation occurence/non-occurence at multiple precipitation station is then predicted through a second stage stochastic model. Precipitation amounts are predicted conditional on the weather class identified from the large area circulation information.

Hydrologic drivers of tree biodiversity: The impact of climate change (Invited)

NASA Astrophysics Data System (ADS)

Rodriguez-Iturbe, I.; Konar, M.; Muneepeerakul, R.; Azaele, S.; Bertuzzo, E.; Rinaldo, A.

2009-12-01

Biodiversity of forests is of major importance for society. The possible impact of climate change on the characteristics of tree diversity is a topic of crucial importance with relevant implications for conservation campaigns and resource management. Here we present the main results of the expected biodiversity changes in the Mississippi-Missouri River Basin (MMRS) and two of its subregions under different scenarios of possible climate change. A mechanistic neutral metapopulation model is developed to study the main drivers of large scale biodiversity signatures in the MMRS system. The region is divided into 824 Direct Tributary Areas (DTAs), each one characterized by its own habitat capacity. Data for the spatial occurrence of the 231 species present in the system is taken from the US Forest Service Inventory and Analysis Database. The model has permeable boundaries to account for immigration from the regions surrounding the MMRS. The model accounts for key aspects of ecological dynamics (e.g., birth, death, speciation, and migration) and is fundamentally driven by the mean annual precipitation characteristic of each of the DTAs in the system. It is found that such a simple model, with only four parameters, yields an excellent representation of the observed local species richness (LSR), between-community (β) diversity, and species rank-occupancy function. The mean annual rainfall of each DTA is then changed according to the climate scenarios and new habitat capacities are thus obtained throughout the MMRS and its subregions. The resulting large-scale biodiversity signatures are computed and compared with those of the present scenario, showing that there are very important changes arising from the climate change conditions. For the dry scenarios, it is shown that there is a considerable decrease of species richness, both at local and regional scales, and a contraction of species' geographic ranges. These findings link the hydrologic and ecological dynamics of the MMRS under climate change conditions and are important for a comprehensive evaluation of the climate change impacts over the United States.

Managed relocation as an adaptation strategy for mitigating climate change threats to the persistence of an endangered lizard.

PubMed

Fordham, Damien A; Watts, Michael J; Delean, Steven; Brook, Brook W; Heard, Lee M B; Bull, C M

2012-09-01

The distributional ranges of many species are contracting with habitat conversion and climate change. For vertebrates, informed strategies for translocations are an essential option for decisions about their conservation management. The pygmy bluetongue lizard, Tiliqua adelaidensis, is an endangered reptile with a highly restricted distribution, known from only a small number of natural grassland fragments in South Australia. Land-use changes over the last century have converted perennial native grasslands into croplands, pastures and urban areas, causing substantial contraction of the species' range due to loss of essential habitat. Indeed, the species was thought to be extinct until its rediscovery in 1992. We develop coupled-models that link habitat suitability with stochastic demographic processes to estimate extinction risk and to explore the efficacy of potential climate adaptation options. These coupled-models offer improvements over simple bioclimatic envelope models for estimating the impacts of climate change on persistence probability. Applying this coupled-model approach to T. adelaidensis, we show that: (i) climate-driven changes will adversely impact the expected minimum abundance of populations and could cause extinction without management intervention, (ii) adding artificial burrows might enhance local population density, however, without targeted translocations this measure has a limited effect on extinction risk, (iii) managed relocations are critical for safeguarding lizard population persistence, as a sole or joint action and (iv) where to source and where to relocate animals in a program of translocations depends on the velocity, extent and nonlinearities in rates of climate-induced habitat change. These results underscore the need to consider managed relocations as part of any multifaceted plan to compensate the effects of habitat loss or shifting environmental conditions on species with low dispersal capacity. More broadly, we provide the first step towards a more comprehensive framework for integrating extinction risk, managed relocations and climate change information into range-wide conservation management. © 2012 Blackwell Publishing Ltd.

a Physical Parameterization of Snow Albedo for Use in Climate Models.

NASA Astrophysics Data System (ADS)

Marshall, Susan Elaine

The albedo of a natural snowcover is highly variable ranging from 90 percent for clean, new snow to 30 percent for old, dirty snow. This range in albedo represents a difference in surface energy absorption of 10 to 70 percent of incident solar radiation. Most general circulation models (GCMs) fail to calculate the surface snow albedo accurately, yet the results of these models are sensitive to the assumed value of the snow albedo. This study replaces the current simple empirical parameterizations of snow albedo with a physically-based parameterization which is accurate (within +/- 3% of theoretical estimates) yet efficient to compute. The parameterization is designed as a FORTRAN subroutine (called SNOALB) which can be easily implemented into model code. The subroutine requires less then 0.02 seconds of computer time (CRAY X-MP) per call and adds only one new parameter to the model calculations, the snow grain size. The snow grain size can be calculated according to one of the two methods offered in this thesis. All other input variables to the subroutine are available from a climate model. The subroutine calculates a visible, near-infrared and solar (0.2-5 μm) snow albedo and offers a choice of two wavelengths (0.7 and 0.9 mu m) at which the solar spectrum is separated into the visible and near-infrared components. The parameterization is incorporated into the National Center for Atmospheric Research (NCAR) Community Climate Model, version 1 (CCM1), and the results of a five -year, seasonal cycle, fixed hydrology experiment are compared to the current model snow albedo parameterization. The results show the SNOALB albedos to be comparable to the old CCM1 snow albedos for current climate conditions, with generally higher visible and lower near-infrared snow albedos using the new subroutine. However, this parameterization offers a greater predictability for climate change experiments outside the range of current snow conditions because it is physically-based and not tuned to current empirical results.

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.

Hydrological regime modifications induced by climate change in Mediterranean area

NASA Astrophysics Data System (ADS)

Pumo, Dario; Caracciolo, Domenico; Viola, Francesco; Valerio Noto, Leonardo

2015-04-01

The knowledge of river flow regimes has a capital importance for a variety of practical applications, in water resource management, including optimal and sustainable use. Hydrological regime is highly dependent on climatic factors, among which the most important is surely the precipitation, in terms of frequency, seasonal distribution and intensity of rainfall events. The streamflow frequency regime of river basins are often summarized by flow duration curves (FDCs), that offer a simple and comprehensive graphical view of the overall historical variability associated with streamflow, and characterize the ability of the basin to provide flows of various magnitudes. Climate change is likely to lead shifts in the hydrological regime, and, consequently, in the FDCs. Staring from this premise, the primary objective of the present study is to explore the effects of potential climate changes on the hydrological regime of some small Mediterranean basins. To this aim it is here used a recent hydrological model, the ModABa model (MODel for Annual flow duration curves assessment in ephemeral small BAsins), for the probabilistic characterization of the daily streamflows in small catchments. The model has been calibrated and successively validated in a unique small catchment, where it has shown a satisfactory accuracy in reproducing the empirical FDC starting from easily derivable parameters arising from basic ecohydrological knowledge of the basin and commonly available climatic data such as daily precipitation and temperatures. Thus, this work also represents a first attempt to apply the ModABa to basins different from that used for its preliminary design in order to testing its generality. Different case studies are selected within the Sicily region; the model is first calibrated at the sites and then forced by future climatic scenarios, highlighting the principal differences emerging from the current scenario and future FDCs. The future climate scenarios are generated using a stochastic downscaling technique based on the weather generator, AWE-GEN. This methodology allows for the downscaling of an ensemble of climate model outputs deriving the frequency distribution functions of factors of change for several statistics of temperature and precipitation from outputs of General Circulation Models (GCMs). The stochastic downscaling is carried out using simulations of GCMs adopted in the IPCC 5AR, for the future periods of 2046-2065 and 2081-2100.

Cirrus clouds and climate feedback: Is the sky falling and should we go tell the king

NASA Technical Reports Server (NTRS)

Stephens, Graeme L.

1990-01-01

It is widely believed that thin cirrus clouds act to enhance the greenhouse effect owing to a particular combination of their optical properties. It is demonstrated how this effect is perhaps based on inadequate resolution of the physics of cirrus clouds and that the more likely impact of cirrus clouds to climate change remains somewhat elusive. These conclusions are developed within the context of a specific feedback mechanism incorporated into a simple mechanistic climate model. A specific scientific question addressed is whether or not the observed relationship between the ice water content and temperature of cirrus provides any significant feedback to the CO2 greenhouse warming. A related question also examined concerns the specific role of cloud microphysics and radiation in this feedback. This raises several pertinent issues about the understanding of cirrus clouds and their likely role in climate change as there presently exists a considerable uncertainty about the microphysics of these clouds (size and shape of ice crystals) and their radiative influences.

Accounting for groundwater in stream fish thermal habitat responses to climate change

USGS Publications Warehouse

Snyder, Craig D.; Hitt, Nathaniel P.; Young, John A.

2015-01-01

Forecasting climate change effects on aquatic fauna and their habitat requires an understanding of how water temperature responds to changing air temperature (i.e., thermal sensitivity). Previous efforts to forecast climate effects on brook trout habitat have generally assumed uniform air-water temperature relationships over large areas that cannot account for groundwater inputs and other processes that operate at finer spatial scales. We developed regression models that accounted for groundwater influences on thermal sensitivity from measured air-water temperature relationships within forested watersheds in eastern North America (Shenandoah National Park, USA, 78 sites in 9 watersheds). We used these reach-scale models to forecast climate change effects on stream temperature and brook trout thermal habitat, and compared our results to previous forecasts based upon large-scale models. Observed stream temperatures were generally less sensitive to air temperature than previously assumed, and we attribute this to the moderating effect of shallow groundwater inputs. Predicted groundwater temperatures from air-water regression models corresponded well to observed groundwater temperatures elsewhere in the study area. Predictions of brook trout future habitat loss derived from our fine-grained models were far less pessimistic than those from prior models developed at coarser spatial resolutions. However, our models also revealed spatial variation in thermal sensitivity within and among catchments resulting in a patchy distribution of thermally suitable habitat. Habitat fragmentation due to thermal barriers therefore may have an increasingly important role for trout population viability in headwater streams. Our results demonstrate that simple adjustments to air-water temperature regression models can provide a powerful and cost-effective approach for predicting future stream temperatures while accounting for effects of groundwater.

Adapting to the effects of climate change [Chapter 14

Treesearch

Jessica E. Halofsky

2018-01-01

Adapting to climate change, or adjusting to current or future climate and its effects (Noble et al. 2014), is critical to minimizing the risks associated with climate change impacts. Adaptation actions can vary from passive (e.g., a "wait and see" approach), to relatively simple (e.g., increasing harvest rotation age), to complex (e.g., managing forest...

Century/millennium internal climate oscillations in an ocean-atmosphere-continental ice sheet model

NASA Technical Reports Server (NTRS)

Birchfield, Edward G.; Wang, Huaxiao; Rich, Jonathan J.

1994-01-01

We demonstrate in a simple climate model that there exist nonlinear feedbacks between the atmosphere, ocean, and ice sheets capable of producing century/millennium timescale internal oscillations resembling those seen in the paleoclimate record. Feedbacks involve meridional heat and salt transports in the North Atlantic, surface ocean freshwater fluxes associated with melting and growing continental ice sheets in the northen hemisphere and with Atlantic to Pacific water vapor transport. The positive feedback between the production of North Atlantic Deep Water (NADW) and the meridional salt transport by the Atlantic thermohaline circulation tends to destabilize the climate system, while the negative feedback between the freshwater flux, either to or from the continental ice sheets, and meridional heat flux to the high-latitude North Atlantic, accomplished by the thermohaline circulation, stabilizes the system. The thermohaline circulation plays a central role in both positive and negative feedbacks because of its transport of both heat and salt. Because of asymmetries between the growth and melt phases the oscillations are, in general, accompanied by a growing or decreasing ice volume over each cycle, which in the model is reflected by increasing or decreasing mean salinity.

Reconstruction of solar spectral irradiance since the Maunder minimum

NASA Astrophysics Data System (ADS)

Krivova, N. A.; Vieira, L. E. A.; Solanki, S. K.

2010-12-01

Solar irradiance is the main external driver of the Earth's climate. Whereas the total solar irradiance is the main source of energy input into the climate system, solar UV irradiance exerts control over chemical and physical processes in the Earth's upper atmosphere. The time series of accurate irradiance measurements are, however, relatively short and limit the assessment of the solar contribution to the climate change. Here we reconstruct solar total and spectral irradiance in the range 115-160,000 nm since 1610. The evolution of the solar photospheric magnetic flux, which is a central input to the model, is appraised from the historical record of the sunspot number using a simple but consistent physical model. The model predicts an increase of 1.25 W/m2, or about 0.09%, in the 11-year averaged solar total irradiance since the Maunder minimum. Also, irradiance in individual spectral intervals has generally increased during the past four centuries, the magnitude of the trend being higher toward shorter wavelengths. In particular, the 11-year averaged Ly-α irradiance has increased by almost 50%. An exception is the spectral interval between about 1500 and 2500 nm, where irradiance has slightly decreased (by about 0.02%).

The dynamics of climate-induced deglacial ice stream acceleration

NASA Astrophysics Data System (ADS)

Robel, A.; Tziperman, E.

2015-12-01

Geological observations indicate that ice streams were a significant contributor to ice flow in the Laurentide Ice Sheet during the Last Glacial Maximum. Conceptual and simple model studies have also argued that the gradual development of ice streams increases the sensitivity of large ice sheets to weak climate forcing. In this study, we use an idealized configuration of the Parallel Ice Sheet Model to explore the role of ice streams in rapid deglaciation. In a growing ice sheet, ice streams develop gradually as the bed warms and the margin expands outward onto the continental shelf. Then, a weak change in equilibrium line altitude commensurate with Milankovitch forcing results in a rapid deglacial response, as ice stream acceleration leads to enhanced calving and surface melting at low elevations. We explain the dynamical mechanism that drives this ice stream acceleration and its broader applicability as a feedback for enhancing ice sheet decay in response to climate forcing. We show how our idealized ice sheet simulations match geomorphological observations of deglacial ice stream variability and previous model-data analyses. We conclude with observations on the potential for interaction between ice streams and other feedback mechanisms within the earth system.

A simple mathematical model to predict sea surface temperature over the northwest Indian Ocean

NASA Astrophysics Data System (ADS)

Noori, Roohollah; Abbasi, Mahmud Reza; Adamowski, Jan Franklin; Dehghani, Majid

2017-10-01

A novel and simple mathematical model was developed in this study to enhance the capacity of a reduced-order model based on eigenvectors (RMEV) to predict sea surface temperature (SST) in the northwest portion of the Indian Ocean, including the Persian and Oman Gulfs and Arabian Sea. Developed using only the first two of 12,416 possible modes, the enhanced RMEV closely matched observed daily optimum interpolation SST (DOISST) values. Spatial distribution of the first mode indicated the greatest variations in DOISST occurred in the Persian Gulf. Also, the slightly increasing trend in the temporal component of the first mode observed in the study area over the last 34 years properly reflected the impact of climate change and rising DOISST. Given its simplicity and high level of accuracy, the enhanced RMEV can be applied to forecast DOISST in oceans, which the poor forecasting performance and large computational-time of other numerical models may not allow.

Climate change communication through networks and partnerships: A successful model of engaging and educating non-specialist audience in India

NASA Astrophysics Data System (ADS)

Choudhary, S.; Nayak, R.; Gore, A.

2013-12-01

There is an overwhelming international scientific consensus on climate change; however, the global community still lacks the resolve to implement meaningful solutions. No meaningful solutions can be found without educating and engaging non-scientific community in addressing the climate change. With more than 41 percent of world's population falling under 10-34 years age group, the future citizens, inspiring them is a great challenge for the climate scientists. In order to educate the youth and students in India, a model program named 'Climeducate' was created with the help of scientists in Indian Polar Research Network (IPRN), trained climate leaders in ';The Climate Reality Project', and a local organization (Planature Consultancy Services). This model was developed keeping in mind the obstacles that may be faced in reaching out to non-specialist audiences in different parts of India. The identified obstacles were 1- making such a presentation that could reveal the truth about the climate crisis in a way that ignites the moral courage in non-specialist audience 2- lack of funding for travel and boarding expenses of a climate communicator, 3- language barrier in educating local audiences, 4- logistical arrangements at the venue. In this presentation we will share how all the four obstacles were overcome. Audiences were also given short questionnaires before and after the presentation. Remarkable changes in the pattern of answers, data would be shared in the presentation, were observed between the two questionnaires. More importantly, a significant difference in audience engagement was observed comparing a presentation that integrated scientific data with audiovisuals prepared by The Climate Reality Project Chairman, Al Gore (also Former US Vice President) and the other using simple PowerPoint slides. With the success of this program which was implemented among 500 audiences in the eastern India, we aim to replicate this program soon in other parts of India. This presentation will outline how scientific story telling through an effective collaboration of network of scientists, climate mentors, school teachers and local organizations would derive significant results in inspiring, engaging and preparing non-specialists audiences to face the realities of climate change.

Effects of aerosol emission pathways on future warming and human health

NASA Astrophysics Data System (ADS)

Partanen, Antti-Ilari; Matthews, Damon

2016-04-01

The peak global temperature is largely determined by cumulative emissions of long-lived greenhouse gases. However, anthropogenic emissions include also so-called short-lived climate forcers (SLCFs), which include aerosol particles and methane. Previous studies with simple models indicate that the timing of SLCF emission reductions has only a small effect on the rate of global warming and even less of an effect on global peak temperatures. However, these simple model analyses do not capture the spatial dynamics of aerosol-climate interactions, nor do they consider the additional effects of aerosol emissions on human health. There is therefore merit in assessing how the timing of aerosol emission reductions affects global temperature and premature mortality caused by elevated aerosol concentrations, using more comprehensive climate models. Here, we used an aerosol-climate model ECHAM-HAMMOZ to simulate the direct and indirect radiative forcing resulting from aerosol emissions. We simulated Representative Concentration Pathway (RCP) scenarios, and we also designed idealized low and high aerosol emission pathways based on RCP4.5 scenario (LOW and HIGH, respectively). From these simulations, we calculated the Effective Radiative Forcing (ERF) from aerosol emissions between 1850 and 2100, as well as aerosol concentrations used to estimate the premature mortality caused by particulate pollution. We then use the University of Victoria Earth System Climate Model to simulate the spatial and temporal pattern of climate response to these aerosol-forcing scenarios, in combination with prescribed emissions of both short and long-lived greenhouse gases according to the RCP4.5 scenario. In the RCP scenarios, global mean ERF declined during the 21st century from -1.3 W m-2 to -0.4 W m-2 (RCP8.5) and -0.2 W m-2 (RCP2.6). In the sensitivity scenarios, the forcing at the end of the 21st century was -1.6 W m-2 (HIGH) and practically zero (LOW). The difference in global mean temperature at the year 2100 between LOW and HIGH was about 0.4 °C. The effect was even more significant on the global mean warming rate that reached 0.4 °C per decade in LOW and only 0.2 °C per decade in HIGH. The global temperature and warming rate were similar to each other in simulations using the aerosol emissions from standard RCP scenarios. Anthropogenic aerosols caused significant premature mortality during the 21st century. In 2005, they caused 1.5 million deaths annually. The annual death rate dropped to 0.13 million per year in LOW and was 0.9 million per year in HIGH by 2100. Total premature mortality caused by anthropogenic aerosol particles between 2005 and 2100 was 27 million in LOW, 52-68 million in RCPs, and 113 million in HIGH. Our results show that both climate and health effects of aerosols are fairly similar across RCP scenarios. However, RCPs share assumptions on effective air-quality policies. Our scenarios LOW and HIGH demonstrate that if strong aerosol policies are not enforced or even more ambitious cuts in aerosol emissions are made, the aerosol impacts on climate and health can differ significantly between scenarios.

Attributing Changing Rates of Temperature Record Breaking to Anthropogenic Influences

NASA Astrophysics Data System (ADS)

King, Andrew D.

2017-11-01

Record-breaking temperatures attract attention from the media, so understanding how and why the rate of record breaking is changing may be useful in communicating the effects of climate change. A simple methodology designed for estimating the anthropogenic influence on rates of record breaking in a given time series is proposed here. The frequency of hot and cold record-breaking temperature occurrences is shown to be changing due to the anthropogenic influence on the climate. Using ensembles of model simulations with and without human-induced forcings, it is demonstrated that the effect of climate change on global record-breaking temperatures can be detected as far back as the 1930s. On local scales, a climate change signal is detected more recently at most locations. The anthropogenic influence on the increased occurrence of hot record-breaking temperatures is clearer than it is for the decreased occurrence of cold records. The approach proposed here could be applied in rapid attribution studies of record extremes to quantify the influence of climate change on the rate of record breaking in addition to the climate anomaly being studied. This application is demonstrated for the global temperature record of 2016 and the Central England temperature record in 2014.

Do psychobiosocial states mediate the relationship between perceived motivational climate and individual motivation in youngsters?

PubMed

Bortoli, Laura; Bertollo, Maurizio; Filho, Edson; Robazza, Claudio

2014-01-01

Grounded in achievement goal theory and self-determination theory, this cross-sectional study examined the relationship between perceived motivational climate and individuals' motivation as well as the mediation effect of psychobiosocial states as conceptualised within the individual zones of optimal functioning (IZOF) model. Young students (N = 167, age range 14-15 years) taking part in physical education classes completed measures of teacher-initiated motivational climate, task and ego orientation, motivation and psychobiosocial states. Simple and serial mediation analyses indicated that a perceived mastery climate and individuals' task orientation were related to intrinsic motivation and identified regulation through the mediation of pleasant/functional psychobiosocial states. In contrast, a perceived performance climate was related to external regulation and amotivation through the mediation of unpleasant/dysfunctional psychobiosocial states. Regression analysis results also showed that discrete psychobiosocial states accounted for a significant proportion of variance in motivational variables. Taken together, findings highlight the role of psychobiosocial states as mediators of the relationship between motivational climate and an individual's motivation, and suggest that educators should consider a wide range of individual's functional and dysfunctional reactions deriving from their instructional activity.

Assessing the Performance of Computationally Simple and Complex Representations of Aerosol Processes using a Testbed Methodology

NASA Astrophysics Data System (ADS)

Fast, J. D.; Ma, P.; Easter, R. C.; Liu, X.; Zaveri, R. A.; Rasch, P.

2012-12-01

Predictions of aerosol radiative forcing in climate models still contain large uncertainties, resulting from a poor understanding of certain aerosol processes, the level of complexity of aerosol processes represented in models, and the ability of models to account for sub-grid scale variability of aerosols and processes affecting them. In addition, comparing the performance and computational efficiency of new aerosol process modules used in various studies is problematic because different studies often employ different grid configurations, meteorology, trace gas chemistry, and emissions that affect the temporal and spatial evolution of aerosols. To address this issue, we have developed an Aerosol Modeling Testbed (AMT) to systematically and objectively evaluate aerosol process modules. The AMT consists of the modular Weather Research and Forecasting (WRF) model, a series of testbed cases for which extensive in situ and remote sensing measurements of meteorological, trace gas, and aerosol properties are available, and a suite of tools to evaluate the performance of meteorological, chemical, aerosol process modules. WRF contains various parameterizations of meteorological, chemical, and aerosol processes and includes interactive aerosol-cloud-radiation treatments similar to those employed by climate models. In addition, the physics suite from a global climate model, Community Atmosphere Model version 5 (CAM5), has also been ported to WRF so that these parameterizations can be tested at various spatial scales and compared directly with field campaign data and other parameterizations commonly used by the mesoscale modeling community. In this study, we evaluate simple and complex treatments of the aerosol size distribution and secondary organic aerosols using the AMT and measurements collected during three field campaigns: the Megacities Initiative Local and Global Observations (MILAGRO) campaign conducted in the vicinity of Mexico City during March 2006, the Carbonaceous Aerosol and Radiative Effects Study (CARES) conducted in the vicinity of Sacramento California during June 2010, and the California Nexus (CalNex) campaign conducted in southern California during May and June of 2010. For the aerosol size distribution, we compare the predictions from the GOCART bulk aerosol model, the MADE/SORGAM modal aerosol model, the Modal Aerosol Model (MAM) employed by CAM5, and the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) which uses a sectional representation. For secondary organic aerosols, we compare simple fixed mass yield approaches with the numerically complex volatility basis set approach. All simulations employ the same emissions, meteorology, trace gas chemistry (except for that involving condensable organic species), and initial and boundary conditions. Performance metrics from the AMT are used to assess performance in terms of simulated mass, composition, size distribution (except for GOCART), and aerosol optical properties in relation to computational expense. In addition to statistical measures, qualitative differences among the different aerosol models over the computational domain are presented to examine variations in how aerosols age among the aerosol models.

Using a rule-based envelope model to predict the expansion of habitat suitability within New Zealand for the tick Haemaphysalis longicornis, with future projections based on two climate change scenarios.

PubMed

Lawrence, K E; Summers, S R; Heath, A C G; McFadden, A M J; Pulford, D J; Tait, A B; Pomroy, W E

2017-08-30

Haemaphysalis longicornis is the only species of tick present in New Zealand which infests livestock and is also the only competent vector for Theileria orientalis. Since 2012, New Zealand has suffered from an epidemic of infectious bovine anaemia associated with T. orientalis, an obligate intracellular protozoan parasite of cattle and buffaloes. The aim of this study was to predict the spatial distribution of habitat suitability of New Zealand for the tick H. longicornis using a simple rule-based climate envelope model, to validate the model against published data and use the validated model to project an expansion in habitat suitability for H. longicornis under two alternative climate change scenarios for the periods 2046-2065 and 2081-2100, relative to the climate of 1981-2010. A rule-based climate envelope model was developed based on the environmental requirements for off-host tick survival. The resulting model was validated against a maximum entropy environmental niche model of environmental suitability for T. orientalis transmission and against a H. longicornis occurrence map. Validation was completed using the I-similarity statistic and by linear regression. The H. longicornis climate envelope model predicted that 75% of cattle farms in the North Island, 3% of cattle farms in the South Island and 54% of cattle farms in New Zealand overall have habitats potentially suitable for the establishment of H. longicornis. The validation methods showed an acceptable level of agreement between the envelope model and published data. Both of the climate change scenarios, for each of the time periods, projected only slight to moderate increases in the average farm habitat suitability scores for all the South Island regions. However, only for the West Coast, Marlborough, Tasman, and Nelson regions did these increases in environmental suitability translate into an increased proportion of cattle farms with low or high H. longicornis habitat suitability. These results will have important implications for the geographical progression of Theileria-associated bovine anaemia (TABA) in New Zealand and will also be of interest to Haemaphysalis longicornis researchers in Australia, Japan, Korea and New Zealand. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessment of the water balance over France using regionalized Turc-Pike formula

NASA Astrophysics Data System (ADS)

Le Lay, Matthieu; Garçon, Rémy; Gailhard, Joël; Garavaglia, Federico

2016-04-01

With extensive use of hydrological models over a wide range of hydro-climatic contexts, bias in hydro-climatic data may lead to unreliable models and thus hydrological forecasts and projections. This issue is particularly pregnant when considering mountainous areas with great uncertainties on precipitations, or when considering complex unconservative catchments (e.g. karstic systems). The Turc-Pike water balance formula, analogous to the classical Budyko formula, is a simple and efficient mathematical formulation relating long-term average streamflow to long-term average precipitation and potential evaporation. In this study, we propose to apply this framework to assess and eventually adjust the water-balance before calibrating an operational hydrologic model (MORDOR model). Considering a large set of 350 french catchments, the Turc-Pike formula is regionalized based on ecohydrologic criterions to handle various hydro-climatic contexts. This interannual regional model is then applied to assess the water-balance over numerous catchments and various conditions, such as karstic, snow-driven or glaciarized and even anthropized catchments. Results show that it is possible to obtain pretty realistic corrections of meteorological inputs (precipitations, temperature or potential evaporation) or hydrologic surface (or runoff). These corrections can often be confirmed a posteriori by exogenous information. Positive impacts on hydrologic model's calibration are also demonstrated. This methodology is now operational for hydrologic applications at EDF (Electricité de France, French electric utility company), and therefore applied on hundreds of catchments.

Predicting the Timing of Cherry Blossoms in Washington, DC and Mid-Atlantic States in Response to Climate Change

PubMed Central

Chung, Uran; Mack, Liz; Yun, Jin I.; Kim, Soo-Hyung

2011-01-01

Cherry blossoms, an icon of spring, are celebrated in many cultures of the temperate region. For its sensitivity to winter and early spring temperatures, the timing of cherry blossoms is an ideal indicator of the impacts of climate change on tree phenology. Here, we applied a process-based phenology model for temperate deciduous trees to predict peak bloom dates (PBD) of flowering cherry trees (Prunus×yedoensis ‘Yoshino’ and Prunus serrulata ‘Kwanzan’) in the Tidal Basin, Washington, DC and the surrounding Mid-Atlantic States in response to climate change. We parameterized the model with observed PBD data from 1991 to 2010. The calibrated model was tested against independent datasets of the past PBD data from 1951 to 1970 in the Tidal Basin and more recent PBD data from other locations (e.g., Seattle, WA). The model performance against these independent data was satisfactory (Yoshino: r2 = 0.57, RMSE = 6.6 days, bias = 0.9 days and Kwanzan: r2 = 0.76, RMSE = 5.5 days, bias = −2.0 days). We then applied the model to forecast future PBD for the region using downscaled climate projections based on IPCC's A1B and A2 emissions scenarios. Our results indicate that PBD at the Tidal Basin are likely to be accelerated by an average of five days by 2050 s and 10 days by 2080 s for these cultivars under a mid-range (A1B) emissions scenario projected by ECHAM5 general circulation model. The acceleration is likely to be much greater (13 days for 2050 s and 29 days for 2080s ) under a higher (A2) emissions scenario projected by CGCM2 general circulation model. Our results demonstrate the potential impacts of climate change on the timing of cherry blossoms and illustrate the utility of a simple process-based phenology model for developing adaptation strategies to climate change in horticulture, conservation planning, restoration and other related disciplines. PMID:22087317

Evaluation of mercury loads from climate change projections

Treesearch

Paul Conrads; Paul M. Bradley; Stephen T. Benedict; Toby D. Feaster

2016-01-01

McTier Creek is a small coastal plain watershed located in Aiken County, South Carolina. McTier Creek forms part of the headwaters for the Edisto River basin, which is noted for having some of the highest recorded fish-tissue mercury concentrations in the United States. A simple water-quality load model, TOPLOAD, which was developed for McTier Creek, utilizes a mass...

The Importance of Planetary Rotation Period for Ocean Heat Transport

PubMed Central

Stevens, D.; Joshi, M.

2014-01-01

Abstract The climate and, hence, potential habitability of a planet crucially depends on how its atmospheric and ocean circulation transports heat from warmer to cooler regions. However, previous studies of planetary climate have concentrated on modeling the dynamics of atmospheres, while dramatically simplifying the treatment of oceans, which neglects or misrepresents the effect of the ocean in the total heat transport. Even the majority of studies with a dynamic ocean have used a simple so-called aquaplanet that has no continental barriers, which is a configuration that dramatically changes the ocean dynamics. Here, the significance of the response of poleward ocean heat transport to planetary rotation period is shown with a simple meridional barrier—the simplest representation of any continental configuration. The poleward ocean heat transport increases significantly as the planetary rotation period is increased. The peak heat transport more than doubles when the rotation period is increased by a factor of ten. There are also significant changes to ocean temperature at depth, with implications for the carbon cycle. There is strong agreement between the model results and a scale analysis of the governing equations. This result highlights the importance of both planetary rotation period and the ocean circulation when considering planetary habitability. Key Words: Exoplanet—Oceans—Rotation—Climate—Habitability. Astrobiology 14, 645–650. PMID:25041658

Process-conditioned bias correction for seasonal forecasting: a case-study with ENSO in Peru

NASA Astrophysics Data System (ADS)

Manzanas, R.; Gutiérrez, J. M.

2018-05-01

This work assesses the suitability of a first simple attempt for process-conditioned bias correction in the context of seasonal forecasting. To do this, we focus on the northwestern part of Peru and bias correct 1- and 4-month lead seasonal predictions of boreal winter (DJF) precipitation from the ECMWF System4 forecasting system for the period 1981-2010. In order to include information about the underlying large-scale circulation which may help to discriminate between precipitation affected by different processes, we introduce here an empirical quantile-quantile mapping method which runs conditioned on the state of the Southern Oscillation Index (SOI), which is accurately predicted by System4 and is known to affect the local climate. Beyond the reduction of model biases, our results show that the SOI-conditioned method yields better ROC skill scores and reliability than the raw model output over the entire region of study, whereas the standard unconditioned implementation provides no added value for any of these metrics. This suggests that conditioning the bias correction on simple but well-simulated large-scale processes relevant to the local climate may be a suitable approach for seasonal forecasting. Yet, further research on the suitability of the application of similar approaches to the one considered here for other regions, seasons and/or variables is needed.

Earth tides, volcanos and climatic change

NASA Technical Reports Server (NTRS)

Roosen, R. G.; Harrington, R. S.; Giles, J.; Browning, I.

1976-01-01

The effect of variations in tidal stresses on the earth caused by the sun and moon on volcanic activity and climate is investigated. A statistically significant correlation is found between the derivatives of the envelopes of peak tidal stresses at high northern latitudes and the mean temperature of the Northern Hemisphere as reflected in oxygen isotope ratios in the Greenland ice cap. It is suggested that variations in tidal stresses cause changes in the amount of stratospheric dust produced by volcanic activity, which affects the thickness of the stratospheric dust veil and the atmospheric radiation balance. For a simple model, periodic variations in tidal stress account for 13% of the variance in the ice-core temperature record.

Interactions Between Mineral Dust, Climate, and Ocean Ecosystems

NASA Technical Reports Server (NTRS)

Gasso, Santiago; Grassian, Vicki H.; Miller, Ron L.

2010-01-01

Over the past decade, technological improvements in the chemical and physical characterization of dust have provided insights into a number of phenomena that were previously unknown or poorly understood. In addition, models are now incorporating a wider range of physical processes, which will allow us to better quantify the climatic and ecological impacts of dust. For example, some models include the effect of dust on oceanic photosynthesis and thus on atmospheric CO 2 (Friedlingstein et al. 2006). The impact of long-range dust transport, with its multiple forcings and feedbacks, is a relatively new and complex area of research, where input from several disciplines is needed. So far, many of these effects have only been parameterized in models in very simple terms. For example, the representation of dust sources remains a major uncertainty in dust modeling and estimates of the global mass of airborne dust. This is a problem where Earth scientists could make an important contribution, by working with climate scientists to determine the type of environments in which easily erodible soil particles might have accumulated over time. Geologists could also help to identify the predominant mineralogical composition of dust sources, which is crucial for calculating the radiative and chemical effects of dust but is currently known for only a few regions. Understanding how climate and geological processes control source extent and characterizing the mineral content of airborne dust are two of the fascinating challenges in future dust research.

Evaluating the effects of terrestrial ecosystems, climate and carbon dioxide on weathering over geological time: a global-scale process-based approach.

PubMed

Taylor, Lyla L; Banwart, Steve A; Valdes, Paul J; Leake, Jonathan R; Beerling, David J

2012-02-19

Global weathering of calcium and magnesium silicate rocks provides the long-term sink for atmospheric carbon dioxide (CO(2)) on a timescale of millions of years by causing precipitation of calcium carbonates on the seafloor. Catchment-scale field studies consistently indicate that vegetation increases silicate rock weathering, but incorporating the effects of trees and fungal symbionts into geochemical carbon cycle models has relied upon simple empirical scaling functions. Here, we describe the development and application of a process-based approach to deriving quantitative estimates of weathering by plant roots, associated symbiotic mycorrhizal fungi and climate. Our approach accounts for the influence of terrestrial primary productivity via nutrient uptake on soil chemistry and mineral weathering, driven by simulations using a dynamic global vegetation model coupled to an ocean-atmosphere general circulation model of the Earth's climate. The strategy is successfully validated against observations of weathering in watersheds around the world, indicating that it may have some utility when extrapolated into the past. When applied to a suite of six global simulations from 215 to 50 Ma, we find significantly larger effects over the past 220 Myr relative to the present day. Vegetation and mycorrhizal fungi enhanced climate-driven weathering by a factor of up to 2. Overall, we demonstrate a more realistic process-based treatment of plant fungal-geosphere interactions at the global scale, which constitutes a first step towards developing 'next-generation' geochemical models.

Improving the complementary methods to estimate evapotranspiration under diverse climatic and physical conditions

NASA Astrophysics Data System (ADS)

Anayah, F. M.; Kaluarachchi, J. J.

2014-06-01

Reliable estimation of evapotranspiration (ET) is important for the purpose of water resources planning and management. Complementary methods, including complementary relationship areal evapotranspiration (CRAE), advection aridity (AA) and Granger and Gray (GG), have been used to estimate ET because these methods are simple and practical in estimating regional ET using meteorological data only. However, prior studies have found limitations in these methods especially in contrasting climates. This study aims to develop a calibration-free universal method using the complementary relationships to compute regional ET in contrasting climatic and physical conditions with meteorological data only. The proposed methodology consists of a systematic sensitivity analysis using the existing complementary methods. This work used 34 global FLUXNET sites where eddy covariance (EC) fluxes of ET are available for validation. A total of 33 alternative model variations from the original complementary methods were proposed. Further analysis using statistical methods and simplified climatic class definitions produced one distinctly improved GG-model-based alternative. The proposed model produced a single-step ET formulation with results equal to or better than the recent studies using data-intensive, classical methods. Average root mean square error (RMSE), mean absolute bias (BIAS) and R2 (coefficient of determination) across 34 global sites were 20.57 mm month-1, 10.55 mm month-1 and 0.64, respectively. The proposed model showed a step forward toward predicting ET in large river basins with limited data and requiring no calibration.

Evaluating the effects of terrestrial ecosystems, climate and carbon dioxide on weathering over geological time: a global-scale process-based approach

PubMed Central

Taylor, Lyla L.; Banwart, Steve A.; Valdes, Paul J.; Leake, Jonathan R.; Beerling, David J.

2012-01-01

Global weathering of calcium and magnesium silicate rocks provides the long-term sink for atmospheric carbon dioxide (CO2) on a timescale of millions of years by causing precipitation of calcium carbonates on the seafloor. Catchment-scale field studies consistently indicate that vegetation increases silicate rock weathering, but incorporating the effects of trees and fungal symbionts into geochemical carbon cycle models has relied upon simple empirical scaling functions. Here, we describe the development and application of a process-based approach to deriving quantitative estimates of weathering by plant roots, associated symbiotic mycorrhizal fungi and climate. Our approach accounts for the influence of terrestrial primary productivity via nutrient uptake on soil chemistry and mineral weathering, driven by simulations using a dynamic global vegetation model coupled to an ocean–atmosphere general circulation model of the Earth's climate. The strategy is successfully validated against observations of weathering in watersheds around the world, indicating that it may have some utility when extrapolated into the past. When applied to a suite of six global simulations from 215 to 50 Ma, we find significantly larger effects over the past 220 Myr relative to the present day. Vegetation and mycorrhizal fungi enhanced climate-driven weathering by a factor of up to 2. Overall, we demonstrate a more realistic process-based treatment of plant fungal–geosphere interactions at the global scale, which constitutes a first step towards developing ‘next-generation’ geochemical models. PMID:22232768

Does The Earth Have an Adaptive Infrared Iris?

NASA Technical Reports Server (NTRS)

Lindzen, Richard S.; Chou, Ming-Dah; Hou, Arthur

2000-01-01

Observations and analyses of water vapor and clouds in the tropics over the past decade suggest a different approach to radiative climate feedbacks: namely, that high clouds and high free-tropospheric relative humidity are largely tied to each other, and that the main feedback consists in changing the relative areas of cloudy/moist regions vis a vis clear/dry regions in response to the surface temperature of the cloudy/moist regions - as opposed to altering the humidity in either of the regions. This is an intrinsically 2-dimensional (horizontal and vertical) effect which does not readily enter simple 1-dimensional (vertical) radiative-convective schemes which emphasize average humidity, etc. Preliminary analyses of cloud data for the eastern part of the Western Pacific from the Japanese GMS-5(Geostationary Meteorological Satellite), are supportive of this suggestion - pointing to a 15% reduction in cloudy/moist area for a 1C increase of the sea surface temperature as measured by the cloud-weighted SST (sea surface temperature). The implication of this result is examined using a simple 2-dimensional radiative-convective model. The calculations show that such a change in the tropics would lead to a strong negative feedback in the global climate, with a feedback factor of about -1.7, which, if correct, would easily dominate the positive water vapor feedback found in current models. This new feedback mechanism, in effect, constitutes an adaptive infrared iris that opens and closes in order to control the OLR (outgoing longwave radiation) in response to changes in surface temperature in a manner similar to the way in which an eye's iris opens and closes in response to changing light levels. The climate sensitivity resulting from this thermostatic mechanism is consistent with the independent determination by Lindzen and Giannitisis (1998). Preliminary attempts to replicate observations with GCMs (General Circulation Models) suggest that models lack such a negative cloud/moist areal feedback.

Modeling vector-borne disease risk in migratory animals under climate change.

PubMed

Hall, Richard J; Brown, Leone M; Altizer, Sonia

2016-08-01

Recent theory suggests that animals that migrate to breed at higher latitudes may benefit from reduced pressure from natural enemies, including pathogens ("migratory escape"), and that migration itself weeds out infected individuals and lowers infection prevalence ("migratory culling"). The distribution and activity period of arthropod disease vectors in temperate regions is expected to respond rapidly to climate change, which could reduce the potential for migratory escape. However, climate change could have the opposite effect of reducing transmission if differential responses in the phenology and distribution of migrants and disease vectors reduce their overlap in space and time. Here we outline a simple modeling framework for exploring the influence of climate change on vector-borne disease dynamics in a migratory host. We investigate two scenarios under which pathogen transmission dynamics might be mediated by climate change: (1) vectors respond more rapidly than migrants to advancing phenology at temperate breeding sites, causing peak susceptible host density and vector emergence to diverge ("migratory mismatch") and (2) reduced migratory propensity allows increased nonbreeding survival of infected hosts and larger breeding-site epidemics (loss of migratory culling, here referred to as "sedentary amplification"). Our results highlight the need for continued surveillance of climate-induced changes to migratory behavior and vector activity to predict pathogen prevalence and its impacts on migratory animals. © The Author 2016. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

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.

Air - water temperature relationships in the trout streams of southeastern Minnesota’s carbonate - sandstone landscape

USGS Publications Warehouse

Krider, Lori A.; Magner, Joseph A.; Perry, Jim; Vondracek, Bruce C.; Ferrington, Leonard C.

2013-01-01

Carbonate-sandstone geology in southeastern Minnesota creates a heterogeneous landscape of springs, seeps, and sinkholes that supply groundwater into streams. Air temperatures are effective predictors of water temperature in surface-water dominated streams. However, no published work investigates the relationship between air and water temperatures in groundwater-fed streams (GWFS) across watersheds. We used simple linear regressions to examine weekly air-water temperature relationships for 40 GWFS in southeastern Minnesota. A 40-stream, composite linear regression model has a slope of 0.38, an intercept of 6.63, and R2 of 0.83. The regression models for GWFS have lower slopes and higher intercepts in comparison to surface-water dominated streams. Regression models for streams with high R2 values offer promise for use as predictive tools for future climate conditions. Climate change is expected to alter the thermal regime of groundwater-fed systems, but will do so at a slower rate than surface-water dominated systems. A regression model of intercept vs. slope can be used to identify streams for which water temperatures are more meteorologically than groundwater controlled, and thus more vulnerable to climate change. Such relationships can be used to guide restoration vs. management strategies to protect trout streams.

Inevitable end-of-21st-century trends toward earlier surface runoff timing in California's Sierra Nevada Mountains

NASA Astrophysics Data System (ADS)

Schwartz, M. A.; Hall, A. D.; Sun, F.; Walton, D.; Berg, N.

2015-12-01

Hybrid dynamical-statistical downscaling is used to produce surface runoff timing projections for California's Sierra Nevada, a high-elevation mountain range with significant seasonal snow cover. First, future climate change projections (RCP8.5 forcing scenario, 2081-2100 period) from five CMIP5 global climate models (GCMs) are dynamically downscaled. These projections reveal that future warming leads to a shift toward earlier snowmelt and surface runoff timing throughout the Sierra Nevada region. Relationships between warming and surface runoff timing from the dynamical simulations are used to build a simple statistical model that mimics the dynamical model's projected surface runoff timing changes given GCM input or other statistically-downscaled input. This statistical model can be used to produce surface runoff timing projections for other GCMs, periods, and forcing scenarios to quantify ensemble-mean changes, uncertainty due to intermodel variability and consequences stemming from choice of forcing scenario. For all CMIP5 GCMs and forcing scenarios, significant trends toward earlier surface runoff timing occur at elevations below 2500m. Thus, we conclude that trends toward earlier surface runoff timing by the end-of-the-21st century are inevitable. The changes to surface runoff timing diagnosed in this study have implications for many dimensions of climate change, including impacts on surface hydrology, water resources, and ecosystems.

Attribution of floods in the Okavango basin, Southern Africa

NASA Astrophysics Data System (ADS)

Wolski, Piotr; Stone, Dáithí; Tadross, Mark; Wehner, Michael; Hewitson, Bruce

2014-04-01

In the charismatic wetlands of the Okavango Delta, Botswana, the annual floods of 2009-2011 reached magnitudes last seen 20-30 years ago, considerably affecting life of local populations and the economically important tourism industry. In this study, we analyse results from an attribution modelling system designed to examine how anthropogenic greenhouse gas emissions have contributed to weather and flood risk in our current climate. The system is based on comparison of real world climate and hydrological simulations with parallel counterfactual simulations of the climate and hydrological responses under conditions that might have been had human activities not emitted greenhouse gases. The analyses allow us to address the question of whether anthropogenic climate change contributed to increasing the risk of these high flood events in the Okavango system. Results show that the probability of occurrence of high floods during 2009-2011 in the current climate is likely lower than it would have been in a climate without anthropogenic greenhouse gases. This result is robust across the two climate models and various data processing procedures, although the exact figures for the associated decrease in risk differ. Results also differ between the three years examined, indicating that the “time-slice” method used here needs to be applied to multiple years in order to accurately estimate the contribution of emissions to current risk. Simple sensitivity analyses indicate that the reduction in flood risk is attributed to higher temperatures (and thus evaporation) in the current world, with little difference in the analysed domain's rainfall simulated in the two scenarios.

Cluster-based analysis of multi-model climate ensembles

NASA Astrophysics Data System (ADS)

Hyde, Richard; Hossaini, Ryan; Leeson, Amber A.

2018-06-01

Clustering - the automated grouping of similar data - can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model-observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry-climate model (CCM) output of tropospheric ozone - an important greenhouse gas - from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ˜ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ˜ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere - where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and useful framework in which to assess and visualise model spread, offering insight into geographical areas of agreement among models and a measure of diversity across an ensemble. Finally, we discuss caveats of the clustering techniques and note that while we have focused on tropospheric ozone, the principles underlying the cluster-based MMMs are applicable to other prognostic variables from climate models.

Trading off Aircraft Fuel Burn and NO x Emissions for Optimal Climate Policy.

PubMed

Freeman, Sarah; Lee, David S; Lim, Ling L; Skowron, Agnieszka; De León, Ruben Rodriguez

2018-03-06

Aviation emits pollutants that affect the climate, including CO 2 and NO x , NO x indirectly so, through the formation of tropospheric ozone and reduction of ambient methane. To improve the fuel performance of engines, combustor temperatures and pressures often increase, increasing NO x emissions. Conversely, combustor modifications to reduce NO x may increase CO 2 . Hence, a technology trade-off exists, which also translates to a trade-off between short-lived climate forcers and a long-lived greenhouse gas, CO 2 . Moreover, the NO x -O 3 -CH 4 system responds in a nonlinear manner, according to both aviation emissions and background NO x . A simple climate model was modified to incorporate nonlinearities parametrized from a complex chemistry model. Case studies showed that for a scenario of a 20% reduction in NO x emissions the consequential CO 2 penalty of 2% actually increased the total radiative forcing (RF). For a 2% fuel penalty, NO x emissions needed to be reduced by >43% to realize an overall benefit. Conversely, to ensure that the fuel penalty for a 20% NO x emission reduction did not increase overall forcing, a 0.5% increase in CO 2 was found to be the "break even" point. The time scales of the climate effects of NO x and CO 2 are quite different, necessitating careful analysis of proposed emissions trade-offs.

Drivers of precipitation change: An energetic understanding

NASA Astrophysics Data System (ADS)

Richardson, T.; Forster, P.; Andrews, T.

2016-12-01

Future precipitation changes are highly uncertain. Different drivers of anthropogenic climate change can cause very different hydrological responses, which could have significant societal implications. Changes in precipitation are tightly linked to the atmospheric energy budget due to the latent heat released through condensation. Through analysis of the atmospheric energy budget we make significant steps forward in understanding and predicting the precipitation response to different forcings. Here we analyse the response to five targeted forcing scenarios (perturbed CO2, CH4, black carbon, sulphate and solar insolation) across eight climate models participating in the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). The resulting changes are split into a rapid adjustment component, due to the near-instantaneous changes in the atmospheric energy budget, and a feedback component which scales with surface temperature change. Globally, CO2 and black carbon produce large negative adjustments in precipitation due to the increase in atmospheric absorption. However, over land it is sulphate and solar forcing which produce the largest precipitation adjustments due to changes in horizontal energy transport associated with rapid circulation changes. Globally, the precipitation feedback response is very consistent between forcing scenarios, driven mainly by increased longwave cooling. The feedback response differs significantly over land and sea, with a larger feedback over the oceans. We use the PDRMIP results to construct a simple model for precipitation change over land and sea based on surface temperature change and top of the atmosphere forcing. The simple model matches well with CMIP5 ensemble mean precipitation change for RCP8.5. Simulated changes in land mean precipitation can be estimated well using the rapid adjustment and feedback framework, and understood through simple energy budget arguments. Up until present day the effects of temperature change on land mean precipitation have been entirely masked by sulphate forcing. However, as projected sulphate forcing decreases, and warming continues, the temperature driven increase in land mean precipitation soon dominates.

Climate variability and vadose zone controls on damping of transient recharge

USGS Publications Warehouse

Corona, Claudia R.; Gurdak, Jason J.; Dickinson, Jesse; Ferré, T.P.A.; Maurer, Edwin P.

2018-01-01

Increasing demand on groundwater resources motivates understanding of the controls on recharge dynamics so model predictions under current and future climate may improve. Here we address questions about the nonlinear behavior of flux variability in the vadose zone that may explain previously reported teleconnections between global-scale climate variability and fluctuations in groundwater levels. We use hundreds of HYDRUS-1D simulations in a sensitivity analysis approach to evaluate the damping depth of transient recharge over a range of periodic boundary conditions and vadose zone geometries and hydraulic parameters that are representative of aquifer systems of the conterminous United States (U.S). Although the models were parameterized based on U.S. aquifers, findings from this study are applicable elsewhere that have mean recharge rates between 3.65 and 730 mm yr–1. We find that mean infiltration flux, period of time varying infiltration, and hydraulic conductivity are statistically significant predictors of damping depth. The resulting framework explains why some periodic infiltration fluxes associated with climate variability dampen with depth in the vadose zone, resulting in steady-state recharge, while other periodic surface fluxes do not dampen with depth, resulting in transient recharge. We find that transient recharge in response to the climate variability patterns could be detected at the depths of water levels in most U.S. aquifers. Our findings indicate that the damping behavior of transient infiltration fluxes is linear across soil layers for a range of texture combinations. The implications are that relatively simple, homogeneous models of the vadose zone may provide reasonable estimates of the damping depth of climate-varying transient recharge in some complex, layered vadose zone profiles.

Attribution of Extreme Rainfall Events in the South of France Using EURO-CORDEX Simulations

NASA Astrophysics Data System (ADS)

Luu, L. N.; Vautard, R.; Yiou, P.

2017-12-01

The Mediterranean region regularly undergoes episodes of intense precipitation in the fall season that exceed 300mm a day. This study focuses on the role of climate change on the dynamics of the events that occur in the South of France. We used an ensemble of 10 EURO-CORDEX model simulations with two horizontal resolutions (EUR-11: 0.11° and EUR-44: 0.44°) for the attribution of extreme rainfall in the fall in the Cevennes mountain range (South of France). The biases of the simulations were corrected with simple scaling adjustment and a quantile correction (CDFt). This produces five datasets including EUR-44 and EUR-11 with and without scaling adjustment and CDFt-EUR-11, on which we test the impact of resolution and bias correction on the extremes. Those datasets, after pooling all of models together, are fitted by a stationary Generalized Extreme Value distribution for several periods to estimate a climate change signal in the tail of distribution of extreme rainfall in the Cévenne region. Those changes are then interpreted by a scaling model that links extreme rainfall with mean and maximum daily temperature. The results show that higher-resolution simulations with bias adjustment provide a robust and confident increase of intensity and likelihood of occurrence of autumn extreme rainfall in the area in current climate in comparison with historical climate. The probability (exceedance probability) of 1-in-1000-year event in historical climate may increase by a factor of 1.8 under current climate with a confident interval of 0.4 to 5.3 following the CDFt bias-adjusted EUR-11. The change of magnitude appears to follow the Clausius-Clapeyron relation that indicates a 7% increase in rainfall per 1oC increase in temperature.

Model-based conservation planning of the genetic diversity of Phellodendron amurense Rupr due to climate change

PubMed Central

Wan, Jizhong; Wang, Chunjing; Yu, Jinghua; Nie, Siming; Han, Shijie; Zu, Yuangang; Chen, Changmei; Yuan, Shusheng; Wang, Qinggui

2014-01-01

Climate change affects both habitat suitability and the genetic diversity of wild plants. Therefore, predicting and establishing the most effective and coherent conservation areas is essential for the conservation of genetic diversity in response to climate change. This is because genetic variance is a product not only of habitat suitability in conservation areas but also of efficient protection and management. Phellodendron amurense Rupr. is a tree species (family Rutaceae) that is endangered due to excessive and illegal harvesting for use in Chinese medicine. Here, we test a general computational method for the prediction of priority conservation areas (PCAs) by measuring the genetic diversity of P. amurense across the entirety of northeast China using a single strand repeat analysis of twenty microsatellite markers. Using computational modeling, we evaluated the geographical distribution of the species, both now and in different future climate change scenarios. Different populations were analyzed according to genetic diversity, and PCAs were identified using a spatial conservation prioritization framework. These conservation areas were optimized to account for the geographical distribution of P. amurense both now and in the future, to effectively promote gene flow, and to have a long period of validity. In situ and ex situ conservation, strategies for vulnerable populations were proposed. Three populations with low genetic diversity are predicted to be negatively affected by climate change, making conservation of genetic diversity challenging due to decreasing habitat suitability. Habitat suitability was important for the assessment of genetic variability in existing nature reserves, which were found to be much smaller than the proposed PCAs. Finally, a simple set of conservation measures was established through modeling. This combined molecular and computational ecology approach provides a framework for planning the protection of species endangered by climate change. PMID:25165526

Development of a national anthropogenic heating database with an extrapolation for international cities

NASA Astrophysics Data System (ADS)

Sailor, David J.; Georgescu, Matei; Milne, Jeffrey M.; Hart, Melissa A.

2015-10-01

Given increasing utility of numerical models to examine urban impacts on meteorology and climate, there exists an urgent need for accurate representation of seasonally and diurnally varying anthropogenic heating data, an important component of the urban energy budget for cities across the world. Incorporation of anthropogenic heating data as inputs to existing climate modeling systems has direct societal implications ranging from improved prediction of energy demand to health assessment, but such data are lacking for most cities. To address this deficiency we have applied a standardized procedure to develop a national database of seasonally and diurnally varying anthropogenic heating profiles for 61 of the largest cities in the United Stated (U.S.). Recognizing the importance of spatial scale, the anthropogenic heating database developed includes the city scale and the accompanying greater metropolitan area. Our analysis reveals that a single profile function can adequately represent anthropogenic heating during summer but two profile functions are required in winter, one for warm climate cities and another for cold climate cities. On average, although anthropogenic heating is 40% larger in winter than summer, the electricity sector contribution peaks during summer and is smallest in winter. Because such data are similarly required for international cities where urban climate assessments are also ongoing, we have made a simple adjustment accounting for different international energy consumption rates relative to the U.S. to generate seasonally and diurnally varying anthropogenic heating profiles for a range of global cities. The methodological approach presented here is flexible and straightforwardly applicable to cities not modeled because of presently unavailable data. Because of the anticipated increase in global urban populations for many decades to come, characterizing this fundamental aspect of the urban environment - anthropogenic heating - is an essential element toward continued progress in urban climate assessment.

Climatic influence of background and volcanic stratosphere aerosol models

NASA Technical Reports Server (NTRS)

Deschamps, P. Y.; Herman, M.; Lenoble, J.; Tanre, D.

1982-01-01

A simple modelization of the earth atmosphere system including tropospheric and stratospheric aerosols has been derived and tested. Analytical expressions are obtained for the albedo variation due to a thin stratospheric aerosol layer. Also outlined are the physical procedures and the respective influence of the main parameters: aerosol optical thickness, single scattering albedo and asymmetry factor, and sublayer albedo. The method is applied to compute the variation of the zonal and planetary albedos due to a stratospheric layer of background H2SO4 particles and of volcanic ash.

Quantification of biophysical adaptation benefits from Climate-Smart Agriculture using a Bayesian Belief Network.

PubMed

de Nijs, Patrick J; Berry, Nicholas J; Wells, Geoff J; Reay, Dave S

2014-10-20

The need for smallholder farmers to adapt their practices to a changing climate is well recognised, particularly in Africa. The cost of adapting to climate change in Africa is estimated to be $20 to $30 billion per year, but the total amount pledged to finance adaptation falls significantly short of this requirement. The difficulty of assessing and monitoring when adaptation is achieved is one of the key barriers to the disbursement of performance-based adaptation finance. To demonstrate the potential of Bayesian Belief Networks for describing the impacts of specific activities on climate change resilience, we developed a simple model that incorporates climate projections, local environmental data, information from peer-reviewed literature and expert opinion to account for the adaptation benefits derived from Climate-Smart Agriculture activities in Malawi. This novel approach allows assessment of vulnerability to climate change under different land use activities and can be used to identify appropriate adaptation strategies and to quantify biophysical adaptation benefits from activities that are implemented. We suggest that multiple-indicator Bayesian Belief Network approaches can provide insights into adaptation planning for a wide range of applications and, if further explored, could be part of a set of important catalysts for the expansion of adaptation finance.

Quantitative Estimation of the Climatic Effects of Carbon Transferred by International Trade.

PubMed

Wei, Ting; Dong, Wenjie; Moore, John; Yan, Qing; Song, Yi; Yang, Zhiyong; Yuan, Wenping; Chou, Jieming; Cui, Xuefeng; Yan, Xiaodong; Wei, Zhigang; Guo, Yan; Yang, Shili; Tian, Di; Lin, Pengfei; Yang, Song; Wen, Zhiping; Lin, Hui; Chen, Min; Feng, Guolin; Jiang, Yundi; Zhu, Xian; Chen, Juan; Wei, Xin; Shi, Wen; Zhang, Zhiguo; Dong, Juan; Li, Yexin; Chen, Deliang

2016-06-22

Carbon transfer via international trade affects the spatial pattern of global carbon emissions by redistributing emissions related to production of goods and services. It has potential impacts on attribution of the responsibility of various countries for climate change and formulation of carbon-reduction policies. However, the effect of carbon transfer on climate change has not been quantified. Here, we present a quantitative estimate of climatic impacts of carbon transfer based on a simple CO2 Impulse Response Function and three Earth System Models. The results suggest that carbon transfer leads to a migration of CO2 by 0.1-3.9 ppm or 3-9% of the rise in the global atmospheric concentrations from developed countries to developing countries during 1990-2005 and potentially reduces the effectiveness of the Kyoto Protocol by up to 5.3%. However, the induced atmospheric CO2 concentration and climate changes (e.g., in temperature, ocean heat content, and sea-ice) are very small and lie within observed interannual variability. Given continuous growth of transferred carbon emissions and their proportion in global total carbon emissions, the climatic effect of traded carbon is likely to become more significant in the future, highlighting the need to consider carbon transfer in future climate negotiations.

Quantification of biophysical adaptation benefits from Climate-Smart Agriculture using a Bayesian Belief Network

NASA Astrophysics Data System (ADS)

de Nijs, Patrick J.; Berry, Nicholas J.; Wells, Geoff J.; Reay, Dave S.

2014-10-01

The need for smallholder farmers to adapt their practices to a changing climate is well recognised, particularly in Africa. The cost of adapting to climate change in Africa is estimated to be $20 to $30 billion per year, but the total amount pledged to finance adaptation falls significantly short of this requirement. The difficulty of assessing and monitoring when adaptation is achieved is one of the key barriers to the disbursement of performance-based adaptation finance. To demonstrate the potential of Bayesian Belief Networks for describing the impacts of specific activities on climate change resilience, we developed a simple model that incorporates climate projections, local environmental data, information from peer-reviewed literature and expert opinion to account for the adaptation benefits derived from Climate-Smart Agriculture activities in Malawi. This novel approach allows assessment of vulnerability to climate change under different land use activities and can be used to identify appropriate adaptation strategies and to quantify biophysical adaptation benefits from activities that are implemented. We suggest that multiple-indicator Bayesian Belief Network approaches can provide insights into adaptation planning for a wide range of applications and, if further explored, could be part of a set of important catalysts for the expansion of adaptation finance.

A method to encapsulate model structural uncertainty in ensemble projections of future climate: EPIC v1.0

NASA Astrophysics Data System (ADS)

Lewis, Jared; Bodeker, Greg E.; Kremser, Stefanie; Tait, Andrew

2017-12-01

A method, based on climate pattern scaling, has been developed to expand a small number of projections of fields of a selected climate variable (X) into an ensemble that encapsulates a wide range of indicative model structural uncertainties. The method described in this paper is referred to as the Ensemble Projections Incorporating Climate model uncertainty (EPIC) method. Each ensemble member is constructed by adding contributions from (1) a climatology derived from observations that represents the time-invariant part of the signal; (2) a contribution from forced changes in X, where those changes can be statistically related to changes in global mean surface temperature (Tglobal); and (3) a contribution from unforced variability that is generated by a stochastic weather generator. The patterns of unforced variability are also allowed to respond to changes in Tglobal. The statistical relationships between changes in X (and its patterns of variability) and Tglobal are obtained in a training phase. Then, in an implementation phase, 190 simulations of Tglobal are generated using a simple climate model tuned to emulate 19 different global climate models (GCMs) and 10 different carbon cycle models. Using the generated Tglobal time series and the correlation between the forced changes in X and Tglobal, obtained in the training phase, the forced change in the X field can be generated many times using Monte Carlo analysis. A stochastic weather generator is used to generate realistic representations of weather which include spatial coherence. Because GCMs and regional climate models (RCMs) are less likely to correctly represent unforced variability compared to observations, the stochastic weather generator takes as input measures of variability derived from observations, but also responds to forced changes in climate in a way that is consistent with the RCM projections. This approach to generating a large ensemble of projections is many orders of magnitude more computationally efficient than running multiple GCM or RCM simulations. Such a large ensemble of projections permits a description of a probability density function (PDF) of future climate states rather than a small number of individual story lines within that PDF, which may not be representative of the PDF as a whole; the EPIC method largely corrects for such potential sampling biases. The method is useful for providing projections of changes in climate to users wishing to investigate the impacts and implications of climate change in a probabilistic way. A web-based tool, using the EPIC method to provide probabilistic projections of changes in daily maximum and minimum temperatures for New Zealand, has been developed and is described in this paper.

Evaluating the accuracy of climate change pattern emulation for low warming targets

NASA Astrophysics Data System (ADS)

Tebaldi, Claudia; Knutti, Reto

2018-05-01

Global climate policy is increasingly debating the value of very low warming targets, yet not many experiments conducted with global climate models in their fully coupled versions are currently available to help inform studies of the corresponding impacts. This raises the question whether a map of warming or precipitation change in a world 1.5 °C warmer than preindustrial can be emulated from existing simulations that reach higher warming targets, or whether entirely new simulations are required. Here we show that also for this type of low warming in strong mitigation scenarios, climate change signals are quite linear as a function of global temperature. Therefore, emulation techniques amounting to linear rescaling on the basis of global temperature change ratios (like simple pattern scaling) provide a viable way forward. The errors introduced are small relative to the spread in the forced response to a given scenario that we can assess from a multi-model ensemble. They are also small relative to the noise introduced into the estimates of the forced response by internal variability within a single model, which we can assess from either control simulations or initial condition ensembles. Challenges arise when scaling inadvertently reduces the inter-model spread or suppresses the internal variability, both important sources of uncertainty for impact assessment, or when the scenarios have very different characteristics in the composition of the forcings. Taking advantage of an available suite of coupled model simulations under low-warming and intermediate scenarios, we evaluate the accuracy of these emulation techniques and show that they are unlikely to represent a substantial contribution to the total uncertainty.

The Jormungand Global Climate State and Implications for the Neoproterozoic Snowball Paradox (Invited)

NASA Astrophysics Data System (ADS)

Abbot, D. S.; Voigt, A.; Koll, D.; Pierrehumbert, R. T.

2010-12-01

We present a previously undescribed global climate state, the Jormungand state, that is nearly ice-covered with a narrow (~10-15 degrees of latitude) strip of open ocean near the equator. This state is sustained by internal dynamics of the hydrological cycle and the cryosphere. There is a new bifurcation in global climate climate associated with the Jormungand state that leads to significant hysteresis. We investigate the Jormungand state in a coupled ocean-atmosphere GCM, in multiple atmospheric GCMs coupled to a mixed layer ocean run in an idealized configuration, and we make a simple modification to the Budyko-Sellers model so that it produces Jormungand states. We suggest that the Jormungand state may be a better model for the Neoproterozoic glaciations (~635 Ma and ~715 Ma) than either the hard Snowball or the Slushball models. A Jormungand state would have a large enough region of open ocean near the equator to explain the micropaleontological and molecular clock evidence that photosynthetic eukaryotes thrived both before and immediately after the Neoproterozoic episodes. Additionally, since there is significant hysteresis associated with the Jormungand state, it can explain the cap carbonate sequences, the oxygen isotopic evidence that suggests high CO2 values, and the various evidence that suggests lifetimes for the glaciations of 1 Myrs or more. Since there is not significant hysteresis associated with the Slushball model, the Slushball model cannot explain these observations. Finally, we note that although the Slushball and Jormungand models share the characteristic of open ocean in the tropics, the Jormungand state is produced by entirely different physics, is entered through a new bifurcation in global climate, and is associated with significant hysteresis. Bifurcation diagram of global climate in the CAM global climate model, run with no continents, a 50 m mixed layer with no ocean heat transport, an eccentricity of zero, and annually and diurnally-varying insolation with a solar constant of 94% of present value. Red diamonds denote simulations initiated from ice-free conditions, blue circles denote simulations initiated from the Jormungand state, and green squares denote simulations initiated from the Snowball state. The black curve shows model equilibria, with dotted unstable solution branches (separatrices) and bifurcations drawn schematically.

An Analytical Model for Basin-scale Glacier Erosion as a Function of Climate and Topography

NASA Astrophysics Data System (ADS)

Jaffrey, M.; Hallet, B.

2017-12-01

Knowledge about glacier erosion has advanced considerably over the last few decades with the emergence of a firm mechanistic understanding of abrasion and quarrying, the growing sophistication of complex numerical models of glacial erosion and the evolution of glacial landforms, and the increase in data from field studies of erosion rates. Interest in glacial erosion has also intensified and diversified substantially as it is increasingly recognized as a key process affecting the heights of mountains, the overall evolution of mountain belts, and the coupling of climate, erosion, and tectonics. Yet, the general controls of glacier erosion rates have not been addressed theoretically, and the large range of published basin-scale erosion rates, covering more than 3 orders of magnitude, remains poorly understood. To help gain insight into glacier erosion rates at the scale of glacier basins, the only scale for which extensive data exist, we develop analytically a simple budget of the total mechanical energy per unit time, the power, dissipated by a steady state glacier in sliding, S, and viscous deformation, V. We hypothesize that the power for the work of erosion derives solely from S and that the basin wide erosion rate scales with S averaged over the basin. We solve the power budget directly in terms of climatic and topographic parameters, showing explicitly that the source of power to drive both S and V is the gravitational power supplied by the net snow accumulation (mass balance). The budget leads to the simple metric φ=mbΔz2 for the basin average of S with Δz being the glacier basin relief and mb the gradient of the mass balance with elevation. The dependence of φ on the square of the relief arises from both the mass balance's and potential energy's linear increases with elevation. We validate φ using results from a comprehensive field study of erosion rates paired with glaciological data along a transect extending from Southern Patagonia to the Antarctic Peninsula (Koppes et al. 2015. Nature, 526(7571), 100). Along this transect, φ accounts for 75% ± 12% of the variation in reported erosion rates. The power budget model illuminates the role of climate and topography in basin scale erosion rates with direct implications for the broad community interested in ice masses and their interconnections with climate and topography.

Can integrative catchment management mitigate future water quality issues caused by climate change and socio-economic development?

NASA Astrophysics Data System (ADS)

Honti, Mark; Schuwirth, Nele; Rieckermann, Jörg; Stamm, Christian

2017-03-01

The design and evaluation of solutions for integrated surface water quality management requires an integrated modelling approach. Integrated models have to be comprehensive enough to cover the aspects relevant for management decisions, allowing for mapping of larger-scale processes such as climate change to the regional and local contexts. Besides this, models have to be sufficiently simple and fast to apply proper methods of uncertainty analysis, covering model structure deficits and error propagation through the chain of sub-models. Here, we present a new integrated catchment model satisfying both conditions. The conceptual iWaQa model was developed to support the integrated management of small streams. It can be used to predict traditional water quality parameters, such as nutrients and a wide set of organic micropollutants (plant and material protection products), by considering all major pollutant pathways in urban and agricultural environments. Due to its simplicity, the model allows for a full, propagative analysis of predictive uncertainty, including certain structural and input errors. The usefulness of the model is demonstrated by predicting future surface water quality in a small catchment with mixed land use in the Swiss Plateau. We consider climate change, population growth or decline, socio-economic development, and the implementation of management strategies to tackle urban and agricultural point and non-point sources of pollution. Our results indicate that input and model structure uncertainties are the most influential factors for certain water quality parameters. In these cases model uncertainty is already high for present conditions. Nevertheless, accounting for today's uncertainty makes management fairly robust to the foreseen range of potential changes in the next decades. The assessment of total predictive uncertainty allows for selecting management strategies that show small sensitivity to poorly known boundary conditions. The identification of important sources of uncertainty helps to guide future monitoring efforts and pinpoints key indicators, whose evolution should be closely followed to adapt management. The possible impact of climate change is clearly demonstrated by water quality substantially changing depending on single climate model chains. However, when all climate trajectories are combined, the human land use and management decisions have a larger influence on water quality against a time horizon of 2050 in the study.

The Feasibility of Avoiding Future Climate Impacts: Results from the AVOID Programmes

NASA Astrophysics Data System (ADS)

Lowe, J. A.; Warren, R.; Arnell, N.; Buckle, S.

2014-12-01

The AVOID programme and its successor, AVOID2, have focused on answering three core questions: how do we characterise potentially dangerous climate change and impacts, which emissions pathways can avoid at least some of these impacts, and how feasible are the future reductions needed to significantly deviate from a business-as-usual future emissions pathway. The first AVOID project succeeded in providing the UK Government with evidence to inform its position on climate change. A key part of the work involved developing a range of global emissions pathways and estimating and understanding the corresponding global impacts. This made use of a combination of complex general circulation models, simple climate models, pattern-scaling and state-of-the art impacts models. The results characterise the range of avoidable impacts across the globe in several key sectors including river and coastal flooding, cooling and heating energy demand, crop productivity and aspects of biodiversity. The avoided impacts between a scenario compatible with a 4ºC global warming and one with a 2ºC global warming were found to be highly sector dependent and avoided fractions typically ranged between 20% and 70%. A further key aspect was characterising the magnitude of the uncertainty involved, which is found to be very large in some impact sectors although the avoided fraction appears a more robust metric. The AVOID2 programme began in 2014 and will provide results in the run up to the Paris CoP in 2015. This includes new post-IPCC 5th assessment evidence to inform the long-term climate goal, a more comprehensive assessment of the uncertainty ranges of feasible emission pathways compatible with the long-term goal and enhanced estimates of global impacts using the latest generation of impact models and scenarios.

Modeling climate change impacts on combined sewer overflow using synthetic precipitation time series.

PubMed

Bendel, David; Beck, Ferdinand; Dittmer, Ulrich

2013-01-01

In the presented study climate change impacts on combined sewer overflows (CSOs) in Baden-Wuerttemberg, Southern Germany, were assessed based on continuous long-term rainfall-runoff simulations. As input data, synthetic rainfall time series were used. The applied precipitation generator NiedSim-Klima accounts for climate change effects on precipitation patterns. Time series for the past (1961-1990) and future (2041-2050) were generated for various locations. Comparing the simulated CSO activity of both periods we observe significantly higher overflow frequencies for the future. Changes in overflow volume and overflow duration depend on the type of overflow structure. Both values will increase at simple CSO structures that merely divide the flow, whereas they will decrease when the CSO structure is combined with a storage tank. However, there is a wide variation between the results of different precipitation time series (representative for different locations).

Final Technical Report for Collaborative Research: Developing and Implementing Ocean-Atmosphere Reanalyses for Climate Applications (OARCA)

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

Compo, Gilbert P

As an important step toward a coupled data assimilation system for generating reanalysis fields needed to assess climate model projections, the Ocean Atmosphere Coupled Reanalysis for Climate Applications (OARCA) project assesses and improves the longest reanalyses currently available of the atmosphere and ocean: the 20th Century Reanalysis Project (20CR) and the Simple Ocean Data Assimilation with sparse observational input (SODAsi) system, respectively. In this project, we make off-line but coordinated improvements in the 20CR and SODAsi datasets, with improvements in one feeding into improvements of the other through an iterative generation of new versions. These datasets now span from themore » 19th to 21st centuries. We then study the extreme weather and variability from days to decades of the resulting datasets. A total of 24 publications have been produced in this project.« less

Estimation of the diesel exhaust exposures of railroad workers: II. National and historical exposures.

PubMed

Woskie, S R; Smith, T J; Hammond, S K; Schenker, M B; Garshick, E; Speizer, F E

1988-01-01

The diesel exhaust exposures of railroad workers in thirteen job groups from four railroads in the United States were used to estimate U.S. national average exposures with a linear statistical model which accounts for the significant variability in exposure caused by climate, the differences among railroads and the uneven distribution of railroad workers across climatic regions. Personal measurements of respirable particulate matter, adjusted to remove the contribution of cigarette smoke particles, were used as a marker for diesel exhaust. The estimated national means of adjusted respirable particulate matter (ARP) averaged 10 micrograms/m3 lower than the simple means for each job group, reflecting the climatic differences between the northern railroads studied and the distribution of railroad workers nationally. Limited historical records, including some industrial hygiene data, were used to evaluate past diesel exhaust exposures, which were estimated to be approximately constant from the 1950's to 1983.

Global climate change research at the U.S. Environmental Protection Agency

EPA Science Inventory

The science surrounding global climate change is complex and has been interpreted in many ways. The concept of the Greenhouse Effect—viewed as the cause of global climate change—is quite simple, but the Earth’s response is not. After more than two decades of intensive research, s...

Applying Descriptive Statistics to Teaching the Regional Classification of Climate.

ERIC Educational Resources Information Center

Lindquist, Peter S.; Hammel, Daniel J.

1998-01-01

Describes an exercise for college and high school students that relates descriptive statistics to the regional climatic classification. The exercise introduces students to simple calculations of central tendency and dispersion, the construction and interpretation of scatterplots, and the definition of climatic regions. Forces students to engage…

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.

Evaluation of additional biogeochemical impacts on mitigation pathways in an energy sytem integrated assessment model.

NASA Astrophysics Data System (ADS)

Dessens, O.

2017-12-01

Within the last IPCC AR5 a large and systematic sensitivity study around available technologies and timing of policies applied in IAMs to achieve the 2°C target has been conducted. However the simple climate representations included in IAMs are generally tuned to the results of ensemble means. This may result in hiding within the ensemble mean results possible challenging mitigation pathways for the economy or the technology future scenarios. This work provides new insights on the sensitivity of the socio-economic response to different climate factors under a 2°C climate change target in order to help guide future efforts to reduce uncertainty in the climate mitigation decisions. The main objective is to understand and bring new insights on how future global warming will affect the natural biochemical feedbacks on the climate system and what could be the consequences of these feedbacks on the anthropogenic emission pathways with a specific focus on the energy-economy system. It specifically focuses on three issues of the climate representation affecting the energy system transformation and GHG emissions pathways: 1- Impacts of the climate sensitivity (or TCR); 2- Impacts of warming on the radiative forcing (cloudiness,...); 3- Impacts of warming on the carbon cycle (carbon cycle feedback). We use the integrated assessment model TIAM-UCL to examine the mitigation pathways compatible with the 2C target depending on assumptions regarding the 3 issues of the climate representation introduced above. The following key conclusions drawn from this study are that mitigation to 2°C is still possible under strong climate sensitivity (TCR), strong carbon cycle amplification or positive radiative forcing feedback. However, this level of climate mitigation will require a significant transformation in the way we produce and consume energy. Carbon capture and sequestration on electricity generation, industry and biomass is part of the technology pool needed to achieve this level of decarbonisation. In extreme condition (positive correlation between the 3 issues discussed) the integrated assessment model TIAM-UCL creates pathways requiring additional negative emission technologies at the end of this century to keep temperature change well below 2°C.

Large ensemble modeling of last deglacial retreat of the West Antarctic Ice Sheet: comparison of simple and advanced statistical techniques

NASA Astrophysics Data System (ADS)

Pollard, D.; Chang, W.; Haran, M.; Applegate, P.; DeConto, R.

2015-11-01

A 3-D hybrid ice-sheet model is applied to the last deglacial retreat of the West Antarctic Ice Sheet over the last ~ 20 000 years. A large ensemble of 625 model runs is used to calibrate the model to modern and geologic data, including reconstructed grounding lines, relative sea-level records, elevation-age data and uplift rates, with an aggregate score computed for each run that measures overall model-data misfit. Two types of statistical methods are used to analyze the large-ensemble results: simple averaging weighted by the aggregate score, and more advanced Bayesian techniques involving Gaussian process-based emulation and calibration, and Markov chain Monte Carlo. Results for best-fit parameter ranges and envelopes of equivalent sea-level rise with the simple averaging method agree quite well with the more advanced techniques, but only for a large ensemble with full factorial parameter sampling. Best-fit parameter ranges confirm earlier values expected from prior model tuning, including large basal sliding coefficients on modern ocean beds. Each run is extended 5000 years into the "future" with idealized ramped climate warming. In the majority of runs with reasonable scores, this produces grounding-line retreat deep into the West Antarctic interior, and the analysis provides sea-level-rise envelopes with well defined parametric uncertainty bounds.

Climate Change Impact on Variability of Rainfall Intensity in Upper Blue Nile Basin, Ethiopia

NASA Astrophysics Data System (ADS)

Worku, L. Y.

2015-12-01

Extreme rainfall events are major problems in Ethiopia with the resulting floods that usually could cause significant damage to agriculture, ecology, infrastructure, disruption to human activities, loss of property, loss of lives and disease outbreak. The aim of this study was to explore the likely changes of precipitation extreme changes due to future climate change. The study specifically focuses to understand the future climate change impact on variability of rainfall intensity-duration-frequency in Upper Blue Nile basin. Precipitations data from two Global Climate Models (GCMs) have been used in the study are HadCM3 and CGCM3. Rainfall frequency analysis was carried out to estimate quantile with different return periods. Probability Weighted Method (PWM) selected estimation of parameter distribution and L-Moment Ratio Diagrams (LMRDs) used to find the best parent distribution for each station. Therefore, parent distributions for derived from frequency analysis are Generalized Logistic (GLOG), Generalized Extreme Value (GEV), and Gamma & Pearson III (P3) parent distribution. After analyzing estimated quantile simple disaggregation model was applied in order to find sub daily rainfall data. Finally the disaggregated rainfall is fitted to find IDF curve and the result shows in most parts of the basin rainfall intensity expected to increase in the future. As a result of the two GCM outputs, the study indicates there will be likely increase of precipitation extremes over the Blue Nile basin due to the changing climate. This study should be interpreted with caution as the GCM model outputs in this part of the world have huge uncertainty.

The Promise and Limitations of Using Analogies to Improve Decision-Relevant Understanding of Climate Change

PubMed Central

Stern, Paul C.; Maki, Alexander

2017-01-01

To make informed choices about how to address climate change, members of the public must develop ways to consider established facts of climate science and the uncertainties about its future trajectories, in addition to the risks attendant to various responses, including non-response, to climate change. One method suggested for educating the public about these issues is the use of simple mental models, or analogies comparing climate change to familiar domains such as medical decision making, disaster preparedness, or courtroom trials. Two studies were conducted using online participants in the U.S.A. to test the use of analogies to highlight seven key decision-relevant elements of climate change, including uncertainties about when and where serious damage may occur, its unprecedented and progressive nature, and tradeoffs in limiting climate change. An internal meta-analysis was then conducted to estimate overall effect sizes across the two studies. Analogies were not found to inform knowledge about climate literacy facts. However, results suggested that people found the medical analogy helpful and that it led people—especially political conservatives—to better recognize several decision-relevant attributes of climate change. These effects were weak, perhaps reflecting a well-documented and overwhelming effect of political ideology on climate change communication and education efforts in the U.S.A. The potential of analogies and similar education tools to improve understanding and communication in a polarized political environment are discussed. PMID:28135337

Losing your edge: climate change and the conservation value of range-edge populations.

PubMed

Rehm, Evan M; Olivas, Paulo; Stroud, James; Feeley, Kenneth J

2015-10-01

Populations occurring at species' range edges can be locally adapted to unique environmental conditions. From a species' perspective, range-edge environments generally have higher severity and frequency of extreme climatic events relative to the range core. Under future climates, extreme climatic events are predicted to become increasingly important in defining species' distributions. Therefore, range-edge genotypes that are better adapted to extreme climates relative to core populations may be essential to species' persistence during periods of rapid climate change. We use relatively simple conceptual models to highlight the importance of locally adapted range-edge populations (leading and trailing edges) for determining the ability of species to persist under future climates. Using trees as an example, we show how locally adapted populations at species' range edges may expand under future climate change and become more common relative to range-core populations. We also highlight how large-scale habitat destruction occurring in some geographic areas where many species range edge converge, such as biome boundaries and ecotones (e.g., the arc of deforestation along the rainforest-cerrado ecotone in the southern Amazonia), can have major implications for global biodiversity. As climate changes, range-edge populations will play key roles in helping species to maintain or expand their geographic distributions. The loss of these locally adapted range-edge populations through anthropogenic disturbance is therefore hypothesized to reduce the ability of species to persist in the face of rapid future climate change.

Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and Implications

NASA Astrophysics Data System (ADS)

DeWeaver, Eric T.; Bitz, Cecilia M.; Tremblay, L.-Bruno

This volume addresses the rapid decline of Arctic sea ice, placing recent sea ice decline in the context of past observations, climate model simulations and projections, and simple models of the climate sensitivity of sea ice. Highlights of the work presented here include • An appraisal of the role played by wind forcing in driving the decline; • A reconstruction of Arctic sea ice conditions prior to human observations, based on proxy data from sediments; • A modeling approach for assessing the impact of sea ice decline on polar bears, used as input to the U.S. Fish and Wildlife Service's decision to list the polar bear as a threatened species under the Endangered Species Act; • Contrasting studies on the existence of a "tipping point," beyond which Arctic sea ice decline will become (or has already become) irreversible, including an examination of the role of the small ice cap instability in global warming simulations; • A significant summertime atmospheric response to sea ice reduction in an atmospheric general circulation model, suggesting a positive feedback and the potential for short-term climate prediction. The book will be of interest to researchers attempting to understand the recent behavior of Arctic sea ice, model projections of future sea ice loss, and the consequences of sea ice loss for the natural and human systems of the Arctic.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department.

PubMed

Calegari, Rafael; Fogliatto, Flavio S; Lucini, Filipe R; Neyeloff, Jeruza; Kuchenbecker, Ricardo S; Schaan, Beatriz D

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department

PubMed Central

Fogliatto, Flavio S.; Neyeloff, Jeruza; Kuchenbecker, Ricardo S.; Schaan, Beatriz D.

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification. PMID:27725842

Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

NASA Astrophysics Data System (ADS)

Hoffman, A.; Forest, C. E.; Kemanian, A.

2016-12-01

A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with traditional crop models, but likely at the cost of removing climate information. Our random forest models consistently discover the positive trend without removing any additional data. The application of random forests as a statistical crop model provides insight into understanding the impact of dust on yields in marginal food producing regions.

Estimating the Risk of Domestic Water Source Contamination following Precipitation Events

PubMed Central

Eisenhauer, Ian F.; Hoover, Christopher M.; Remais, Justin V.; Monaghan, Andrew; Celada, Marco; Carlton, Elizabeth J.

2016-01-01

Climate change is expected to increase precipitation extremes, threatening water quality. In low resource settings, it is unclear which water sources are most vulnerable to contamination following rainfall events. We evaluated the relationship between rainfall and drinking water quality in southwest Guatemala where heavy rainfall is frequent and access to safe water is limited. We surveyed 59 shallow household wells, measured precipitation, and calculated simple hydrological variables. We compared Escherichia coli concentration at wells where recent rainfall had occurred versus had not occurred, and evaluated variability in the association between rainfall and E. coli concentration under different conditions using interaction models. Rainfall in the past 24 hours was associated with greater E. coli concentrations, with the strongest association between rainfall and fecal contamination at wells where pigs were nearby. Because of the small sample size, these findings should be considered preliminary, but provide a model to evaluate vulnerability to climate change. PMID:27114298

Simple and Multiple Endmember Mixture Analysis in the Boreal Forest

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Gamon, John A.; Qiu, Hong-Lie

2000-01-01

A key scientific objective of the original Boreal Ecosystem-Atmospheric Study (BOREAS) field campaign (1993-1996) was to obtain the baseline data required for modeling and predicting fluxes of energy, mass, and trace gases in the boreal forest biome. These data sets are necessary to determine the sensitivity of the boreal forest biome to potential climatic changes and potential biophysical feedbacks on climate. A considerable volume of remotely sensed and supporting field data were acquired by numerous researchers to meet this objective. By design, remote sensing and modeling were considered critical components for scaling efforts, extending point measurements from flux towers and field sites over larger spatial and longer temporal scales. A major focus of the BOREAS Follow-on program was concerned with integrating the diverse remotely sensed and ground-based data sets to address specific questions such as carbon dynamics at local to regional scales.

The role of bias in simulation of the Indian monsoon and its relationship to predictability

NASA Astrophysics Data System (ADS)

Kelly, P.

2016-12-01

Confidence in future projections of how climate change will affect the Indian monsoon is currently limited by- among other things-model biases. That is, the systematic error in simulating the mean present day climate. An important priority question in seamless prediction involves the role of the mean state. How much of the prediction error in imperfect models stems from a biased mean state (itself a result of many interacting process errors), and how much stems from the flow dependence of processes during an oscillation or variation we are trying to predict? Using simple but effective nudging techniques, we are able to address this question in a clean and incisive framework that teases apart the roles of the mean state vs. transient flow dependence in constraining predictability. The role of bias in model fidelity of simulations of the Indian monsoon is investigated in CAM5, and the relationship to predictability in remote regions in the "free" (non-nudged) domain is explored.

Physical climate response to a reduction of anthropogenic climate forcing

NASA Astrophysics Data System (ADS)

Myneni, R. B.; Samanta, A.; Anderson, B. T.; Ganguly, S.; Knyazikhin, Y.; Nemani, R. R.

2009-12-01

Recent research indicates that the warming of the climate system resulting from increased greenhouse gas (GHG) emissions over the next century will persist for many centuries after the cessation of these emissions, due principally to the persistence of elevated atmospheric carbon dioxide (CO2) concentrations and their attendant radiative forcing. However, it is unknown whether the responses of other components of the climate system—including those related to Greenland and Antarctic ice cover, the Atlantic thermohaline circulation, the West African monsoon, and ecosystems and human welfare—would be reversed even if atmospheric CO2 concentrations were to recover to 1990 levels. Here, using a simple set of experiments employing a current-generation numerical climate model, we show that many physical characteristics of the climate system, including global temperatures, precipitation, soil moisture and sea ice, recover as CO2 concentrations decrease. In contrast, stratospheric water vapor, especially in the high latitudes, exhibits non-linear hysteresis. In these regions, increases in water vapor, which initially result from increased CO2 concentrations, remain present even as CO2 concentrations recover. This result has implications for the sensitivity of the global climate system, the evolution and recovery of stratospheric ozone, and the persistence of weather patterns in the high latitudes. Our work also demonstrates that further identification of threshold behavior in response to human-induced global climate change requires an examination of the full Earth system, including cryosphere, biosphere, and chemistry.

An end-to-end assessment of extreme weather impacts on food security

NASA Astrophysics Data System (ADS)

Chavez, Erik; Conway, Gordon; Ghil, Michael; Sadler, Marc

2015-11-01

Both governments and the private sector urgently require better estimates of the likely incidence of extreme weather events, their impacts on food crop production and the potential consequent social and economic losses. Current assessments of climate change impacts on agriculture mostly focus on average crop yield vulnerability to climate and adaptation scenarios. Also, although new-generation climate models have improved and there has been an exponential increase in available data, the uncertainties in their projections over years and decades, and at regional and local scale, have not decreased. We need to understand and quantify the non-stationary, annual and decadal climate impacts using simple and communicable risk metrics that will help public and private stakeholders manage the hazards to food security. Here we present an `end-to-end’ methodological construct based on weather indices and machine learning that integrates current understanding of the various interacting systems of climate, crops and the economy to determine short- to long-term risk estimates of crop production loss, in different climate and adaptation scenarios. For provinces north and south of the Yangtze River in China, we have found that risk profiles for crop yields that translate climate into economic variability follow marked regional patterns, shaped by drivers of continental-scale climate. We conclude that to be cost-effective, region-specific policies have to be tailored to optimally combine different categories of risk management instruments.

Synergistic impacts of deforestation, climate change and fire on the future biomes distribution in Amazonia

NASA Astrophysics Data System (ADS)

Sampaio, G.; Cardoso, M. F.; Nobre, C. A.; Salazar, L. F.

2013-05-01

Several studies indicate future increase of environmental risks for the ecosystems in the Amazon region as a result of climate and land-use change, and their synergistic interactions. Modeling studies (e.g. Oyama and Nobre 2004, Salazar et al. 2007, Malhi et al. 2008) project rapid and irreversible replacement of forests by savannas with large-scale losses of biodiversity and livelihoods for people in the region. This process is referred to as the Amazon Dieback, where accelerated plant mortality due to environmental changes lead to forest collapse and savannas expansion after "tipping points" in climate and land surface changes are achieved. In this study we performed new analyses to quantify how deforestation, climate change and fire may combine to affect the distribution of major biomes in Amazonia. Changes in land use consider deforestation scenarios of 0%, 20%, 40%, and 50% (Sampaio et al., 2007), with and without fires (Cardoso et al., 2008), under the two greenhouse gases scenarios B1 and A2 and three "representative concentration pathways" (RCPs): 2.6, 4.5 and 8.5, for years 2015-2034 and 2040-2059 ("2025" and "2050" time-slices), from IPCC AR4 and CMIP5. The results show that the area affected in scenarios A2 and RCP 8.5 is larger than in the climate scenario B1 and RCP 2.6, and in both cases the effect is progressively higher in time. Most important changes occur in the East and South of the Amazon, with replacement of tropical forest by seasonal forest and savanna. The effect of fire in this region is important in all scenarios. The Northwest Amazon presents the smallest changes in the area of tropical forest, indicating that even for substantial land-use modifications and global climate change, the resulting atmospheric conditions would still support tropical forest in the region. In summary, we conclude that the synergistic combination of deforestation, climate change resulting from global warming, and the potential for higher fire occurrence may lead to important impacts that add considerably to the vulnerability of tropical forest ecosystems in the study region. REFERENCES Cardoso, M. F. ; Nobre, C. A. ; Sampaio, G. ; Hirota, M. ; Valeriano, D. ; Câmara, G. Long-term potential for tropical-forest degradation due to deforestation and fires in the Brazilian Amazon. Biologia (Bratislava), v. 64, p. 433-437, 2009. Malhi Y, et al. (2008) Climate change, deforestation, and the fate of the Amazon. Science 319:169-172. Oyama, M.D. and C.A. Nobre (2004), A simple potencial vegetation model for coupling with the Simple Biosphere Model (SIB). Revista Brasileira de Meteorologia, v. 19, n. 2, p. 203-216, 2004. Salazar, L. F., C. A. Nobre, and M. D. Oyama (2007), Climate change consequences on the biome distribution in tropical South America, Geophys. Res. Lett., 34, L09708, doi:10.1029/2007GL029695 Sampaio, G., C. A. Nobre, M. H. Costa, P. Satyamurty, B. S. Soares-Filho and, M. Cardoso (2007), Regional climate change over eastern Amazonia caused by pasture and soybean cropland expansion. Geophys. Res. Lett., 34, L17709, doi:10.1029/2007GL030612.

Where to Dig for Fossils: Combining Climate-Envelope, Taphonomy and Discovery Models

PubMed Central

Block, Sebastián; Saltré, Frédérik; Rodríguez-Rey, Marta; Fordham, Damien A.; Unkel, Ingmar; Bradshaw, Corey J. A.

2016-01-01

Fossils represent invaluable data to reconstruct the past history of life, yet fossil-rich sites are often rare and difficult to find. The traditional fossil-hunting approach focuses on small areas and has not yet taken advantage of modelling techniques commonly used in ecology to account for an organism’s past distributions. We propose a new method to assist finding fossils at continental scales based on modelling the past distribution of species, the geological suitability of fossil preservation and the likelihood of fossil discovery in the field, and apply it to several genera of Australian megafauna that went extinct in the Late Quaternary. Our models predicted higher fossil potentials for independent sites than for randomly selected locations (mean Kolmogorov-Smirnov statistic = 0.66). We demonstrate the utility of accounting for the distribution history of fossil taxa when trying to find the most suitable areas to look for fossils. For some genera, the probability of finding fossils based on simple climate-envelope models was higher than the probability based on models incorporating current conditions associated with fossil preservation and discovery as predictors. However, combining the outputs from climate-envelope, preservation, and discovery models resulted in the most accurate predictions of potential fossil sites at a continental scale. We proposed potential areas to discover new fossils of Diprotodon, Zygomaturus, Protemnodon, Thylacoleo, and Genyornis, and provide guidelines on how to apply our approach to assist fossil hunting in other continents and geological settings. PMID:27027874

Where to Dig for Fossils: Combining Climate-Envelope, Taphonomy and Discovery Models.

PubMed

Block, Sebastián; Saltré, Frédérik; Rodríguez-Rey, Marta; Fordham, Damien A; Unkel, Ingmar; Bradshaw, Corey J A

2016-01-01

Fossils represent invaluable data to reconstruct the past history of life, yet fossil-rich sites are often rare and difficult to find. The traditional fossil-hunting approach focuses on small areas and has not yet taken advantage of modelling techniques commonly used in ecology to account for an organism's past distributions. We propose a new method to assist finding fossils at continental scales based on modelling the past distribution of species, the geological suitability of fossil preservation and the likelihood of fossil discovery in the field, and apply it to several genera of Australian megafauna that went extinct in the Late Quaternary. Our models predicted higher fossil potentials for independent sites than for randomly selected locations (mean Kolmogorov-Smirnov statistic = 0.66). We demonstrate the utility of accounting for the distribution history of fossil taxa when trying to find the most suitable areas to look for fossils. For some genera, the probability of finding fossils based on simple climate-envelope models was higher than the probability based on models incorporating current conditions associated with fossil preservation and discovery as predictors. However, combining the outputs from climate-envelope, preservation, and discovery models resulted in the most accurate predictions of potential fossil sites at a continental scale. We proposed potential areas to discover new fossils of Diprotodon, Zygomaturus, Protemnodon, Thylacoleo, and Genyornis, and provide guidelines on how to apply our approach to assist fossil hunting in other continents and geological settings.

A Mechanistically Informed User-Friendly Model to Predict Greenhouse Gas (GHG) Fluxes and Carbon Storage from Coastal Wetlands

NASA Astrophysics Data System (ADS)

Abdul-Aziz, O. I.; Ishtiaq, K. S.

2015-12-01

We present a user-friendly modeling tool on MS Excel to predict the greenhouse gas (GHG) fluxes and estimate potential carbon sequestration from the coastal wetlands. The dominant controls of wetland GHG fluxes and their relative mechanistic linkages with various hydro-climatic, sea level, biogeochemical and ecological drivers were first determined by employing a systematic data-analytics method, including Pearson correlation matrix, principal component and factor analyses, and exploratory partial least squares regressions. The mechanistic knowledge and understanding was then utilized to develop parsimonious non-linear (power-law) models to predict wetland carbon dioxide (CO2) and methane (CH4) fluxes based on a sub-set of climatic, hydrologic and environmental drivers such as the photosynthetically active radiation, soil temperature, water depth, and soil salinity. The models were tested with field data for multiple sites and seasons (2012-13) collected from the Waquoit Bay, MA. The model estimated the annual wetland carbon storage by up-scaling the instantaneous predicted fluxes to an extended growing season (e.g., May-October) and by accounting for the net annual lateral carbon fluxes between the wetlands and estuary. The Excel Spreadsheet model is a simple ecological engineering tool for coastal carbon management and their incorporation into a potential carbon market under a changing climate, sea level and environment. Specifically, the model can help to determine appropriate GHG offset protocols and monitoring plans for projects that focus on tidal wetland restoration and maintenance.

Controls on Arctic sea ice from first-year and multi-year ice survival rates

NASA Astrophysics Data System (ADS)

Armour, K.; Bitz, C. M.; Hunke, E. C.; Thompson, L.

2009-12-01

The recent decrease in Arctic sea ice cover has transpired with a significant loss of multi-year (MY) ice. The transition to an Arctic that is populated by thinner first-year (FY) sea ice has important implications for future trends in area and volume. We develop a reduced model for Arctic sea ice with which we investigate how the survivability of FY and MY ice control various aspects of the sea-ice system. We demonstrate that Arctic sea-ice area and volume behave approximately as first-order autoregressive processes, which allows for a simple interpretation of September sea-ice in which its mean state, variability, and sensitivity to climate forcing can be described naturally in terms of the average survival rates of FY and MY ice. This model, used in concert with a sea-ice simulation that traces FY and MY ice areas to estimate the survival rates, reveals that small trends in the ice survival rates explain the decline in total Arctic ice area, and the relatively larger loss of MY ice area, over the period 1979-2006. Additionally, our model allows for a calculation of the persistence time scales of September area and volume anomalies. A relatively short memory time scale for ice area (~ 1 year) implies that Arctic ice area is nearly in equilibrium with long-term climate forcing at all times, and therefore observed trends in area are a clear indication of a changing climate. A longer memory time scale for ice volume (~ 5 years) suggests that volume can be out of equilibrium with climate forcing for long periods of time, and therefore trends in ice volume are difficult to distinguish from its natural variability. With our reduced model, we demonstrate the connection between memory time scale and sensitivity to climate forcing, and discuss the implications that a changing memory time scale has on the trajectory of ice area and volume in a warming climate. Our findings indicate that it is unlikely that a “tipping point” in September ice area and volume will be reached as the climate is further warmed. Finally, we suggest novel model validation techniques based upon comparing the characteristics of FY and MY ice within models to observations. We propose that keeping an account of FY and MY ice area within sea ice models offers a powerful new way to evaluate model projections of sea ice in a greenhouse warming climate.

A user-friendly earth system model of low complexity: the ESCIMO system dynamics model of global warming towards 2100

NASA Astrophysics Data System (ADS)

Randers, Jorgen; Golüke, Ulrich; Wenstøp, Fred; Wenstøp, Søren

2016-11-01

We have made a simple system dynamics model, ESCIMO (Earth System Climate Interpretable Model), which runs on a desktop computer in seconds and is able to reproduce the main output from more complex climate models. ESCIMO represents the main causal mechanisms at work in the Earth system and is able to reproduce the broad outline of climate history from 1850 to 2015. We have run many simulations with ESCIMO to 2100 and beyond. In this paper we present the effects of introducing in 2015 six possible global policy interventions that cost around USD 1000 billion per year - around 1 % of world GDP. We tentatively conclude (a) that these policy interventions can at most reduce the global mean surface temperature - GMST - by up to 0.5 °C in 2050 and up to 1.0 °C in 2100 relative to no intervention. The exception is injection of aerosols into the stratosphere, which can reduce the GMST by more than 1.0 °C in a decade but creates other serious problems. We also conclude (b) that relatively cheap human intervention can keep global warming in this century below +2 °C relative to preindustrial times. Finally, we conclude (c) that run-away warming is unlikely to occur in this century but is likely to occur in the longer run. The ensuing warming is slow, however. In ESCIMO, it takes several hundred years to lift the GMST to +3 °C above preindustrial times through gradual self-reinforcing melting of the permafrost. We call for research to test whether more complex climate models support our tentative conclusions from ESCIMO.

Effective Engagement of Hostile Audiences on Climate Change

NASA Astrophysics Data System (ADS)

Denning, S.

2012-12-01

Communicating effectively about climate change can be very frustrating because hostility to climate science is rooted in deeply held beliefs rather than facts. Opposition can be more effectively countered by respecting ideological objections than by aggressive insistence on acceptance of consensus evidence. When presented with a stark choice between sacred beliefs and factual evidence, social science research shows that nearly everyone will choose the latter. Rational argument from authority is often the weakest approach in such situations. Climate change is Simple, Serious, and Solvable. Effective communication of these three key ideas can succeed when the science argument is carefully framed to avoid attack of the audience's ethical identity. Simple arguments from common sense and everyday experience are more successful than data. Serious consequences to values that resonate with the audience can be avoided by solutions that don't threaten those values.

Chicken barn climate and hazardous volatile compounds control using simple linear regression and PID

NASA Astrophysics Data System (ADS)

Abdullah, A. H.; Bakar, M. A. A.; Shukor, S. A. A.; Saad, F. S. A.; Kamis, M. S.; Mustafa, M. H.; Khalid, N. S.

2016-07-01

The hazardous volatile compounds from chicken manure in chicken barn are potentially to be a health threat to the farm animals and workers. Ammonia (NH3) and hydrogen sulphide (H2S) produced in chicken barn are influenced by climate changes. The Electronic Nose (e-nose) is used for the barn's air, temperature and humidity data sampling. Simple Linear Regression is used to identify the correlation between temperature-humidity, humidity-ammonia and ammonia-hydrogen sulphide. MATLAB Simulink software was used for the sample data analysis using PID controller. Results shows that the performance of PID controller using the Ziegler-Nichols technique can improve the system controller to control climate in chicken barn.

Simple versus complex models of trait evolution and stasis as a response to environmental change

NASA Astrophysics Data System (ADS)

Hunt, Gene; Hopkins, Melanie J.; Lidgard, Scott

2015-04-01

Previous analyses of evolutionary patterns, or modes, in fossil lineages have focused overwhelmingly on three simple models: stasis, random walks, and directional evolution. Here we use likelihood methods to fit an expanded set of evolutionary models to a large compilation of ancestor-descendant series of populations from the fossil record. In addition to the standard three models, we assess more complex models with punctuations and shifts from one evolutionary mode to another. As in previous studies, we find that stasis is common in the fossil record, as is a strict version of stasis that entails no real evolutionary changes. Incidence of directional evolution is relatively low (13%), but higher than in previous studies because our analytical approach can more sensitively detect noisy trends. Complex evolutionary models are often favored, overwhelmingly so for sequences comprising many samples. This finding is consistent with evolutionary dynamics that are, in reality, more complex than any of the models we consider. Furthermore, the timing of shifts in evolutionary dynamics varies among traits measured from the same series. Finally, we use our empirical collection of evolutionary sequences and a long and highly resolved proxy for global climate to inform simulations in which traits adaptively track temperature changes over time. When realistically calibrated, we find that this simple model can reproduce important aspects of our paleontological results. We conclude that observed paleontological patterns, including the prevalence of stasis, need not be inconsistent with adaptive evolution, even in the face of unstable physical environments.

Using Data from Climate Science to Teach Introductory Statistics

ERIC Educational Resources Information Center

Witt, Gary

2013-01-01

This paper shows how the application of simple statistical methods can reveal to students important insights from climate data. While the popular press is filled with contradictory opinions about climate science, teachers can encourage students to use introductory-level statistics to analyze data for themselves on this important issue in public…

Seasonal Synchronization of a Simple Stochastic Dynamical Model Capturing El Niño Diversity

NASA Astrophysics Data System (ADS)

Thual, S.; Majda, A.; Chen, N.

2017-12-01

The El Niño-Southern Oscillation (ENSO) has significant impact on global climate and seasonal prediction. Recently, a simple ENSO model was developed that automatically captures the ENSO diversity and intermittency in nature, where state-dependent stochastic wind bursts and nonlinear advection of sea surface temperature (SST) are coupled to simple ocean-atmosphere processes that are otherwise deterministic, linear and stable. In the present article, it is further shown that the model can reproduce qualitatively the ENSO synchronization (or phase-locking) to the seasonal cycle in nature. This goal is achieved by incorporating a cloud radiative feedback that is derived naturally from the model's atmosphere dynamics with no ad-hoc assumptions and accounts in simple fashion for the marked seasonal variations of convective activity and cloud cover in the eastern Pacific. In particular, the weak convective response to SSTs in boreal fall favors the eastern Pacific warming that triggers El Niño events while the increased convective activity and cloud cover during the following spring contributes to the shutdown of those events by blocking incoming shortwave solar radiations. In addition to simulating the ENSO diversity with realistic non-Gaussian statistics in different Niño regions, both the eastern Pacific moderate and super El Niño, the central Pacific El Niño as well as La Niña show a realistic chronology with a tendency to peak in boreal winter as well as decreased predictability in spring consistent with the persistence barrier in nature. The incorporation of other possible seasonal feedbacks in the model is also documented for completeness.

A modeling study of marine boundary layer clouds

NASA Technical Reports Server (NTRS)

Wang, Shouping; Fitzjarrald, Daniel E.

1993-01-01

Marine boundary layer (MBL) clouds are important components of the earth's climate system. These clouds drastically reduce the amount of solar radiation absorbed by the earth, but have little effect on the emitted infrared radiation on top of the atmosphere. In addition, these clouds are intimately involved in regulating boundary layer turbulent fluxes. For these reasons, it is important that general circulation models used for climate studies must realistically simulate the global distribution of the MBL. While the importance of these cloud systems is well recognized, many physical processes involved in these clouds are poorly understood and their representation in large-scale models remains an unresolved problem. The present research aims at the development and improvement of the parameterization of these cloud systems and an understanding of physical processes involved. This goal is addressed in two ways. One is to use regional modeling approach to validate and evaluate two-layer marine boundary layer models using satellite and ground-truth observations; the other is to combine this simple model with a high-order turbulence closure model to study the transition processes from stratocumulus to shallow cumulus clouds. Progress made in this effort is presented.

A remote sensing based vegetation classification logic for global land cover analysis

USGS Publications Warehouse

Running, Steven W.; Loveland, Thomas R.; Pierce, Lars L.; Nemani, R.R.; Hunt, E. Raymond

1995-01-01

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

Keno-21: Fundamental Issues in the Design of Geophysical Simulation Experiments and Resource Allocation in Climate Modelling

NASA Astrophysics Data System (ADS)

Smith, L. A.

2001-05-01

Many sources of uncertainty come into play when modelling geophysical systems by simulation. These include uncertainty in the initial condition, uncertainty in model parameter values (and the parameterisations themselves) and error in the model class from which the model(s) was selected. In recent decades, climate simulations have focused resources on reducing the last of these by including more and more details into the model. One can question when this ``kitchen sink'' approach should be complimented with realistic estimates of the impact from other uncertainties noted above. Indeed while the impact of model error can never be fully quantified, as all simulation experiments are interpreted a the rosy scenario which assumes a priori that nothing crucial is missing, the impact of other uncertainties can be quantified at only the cost of computational power; as illustrated, for example, in ensemble climate modelling experiments like Casino-21. This talk illustrates the interplay uncertainties in the context of a trivial nonlinear system and an ensemble of models. The simple systems considered in this small scale experiment, Keno-21, are meant to illustrate issues of experimental design; they are not intended to provide true climate simulations. The use of simulation models with huge numbers of parameters given limited data is usually justified by an appeal to the Laws of Physics: the number of free degrees-of-freedom are many fewer than the number of variables; both variables, parameterisations, and parameter values are constrained by ``the physics" and the resulting simulation yields a realistic reproduction of the entire planet's climate system to within reasonable bounds. But what bounds? exactly? In a single model run under transient forcing scenario, there are good statistical grounds for considering only large space and time averages; most of these reasons vanish if an ensemble of runs are made. Ensemble runs can quantify the (in)ability of a model to provide insight on regional changes: if a model cannot capture regional variations in the data on which the model was constructed (that is, in-sample) claims that out-of-sample predictions of those same regional averages should be used in policy making are vacuous. While motivated by climate modelling and illustrated on a trivial nonlinear system, these issues have implications across the range of geophysical modelling. These include implications for appropriate resource allocation, on the making of science policy, and on the public understanding of science and the role of uncertainty in decision making.

Climate Twins - a tool to explore future climate impacts by assessing real world conditions: Exploration principles, underlying data, similarity conditions and uncertainty ranges

NASA Astrophysics Data System (ADS)

Loibl, Wolfgang; Peters-Anders, Jan; Züger, Johann

2010-05-01

To achieve public awareness and thorough understanding about expected climate changes and their future implications, ways have to be found to communicate model outputs to the public in a scientifically sound and easily understandable way. The newly developed Climate Twins tool tries to fulfil these requirements via an intuitively usable web application, which compares spatial patterns of current climate with future climate patterns, derived from regional climate model results. To get a picture of the implications of future climate in an area of interest, users may click on a certain location within an interactive map with underlying future climate information. A second map depicts the matching Climate Twin areas according to current climate conditions. In this way scientific output can be communicated to the public which allows for experiencing climate change through comparison with well-known real world conditions. To identify climatic coincidence seems to be a simple exercise, but the accuracy and applicability of the similarity identification depends very much on the selection of climate indicators, similarity conditions and uncertainty ranges. Too many indicators representing various climate characteristics and too narrow uncertainty ranges will judge little or no area as regions with similar climate, while too little indicators and too wide uncertainty ranges will address too large regions as those with similar climate which may not be correct. Similarity cannot be just explored by comparing mean values or by calculating correlation coefficients. As climate change triggers an alteration of various indicators, like maxima, minima, variation magnitude, frequency of extreme events etc., the identification of appropriate similarity conditions is a crucial question to be solved. For Climate Twins identification, it is necessary to find a right balance of indicators, similarity conditions and uncertainty ranges, unless the results will be too vague conducting a useful Climate Twins regions search. The Climate Twins tool works actually comparing future climate conditions of a certain source area in the Greater Alpine Region with current climate conditions of entire Europe and the neighbouring southern as well south-eastern areas as target regions. A next version will integrate web crawling features for searching information about climate-related local adaptations observed today in the target region which may turn out as appropriate solution for the source region under future climate conditions. The contribution will present the current tool functionally and will discuss which indicator sets, similarity conditions and uncertainty ranges work best to deliver scientifically sound climate comparisons and distinct mapping results.

Reconstructing Common Era Climate Fields Using Data Assimilation, Proxies, and a Linear Climate Model

NASA Astrophysics Data System (ADS)

Perkins, W. A.; Hakim, G. J.

2016-12-01

In this work, we examine the skill of a new approach to performing climate field reconstructions (CFRs) using a form of online paleoclimate data assimilation (PDA). Many previous studies have foregone climate model forecasts during assimilation due to the computational expense of running coupled global climate models (CGCMs), and the relatively low skill of these forecasts on longer timescales. Here we greatly diminish the computational costs by employing an empirical forecast model (known as a linear inverse model; LIM), which has been shown to have comparable skill to CGCMs. CFRs of annually averaged 2m air temperature anomalies are compared between the Last Millennium Reanalysis framework (no forecasting or "offline"), a persistence forecast, and four LIM forecasting experiments over the instrumental period (1850 - 2000). We test LIM calibrations for observational (Berkeley Earth), reanalysis (20th Century Reanalysis), and CMIP5 climate model (CCSM4 and MPI) data. Generally, we find that the usage of LIM forecasts for online PDA increases reconstruction agreement with the instrumental record for both spatial and global mean temperature (GMT). The detrended GMT skill metrics show the most dramatic increases in skill with coefficient of efficiency (CE) improvements over the no-forecasting benchmark averaging 57%. LIM experiments display a common pattern of spatial field increases in CE skill over northern hemisphere land areas and in the high-latitude North Atlantic - Barents Sea corridor (Figure 1). However, the non-GCM-calibrated LIMs introduce other deficiencies into the spatial skill of these reconstructions, likely due to aspects of the LIM calibration process. Overall, the CMIP5 LIMs have the best performance when considering both spatial fields and GMT. A comparison with the persistence forecast experiment suggests that improvements are associated with the usage of the LIM forecasts, and not simple persistence of temperature anomalies over time. These results show that the use of LIM forecasting can help add further dynamical constraint to CFRs. As we move forward, this will be an important factor in fully utilizing dynamically consistent information from the proxy record while reconstructing the past millennium.

Black Carbon and Kerosene Lighting: An Opportunity for Rapid Action on Climate Change and Clean Energy for Development

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

Jacobson, Arne; Bond, Tami C.; Lam, Nicholoas L.

2013-04-15

Replacing inefficient kerosene lighting with electric lighting or other clean alternatives can rapidly achieve development and energy access goals, save money and reduce climate warming. Many of the 250 million households that lack reliable access to electricity rely on inefficient and dangerous simple wick lamps and other kerosene-fueled light sources, using 4 to 25 billion liters of kerosene annually to meet basic lighting needs. Kerosene costs can be a significant household expense and subsidies are expensive. New information on kerosene lamp emissions reveals that their climate impacts are substantial. Eliminating current annual black carbon emissions would provide a climate benefitmore » equivalent to 5 gigatons of carbon dioxide reductions over the next 20 years. Robust and low-cost technologies for supplanting simple wick and other kerosene-fueled lamps exist and are easily distributed and scalable. Improving household lighting offers a low-cost opportunity to improve development, cool the climate and reduce costs.« less

Disentangling climatic versus biotic drivers of tree range constraints: Broad scale tradeoffs between climate and competion rarely explain local range boundaries

NASA Astrophysics Data System (ADS)

Anderegg, L. D. L.; Hillerislambers, J.

2016-12-01

Accurate prediction of climatically-driven range shifts requires knowledge of the dominant forces constraining species ranges, because climatically controlled range boundaries will likely behave differently from biotically controlled range boundaries in a changing climate. Yet the roles of climatic constraints (due to species physiological tolerance) versus biotic constraints (caused by species interactions) on geographic ranges are largely unknown, infusing large uncertainty into projections of future range shifts. Plant species ranges across strong climatic gradients such as elevation gradients are often assumed to represent a tradeoff between climatic constraints on the harsh side of the range and biotic constraints (often competitive constraints) on the climatically benign side. To test this assumption, we collected tree cores from across the elevational range of the three dominant tree species inhabiting each of three climatically disparate mountain slopes and assessed climatic versus competitive constraints on growth at each species' range margins. Across all species and mountains, we found evidence for a tradeoff between climatic and competitve growth constraints. We also found that some individual species did show an apparent trade-off between a climatic constraint at one range margin and a competitive constraint at the other. However, even these simple elevation gradients resulted in complex interactions between temperature, moisture, and competitive constraints such that a climate-competition tradeoff did not explain range constraints for many species. Our results suggest that tree species can be constrained by a simple trade-off between climate and competition, but that the intricacies of real world climate gradients complicate the application of this theory even in apparently harsh environments, such as near high elevation tree line.

Understanding the varied response of the extratropical storm tracks to climate change

PubMed Central

O’Gorman, Paul A.

2010-01-01

Transient eddies in the extratropical storm tracks are a primary mechanism for the transport of momentum, energy, and water in the atmosphere, and as such are a major component of the climate system. Changes in the extratropical storm tracks under global warming would impact these transports, the ocean circulation and carbon cycle, and society through changing weather patterns. I show that the southern storm track intensifies in the multimodel mean of simulations of 21st century climate change, and that the seasonal cycle of storm-track intensity increases in amplitude in both hemispheres. I use observations of the present-day seasonal cycle to confirm the relationship between storm-track intensity and the mean available potential energy of the atmosphere, and show how this quantitative relationship can be used to account for much of the varied response in storm-track intensity to global warming, including substantially different responses in simulations with different climate models. The results suggest that storm-track intensity is not related in a simple way to global-mean surface temperature, so that, for example, a stronger southern storm track in response to present-day global warming does not imply it was also stronger in hothouse climates of the past. PMID:20974916

Understanding the varied response of the extratropical storm tracks to climate change.

PubMed

O'Gorman, Paul A

2010-11-09

Transient eddies in the extratropical storm tracks are a primary mechanism for the transport of momentum, energy, and water in the atmosphere, and as such are a major component of the climate system. Changes in the extratropical storm tracks under global warming would impact these transports, the ocean circulation and carbon cycle, and society through changing weather patterns. I show that the southern storm track intensifies in the multimodel mean of simulations of 21st century climate change, and that the seasonal cycle of storm-track intensity increases in amplitude in both hemispheres. I use observations of the present-day seasonal cycle to confirm the relationship between storm-track intensity and the mean available potential energy of the atmosphere, and show how this quantitative relationship can be used to account for much of the varied response in storm-track intensity to global warming, including substantially different responses in simulations with different climate models. The results suggest that storm-track intensity is not related in a simple way to global-mean surface temperature, so that, for example, a stronger southern storm track in response to present-day global warming does not imply it was also stronger in hothouse climates of the past.

A simple stochastic rainstorm generator for simulating spatially and temporally varying rainfall

NASA Astrophysics Data System (ADS)

Singer, M. B.; Michaelides, K.; Nichols, M.; Nearing, M. A.

2016-12-01

In semi-arid to arid drainage basins, rainstorms often control both water supply and flood risk to marginal communities of people. They also govern the availability of water to vegetation and other ecological communities, as well as spatial patterns of sediment, nutrient, and contaminant transport and deposition on local to basin scales. All of these landscape responses are sensitive to changes in climate that are projected to occur throughout western North America. Thus, it is important to improve characterization of rainstorms in a manner that enables statistical assessment of rainfall at spatial scales below that of existing gauging networks and the prediction of plausible manifestations of climate change. Here we present a simple, stochastic rainstorm generator that was created using data from a rich and dense network of rain gauges at the Walnut Gulch Experimental Watershed (WGEW) in SE Arizona, but which is applicable anywhere. We describe our methods for assembling pdfs of relevant rainstorm characteristics including total annual rainfall, storm area, storm center location, and storm duration. We also generate five fitted intensity-duration curves and apply a spatial rainfall gradient to generate precipitation at spatial scales below gauge spacing. The model then runs by Monte Carlo simulation in which a total annual rainfall is selected before we generate rainstorms until the annual precipitation total is reached. The procedure continues for decadal simulations. Thus, we keep track of the hydrologic impact of individual storms and the integral of precipitation over multiple decades. We first test the model using ensemble predictions until we reach statistical similarity to the input data from WGEW. We then employ the model to assess decadal precipitation under simulations of climate change in which we separately vary the distribution of total annual rainfall (trend in moisture) and the intensity-duration curves used for simulation (trends in storminess). We demonstrate the model output through spatial maps of rainfall and through statistical comparisons of relevant parameters and distributions. Finally, discuss how the model can be used to understand basin-scale hydrology in terms of soil moisture, runoff, and erosion.

Determinants of adaptation choices to climate change by sheep and goat farmers in Northern Ethiopia: the case of Southern and Central Tigray, Ethiopia.

PubMed

Feleke, Fikeremaryam Birara; Berhe, Melaku; Gebru, Getachew; Hoag, Dana

2016-01-01

The livestock sector serves as a foremost source of revenue for rural people, particularly in many developing countries. Among the livestock species, sheep and goats are the main source of livelihood for rural people in Ethiopia; they can quickly multiply, resilient and are easily convertible to cash to meet financial needs of the rural producers. The multiple contributions of sheep and goat and other livestock to rural farmers are however being challenged by climate change and variability. Farmers are responding to the impacts of climate change by adopting different mechanisms, where choices are largely dependent on many factors. This study, therefore, aims to analyze the determinants of choices of adaptation practices to climate change that causes scarcity of feed, heat stress, shortage of water and pasture on sheep and goat production. The study used 318 sample households drawn from potential livestock producing districts representing 3 agro-ecological settings. Data was analyzed using simple descriptive statistical tools, a multivariate probit model and Ordinary Least Squares (OLS). Most of the respondents (98.6 %) noted that climate is changing. Respondents' perception is that climate change is expressed through increased temperature (88 %) and decline in rainfall (73 %) over the last 10 years. The most commonly used adaptation strategy was marketing during forage shock (96.5 %), followed by home feeding (89.6 %). The estimation from the multivariate probit model showed that access to information, farming experience, number of households in one village, distance to main market, income of household, and agro-ecological settings influenced farmers' adaptation choices to climate change. Furthermore, OLS revealed that the adaptation strategies had positive influence on the household income.

Impact of the time scale of model sensitivity response on coupled model parameter estimation

NASA Astrophysics Data System (ADS)

Liu, Chang; Zhang, Shaoqing; Li, Shan; Liu, Zhengyu

2017-11-01

That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter estimation using filtering theory and methodology. Depending on the nature of associated physics and characteristic variability of the fluid in a coupled system, the response time scales of a model to parameters can be different, from hourly to decadal. Unlike state estimation, where the update frequency is usually linked with observational frequency, the update frequency for parameter estimation must be associated with the time scale of the model sensitivity response to the parameter being estimated. Here, with a simple coupled model, the impact of model sensitivity response time scales on coupled model parameter estimation is studied. The model includes characteristic synoptic to decadal scales by coupling a long-term varying deep ocean with a slow-varying upper ocean forced by a chaotic atmosphere. Results show that, using the update frequency determined by the model sensitivity response time scale, both the reliability and quality of parameter estimation can be improved significantly, and thus the estimated parameters make the model more consistent with the observation. These simple model results provide a guideline for when real observations are used to optimize the parameters in a coupled general circulation model for improving climate analysis and prediction initialization.

Modeling Studies of the Effects of Winds and Heat Flux on the Tropical Oceans

NASA Technical Reports Server (NTRS)

Seager, R.

1999-01-01

Over a decade ago, funding from this NASA grant supported the development of the Cane-Zebiak ENSO prediction model which remains in use to this day. It also supported our work developing schemes for modeling the air-sea heat flux in ocean models used for studying climate variability. We introduced a succession of simple boundary layer models that allow the fluxes to be computed internally in the model and avoid the need to specify the atmospheric thermodynamic state. These models have now reached a level of generality that allows modeling of the global, rather than just tropical, ocean, including sea ice cover. The most recent versions of these boundary layer models have been widely distributed around the world and are in use by many ocean modeling groups.

Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses

NASA Astrophysics Data System (ADS)

Rowland, L.; Harper, A.; Christoffersen, B. O.; Galbraith, D. R.; Imbuzeiro, H. M. A.; Powell, T. L.; Doughty, C.; Levine, N. M.; Malhi, Y.; Saleska, S. R.; Moorcroft, P. R.; Meir, P.; Williams, M.

2015-04-01

Accurately predicting the response of Amazonia to climate change is important for predicting climate change across the globe. Changes in multiple climatic factors simultaneously result in complex non-linear ecosystem responses, which are difficult to predict using vegetation models. Using leaf- and canopy-scale observations, this study evaluated the capability of five vegetation models (Community Land Model version 3.5 coupled to the Dynamic Global Vegetation model - CLM3.5-DGVM; Ecosystem Demography model version 2 - ED2; the Joint UK Land Environment Simulator version 2.1 - JULES; Simple Biosphere model version 3 - SiB3; and the soil-plant-atmosphere model - SPA) to simulate the responses of leaf- and canopy-scale productivity to changes in temperature and drought in an Amazonian forest. The models did not agree as to whether gross primary productivity (GPP) was more sensitive to changes in temperature or precipitation, but all the models were consistent with the prediction that GPP would be higher if tropical forests were 5 °C cooler than current ambient temperatures. There was greater model-data consistency in the response of net ecosystem exchange (NEE) to changes in temperature than in the response to temperature by net photosynthesis (An), stomatal conductance (gs) and leaf area index (LAI). Modelled canopy-scale fluxes are calculated by scaling leaf-scale fluxes using LAI. At the leaf-scale, the models did not agree on the temperature or magnitude of the optimum points of An, Vcmax or gs, and model variation in these parameters was compensated for by variations in the absolute magnitude of simulated LAI and how it altered with temperature. Across the models, there was, however, consistency in two leaf-scale responses: (1) change in An with temperature was more closely linked to stomatal behaviour than biochemical processes; and (2) intrinsic water use efficiency (IWUE) increased with temperature, especially when combined with drought. These results suggest that even up to fairly extreme temperature increases from ambient levels (+6 °C), simulated photosynthesis becomes increasingly sensitive to gs and remains less sensitive to biochemical changes. To improve the reliability of simulations of the response of Amazonian rainforest to climate change, the mechanistic underpinnings of vegetation models need to be validated at both leaf- and canopy-scales to improve accuracy and consistency in the quantification of processes within and across an ecosystem.

A global assessment of climate-water quality relationships in large rivers: an elasticity perspective.

PubMed

Jiang, Jiping; Sharma, Ashish; Sivakumar, Bellie; Wang, Peng

2014-01-15

To uncover climate-water quality relationships in large rivers on a global scale, the present study investigates the climate elasticity of river water quality (CEWQ) using long-term monthly records observed at 14 large rivers. Temperature and precipitation elasticities of 12 water quality parameters, highlighted by N- and P-nutrients, are assessed. General observations on elasticity values show the usefulness of this approach to describe the magnitude of stream water quality responses to climate change, which improves that of simple statistical correlation. Sensitivity type, intensity and variability rank of CEWQ are reported and specific characteristics and mechanism of elasticity of nutrient parameters are also revealed. Among them, the performance of ammonia, total phosphorus-air temperature models, and nitrite, orthophosphorus-precipitation models are the best. Spatial and temporal assessment shows that precipitation elasticity is more variable in space than temperature elasticity and that seasonal variation is more evident for precipitation elasticity than for temperature elasticity. Moreover, both anthropogenic activities and environmental factors are found to impact CEWQ for select variables. The major relationships that can be inferred include: (1) human population has a strong linear correlation with temperature elasticity of turbidity and total phosphorus; and (2) latitude has a strong linear correlation with precipitation elasticity of turbidity and N nutrients. As this work improves our understanding of the relation between climate factors and surface water quality, it is potentially helpful for investigating the effect of climate change on water quality in large rivers, such as on the long-term change of nutrient concentrations. © 2013.

Using prior information to separate the temperature response to greenhouse gas forcing from that of aerosols - Estimating the transient climate response

NASA Astrophysics Data System (ADS)

Schurer, Andrew; Hegerl, Gabriele

2016-04-01

The evaluation of the transient climate response (TCR) is of critical importance to policy makers as it can be used to calculate a simple estimate of the expected warming given predicted greenhouse gas emissions. Previous studies using optimal detection techniques have been able to estimate a TCR value from the historic record using simulations from some of the models which took part in the Coupled Model Intercomparison Project Phase 5 (CMIP5) but have found that others give unconstrained results. At least partly this is due to degeneracy between the greenhouse gas and aerosol signals which makes separation of the temperature response to these forcings problematic. Here we re-visit this important topic by using an adapted optimal detection analysis within a Bayesian framework. We account for observational uncertainty by the use of an ensemble of instrumental observations, and model uncertainty by combining the results from several different models. This framework allows the use of prior information which is found to help separate the response to the different forcings leading to a more constrained estimate of TCR.

Do downscaled general circulation models reliably simulate historical climatic conditions?

USGS Publications Warehouse

Bock, Andrew R.; Hay, Lauren E.; McCabe, Gregory J.; Markstrom, Steven L.; Atkinson, R. Dwight

2018-01-01

The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated using CMIP5 generally are more reliable than those derived from CMIP3, likely due to improvements in PPT simulations. At most locations across the CONUS, the largest differences between GSD and SD PPT and RUN occurred in the lowest part of the distributions (i.e., low-flow RUN and low-magnitude PPT). Results indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate historical climatic conditions. But, in some geographic locations, none of the SD GCMs replicated historical conditions for two of the three variables (PPT and RUN) based on the KS test, with a significance level of 0.05. In these locations, improved GCM simulations of PPT are needed to more reliably estimate components of the hydrologic cycle. Simple metrics and statistical tests, such as those described here, can provide an initial set of criteria to help simplify GCM selection.