A public health approach to the impact of climate change on health in southern Africa - identifying priority modifiable risks.

PubMed

Myers, J; Young, T; Galloway, M; Manyike, P; Tucker, T

2011-11-01

Anthropogenic climate change and anticipated adverse impacts on human health as outlined by the Intergovernmental Panel on Climate Change (IPCC) are taken as given. A conceptual model for thinking about the spectrum of climate-related health risks ranging from distal and infrastructural to proximal and behavioural and their relation to the burden of disease pattern typical of sub-Saharan Africa is provided. The model provides a tool for identifying modifiable risk factors with a view to future research, specifically into the performance of interventions to reduce the impact of climate change.

Climate model biases in jet streams, blocking and storm tracks resulting from missing orographic drag

NASA Astrophysics Data System (ADS)

Pithan, Felix; Shepherd, Theodore G.; Zappa, Giuseppe; Sandu, Irina

2016-07-01

State-of-the art climate models generally struggle to represent important features of the large-scale circulation. Common model deficiencies include an equatorward bias in the location of the midlatitude westerlies and an overly zonal orientation of the North Atlantic storm track. Orography is known to strongly affect the atmospheric circulation and is notoriously difficult to represent in coarse-resolution climate models. Yet how the representation of orography affects circulation biases in current climate models is not understood. Here we show that the effects of switching off the parameterization of drag from low-level orographic blocking in one climate model resemble the biases of the Coupled Model Intercomparison Project Phase 5 ensemble: An overly zonal wintertime North Atlantic storm track and less European blocking events, and an equatorward shift in the Southern Hemispheric jet and increase in the Southern Annular Mode time scale. This suggests that typical circulation biases in coarse-resolution climate models may be alleviated by improved parameterizations of low-level drag.

Modeling Future Fire danger over North America in a Changing Climate

NASA Astrophysics Data System (ADS)

Jain, P.; Paimazumder, D.; Done, J.; Flannigan, M.

2016-12-01

Fire danger ratings are used to determine wildfire potential due to weather and climate factors. The Fire Weather Index (FWI), part of the Canadian Forest Fire Danger Rating System (CFFDRS), incorporates temperature, relative humidity, windspeed and precipitation to give a daily fire danger rating that is used by wildfire management agencies in an operational context. Studies using GCM output have shown that future wildfire danger will increase in a warming climate. However, these studies are somewhat limited by the coarse spatial resolution (typically 100-400km) and temporal resolution (typically 6-hourly to monthly) of the model output. Future wildfire potential over North America based on FWI is calculated using output from the Weather, Research and Forecasting (WRF) model, which is used to downscale future climate scenarios from the bias-corrected Community Climate System Model (CCSM) under RCP8.5 scenarios at a spatial resolution of 36km. We consider five eleven year time slices: 1990-2000, 2020-2030, 2030-2040, 2050-2060 and 2080-2090. The dynamically downscaled simulation improves determination of future extreme weather by improving both spatial and temporal resolution over most GCM models. To characterize extreme fire weather we calculate annual numbers of spread days (days for which FWI > 19) and annual 99th percentile of FWI. Additionally, an extreme value analysis based on the peaks-over-threshold method allows us to calculate the return values for extreme FWI values.

Integrating a Detailed Agricultural Model in a Global Economic Framework: New methods for assessment of climate mitigation and adaptation opportunities

NASA Astrophysics Data System (ADS)

Thomson, A. M.; Izaurralde, R. C.; Calvin, K.; Zhang, X.; Wise, M.; West, T. O.

2010-12-01

Climate change and food security are global issues increasingly linked through human decision making that takes place across all scales from on-farm management actions to international climate negotiations. Understanding how agricultural systems can respond to climate change, through mitigation or adaptation, while still supplying sufficient food to feed a growing global population, thus requires a multi-sector tool in a global economic framework. Integrated assessment models are one such tool, however they are typically driven by historical aggregate statistics of production in combination with exogenous assumptions of future trends in agricultural productivity; they are not yet capable of exploring agricultural management practices as climate adaptation or mitigation strategies. Yet there are agricultural models capable of detailed biophysical modeling of farm management and climate impacts on crop yield, soil erosion and C and greenhouse gas emissions, although these are typically applied at point scales that are incompatible with coarse resolution integrated assessment modeling. To combine the relative strengths of these modeling systems, we are using the agricultural model EPIC (Environmental Policy Integrated Climate), applied in a geographic data framework for regional analyses, to provide input to the global economic model GCAM (Global Change Assessment Model). The initial phase of our approach focuses on a pilot region of the Midwest United States, a highly productive agricultural area. We apply EPIC, a point based biophysical process model, at 60 m spatial resolution within this domain and aggregate the results to GCAM agriculture and land use subregions for the United States. GCAM is then initialized with multiple management options for key food and bioenergy crops. Using EPIC to distinguish these management options based on grain yield, residue yield, soil C change and cost differences, GCAM then simulates the optimum distribution of the available management options to meet demands for food and energy over the next century. The coupled models provide a new platform for evaluating future changes in agricultural management based on food demand, bioenergy demand, and changes in crop yield and soil C under a changing climate. This framework can be applied to evaluate the economically and biophysically optimal distribution of management under future climates.

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

Buechler, Elizabeth D.; Pallin, Simon B.; Boudreaux, Philip R.

The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, HVAC system performance, and occupant comfort. Therefore, indoor climate data is generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies is influenced by weather, occupant behavior and internal loads, and is generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The

Towards process-informed bias correction of climate change simulations

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Shepherd, Theodore G.; Widmann, Martin; Zappa, Giuseppe; Walton, Daniel; Gutiérrez, José M.; Hagemann, Stefan; Richter, Ingo; Soares, Pedro M. M.; Hall, Alex; Mearns, Linda O.

2017-11-01

Biases in climate model simulations introduce biases in subsequent impact simulations. Therefore, bias correction methods are operationally used to post-process regional climate projections. However, many problems have been identified, and some researchers question the very basis of the approach. Here we demonstrate that a typical cross-validation is unable to identify improper use of bias correction. Several examples show the limited ability of bias correction to correct and to downscale variability, and demonstrate that bias correction can cause implausible climate change signals. Bias correction cannot overcome major model errors, and naive application might result in ill-informed adaptation decisions. We conclude with a list of recommendations and suggestions for future research to reduce, post-process, and cope with climate model biases.

Modeling the potential impacts of climate change on the water table level of selected forested wetlands in the southeastern United States

NASA Astrophysics Data System (ADS)

Zhu, Jie; Sun, Ge; Li, Wenhong; Zhang, Yu; Miao, Guofang; Noormets, Asko; McNulty, Steve G.; King, John S.; Kumar, Mukesh; Wang, Xuan

2017-12-01

The southeastern United States hosts extensive forested wetlands, providing ecosystem services including carbon sequestration, water quality improvement, groundwater recharge, and wildlife habitat. However, these wetland ecosystems are dependent on local climate and hydrology, and are therefore at risk due to climate and land use change. This study develops site-specific empirical hydrologic models for five forested wetlands with different characteristics by analyzing long-term observed meteorological and hydrological data. These wetlands represent typical cypress ponds/swamps, Carolina bays, pine flatwoods, drained pocosins, and natural bottomland hardwood ecosystems. The validated empirical models are then applied at each wetland to predict future water table changes using climate projections from 20 general circulation models (GCMs) participating in Coupled Model Inter-comparison Project 5 (CMIP5) under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. We show that combined future changes in precipitation and potential evapotranspiration would significantly alter wetland hydrology including groundwater dynamics by the end of the 21st century. Compared to the historical period, all five wetlands are predicted to become drier over time. The mean water table depth is predicted to drop by 4 to 22 cm in response to the decrease in water availability (i.e., precipitation minus potential evapotranspiration) by the year 2100. Among the five examined wetlands, the depressional wetland in hot and humid Florida appears to be most vulnerable to future climate change. This study provides quantitative information on the potential magnitude of wetland hydrological response to future climate change in typical forested wetlands in the southeastern US.

A new eco-hydrological distributed model for the predictions of the climate change impact on water resources of Mediterranean water-limited basins: the Mulargia basin case study in Sardinia.

NASA Astrophysics Data System (ADS)

Sarigu, Alessio; Montaldo, Nicola

2017-04-01

In the last three decades, climate change and human activities increased desertification process in Mediterranean regions, with dramatic consequences for agriculture and water availability. For instance in the main reservoir systems in Sardinia the average annual runoff in the latter part of the 20th century decreased of more than 50% compared with the previous period, while the precipitation over the Sardinia basin has decreased, but not at such a drastic rate as the discharge, with an high precipitation elasticity to streamflow, highlighting the key role of the rainfall seasonality on runoff production. IPCC climate change scenarios predict a further decrease of winter rainfall, which is the key term for runoff production in these typical Mediterranean climate basins, and air temperature increase, which can potentially impact on evapotranspiration, soil moisture and runoff. Only the use of an accurate ecohydrological physically based distributed model allow to well predict the impact of the climate change scenarios on the basin water resources. A new eco-hydrological model is developed that couples a distributed hydrological model of and a vegetation dynamic model (VDM). The hydrological model estimates the soil water balance of each basin cell using the force-restore method, the Philips model for infiltration estimate and the Penman-Monteith equation for evapotranspiration estimate. The VDM evaluates the changes in biomass over time for each cell and provides the leaf area index (LAI), which is then used by the hydrological model for evapotranspiration and rainfall interception estimates. Case study is the Mulargia basin (Sardinia, basin area of about 70 km2), where an extended field campaign started from 2003, with rain and discharge data observed at the basin outlet, periodic field measurements of soil moisture and LAI all over the basin, and evapotraspiration estimates using an eddy correlation based tower. The Mulargia basin case study is a very interesting laboratory of Mediterranean basins, thanks to its typical Mediterranean climate, its typical physiografic characteristics, its low human activities and influences and its attractive hydrologic database. The model has been successfully and deeply calibrated for the 2003 and validated for the 2004-2005 period, using both field data and satellite Modis data. Three future climate change scenarios has been generated using a stochastic model (Richardson, 1991), opportunely adapted for accounting the future changes of climate conditions. The scenarios (A1-A1B-A2) assume that in the next century there will be a drastic reduction of precipitation (with maximum reduction of 30% in A2) and that will continue the warming process. A reduction of soil moisture (about 40%) is predicted, especially during winter month and also the LAI will drastically decrease (more than 50% for woody vegetation and 75% for grass especially during the spring). Runoff will decrease even more (up to 70%) during the winter season, which is the key season for the water resource management and planning of these Mediterranean basins. These results anticipate a dramatic reduction of water resources availability, a change of vegetation species and ecosystems, increasing the desertification process in this typical Mediterranean area.

The influence of climatic conditions on the heat balance of the human body

NASA Astrophysics Data System (ADS)

Blażejeczyk, Krzysztof; Krawczyk, Barbara

1991-06-01

The structure of heat exchange between the human body and its surroundings has been studied according to M.I. Budyko's model. Comparative measurements were carried out in the Polish Lakeland (maritime, temperate warm climate), in Central Mongolia (continental, temperate cool climate), and in the Kara Kum desert (dry subtropical climate). The results deal with the summer and early autumn seasons. The calculations indicate that the quantitative apportionment of various forms of heat exchange depend on specific weather conditions, which are typical for the distinguished climatic zones.

Climate Model Diagnostic Analyzer

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Pan, Lei; Zhai, Chengxing; Tang, Benyang; Kubar, Terry; Zhang, Zia; Wang, Wei

2015-01-01

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

On the Hydrologic Adjustment of Climate-Model Projections: The Potential Pitfall of Potential Evapotranspiration

USGS Publications Warehouse

Milly, Paul C.D.; Dunne, Krista A.

2011-01-01

Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement ("downscaling"), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median -11%) caused by the hydrologic model’s apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen–Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors’ findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climate-change impacts on water.

On the role of ozone feedback in the ENSO amplitude response under global warming.

PubMed

Nowack, Peer J; Braesicke, Peter; Luke Abraham, N; Pyle, John A

2017-04-28

The El Niño-Southern Oscillation (ENSO) in the tropical Pacific Ocean is of key importance to global climate and weather. However, state-of-the-art climate models still disagree on the ENSO's response under climate change. The potential role of atmospheric ozone changes in this context has not been explored before. Here we show that differences between typical model representations of ozone can have a first-order impact on ENSO amplitude projections in climate sensitivity simulations. The vertical temperature gradient of the tropical middle-to-upper troposphere adjusts to ozone changes in the upper troposphere and lower stratosphere, modifying the Walker circulation and consequently tropical Pacific surface temperature gradients. We show that neglecting ozone changes thus results in a significant increase in the number of extreme ENSO events in our model. Climate modeling studies of the ENSO often neglect changes in ozone. We therefore highlight the need to understand better the coupling between ozone, the tropospheric circulation, and climate variability.

FOREST ECOLOGY. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models.

PubMed

Anderegg, W R L; Schwalm, C; Biondi, F; Camarero, J J; Koch, G; Litvak, M; Ogle, K; Shaw, J D; Shevliakova, E; Williams, A P; Wolf, A; Ziaco, E; Pacala, S

2015-07-31

The impacts of climate extremes on terrestrial ecosystems are poorly understood but important for predicting carbon cycle feedbacks to climate change. Coupled climate-carbon cycle models typically assume that vegetation recovery from extreme drought is immediate and complete, which conflicts with the understanding of basic plant physiology. We examined the recovery of stem growth in trees after severe drought at 1338 forest sites across the globe, comprising 49,339 site-years, and compared the results with simulated recovery in climate-vegetation models. We found pervasive and substantial "legacy effects" of reduced growth and incomplete recovery for 1 to 4 years after severe drought. Legacy effects were most prevalent in dry ecosystems, among Pinaceae, and among species with low hydraulic safety margins. In contrast, limited or no legacy effects after drought were simulated by current climate-vegetation models. Our results highlight hysteresis in ecosystem-level carbon cycling and delayed recovery from climate extremes. Copyright © 2015, American Association for the Advancement of Science.

The AgMIP GRIDded Crop Modeling Initiative (AgGRID) and the Global Gridded Crop Model Intercomparison (GGCMI)

NASA Technical Reports Server (NTRS)

Elliott, Joshua; Muller, Christoff

2015-01-01

Climate change is a significant risk for agricultural production. Even under optimistic scenarios for climate mitigation action, present-day agricultural areas are likely to face significant increases in temperatures in the coming decades, in addition to changes in precipitation, cloud cover, and the frequency and duration of extreme heat, drought, and flood events (IPCC, 2013). These factors will affect the agricultural system at the global scale by impacting cultivation regimes, prices, trade, and food security (Nelson et al., 2014a). Global-scale evaluation of crop productivity is a major challenge for climate impact and adaptation assessment. Rigorous global assessments that are able to inform planning and policy will benefit from consistent use of models, input data, and assumptions across regions and time that use mutually agreed protocols designed by the modeling community. To ensure this consistency, large-scale assessments are typically performed on uniform spatial grids, with spatial resolution of typically 10 to 50 km, over specified time-periods. Many distinct crop models and model types have been applied on the global scale to assess productivity and climate impacts, often with very different results (Rosenzweig et al., 2014). These models are based to a large extent on field-scale crop process or ecosystems models and they typically require resolved data on weather, environmental, and farm management conditions that are lacking in many regions (Bondeau et al., 2007; Drewniak et al., 2013; Elliott et al., 2014b; Gueneau et al., 2012; Jones et al., 2003; Liu et al., 2007; M¨uller and Robertson, 2014; Van den Hoof et al., 2011;Waha et al., 2012; Xiong et al., 2014). Due to data limitations, the requirements of consistency, and the computational and practical limitations of running models on a large scale, a variety of simplifying assumptions must generally be made regarding prevailing management strategies on the grid scale in both the baseline and future periods. Implementation differences in these and other modeling choices contribute to significant variation among global-scale crop model assessments in addition to differences in crop model implementations that also cause large differences in site-specific crop modeling (Asseng et al., 2013; Bassu et al., 2014).

Evaluation of Aerosol Mixing State Classes in the GISS Modele-matrix Climate Model Using Single-particle Mass Spectrometry Measurements

NASA Technical Reports Server (NTRS)

Bauer, Susanne E.; Ault, Andrew; Prather, Kimberly A.

2013-01-01

Aerosol particles in the atmosphere are composed of multiple chemical species. The aerosol mixing state, which describes how chemical species are mixed at the single-particle level, provides critical information on microphysical characteristics that determine the interaction of aerosols with the climate system. The evaluation of mixing state has become the next challenge. This study uses aerosol time-of-flight mass spectrometry (ATOFMS) data and compares the results to those of the Goddard Institute for Space Studies modelE-MATRIX (Multiconfiguration Aerosol TRacker of mIXing state) model, a global climate model that includes a detailed aerosol microphysical scheme. We use data from field campaigns that examine a variety of air mass regimens (urban, rural, and maritime). At all locations, polluted areas in California (Riverside, La Jolla, and Long Beach), a remote location in the Sierra Nevada Mountains (Sugar Pine) and observations from Jeju (South Korea), the majority of aerosol species are internally mixed. Coarse aerosol particles, those above 1 micron, are typically aged, such as coated dust or reacted sea-salt particles. Particles below 1 micron contain large fractions of organic material, internally-mixed with sulfate and black carbon, and few external mixtures. We conclude that observations taken over multiple weeks characterize typical air mass types at a given location well; however, due to the instrumentation, we could not evaluate mass budgets. These results represent the first detailed comparison of single-particle mixing states in a global climate model with real-time single-particle mass spectrometry data, an important step in improving the representation of mixing state in global climate models.

Clouds and ocean-atmosphere interactions. Final report, September 15, 1992--September 14, 1995

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

Randall, D.A.; Jensen, T.G.

1995-10-01

Predictions of global change based on climate models are influencing both national and international policies on energy and the environment. Existing climate models show some skill in simulating the present climate, but suffer from many widely acknowledged deficiencies. Among the most serious problems is the need to apply ``flux corrections`` to prevent the models from drifting away from the observed climate in control runs that do not include external perturbing influences such as increased carbon dioxide (CO{sub 2}) concentrations. The flux corrections required to prevent climate drift are typically comparable in magnitude to the observed fluxes themselves. Although there canmore » be many contributing reasons for the climate drift problem, clouds and their effects on the surface energy budget are among the prime suspects. The authors have conducted a research program designed to investigate global air-sea interaction as it relates to the global warming problem, with special emphasis on the role of clouds. Their research includes model development efforts; application of models to simulation of present and future climates, with comparison to observations wherever possible; and vigorous participation in ongoing efforts to intercompare the present generation of atmospheric general circulation models.« less

Regional climate models reduce biases of global models and project smaller European summer warming

NASA Astrophysics Data System (ADS)

Soerland, S.; Schar, C.; Lüthi, D.; Kjellstrom, E.

2017-12-01

The assessment of regional climate change and the associated planning of adaptation and response strategies are often based on complex model chains. Typically, these model chains employ global and regional climate models (GCMs and RCMs), as well as one or several impact models. It is a common belief that the errors in such model chains behave approximately additive, thus the uncertainty should increase with each modeling step. If this hypothesis were true, the application of RCMs would not lead to any intrinsic improvement (beyond higher-resolution detail) of the GCM results. Here, we investigate the bias patterns (offset during the historical period against observations) and climate change signals of two RCMs that have downscaled a comprehensive set of GCMs following the EURO-CORDEX framework. The two RCMs reduce the biases of the driving GCMs, reduce the spread and modify the amplitude of the GCM projected climate change signal. The GCM projected summer warming at the end of the century is substantially reduced by both RCMs. These results are important, as the projected summer warming and its likely impact on the water cycle are among the most serious concerns regarding European climate change.

Improving Seasonal Climate Predictability in the Colorado River Basin for Enhanced Decision Support

NASA Astrophysics Data System (ADS)

Rajagopal, S.; Mahmoud, M. I.

2016-12-01

The water resource management community is increasingly seeking skillful seasonal climate forecasts with long lead times. But predicting wet or dry climate with sufficient lead time (3 months) for a season (especially winter) in the Colorado River Basin (CRB) is a challenging problem. The typical approach taken to predicting winter climate is based on using climate indices and climate models to predict precipitation or streamflow in the Colorado River Basin. In addition to this approach; which may have a long lead time, water supply forecasts are also generated based on current observations by the Colorado River Forecast Center. Recently, the effects of coupled atmospheric-ocean phenomena such as ENSO over North America, and atmospheric circulation patterns at the 500 mb pressure level, which make the CRB wet or dry, have been studied separately. In the current work we test whether combining climate indices and circulation patterns improve predictability in the CRB. To accomplish this, the atmospheric circulation data from the Earth System Research Laboratory (ESRL) and climate indices data from the Climate Prediction Center were combined using logical functions. To quantify the skill in prediction, statistics such as the hit ratio and false alarm ratio were computed. The results from using a combination of climate indices and atmospheric circulation patterns suggest that there is an improvement in the prediction skill with hit ratios higher than 0.8, as compared to using either predictor individually (which typically produced a hit ratio of 0.6). Based on this result, there is value in using this hybrid approach when compared to a black box statistical model, as the climate index is an analog to the moisture availability and the right atmospheric circulation pattern helps in transporting that moisture to the Basin.

Modeling Climate Change in the Absence of Climate Change Data. Editorial Comment

NASA Technical Reports Server (NTRS)

Skiles, J. W.

1995-01-01

Practitioners of climate change prediction base many of their future climate scenarios on General Circulation Models (GCM's), each model with differing assumptions and parameter requirements. For representing the atmosphere, GCM's typically contain equations for calculating motion of particles, thermodynamics and radiation, and continuity of water vapor. Hydrology and heat balance are usually included for continents, and sea ice and heat balance are included for oceans. The current issue of this journal contains a paper by Van Blarcum et al. (1995) that predicts runoff from nine high-latitude rivers under a doubled CO2 atmosphere. The paper is important since river flow is an indicator variable for climate change. The authors show that precipitation will increase under the imposed perturbations and that owing to higher temperatures earlier in the year that cause the snow pack to melt sooner, runoff will also increase. They base their simulations on output from a GCM coupled with an interesting water routing scheme they have devised. Climate change models have been linked to other models to predict deforestation.

Insensitivity of evapotranspiration to seasonal rainfall distribution directs climate change impacts at water yield

NASA Astrophysics Data System (ADS)

Montaldo, N.; Oren, R.

2017-12-01

Over the past century, climate change is affecting precipitation regimes across the world. In the Mediterranean regions there is a persistent trend of precipitation and runoff decreases, generating a desertification process. Given the past winter precipitation shifts, the impacts on evapotranspiration (ET) need to be carefully evaluated, and the compelling question is what will be the impact of future climate change scenarios (predicting changes of precipitation and vapor pressure deficit, VPD) on evapotranspiration and water yield? Looking for the key elements of the climate change that are impacting annual ET, we investigate main climate conditions (e.g. precipitation and VPD) and basin physiographic properties contributing to annual ET. We propose a simplified model for annual ET predictions that accounts for the strong meteo seasonality typical of Mediterranean climates, using the steady state assumption of the basin water balance at mean annual scale. We investigate the Sardinia case study because the position of the island of Sardinia in the center of the western Mediterranean Sea basin and its low urbanization and human activity make Sardinia a perfect reference laboratory for Mediterranean hydrologic studies. Sardinian runoff decreased drastically over the 1975-2010 period, with mean yearly runoff reduced by more than 40% compared to the previous 1922-1974 period, and most yearly runoff in the Sardinian basins (70% on average) is produced by winter precipitation due to the seasonality typical of the Mediterranean climate regime. The use of our proposed model allows to predict future ET and water yield using future climate scenarios. We use the future climate scenarios predicted by Global climate models (GCM) in the Fifth Assessment report of the Intergovernmental Panel on Climate Change (IPCC), and we select most reliable models testing the past GCM predictions with historical data. Contrasting shifts of precipitation (both positive and negative) are predicted in the future scenarios by GCMs but these changes will produce significant changes (level of significance > 90%) only in runoff and not in ET. Surprisingly, we show that ET is insensitive to intra-annual rainfall distribution changes, and is insensitive to VPD scenario changes.

Impacts of climate change on the formation and stability of late Quaternary sand sheets and falling dunes, Black Mesa region, southern Colorado Plateau, USA

USGS Publications Warehouse

Ellwein, Amy L.; Mahan, Shannon; McFadden, Leslie D.

2015-01-01

Widely used predictive models of eolian system dynamics are typically based entirely on climatic variables and do not account for landscape complexity and geomorphic history. Climate-only assumptions fail to give accurate predictions of the dynamics of this and many other dune fields. A growing body of work suggests that eolian deposits in wind-driven semiarid climates may be more strongly related to increases in sediment supply than to increases in aridity.

On the hydrologic adjustment of climate-model projections: The potential pitfall of potential evapotranspiration

USGS Publications Warehouse

Milly, P.C.D.; Dunne, K.A.

2011-01-01

Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement ("downscaling"), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median 211%) caused by the hydrologic model's apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen-Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors' findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climatechange impacts on water. Copyright ?? 2011, Paper 15-001; 35,952 words, 3 Figures, 0 Animations, 1 Tables.

Towards a climate-dependent paradigm of ammonia emission and deposition

EPA Science Inventory

Existing descriptions of bi-directional ammonia (NH3) land–atmosphere exchange incorporate temperature and moisture controls, and are beginning to be used in regional chemical transport models. However, such models have typically applied simpler emission factors to upscale ...

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.

Assessing the Robustness of Green Infrastructure under Stochastic Design Storms and Climate Change Scenarios

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Creation of Synthetic Surface Temperature and Precipitation Ensembles Through A Computationally Efficient, Mixed Method Approach

NASA Astrophysics Data System (ADS)

Hartin, C.; Lynch, C.; Kravitz, B.; Link, R. P.; Bond-Lamberty, B. P.

2017-12-01

Typically, uncertainty quantification of internal variability relies on large ensembles of climate model runs under multiple forcing scenarios or perturbations in a parameter space. Computationally efficient, standard pattern scaling techniques only generate one realization and do not capture the complicated dynamics of the climate system (i.e., stochastic variations with a frequency-domain structure). In this study, we generate large ensembles of climate data with spatially and temporally coherent variability across a subselection of Coupled Model Intercomparison Project Phase 5 (CMIP5) models. First, for each CMIP5 model we apply a pattern emulation approach to derive the model response to external forcing. We take all the spatial and temporal variability that isn't explained by the emulator and decompose it into non-physically based structures through use of empirical orthogonal functions (EOFs). Then, we perform a Fourier decomposition of the EOF projection coefficients to capture the input fields' temporal autocorrelation so that our new emulated patterns reproduce the proper timescales of climate response and "memory" in the climate system. Through this 3-step process, we derive computationally efficient climate projections consistent with CMIP5 model trends and modes of variability, which address a number of deficiencies inherent in the ability of pattern scaling to reproduce complex climate model behavior.

Health Impacts of Air Pollution Under a Changing Climate

NASA Astrophysics Data System (ADS)

Kinney, P. L.; Knowlton, K.; Rosenthal, J.; Hogrefe, C.; Rosenzweig, C.; Solecki, W.

2003-12-01

Outdoor air pollution remains a serious public health problem in cities throughout the world. In the US, despite considerable progress in reducing emissions over the past 30 years, as many as 50,000 premature deaths each year have been attributed to airborne particulate matter alone. Tropospheric ozone has been associated with increased daily mortality and hospitalization rates, and with a variety of related respiratory problems. Weather plays an important role in the transport and transformation of air pollution. In particular, a warming climate is likely to promote the atmospheric reactions that are responsible for ozone and secondary aerosol production, as well as increasing emissions of many of their volatile precursors. Increasingly, efforts to address urban air pollution problems throughout the world will be complicated by trends and variability in climate. The New York Climate and Health Project (NYCHP) is developing and applying tools for integrated assessment of health impacts from air pollution and heat associated with climate and land-use changes in the New York City metropolitan region. Global climate change is modeled over the 21st century based on the Intergovernmental Panel on Climate Change (IPCC) A2 greenhouse gas emissions scenario using the Goddard Institute for Space Studies (GISS) Global Atmosphere-Ocean Model (GCM). Meteorological fields are downscaled to a 36 km grid over the eastern US using the Penn State/NCAR MM5 mesoscale meteorological model. MM5 results are then used as input to the Community Multiscale Air Quality (CMAQ) model for simulating air quality, with emissions based on the Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE). To date, simulations have been performed for five summer seasons each during the 1990s and the 2050s. An evaluation of the present-day climate and air quality predictions indicates that the modeling system largely captures the observed climate-ozone system. Analysis of future-year predictions shows an increase in temperature and humidity as well as mean and extreme ozone concentrations under the IPCC A2 emission scenario. To address public health impacts, a risk assessment framework is used to estimate ozone-related mortality in the region, with a focus on comparing health impact estimates for the 1990s versus the 2050s. This endpoint represents a potentially appreciable public health impact resulting from climate change-induced alterations in regional air quality profiles. Concentration-response functions from the epidemiological literature describing ozone-mortality relationships are used to estimate numbers of regional deaths in a typical 1990s summer and a typical 2050s summer. Preliminary analysis of future-year ozone-related mortality suggests a subtle increase in the number of summer ozone-related deaths in the New York region in the 2050s as compared to the 1990s. A parallel evaluation of heat-related mortality in a typical summer of the 2050s suggests a greater relative increase as compared to the 1990s, with a doubling to tripling of regional summer heat deaths possible by the 2050s.

Influence of global climate change on chemical fate and bioaccumulation: the role of multimedia models.

PubMed

Gouin, Todd; Armitage, James M; Cousins, Ian T; Muir, Derek C G; Ng, Carla A; Reid, Liisa; Tao, Shu

2013-01-01

Multimedia environmental fate models are valuable tools for investigating potential changes associated with global climate change, particularly because thermodynamic forcing on partitioning behavior as well as diffusive and nondiffusive exchange processes are implicitly considered. Similarly, food-web bioaccumulation models are capable of integrating the net effect of changes associated with factors such as temperature, growth rates, feeding preferences, and partitioning behavior on bioaccumulation potential. For the climate change scenarios considered in the present study, such tools indicate that alterations to exposure concentrations are typically within a factor of 2 of the baseline output. Based on an appreciation for the uncertainty in model parameters and baseline output, the authors recommend caution when interpreting or speculating on the relative importance of global climate change with respect to how changes caused by it will influence chemical fate and bioavailability. Copyright © 2012 SETAC.

Modernizing Earth and Space Science Modeling Workflows in the Big Data Era

NASA Astrophysics Data System (ADS)

Kinter, J. L.; Feigelson, E.; Walker, R. J.; Tino, C.

2017-12-01

Modeling is a major aspect of the Earth and space science research. The development of numerical models of the Earth system, planetary systems or astrophysical systems is essential to linking theory with observations. Optimal use of observations that are quite expensive to obtain and maintain typically requires data assimilation that involves numerical models. In the Earth sciences, models of the physical climate system are typically used for data assimilation, climate projection, and inter-disciplinary research, spanning applications from analysis of multi-sensor data sets to decision-making in climate-sensitive sectors with applications to ecosystems, hazards, and various biogeochemical processes. In space physics, most models are from first principles, require considerable expertise to run and are frequently modified significantly for each case study. The volume and variety of model output data from modeling Earth and space systems are rapidly increasing and have reached a scale where human interaction with data is prohibitively inefficient. A major barrier to progress is that modeling workflows isn't deemed by practitioners to be a design problem. Existing workflows have been created by a slow accretion of software, typically based on undocumented, inflexible scripts haphazardly modified by a succession of scientists and students not trained in modern software engineering methods. As a result, existing modeling workflows suffer from an inability to onboard new datasets into models; an inability to keep pace with accelerating data production rates; and irreproducibility, among other problems. These factors are creating an untenable situation for those conducting and supporting Earth system and space science. Improving modeling workflows requires investments in hardware, software and human resources. This paper describes the critical path issues that must be targeted to accelerate modeling workflows, including script modularization, parallelization, and automation in the near term, and longer term investments in virtualized environments for improved scalability, tolerance for lossy data compression, novel data-centric memory and storage technologies, and tools for peer reviewing, preserving and sharing workflows, as well as fundamental statistical and machine learning algorithms.

Skilful multi-year predictions of tropical trans-basin climate variability

PubMed Central

Chikamoto, Yoshimitsu; Timmermann, Axel; Luo, Jing-Jia; Mochizuki, Takashi; Kimoto, Masahide; Watanabe, Masahiro; Ishii, Masayoshi; Xie, Shang-Ping; Jin, Fei-Fei

2015-01-01

Tropical Pacific sea surface temperature anomalies influence the atmospheric circulation, impacting climate far beyond the tropics. The predictability of the corresponding atmospheric signals is typically limited to less than 1 year lead time. Here we present observational and modelling evidence for multi-year predictability of coherent trans-basin climate variations that are characterized by a zonal seesaw in tropical sea surface temperature and sea-level pressure between the Pacific and the other two ocean basins. State-of-the-art climate model forecasts initialized from a realistic ocean state show that the low-frequency trans-basin climate variability, which explains part of the El Niño Southern Oscillation flavours, can be predicted up to 3 years ahead, thus exceeding the predictive skill of current tropical climate forecasts for natural variability. This low-frequency variability emerges from the synchronization of ocean anomalies in all basins via global reorganizations of the atmospheric Walker Circulation. PMID:25897996

Skilful multi-year predictions of tropical trans-basin climate variability.

PubMed

Chikamoto, Yoshimitsu; Timmermann, Axel; Luo, Jing-Jia; Mochizuki, Takashi; Kimoto, Masahide; Watanabe, Masahiro; Ishii, Masayoshi; Xie, Shang-Ping; Jin, Fei-Fei

2015-04-21

Tropical Pacific sea surface temperature anomalies influence the atmospheric circulation, impacting climate far beyond the tropics. The predictability of the corresponding atmospheric signals is typically limited to less than 1 year lead time. Here we present observational and modelling evidence for multi-year predictability of coherent trans-basin climate variations that are characterized by a zonal seesaw in tropical sea surface temperature and sea-level pressure between the Pacific and the other two ocean basins. State-of-the-art climate model forecasts initialized from a realistic ocean state show that the low-frequency trans-basin climate variability, which explains part of the El Niño Southern Oscillation flavours, can be predicted up to 3 years ahead, thus exceeding the predictive skill of current tropical climate forecasts for natural variability. This low-frequency variability emerges from the synchronization of ocean anomalies in all basins via global reorganizations of the atmospheric Walker Circulation.

Palaeoclimatic insights into future climate challenges.

PubMed

Alley, Richard B

2003-09-15

Palaeoclimatic data document a sensitive climate system subject to large and perhaps difficult-to-predict abrupt changes. These data suggest that neither the sensitivity nor the variability of the climate are fully captured in some climate-change projections, such as the Intergovernmental Panel on Climate Change (IPCC) Summary for Policymakers. Because larger, faster and less-expected climate changes can cause more problems for economies and ecosystems, the palaeoclimatic data suggest the hypothesis that the future may be more challenging than anticipated in ongoing policy making. Large changes have occurred repeatedly with little net forcing. Increasing carbon dioxide concentration appears to have globalized deglacial warming, with climate sensitivity near the upper end of values from general circulation models (GCMs) used to project human-enhanced greenhouse warming; data from the warm Cretaceous period suggest a similarly high climate sensitivity to CO(2). Abrupt climate changes of the most recent glacial-interglacial cycle occurred during warm as well as cold times, linked especially to changing North Atlantic freshwater fluxes. GCMs typically project greenhouse-gas-induced North Atlantic freshening and circulation changes with notable but not extreme consequences; however, such models often underestimate the magnitude, speed or extent of past changes. Targeted research to assess model uncertainties would help to test these hypotheses.

Variance decomposition shows the importance of human-climate feedbacks in the Earth system

NASA Astrophysics Data System (ADS)

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

2017-12-01

The human and Earth systems are intricately linked: climate influences agricultural production, renewable energy potential, and water availability, for example, while anthropogenic emissions from industry and land use change alter temperature and precipitation. Such feedbacks have the potential to significantly alter future climate change. Current climate change projections contain significant uncertainties, however, and because Earth System Models do not generally include dynamic human (demography, economy, energy, water, land use) components, little is known about how climate feedbacks contribute to that uncertainty. Here we use variance decomposition of a novel coupled human-earth system model to show that the influence of human-climate feedbacks can be as large as 17% of the total variance in the near term for global mean temperature rise, and 11% in the long term for cropland area. The near-term contribution of energy and land use feedbacks to the climate on global mean temperature rise is as large as that from model internal variability, a factor typically considered in modeling studies. Conversely, the contribution of climate feedbacks to cropland extent, while non-negligible, is less than that from socioeconomics, policy, or model. Previous assessments have largely excluded these feedbacks, with the climate community focusing on uncertainty due to internal variability, scenario, and model and the integrated assessment community focusing on uncertainty due to socioeconomics, technology, policy, and model. Our results set the stage for a new generation of models and hypothesis testing to determine when and how bidirectional feedbacks between human and Earth systems should be considered in future assessments of climate change.

Detection and Attribution of Simulated Climatic Extreme Events and Impacts: High Sensitivity to Bias Correction

NASA Astrophysics Data System (ADS)

Sippel, S.; Otto, F. E. L.; Forkel, M.; Allen, M. R.; Guillod, B. P.; Heimann, M.; Reichstein, M.; Seneviratne, S. I.; Kirsten, T.; Mahecha, M. D.

2015-12-01

Understanding, quantifying and attributing the impacts of climatic extreme events and variability is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit pronounced biases in their output that hinders any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies most of which have been criticized for physical inconsistency and the non-preservation of the multivariate correlation structure. We assess how biases and their correction affect the quantification and attribution of simulated extremes and variability in i) climatological variables and ii) impacts on ecosystem functioning as simulated by a terrestrial biosphere model. Our study demonstrates that assessments of simulated climatic extreme events and impacts in the terrestrial biosphere are highly sensitive to bias correction schemes with major implications for the detection and attribution of these events. We introduce a novel ensemble-based resampling scheme based on a large regional climate model ensemble generated by the distributed weather@home setup[1], which fully preserves the physical consistency and multivariate correlation structure of the model output. We use extreme value statistics to show that this procedure considerably improves the representation of climatic extremes and variability. Subsequently, biosphere-atmosphere carbon fluxes are simulated using a terrestrial ecosystem model (LPJ-GSI) to further demonstrate the sensitivity of ecosystem impacts to the methodology of bias correcting climate model output. We find that uncertainties arising from bias correction schemes are comparable in magnitude to model structural and parameter uncertainties. The present study consists of a first attempt to alleviate climate model biases in a physically consistent way and demonstrates that this yields improved simulations of climate extremes and associated impacts. [1] http://www.climateprediction.net/weatherathome/

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.

A multiscale climate emulator for long-term morphodynamics (MUSCLE-morpho)

NASA Astrophysics Data System (ADS)

Antolínez, José Antonio A.; Méndez, Fernando J.; Camus, Paula; Vitousek, Sean; González, E. Mauricio; Ruggiero, Peter; Barnard, Patrick

2016-01-01

Interest in understanding long-term coastal morphodynamics has recently increased as climate change impacts become perceptible and accelerated. Multiscale, behavior-oriented and process-based models, or hybrids of the two, are typically applied with deterministic approaches which require considerable computational effort. In order to reduce the computational cost of modeling large spatial and temporal scales, input reduction and morphological acceleration techniques have been developed. Here we introduce a general framework for reducing dimensionality of wave-driver inputs to morphodynamic models. The proposed framework seeks to account for dependencies with global atmospheric circulation fields and deals simultaneously with seasonality, interannual variability, long-term trends, and autocorrelation of wave height, wave period, and wave direction. The model is also able to reproduce future wave climate time series accounting for possible changes in the global climate system. An application of long-term shoreline evolution is presented by comparing the performance of the real and the simulated wave climate using a one-line model. This article was corrected on 2 FEB 2016. See the end of the full text for details.

Ocean-Atmosphere Coupled Model Simulations of Precipitation in the Central Andes

NASA Technical Reports Server (NTRS)

Nicholls, Stephen D.; Mohr, Karen I.

2015-01-01

The meridional extent and complex orography of the South American continent contributes to a wide diversity of climate regimes ranging from hyper-arid deserts to tropical rainforests to sub-polar highland regions. In addition, South American meteorology and climate are also made further complicated by ENSO, a powerful coupled ocean-atmosphere phenomenon. Modelling studies in this region have typically resorted to either atmospheric mesoscale or atmosphere-ocean coupled global climate models. The latter offers full physics and high spatial resolution, but it is computationally inefficient typically lack an interactive ocean, whereas the former offers high computational efficiency and ocean-atmosphere coupling, but it lacks adequate spatial and temporal resolution to adequate resolve the complex orography and explicitly simulate precipitation. Explicit simulation of precipitation is vital in the Central Andes where rainfall rates are light (0.5-5 mm hr-1), there is strong seasonality, and most precipitation is associated with weak mesoscale-organized convection. Recent increases in both computational power and model development have led to the advent of coupled ocean-atmosphere mesoscale models for both weather and climate study applications. These modelling systems, while computationally expensive, include two-way ocean-atmosphere coupling, high resolution, and explicit simulation of precipitation. In this study, we use the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST), a fully-coupled mesoscale atmosphere-ocean modeling system. Previous work has shown COAWST to reasonably simulate the entire 2003-2004 wet season (Dec-Feb) as validated against both satellite and model analysis data when ECMWF interim analysis data were used for boundary conditions on a 27-9-km grid configuration (Outer grid extent: 60.4S to 17.7N and 118.6W to 17.4W).

On the use of tower-flux measurements to assess the performance of global ecosystem models

NASA Astrophysics Data System (ADS)

El Maayar, M.; Kucharik, C.

2003-04-01

Global ecosystem models are important tools for the study of biospheric processes and their responses to environmental changes. Such models typically translate knowledge, gained from local observations, into estimates of regional or even global outcomes of ecosystem processes. A typical test of ecosystem models consists of comparing their output against tower-flux measurements of land surface-atmosphere exchange of heat and mass. To perform such tests, models are typically run using detailed information on soil properties (texture, carbon content,...) and vegetation structure observed at the experimental site (e.g., vegetation height, vegetation phenology, leaf photosynthetic characteristics,...). In global simulations, however, earth's vegetation is typically represented by a limited number of plant functional types (PFT; group of plant species that have similar physiological and ecological characteristics). For each PFT (e.g., temperate broadleaf trees, boreal conifer evergreen trees,...), which can cover a very large area, a set of typical physiological and physical parameters are assigned. Thus, a legitimate question arises: How does the performance of a global ecosystem model run using detailed site-specific parameters compare with the performance of a less detailed global version where generic parameters are attributed to a group of vegetation species forming a PFT? To answer this question, we used a multiyear dataset, measured at two forest sites with contrasting environments, to compare seasonal and interannual variability of surface-atmosphere exchange of water and carbon predicted by the Integrated BIosphere Simulator-Dynamic Global Vegetation Model. Two types of simulations were, thus, performed: a) Detailed runs: observed vegetation characteristics (leaf area index, vegetation height,...) and soil carbon content, in addition to climate and soil type, are specified for model run; and b) Generic runs: when only observed climates and soil types at the measurement sites are used to run the model. The generic runs were performed for the number of years equal to the current age of the forests, initialized with no vegetation and a soil carbon density equal to zero.

Exploring Typical and Atypical Safety Climate Perceptions of Practitioners in the Repair, Maintenance, Minor Alteration and Addition (RMAA) Sector in Hong Kong.

PubMed

Hon, Carol K H; Liu, Yulin

2016-09-22

The safety of repair, maintenance, minor alteration and addition (RMAA) work is an under-explored area. This study explored the typical and atypical safety climate perceptions of practitioners in the RMAA sector in Hong Kong, based on a self-administered questionnaire survey of 662 local practitioners in the industry. Profile analysis, via multidimensional scaling of the respondents' scores of three safety climate scales, identified one typical perception: high in management commitment to occupational health and safety (OHS) and employee involvement, low in applicability for safety rules and regulations, and low in responsibility for OHS. The respondents were clustered into typical and atypical perception groups according to their safety climate scores' match to the typical perception. A comparison of demographics between the two groups with logistic regression found that work level and direct employer significantly affect their classification. A multivariate analysis of variance of safety performance measures between the two groups indicated that the typical group had a significantly higher level of safety compliance than the atypical group, with no significant difference in safety participation or injury. The significance of this study lies in revealing the typical safety climate perception profile pattern of RMAA works and offering a new perspective of safety climate research.

Exploring Typical and Atypical Safety Climate Perceptions of Practitioners in the Repair, Maintenance, Minor Alteration and Addition (RMAA) Sector in Hong Kong

PubMed Central

Hon, Carol K.H.; Liu, Yulin

2016-01-01

The safety of repair, maintenance, minor alteration and addition (RMAA) work is an under-explored area. This study explored the typical and atypical safety climate perceptions of practitioners in the RMAA sector in Hong Kong, based on a self-administered questionnaire survey of 662 local practitioners in the industry. Profile analysis, via multidimensional scaling of the respondents’ scores of three safety climate scales, identified one typical perception: high in management commitment to occupational health and safety (OHS) and employee involvement, low in applicability for safety rules and regulations, and low in responsibility for OHS. The respondents were clustered into typical and atypical perception groups according to their safety climate scores’ match to the typical perception. A comparison of demographics between the two groups with logistic regression found that work level and direct employer significantly affect their classification. A multivariate analysis of variance of safety performance measures between the two groups indicated that the typical group had a significantly higher level of safety compliance than the atypical group, with no significant difference in safety participation or injury. The significance of this study lies in revealing the typical safety climate perception profile pattern of RMAA works and offering a new perspective of safety climate research. PMID:27669269

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.

Statistical link between external climate forcings and modes of ocean variability

NASA Astrophysics Data System (ADS)

Malik, Abdul; Brönnimann, Stefan; Perona, Paolo

2017-07-01

In this study we investigate statistical link between external climate forcings and modes of ocean variability on inter-annual (3-year) to centennial (100-year) timescales using de-trended semi-partial-cross-correlation analysis technique. To investigate this link we employ observations (AD 1854-1999), climate proxies (AD 1600-1999), and coupled Atmosphere-Ocean-Chemistry Climate Model simulations with SOCOL-MPIOM (AD 1600-1999). We find robust statistical evidence that Atlantic multi-decadal oscillation (AMO) has intrinsic positive correlation with solar activity in all datasets employed. The strength of the relationship between AMO and solar activity is modulated by volcanic eruptions and complex interaction among modes of ocean variability. The observational dataset reveals that El Niño southern oscillation (ENSO) has statistically significant negative intrinsic correlation with solar activity on decadal to multi-decadal timescales (16-27-year) whereas there is no evidence of a link on a typical ENSO timescale (2-7-year). In the observational dataset, the volcanic eruptions do not have a link with AMO on a typical AMO timescale (55-80-year) however the long-term datasets (proxies and SOCOL-MPIOM output) show that volcanic eruptions have intrinsic negative correlation with AMO on inter-annual to multi-decadal timescales. The Pacific decadal oscillation has no link with solar activity, however, it has positive intrinsic correlation with volcanic eruptions on multi-decadal timescales (47-54-year) in reconstruction and decadal to multi-decadal timescales (16-32-year) in climate model simulations. We also find evidence of a link between volcanic eruptions and ENSO, however, the sign of relationship is not consistent between observations/proxies and climate model simulations.

Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise.

PubMed

Brown, Patrick T; Li, Wenhong; Cordero, Eugene C; Mauget, Steven A

2015-04-21

The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20(th) century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal.

Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise

PubMed Central

Brown, Patrick T.; Li, Wenhong; Cordero, Eugene C.; Mauget, Steven A.

2015-01-01

The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20th century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal. PMID:25898351

The rogue nature of hiatuses in a global warming climate

NASA Astrophysics Data System (ADS)

Sévellec, F.; Sinha, B.; Skliris, N.

2016-08-01

The nature of rogue events is their unlikelihood and the recent unpredicted decade-long slowdown in surface warming, the so-called hiatus, may be such an event. However, given decadal variability in climate, global surface temperatures were never expected to increase monotonically with increasing radiative forcing. Here surface air temperature from 20 climate models is analyzed to estimate the historical and future likelihood of hiatuses and "surges" (faster than expected warming), showing that the global hiatus of the early 21st century was extremely unlikely. A novel analysis of future climate scenarios suggests that hiatuses will almost vanish and surges will strongly intensify by 2100 under a "business as usual" scenario. For "CO2 stabilisation" scenarios, hiatus, and surge characteristics revert to typical 1940s values. These results suggest to study the hiatus of the early 21st century and future reoccurrences as rogue events, at the limit of the variability of current climate modelling capability.

Climate Model Diagnostic Analyzer Web Service System

NASA Astrophysics Data System (ADS)

Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Kubar, T. L.; Li, J.; Zhang, J.; Wang, W.

2015-12-01

Both the National Research Council Decadal Survey and the latest Intergovernmental Panel on Climate Change Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with the synergistic use of global satellite observations in order to improve our weather and climate simulation and prediction capabilities. The abundance of satellite observations for fundamental climate parameters and the availability of coordinated model outputs from CMIP5 for the same parameters offer a great opportunity to understand and diagnose model biases in climate models. In addition, the Obs4MIPs efforts have created several key global observational datasets that are readily usable for model evaluations. However, a model diagnostic evaluation process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. In response, we have developed a novel methodology to diagnose model biases in contemporary climate models and implementing the methodology as a web-service based, cloud-enabled, provenance-supported climate-model evaluation system. The evaluation system is named Climate Model Diagnostic Analyzer (CMDA), which is the product of the research and technology development investments of several current and past NASA ROSES programs. The current technologies and infrastructure of CMDA are designed and selected to address several technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. In particular, we have three key technology components: (1) diagnostic analysis methodology; (2) web-service based, cloud-enabled technology; (3) provenance-supported technology. The diagnostic analysis methodology includes random forest feature importance ranking, conditional probability distribution function, conditional sampling, and time-lagged correlation map. We have implemented the new methodology as web services and incorporated the system into the Cloud. We have also developed a provenance management system for CMDA where CMDA service semantics modeling, service search and recommendation, and service execution history management are designed and implemented.

The Impact of Different Absolute Solar Irradiance Values on Current Climate Model Simulations

NASA Technical Reports Server (NTRS)

Rind, David H.; Lean, Judith L.; Jonas, Jeffrey

2014-01-01

Simulations of the preindustrial and doubled CO2 climates are made with the GISS Global Climate Middle Atmosphere Model 3 using two different estimates of the absolute solar irradiance value: a higher value measured by solar radiometers in the 1990s and a lower value measured recently by the Solar Radiation and Climate Experiment. Each of the model simulations is adjusted to achieve global energy balance; without this adjustment the difference in irradiance produces a global temperature change of 0.48C, comparable to the cooling estimated for the Maunder Minimum. The results indicate that by altering cloud cover the model properly compensates for the different absolute solar irradiance values on a global level when simulating both preindustrial and doubled CO2 climates. On a regional level, the preindustrial climate simulations and the patterns of change with doubled CO2 concentrations are again remarkably similar, but there are some differences. Using a higher absolute solar irradiance value and the requisite cloud cover affects the model's depictions of high-latitude surface air temperature, sea level pressure, and stratospheric ozone, as well as tropical precipitation. In the climate change experiments it leads to an underestimation of North Atlantic warming, reduced precipitation in the tropical western Pacific, and smaller total ozone growth at high northern latitudes. Although significant, these differences are typically modest compared with the magnitude of the regional changes expected for doubled greenhouse gas concentrations. Nevertheless, the model simulations demonstrate that achieving the highest possible fidelity when simulating regional climate change requires that climate models use as input the most accurate (lower) solar irradiance value.

Seltzer_et_al_2016

EPA Pesticide Factsheets

This dataset supports the modeling study of Seltzer et al. (2016) published in Atmospheric Environment. In this study, techniques typically used for future air quality projections are applied to a historical 11-year period to assess the performance of the modeling system when the driving meteorological conditions are obtained using dynamical downscaling of coarse-scale fields without correcting toward higher resolution observations. The Weather Research and Forecasting model and the Community Multiscale Air Quality model are used to simulate regional climate and air quality over the contiguous United States for 2000-2010. The air quality simulations for that historical period are then compared to observations from four national networks. Comparisons are drawn between defined performance metrics and other published modeling results for predicted ozone, fine particulate matter, and speciated fine particulate matter. The results indicate that the historical air quality simulations driven by dynamically downscaled meteorology are typically within defined modeling performance benchmarks and are consistent with results from other published modeling studies using finer-resolution meteorology. This indicates that the regional climate and air quality modeling framework utilized here does not introduce substantial bias, which provides confidence in the method??s use for future air quality projections.This dataset is associated with the following publication:Seltzer, K., C

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.

Transient simulations of historical climate change including interactive carbon emissions from land-use change.

NASA Astrophysics Data System (ADS)

Matveev, A.; Matthews, H. D.

2009-04-01

Carbon fluxes from land conversion are among the most uncertain variables in our understanding of the contemporary carbon cycle, which limits our ability to estimate both the total human contribution to current climate forcing and the net effect of terrestrial biosphere changes on atmospheric CO2 increases. The current generation of coupled climate-carbon models have made significant progress in simulating the coupled climate and carbon cycle response to anthropogenic CO2 emissions, but do not typically include land-use change as a dynamic component of the simulation. In this work we have incorporated a book-keeping land-use carbon accounting model into the University of Victoria Earth System Climate Model (UVic ESCM), and intermediate-complexity coupled climate-carbon model. The terrestrial component of the UVic ESCM allows an aerial competition of five plant functional types (PFTs) in response to climatic conditions and area availability, and tracks the associated changes in affected carbon pools. In order to model CO2 emissions from land conversion in the terrestrial component of the model, we calculate the allocation of carbon to short and long-lived wood products following specified land-cover change, and use varying decay timescales to estimate CO2 emissions. We use recently available spatial datasets of both crop and pasture distributions to drive a series of transient simulations and estimate the net contribution of human land-use change to historical carbon emissions and climate change.

Climate and southern Africa's water-energy-food nexus

NASA Astrophysics Data System (ADS)

Conway, Declan; van Garderen, Emma Archer; Deryng, Delphine; Dorling, Steve; Krueger, Tobias; Landman, Willem; Lankford, Bruce; Lebek, Karen; Osborn, Tim; Ringler, Claudia; Thurlow, James; Zhu, Tingju; Dalin, Carole

2015-09-01

In southern Africa, the connections between climate and the water-energy-food nexus are strong. Physical and socioeconomic exposure to climate is high in many areas and in crucial economic sectors. Spatial interdependence is also high, driven, for example, by the regional extent of many climate anomalies and river basins and aquifers that span national boundaries. There is now strong evidence of the effects of individual climate anomalies, but associations between national rainfall and gross domestic product and crop production remain relatively weak. The majority of climate models project decreases in annual precipitation for southern Africa, typically by as much as 20% by the 2080s. Impact models suggest these changes would propagate into reduced water availability and crop yields. Recognition of spatial and sectoral interdependencies should inform policies, institutions and investments for enhancing water, energy and food security. Three key political and economic instruments could be strengthened for this purpose: the Southern African Development Community, the Southern African Power Pool and trade of agricultural products amounting to significant transfers of embedded water.

Toward GEOS-6, A Global Cloud System Resolving Atmospheric Model

NASA Technical Reports Server (NTRS)

Putman, William M.

2010-01-01

NASA is committed to observing and understanding the weather and climate of our home planet through the use of multi-scale modeling systems and space-based observations. Global climate models have evolved to take advantage of the influx of multi- and many-core computing technologies and the availability of large clusters of multi-core microprocessors. GEOS-6 is a next-generation cloud system resolving atmospheric model that will place NASA at the forefront of scientific exploration of our atmosphere and climate. Model simulations with GEOS-6 will produce a realistic representation of our atmosphere on the scale of typical satellite observations, bringing a visual comprehension of model results to a new level among the climate enthusiasts. In preparation for GEOS-6, the agency's flagship Earth System Modeling Framework [JDl] has been enhanced to support cutting-edge high-resolution global climate and weather simulations. Improvements include a cubed-sphere grid that exposes parallelism; a non-hydrostatic finite volume dynamical core, and algorithm designed for co-processor technologies, among others. GEOS-6 represents a fundamental advancement in the capability of global Earth system models. The ability to directly compare global simulations at the resolution of spaceborne satellite images will lead to algorithm improvements and better utilization of space-based observations within the GOES data assimilation system

Towards a threshold climate for emergency lower respiratory hospital admissions.

PubMed

Islam, Muhammad Saiful; Chaussalet, Thierry J; Koizumi, Naoru

2017-02-01

Identification of 'cut-points' or thresholds of climate factors would play a crucial role in alerting risks of climate change and providing guidance to policymakers. This study investigated a 'Climate Threshold' for emergency hospital admissions of chronic lower respiratory diseases by using a distributed lag non-linear model (DLNM). We analysed a unique longitudinal dataset (10 years, 2000-2009) on emergency hospital admissions, climate, and pollution factors for the Greater London. Our study extends existing work on this topic by considering non-linearity, lag effects between climate factors and disease exposure within the DLNM model considering B-spline as smoothing technique. The final model also considered natural cubic splines of time since exposure and 'day of the week' as confounding factors. The results of DLNM indicated a significant improvement in model fitting compared to a typical GLM model. The final model identified the thresholds of several climate factors including: high temperature (≥27°C), low relative humidity (≤ 40%), high Pm10 level (≥70-µg/m 3 ), low wind speed (≤ 2 knots) and high rainfall (≥30mm). Beyond the threshold values, a significantly higher number of emergency admissions due to lower respiratory problems would be expected within the following 2-3 days after the climate shift in the Greater London. The approach will be useful to initiate 'region and disease specific' climate mitigation plans. It will help identify spatial hot spots and the most sensitive areas and population due to climate change, and will eventually lead towards a diversified health warning system tailored to specific climate zones and populations. Copyright © 2016 Elsevier Inc. All rights reserved.

Quantifying Key Climate Parameter Uncertainties Using an Earth System Model with a Dynamic 3D Ocean

NASA Astrophysics Data System (ADS)

Olson, R.; Sriver, R. L.; Goes, M. P.; Urban, N.; Matthews, D.; Haran, M.; Keller, K.

2011-12-01

Climate projections hinge critically on uncertain climate model parameters such as climate sensitivity, vertical ocean diffusivity and anthropogenic sulfate aerosol forcings. Climate sensitivity is defined as the equilibrium global mean temperature response to a doubling of atmospheric CO2 concentrations. Vertical ocean diffusivity parameterizes sub-grid scale ocean vertical mixing processes. These parameters are typically estimated using Intermediate Complexity Earth System Models (EMICs) that lack a full 3D representation of the oceans, thereby neglecting the effects of mixing on ocean dynamics and meridional overturning. We improve on these studies by employing an EMIC with a dynamic 3D ocean model to estimate these parameters. We carry out historical climate simulations with the University of Victoria Earth System Climate Model (UVic ESCM) varying parameters that affect climate sensitivity, vertical ocean mixing, and effects of anthropogenic sulfate aerosols. We use a Bayesian approach whereby the likelihood of each parameter combination depends on how well the model simulates surface air temperature and upper ocean heat content. We use a Gaussian process emulator to interpolate the model output to an arbitrary parameter setting. We use Markov Chain Monte Carlo method to estimate the posterior probability distribution function (pdf) of these parameters. We explore the sensitivity of the results to prior assumptions about the parameters. In addition, we estimate the relative skill of different observations to constrain the parameters. We quantify the uncertainty in parameter estimates stemming from climate variability, model and observational errors. We explore the sensitivity of key decision-relevant climate projections to these parameters. We find that climate sensitivity and vertical ocean diffusivity estimates are consistent with previously published results. The climate sensitivity pdf is strongly affected by the prior assumptions, and by the scaling parameter for the aerosols. The estimation method is computationally fast and can be used with more complex models where climate sensitivity is diagnosed rather than prescribed. The parameter estimates can be used to create probabilistic climate projections using the UVic ESCM model in future studies.

Sensitivity of leaf size and shape to climate: Global patterns and paleoclimatic applications

USGS Publications Warehouse

Peppe, D.J.; Royer, D.L.; Cariglino, B.; Oliver, S.Y.; Newman, S.; Leight, E.; Enikolopov, G.; Fernandez-Burgos, M.; Herrera, F.; Adams, J.M.; Correa, E.; Currano, E.D.; Erickson, J.M.; Hinojosa, L.F.; Hoganson, J.W.; Iglesias, A.; Jaramillo, C.A.; Johnson, K.R.; Jordan, G.J.; Kraft, N.J.B.; Lovelock, E.C.; Lusk, C.H.; Niinemets, U.; Penuelas, J.; Rapson, G.; Wing, S.L.; Wright, I.J.

2011-01-01

Paleobotanists have long used models based on leaf size and shape to reconstruct paleoclimate. However, most models incorporate a single variable or use traits that are not physiologically or functionally linked to climate, limiting their predictive power. Further, they often underestimate paleotemperature relative to other proxies. Here we quantify leaf-climate correlations from 92 globally distributed, climatically diverse sites, and explore potential confounding factors. Multiple linear regression models for mean annual temperature (MAT) and mean annual precipitation (MAP) are developed and applied to nine well-studied fossil floras. We find that leaves in cold climates typically have larger, more numerous teeth, and are more highly dissected. Leaf habit (deciduous vs evergreen), local water availability, and phylogenetic history all affect these relationships. Leaves in wet climates are larger and have fewer, smaller teeth. Our multivariate MAT and MAP models offer moderate improvements in precision over univariate approaches (??4.0 vs 4.8??C for MAT) and strong improvements in accuracy. For example, our provisional MAT estimates for most North American fossil floras are considerably warmer and in better agreement with independent paleoclimate evidence. Our study demonstrates that the inclusion of additional leaf traits that are functionally linked to climate improves paleoclimate reconstructions. This work also illustrates the need for better understanding of the impact of phylogeny and leaf habit on leaf-climate relationships. ?? 2011 The Authors. New Phytologist ?? 2011 New Phytologist Trust.

Communicating uncertainty in circulation aspects of climate change

NASA Astrophysics Data System (ADS)

Shepherd, Ted

2017-04-01

The usual way of representing uncertainty in climate change is to define a likelihood range of possible futures, conditioned on a particular pathway of greenhouse gas concentrations (RCPs). Typically these likelihood ranges are derived from multi-model ensembles. However, there is no obvious basis for treating such ensembles as probability distributions. Moreover, for aspects of climate related to atmospheric circulation, such an approach generally leads to large uncertainty and low confidence in projections. Yet this does not mean that the associated climate risks are small. We therefore need to develop suitable ways of communicating climate risk whilst acknowledging the uncertainties. This talk will outline an approach based on conditioning the purely thermodynamic aspects of climate change, concerning which there is comparatively high confidence, on circulation-related aspects, and treating the latter through non-probabilistic storylines.

High-resolution regional climate model evaluation using variable-resolution CESM over California

NASA Astrophysics Data System (ADS)

Huang, X.; Rhoades, A.; Ullrich, P. A.; Zarzycki, C. M.

2015-12-01

Understanding the effect of climate change at regional scales remains a topic of intensive research. Though computational constraints remain a problem, high horizontal resolution is needed to represent topographic forcing, which is a significant driver of local climate variability. Although regional climate models (RCMs) have traditionally been used at these scales, variable-resolution global climate models (VRGCMs) have recently arisen as an alternative for studying regional weather and climate allowing two-way interaction between these domains without the need for nudging. In this study, the recently developed variable-resolution option within the Community Earth System Model (CESM) is assessed for long-term regional climate modeling over California. Our variable-resolution simulations will focus on relatively high resolutions for climate assessment, namely 28km and 14km regional resolution, which are much more typical for dynamically downscaled studies. For comparison with the more widely used RCM method, the Weather Research and Forecasting (WRF) model will be used for simulations at 27km and 9km. All simulations use the AMIP (Atmospheric Model Intercomparison Project) protocols. The time period is from 1979-01-01 to 2005-12-31 (UTC), and year 1979 was discarded as spin up time. The mean climatology across California's diverse climate zones, including temperature and precipitation, is analyzed and contrasted with the Weather Research and Forcasting (WRF) model (as a traditional RCM), regional reanalysis, gridded observational datasets and uniform high-resolution CESM at 0.25 degree with the finite volume (FV) dynamical core. The results show that variable-resolution CESM is competitive in representing regional climatology on both annual and seasonal time scales. This assessment adds value to the use of VRGCMs for projecting climate change over the coming century and improve our understanding of both past and future regional climate related to fine-scale processes. This assessment is also relevant for addressing the scale limitation of current RCMs or VRGCMs when next-generation model resolution increases to ~10km and beyond.

The influence of climatic changes on distribution pattern of six typical Kobresia species in Tibetan Plateau based on MaxEnt model and geographic information system

NASA Astrophysics Data System (ADS)

Hu, Zhongjun; Guo, Ke; Jin, Shulan; Pan, Huahua

2018-01-01

The issue that climatic change has great influence on species distribution is currently of great interest in field of biogeography. Six typical Kobresia species are selected from alpine grassland of Tibetan Plateau (TP) as research objects which are the high-quality forage for local husbandry, and their distribution changes are modeled in four periods by using MaxEnt model and GIS technology. The modeling results have shown that the distribution of these six typical Kobresia species in TP was strongly affected by two factors of "the annual precipitation" and "the precipitation in the wettest and driest quarters of the year". The modeling results have also shown that the most suitable habitats of K. pygmeae were located in the area around Qinghai Lake, the Hengduan-Himalayan mountain area, and the hinterland of TP. The most suitable habitats of K. humilis were mainly located in the area around Qinghai Lake and the hinterland of TP during the Last Interglacial period, and gradually merged into a bigger area; K. robusta and K. tibetica were located in the area around Qinghai Lake and the hinterland of TP, but they did not integrate into one area all the time, and K. capillifolia were located in the area around Qinghai Lake and extended to the southwest of the original distributing area, whereas K. macrantha were mainly distributed along the area of the Himalayan mountain chain, which had the smallest distribution area among them, and all these six Kobresia species can be divided into four types of "retreat/expansion" styles according to the changes of suitable habitat areas during the four periods; all these change styles are the result of long-term adaptations of the different species to the local climate changes in regions of TP and show the complexity of relationships between different species and climate. The research results have positive reference value to the protection of species diversity and sustainable development of the local husbandry in TP.

A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

NASA Astrophysics Data System (ADS)

Duveiller, G.; Donatelli, M.; Fumagalli, D.; Zucchini, A.; Nelson, R.; Baruth, B.

2017-02-01

Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure deficit are estimated ensuring consistency within daily records. The weather generator ClimGen is then used to create 30 synthetic years of all variables to characterize the time horizons of 2000, 2020 and 2030, which can readily be used for crop modelling studies.

How does the sensitivity of climate affect stratospheric solar radiation management?

NASA Astrophysics Data System (ADS)

Ricke, K.; Rowlands, D. J.; Ingram, W.; Keith, D.; Morgan, M. G.

2011-12-01

If implementation of proposals to engineer the climate through solar radiation management (SRM) ever occurs, it is likely to be contingent upon climate sensitivity. Despite this, no modeling studies have examined how the effectiveness of SRM forcings differs between the typical Atmosphere-Ocean General Circulation Models (AOGCMs) with climate sensitivities close to the Coupled Model Intercomparison Project (CMIP) mean and ones with high climate sensitivities. Here, we use a perturbed physics ensemble modeling experiment to examine variations in the response of climate to SRM under different climate sensitivities. When SRM is used as a substitute for mitigation its ability to maintain the current climate state gets worse with increased climate sensitivity and with increased concentrations of greenhouse gases. However, our results also demonstrate that the potential of SRM to slow climate change, even at the regional level, grows with climate sensitivity. On average, SRM reduces regional rates of temperature change by more than 90 percent and rates of precipitation change by more than 50 percent in these higher sensitivity model configurations. To investigate how SRM might behave in models with high climate sensitivity that are also consistent with recent observed climate change we perform a "perturbed physics" ensemble (PPE) modelling experiment with the climateprediction.net (cpdn) version of the HadCM3L AOGCM. Like other perturbed physics climate modelling experiments, we simulate past and future climate scenarios using a wide range of model parameter combinations that both reproduce past climate within a specified level of accuracy and simulate future climates with a wide range of climate sensitivities. We chose 43 members ("model versions") from a subset of the 1,550 from the British Broadcasting Corporation (BBC) climateprediction.net project that have data that allow restarts. We use our results to explore how much assessments of SRM that use best-estimate models, and so near-median climate sensitivity, may be ignoring important contingencies associated with implementing SRM in reality. A primary motivation for studying SRM via the injection of aerosols in the stratosphere is to evaluate its potential effectiveness as "insurance" in the case of higher-than-expected climate response to global warming. We find that this is precisely when SRM appears to be least effective in returning regional climates to their baseline states and reducing regional rates of precipitation change. On the other hand, given the very high regional temperature anomalies associated with rising greenhouse gas concentrations in high sensitivity models, it is also where SRM is most effective in reducing rates of change relative to a no SRM alternative.

The UTCI-clothing model.

PubMed

Havenith, George; Fiala, Dusan; Błazejczyk, Krzysztof; Richards, Mark; Bröde, Peter; Holmér, Ingvar; Rintamaki, Hannu; Benshabat, Yael; Jendritzky, Gerd

2012-05-01

The Universal Thermal Climate Index (UTCI) was conceived as a thermal index covering the whole climate range from heat to cold. This would be impossible without considering clothing as the interface between the person (here, the physiological model of thermoregulation) and the environment. It was decided to develop a clothing model for this application in which the following three factors were considered: (1) typical dressing behaviour in different temperatures, as observed in the field, resulting in a model of the distribution of clothing over the different body segments in relation to the ambient temperature, (2) the changes in clothing insulation and vapour resistance caused by wind and body movement, and (3) the change in wind speed in relation to the height above ground. The outcome was a clothing model that defines in detail the effective clothing insulation and vapour resistance for each of the thermo-physiological model's body segments over a wide range of climatic conditions. This paper details this model's conception and documents its definitions.

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

Meehl, G A; Covey, C; McAvaney, B

The Coupled Model Intercomparison Project (CMIP) is designed to allow study and intercomparison of multi-model simulations of present-day and future climate. The latter are represented by idealized forcing of compounded 1% per year CO2 increase to the time of CO2 doubling near year 70 in simulations with global coupled models that contain, typically, components representing atmosphere, ocean, sea ice and land surface. Results from CMIP diagnostic subprojects were presented at the Second CMIP Workshop held at the Max Planck Institute for Meteorology in Hamburg, Germany, in September, 2003. Significant progress in diagnosing and understanding results from global coupled models hasmore » been made since the First CMIP Workshop in Melbourne, Australia in 1998. For example, the issue of flux adjustment is slowly fading as more and more models obtain stable multi-century surface climates without them. El Nino variability, usually about half the observed amplitude in the previous generation of coupled models, is now more accurately simulated in the present generation of global coupled models, though there are still biases in simulating the patterns of maximum variability. Typical resolutions of atmospheric component models contained in coupled models is now usually around 2.5 degrees latitude-longitude, with the ocean components often having about twice the atmospheric model resolution, with even higher resolution in the equatorial tropics. Some new-generation coupled models have atmospheric model resolutions of around 1.5 degrees latitude-longitude. Modeling groups now routinely run the CMIP control and 1% CO2 simulations in addition to 20th and 21st century climate simulations with a variety of forcings (e.g. volcanoes, solar variability, anthropogenic sulfate aerosols, ozone, and greenhouse gases (GHGs), with the anthropogenic forcings for future climate as well). However, persistent systematic errors noted in previous generations of global coupled models still are present in the present generation (e.g. over-extensive equatorial Pacific cold tongue, double ITCZ). This points to the next challenge for the global coupled climate modeling community. Planning and imminent commencement of the IPCC Fourth Assessment Report (AR4) has prompted rapid coupled model development, which will lead to an expanded CMIP-like activity to collect and analyze results for the control, 1% CO2, 20th, 21st and 22nd century simulations performed for the AR4. The international climate community is encouraged to become involved in this analysis effort, and details are provided below in how to do so.« less

Decision- rather than scenario-centred downscaling: Towards smarter use of climate model outputs

NASA Astrophysics Data System (ADS)

Wilby, Robert L.

2013-04-01

Climate model output has been used for hydrological impact assessments for at least 25 years. Scenario-led methods raise awareness about risks posed by climate variability and change to the security of supplies, performance of water infrastructure, and health of freshwater ecosystems. However, it is less clear how these analyses translate into actionable information for adaptation. One reason is that scenario-led methods typically yield very large uncertainty bounds in projected impacts at regional and river catchment scales. Consequently, there is growing interest in vulnerability-based frameworks and strategies for employing climate model output in decision-making contexts. This talk begins by summarising contrasting perspectives on climate models and principles for testing their utility for water sector applications. Using selected examples it is then shown how water resource systems may be adapted with varying levels of reliance on climate model information. These approaches include the conventional scenario-led risk assessment, scenario-neutral strategies, safety margins and sensitivity testing, and adaptive management of water systems. The strengths and weaknesses of each approach are outlined and linked to selected water management activities. These cases show that much progress can be made in managing water systems without dependence on climate models. Low-regret measures such as improved forecasting, better inter-agency co-operation, and contingency planning, yield benefits regardless of the climate outlook. Nonetheless, climate model scenarios are useful for evaluating adaptation portfolios, identifying system thresholds and fixing weak links, exploring the timing of investments, improving operating rules, or developing smarter licensing regimes. The most problematic application remains the climate change safety margin because of the very low confidence in extreme precipitation and river flows generated by climate models. In such cases, it is necessary to understand the trade-offs that exist between the additional costs of a scheme and the level of risk that is accommodated.

Could geoengineering research help answer one of the biggest questions in climate science?

NASA Astrophysics Data System (ADS)

Wood, Robert; Ackerman, Thomas; Rasch, Philip; Wanser, Kelly

2017-07-01

Anthropogenic aerosol impacts on clouds constitute the largest source of uncertainty in quantifying the radiative forcing of climate, and hinders our ability to determine Earth's climate sensitivity to greenhouse gas increases. Representation of aerosol-cloud interactions in global models is particularly challenging because these interactions occur on typically unresolved scales. Observational studies show influences of aerosol on clouds, but correlations between aerosol and clouds are insufficient to constrain aerosol forcing because of the difficulty in separating aerosol and meteorological impacts. In this commentary, we argue that this current impasse may be overcome with the development of approaches to conduct control experiments whereby aerosol particle perturbations can be introduced into patches of marine low clouds in a systematic manner. Such cloud perturbation experiments constitute a fresh approach to climate science and would provide unprecedented data to untangle the effects of aerosol particles on cloud microphysics and the resulting reflection of solar radiation by clouds. The control experiments would provide a critical test of high-resolution models that are used to develop an improved representation aerosol-cloud interactions needed to better constrain aerosol forcing in global climate models.

On the generation of climate model ensembles

NASA Astrophysics Data System (ADS)

Haughton, Ned; Abramowitz, Gab; Pitman, Andy; Phipps, Steven J.

2014-10-01

Climate model ensembles are used to estimate uncertainty in future projections, typically by interpreting the ensemble distribution for a particular variable probabilistically. There are, however, different ways to produce climate model ensembles that yield different results, and therefore different probabilities for a future change in a variable. Perhaps equally importantly, there are different approaches to interpreting the ensemble distribution that lead to different conclusions. Here we use a reduced-resolution climate system model to compare three common ways to generate ensembles: initial conditions perturbation, physical parameter perturbation, and structural changes. Despite these three approaches conceptually representing very different categories of uncertainty within a modelling system, when comparing simulations to observations of surface air temperature they can be very difficult to separate. Using the twentieth century CMIP5 ensemble for comparison, we show that initial conditions ensembles, in theory representing internal variability, significantly underestimate observed variance. Structural ensembles, perhaps less surprisingly, exhibit over-dispersion in simulated variance. We argue that future climate model ensembles may need to include parameter or structural perturbation members in addition to perturbed initial conditions members to ensure that they sample uncertainty due to internal variability more completely. We note that where ensembles are over- or under-dispersive, such as for the CMIP5 ensemble, estimates of uncertainty need to be treated with care.

Predictors of Drought Recovery across Forest Ecosystems

NASA Astrophysics Data System (ADS)

Anderegg, W.

2016-12-01

The impacts of climate extremes on terrestrial ecosystems are poorly understood but central for predicting carbon cycle feedbacks to climate change. Coupled climate-carbon cycle models typically assume that vegetation recovery from extreme drought is immediate and complete, which conflicts with basic plant physiological understanding. Here, we discuss what we have learned about forest ecosystem recovery from extreme drought across spatial and temporal scales, drawing on inference from tree rings, eddy covariance data, large scale gross primary productivity products, and ecosystem models. In tree rings, we find pervasive and substantial "legacy effects" of reduced growth and incomplete recovery for 1-4 years after severe drought, and that legacy effects are most prevalent in dry ecosystems, Pinaceae, and species with low hydraulic safety margins. At larger scales, we see relatively rapid recovery of ecosystem fluxes, with strong influences of ecosystem productivity and diversity and longer recovery periods in high latidue forests. In contrast, no or limited legacy effects are simulated in current climate-vegetation models after drought, and we highlight some of the key missing mechanisms in dynamic vegetation models. Our results reveal hysteresis in forest ecosystem carbon cycling and delayed recovery from climate extremes and help advance a predictive understanding of ecosystem recovery.

Natural and anthropogenic land cover change and its impact on the regional climate and hydrological extremes over Sanjiangyuan region

NASA Astrophysics Data System (ADS)

Ji, P.; Yuan, X.

2017-12-01

Located in the northern Tibetan Plateau, Sanjiangyuan is the headwater region of the Yellow River, Yangtze River and Mekong River. Besides climate change, natural and human-induced land cover change (e.g., Graze for Grass Project) is also influencing the regional hydro-climate and hydrological extremes significantly. To quantify their impacts, a land surface model (LSM) with consideration of soil moisture-lateral surface flow interaction and quasi-three-dimensional subsurface flow, is used to conduct long-term high resolution simulations driven by China Meteorological Administration Land Data Assimilation System forcing data and different land cover scenarios. In particular, the role of surface and subsurface lateral flows is also analyzed by comparing with typical one-dimensional models. Lateral flows help to simulate soil moisture variability caused by topography at hyper-resolution (e.g., 100m), which is also essential for simulating hydrological extremes including soil moisture dryness/wetness and high/low flows. The LSM will also be coupled with a regional climate model to simulate the effect of natural and anthropogenic land cover change on regional climate, with particular focus on the land-atmosphere coupling at different resolutions with different configurations in modeling land surface hydrology.

Accelerating Climate Simulations Through Hybrid Computing

NASA Technical Reports Server (NTRS)

Zhou, Shujia; Sinno, Scott; Cruz, Carlos; Purcell, Mark

2009-01-01

Unconventional multi-core processors (e.g., IBM Cell B/E and NYIDIDA GPU) have emerged as accelerators in climate simulation. However, climate models typically run on parallel computers with conventional processors (e.g., Intel and AMD) using MPI. Connecting accelerators to this architecture efficiently and easily becomes a critical issue. When using MPI for connection, we identified two challenges: (1) identical MPI implementation is required in both systems, and; (2) existing MPI code must be modified to accommodate the accelerators. In response, we have extended and deployed IBM Dynamic Application Virtualization (DAV) in a hybrid computing prototype system (one blade with two Intel quad-core processors, two IBM QS22 Cell blades, connected with Infiniband), allowing for seamlessly offloading compute-intensive functions to remote, heterogeneous accelerators in a scalable, load-balanced manner. Currently, a climate solar radiation model running with multiple MPI processes has been offloaded to multiple Cell blades with approx.10% network overhead.

Late Noachian Climate Of Mars: Constraints From Valley Network System Formation Times And The Intermittencies (Episodic/Periodic And Punctuated).

NASA Astrophysics Data System (ADS)

Head, James

2017-04-01

Formation of Late Noachian-Early Hesperian (LN-EH) valley network systems (VNS) signaled the presence of warm/wet conditions generating several hypotheses for climates permissive of these conditions. To constrain options for the ambient Noachian climate, we examine estimates for time required to carve channels/deltas and total duration implied by plausible intermittencies. Formation Times for VN, OBL, Deltas, Fans: A synthesis of required timescales show that even with the longest estimated continuous duration of VN formation/intermittencies, total time to carve the VN does not exceed 106 years, <˜0.25% of the total Noachian. Intermittency/episodicity assumptions are climate-model dependent (e.g., most workers use Earth-like fluvial activity and intermittency). Noachian-Early Hesperian Climate Models: 1) Warm and wet/semiarid/arid climate: Sustained background MAT >273 K, hydrological system vertically integrated, and rainfall occurs to recharge the aquifer. Two subtypes: a) "Rainfall/Fluvial Erosion-Dominated Warm and Wet Model": "Rainfall and surface runoff" persist throughout Noachian to explain crater degradation, and a LN-EH short rapidly ending terminal epoch. b) "Recharge Evaporation/Evaporite Dominated Warm and Wet Model": Sustained period of equatorial/mid-latitude precipitation and a vertically integrated hydrological system driven by evaporative upwelling and fluctuating shallow water table playa environments account for sulfate evaporate environments at Meridiani Planum. Sustained temperatures >273 K are required for extended periods (107-108 years). 2) Cold and icy climate: Sustained background temperatures extremely low (MAT ˜225 K), cryosphere is globally continuous, hydrological system is horizontally stratified, separating groundwater system from surface; no combination of spin-axis/orbital perturbations can raise MAT to 273 K. Adiabatic cooling effects transfer water to high altitudes, leading to "Late Noachian Icy Highlands Model". VNS cannot form in this nominal climate environment without special circumstances (e.g., impacts or volcanic eruptions elevate of temperatures by >˜50 K to induce melting and fluvial/lacustrine activity). 3) Cold and Icy climate warmed by greenhouse gases: The climate is sustained cold/icy model, but greenhouse gases of unspecified nature/amount/duration elevate MAT by several tens of Kelvins (say 25 K, to MAT 250 K), bringing annual temperature range into the realm where peak seasonal temperatures (PST) exceed 273 K. In this climate environment, analogous to the Antarctic Dry Valleys, seasonal summer temperatures above 273 K are sufficient to melt snow/ice and form fluvial and lacustrine features, but MAT is well below 273 K (253 K). Fluvial systems driven by episodic/periodic intermittency typically involve short intermittency time-scales (10-106 years) but require a warm climate (MAT >273 K) to be sustained for >0.4 x 109 years. Fluvial systems driven by punctuated intermittency typically involve short duration time-scales (10-105 years) but only require a warm climate (MAT >273 K) for the very short duration of the climatic impact of the punctuated event (102-105 years). We conclude that a cold and icy background climate with punctuated intermittency of warming and melting events is consistent with: 1) the estimated durations of continuous VN formation (<105 years) and 2) VN system estimated recurrence rates (106-107 years).

A Continental United States High Resolution NLCD Land Cover – MODIS Albedo Database to Examine Albedo and Land Cover Change Relationships

EPA Science Inventory

Surface albedo influences climate by affecting the amount of solar radiation that is reflected at the Earth’s surface, and surface albedo is, in turn, affected by land cover. General Circulation Models typically use modeled or prescribed albedo to assess the influence of land co...

Geographic variation in opinions on climate change at state and local scales in the USA

NASA Astrophysics Data System (ADS)

Howe, Peter D.; Mildenberger, Matto; Marlon, Jennifer R.; Leiserowitz, Anthony

2015-06-01

Addressing climate change in the United States requires enactment of national, state and local mitigation and adaptation policies. The success of these initiatives depends on public opinion, policy support and behaviours at appropriate scales. Public opinion, however, is typically measured with national surveys that obscure geographic variability across regions, states and localities. Here we present independently validated high-resolution opinion estimates using a multilevel regression and poststratification model. The model accurately predicts climate change beliefs, risk perceptions and policy preferences at the state, congressional district, metropolitan and county levels, using a concise set of demographic and geographic predictors. The analysis finds substantial variation in public opinion across the nation. Nationally, 63% of Americans believe global warming is happening, but county-level estimates range from 43 to 80%, leading to a diversity of political environments for climate policy. These estimates provide an important new source of information for policymakers, educators and scientists to more effectively address the challenges of climate change.

High regional climate sensitivity over continental China constrained by glacial-recent changes in temperature and the hydrological cycle.

PubMed

Eagle, Robert A; Risi, Camille; Mitchell, Jonathan L; Eiler, John M; Seibt, Ulrike; Neelin, J David; Li, Gaojun; Tripati, Aradhna K

2013-05-28

The East Asian monsoon is one of Earth's most significant climatic phenomena, and numerous paleoclimate archives have revealed that it exhibits variations on orbital and suborbital time scales. Quantitative constraints on the climate changes associated with these past variations are limited, yet are needed to constrain sensitivity of the region to changes in greenhouse gas levels. Here, we show central China is a region that experienced a much larger temperature change since the Last Glacial Maximum than typically simulated by climate models. We applied clumped isotope thermometry to carbonates from the central Chinese Loess Plateau to reconstruct temperature and water isotope shifts from the Last Glacial Maximum to present. We find a summertime temperature change of 6-7 °C that is reproduced by climate model simulations presented here. Proxy data reveal evidence for a shift to lighter isotopic composition of meteoric waters in glacial times, which is also captured by our model. Analysis of model outputs suggests that glacial cooling over continental China is significantly amplified by the influence of stationary waves, which, in turn, are enhanced by continental ice sheets. These results not only support high regional climate sensitivity in Central China but highlight the fundamental role of planetary-scale atmospheric dynamics in the sensitivity of regional climates to continental glaciation, changing greenhouse gas levels, and insolation.

CERES-Maize model-based simulation of climate change impacts on maize yields and potential adaptive measures in Heilongjiang Province, China.

PubMed

Lin, Yumei; Wu, Wenxiang; Ge, Quansheng

2015-11-01

Climate change would cause negative impacts on future agricultural production and food security. Adaptive measures should be taken to mitigate the adverse effects. The objectives of this study were to simulate the potential effects of climate change on maize yields in Heilongjiang Province and to evaluate two selected typical household-level autonomous adaptive measures (cultivar changes and planting time adjustments) for mitigating the risks of climate change based on the CERES-Maize model. The results showed that flowering duration and maturity duration of maize would be shortened in the future climate and thus maize yield would reduce by 11-46% during 2011-2099 relative to 1981-2010. Increased CO2 concentration would not benefit maize production significantly. However, substituting local cultivars with later-maturing ones and delaying the planting date could increase yields as the climate changes. The results provide insight regarding the likely impacts of climate change on maize yields and the efficacy of selected adaptive measures by presenting evidence-based implications and mitigation strategies for the potential negative impacts of future climate change. © 2014 Society of Chemical Industry.

Addressing climate challenges in developing countries

NASA Astrophysics Data System (ADS)

Tilmes, Simone; Monaghan, Andrew; Done, James

2012-04-01

Advanced Study Program/Early Career Scientist Assembly Workshop on Regional Climate Issues in Developing Countries; Boulder, Colorado, 19-22 October 2011 The Early Career Scientist Assembly (ECSA) and the Advanced Study Program of the National Center for Atmospheric Research (NCAR) invited 35 early-career scientists from nearly 20 countries to attend a 3-day workshop at the NCAR Mesa Laboratory prior to the World Climate Research Programme (WCRP) Open Science Conference in October 2011. The goal of the workshop was to examine a range of regional climate challenges in developing countries. Topics included regional climate modeling, climate impacts, water resources, and air quality. The workshop fostered new ideas and collaborations between early-career scientists from around the world. The discussions underscored the importance of establishing partnerships with scientists located in typically underrepresented countries to understand and account for the local political, economic, and cultural factors on which climate change is superimposed.

Building Quantitative Hydrologic Storylines from Process-based Models for Managing Water Resources in the U.S. Under Climate-changed Futures

NASA Astrophysics Data System (ADS)

Arnold, J.; Gutmann, E. D.; Clark, M. P.; Nijssen, B.; Vano, J. A.; Addor, N.; Wood, A.; Newman, A. J.; Mizukami, N.; Brekke, L. D.; Rasmussen, R.; Mendoza, P. A.

2016-12-01

Climate change narratives for water-resource applications must represent the change signals contextualized by hydroclimatic process variability and uncertainty at multiple scales. Building narratives of plausible change includes assessing uncertainties across GCM structure, internal climate variability, climate downscaling methods, and hydrologic models. Work with this linked modeling chain has dealt mostly with GCM sampling directed separately to either model fidelity (does the model correctly reproduce the physical processes in the world?) or sensitivity (of different model responses to CO2 forcings) or diversity (of model type, structure, and complexity). This leaves unaddressed any interactions among those measures and with other components in the modeling chain used to identify water-resource vulnerabilities to specific climate threats. However, time-sensitive, real-world vulnerability studies typically cannot accommodate a full uncertainty ensemble across the whole modeling chain, so a gap has opened between current scientific knowledge and most routine applications for climate-changed hydrology. To close that gap, the US Army Corps of Engineers, the Bureau of Reclamation, and the National Center for Atmospheric Research are working on techniques to subsample uncertainties objectively across modeling chain components and to integrate results into quantitative hydrologic storylines of climate-changed futures. Importantly, these quantitative storylines are not drawn from a small sample of models or components. Rather, they stem from the more comprehensive characterization of the full uncertainty space for each component. Equally important from the perspective of water-resource practitioners, these quantitative hydrologic storylines are anchored in actual design and operations decisions potentially affected by climate change. This talk will describe part of our work characterizing variability and uncertainty across modeling chain components and their interactions using newly developed observational data, models and model outputs, and post-processing tools for making the resulting quantitative storylines most useful in practical hydrology applications.

Characterizing sources of uncertainty from global climate models and downscaling techniques

USGS Publications Warehouse

Wootten, Adrienne; Terando, Adam; Reich, Brian J.; Boyles, Ryan; Semazzi, Fred

2017-01-01

In recent years climate model experiments have been increasingly oriented towards providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here we present a method, based on a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. We apply the method to the Southeast U.S. using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios are typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast U.S. for precipitation and ~30% for extreme heat days (> 35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a sub-sample of all models are available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. We conclude with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

High-resolution downscaling for hydrological management

NASA Astrophysics Data System (ADS)

Ulbrich, Uwe; Rust, Henning; Meredith, Edmund; Kpogo-Nuwoklo, Komlan; Vagenas, Christos

2017-04-01

Hydrological modellers and water managers require high-resolution climate data to model regional hydrologies and how these may respond to future changes in the large-scale climate. The ability to successfully model such changes and, by extension, critical infrastructure planning is often impeded by a lack of suitable climate data. This typically takes the form of too-coarse data from climate models, which are not sufficiently detailed in either space or time to be able to support water management decisions and hydrological research. BINGO (Bringing INnovation in onGOing water management; ) aims to bridge the gap between the needs of hydrological modellers and planners, and the currently available range of climate data, with the overarching aim of providing adaptation strategies for climate change-related challenges. Producing the kilometre- and sub-daily-scale climate data needed by hydrologists through continuous simulations is generally computationally infeasible. To circumvent this hurdle, we adopt a two-pronged approach involving (1) selective dynamical downscaling and (2) conditional stochastic weather generators, with the former presented here. We take an event-based approach to downscaling in order to achieve the kilometre-scale input needed by hydrological modellers. Computational expenses are minimized by identifying extremal weather patterns for each BINGO research site in lower-resolution simulations and then only downscaling to the kilometre-scale (convection permitting) those events during which such patterns occur. Here we (1) outline the methodology behind the selection of the events, and (2) compare the modelled precipitation distribution and variability (preconditioned on the extremal weather patterns) with that found in observations.

From Global Climate Model Projections to Local Impacts Assessments: Analyses in Support of Planning for Climate Change

NASA Astrophysics Data System (ADS)

Snover, A. K.; Littell, J. S.; Mantua, N. J.; Salathe, E. P.; Hamlet, A. F.; McGuire Elsner, M.; Tohver, I.; Lee, S.

2010-12-01

Assessing and planning for the impacts of climate change require regionally-specific information. Information is required not only about projected changes in climate but also the resultant changes in natural and human systems at the temporal and spatial scales of management and decision making. Therefore, climate impacts assessment typically results in a series of analyses, in which relatively coarse-resolution global climate model projections of changes in regional climate are downscaled to provide appropriate input to local impacts models. This talk will describe recent examples in which coarse-resolution (~150 to 300km) GCM output was “translated” into information requested by decision makers at relatively small (watershed) and large (multi-state) scales using regional climate modeling, statistical downscaling, hydrologic modeling, and sector-specific impacts modeling. Projected changes in local air temperature, precipitation, streamflow, and stream temperature were developed to support Seattle City Light’s assessment of climate change impacts on hydroelectric operations, future electricity load, and resident fish populations. A state-wide assessment of climate impacts on eight sectors (agriculture, coasts, energy, forests, human health, hydrology and water resources, salmon, and urban stormwater infrastructure) was developed for Washington State to aid adaptation planning. Hydro-climate change scenarios for approximately 300 streamflow locations in the Columbia River basin and selected coastal drainages west of the Cascades were developed in partnership with major water management agencies in the Pacific Northwest to allow planners to consider how hydrologic changes may affect management objectives. Treatment of uncertainty in these assessments included: using “bracketing” scenarios to describe a range of impacts, using ensemble averages to characterize the central estimate of future conditions (given an emissions scenario), and explicitly assessing the impacts of multiple GCM ensemble members. The implications of various approaches to dealing with uncertainty, such as these, must be carefully communicated to decision makers in order for projected climate impacts to be viewed as credible and used appropriately.

Amplified plant turnover in response to climate change forecast by Late Quaternary records

NASA Astrophysics Data System (ADS)

Nogués-Bravo, D.; Veloz, S.; Holt, B. G.; Singarayer, J.; Valdes, P.; Davis, B.; Brewer, S. C.; Williams, J. W.; Rahbek, C.

2016-12-01

Conservation decisions are informed by twenty-first-century climate impact projections that typically predict high extinction risk. Conversely, the palaeorecord shows strong sensitivity of species abundances and distributions to past climate changes, but few clear instances of extinctions attributable to rising temperatures. However, few studies have incorporated palaeoecological data into projections of future distributions. Here we project changes in abundance and conservation status under a climate warming scenario for 187 European and North American plant taxa using niche-based models calibrated against taxa-climate relationships for the past 21,000 years. We find that incorporating long-term data into niche-based models increases the magnitude of projected future changes for plant abundances and community turnover. The larger projected changes in abundances and community turnover translate into different, and often more threatened, projected IUCN conservation status for declining tree taxa, compared with traditional approaches. An average of 18.4% (North America) and 15.5% (Europe) of taxa switch IUCN categories when compared with single-time model results. When taxa categorized as `Least Concern' are excluded, the palaeo-calibrated models increase, on average, the conservation threat status of 33.2% and 56.8% of taxa. Notably, however, few models predict total disappearance of taxa, suggesting resilience for these taxa, if climate were the only extinction driver. Long-term studies linking palaeorecords and forecasting techniques have the potential to improve conservation assessments.

What are the effects of Agro-Ecological Zones and land use region boundaries on land resource projection using the Global Change Assessment Model?

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

Di Vittorio, Alan V.; Kyle, Page; Collins, William D.

Understanding the potential impacts of climate change is complicated by mismatched spatial representations between gridded Earth System Models (ESMs) and Integrated Assessment Models (IAMs), whose regions are typically larger and defined by geopolitical and biophysical criteria. In this study we address uncertainty stemming from the construction of land use regions in an IAM, the Global Change Assessment Model (GCAM), whose regions are currently based on historical climatic conditions (1961-1990). We re-define GCAM’s regions according to projected climatic conditions (2070-2099), and investigate how this changes model outcomes for land use, agriculture, and forestry. By 2100, we find potentially large differences inmore » projected global and regional area of biomass energy crops, fodder crops, harvested forest, and intensive pasture. These land area differences correspond with changes in agricultural commodity prices and production. These results have broader implications for understanding policy scenarios and potential impacts, and for evaluating and comparing IAM and ESM simulations.« less

Insights into low-latitude cloud feedbacks from high-resolution models.

PubMed

Bretherton, Christopher S

2015-11-13

Cloud feedbacks are a leading source of uncertainty in the climate sensitivity simulated by global climate models (GCMs). Low-latitude boundary-layer and cumulus cloud regimes are particularly problematic, because they are sustained by tight interactions between clouds and unresolved turbulent circulations. Turbulence-resolving models better simulate such cloud regimes and support the GCM consensus that they contribute to positive global cloud feedbacks. Large-eddy simulations using sub-100 m grid spacings over small computational domains elucidate marine boundary-layer cloud response to greenhouse warming. Four observationally supported mechanisms contribute: 'thermodynamic' cloudiness reduction from warming of the atmosphere-ocean column, 'radiative' cloudiness reduction from CO2- and H2O-induced increase in atmospheric emissivity aloft, 'stability-induced' cloud increase from increased lower tropospheric stratification, and 'dynamical' cloudiness increase from reduced subsidence. The cloudiness reduction mechanisms typically dominate, giving positive shortwave cloud feedback. Cloud-resolving models with horizontal grid spacings of a few kilometres illuminate how cumulonimbus cloud systems affect climate feedbacks. Limited-area simulations and superparameterized GCMs show upward shift and slight reduction of cloud cover in a warmer climate, implying positive cloud feedbacks. A global cloud-resolving model suggests tropical cirrus increases in a warmer climate, producing positive longwave cloud feedback, but results are sensitive to subgrid turbulence and ice microphysics schemes. © 2015 The Author(s).

Increased wind risk from sting-jet windstorms with climate change

NASA Astrophysics Data System (ADS)

Martínez-Alvarado, Oscar; Gray, Suzanne L.; Hart, Neil C. G.; Clark, Peter A.; Hodges, Kevin; Roberts, Malcolm J.

2018-04-01

Extra-tropical cyclones dominate autumn and winter weather over western Europe. The strongest cyclones, often termed windstorms, have a large socio-economic impact on landfall due to strong surface winds and coastal storm surges. Climate model integrations have predicted a future increase in the frequency of, and potential damage from, European windstorms and yet these integrations cannot properly represent localised jets, such as sting jets, that may significantly enhance damage. Here we present the first prediction of how the climatology of sting-jet-containing cyclones will change in a future warmer climate, considering the North Atlantic and Europe. A proven sting-jet precursor diagnostic is applied to 13 year present-day and future (~2100) climate integrations from the Met Office Unified Model in its Global Atmosphere 3.0 configuration. The present-day climate results are consistent with previously-published results from a reanalysis dataset (with around 32% of cyclones exhibiting the sing-jet precursor), lending credibility to the analysis of the future-climate integration. The proportion of cyclones exhibiting the sting-jet precursor in the future-climate integration increases to 45%. Furthermore, while the proportion of explosively-deepening storms increases only slightly in the future climate, the proportion of those storms with the sting-jet precursor increases by 60%. The European resolved-wind risk associated with explosively-deepening storms containing a sting-jet precursor increases substantially in the future climate; in reality this wind risk is likely to be further enhanced by the release of localised moist instability, unresolved by typical climate models.

Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America

PubMed Central

Wang, Tongli; Hamann, Andreas; Spittlehouse, Dave; Carroll, Carlos

2016-01-01

Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901–2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011–2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data. PMID:27275583

Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America.

PubMed

Wang, Tongli; Hamann, Andreas; Spittlehouse, Dave; Carroll, Carlos

2016-01-01

Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901-2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011-2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data.

Identifying stakeholder-relevant climate change impacts: a case study in the Yakima River Basin, Washington, USA

USGS Publications Warehouse

Jenni, K.; Graves, D.; Hardiman, Jill M.; Hatten, James R.; Mastin, Mark C.; Mesa, Matthew G.; Montag, J.; Nieman, Timothy; Voss, Frank D.; Maule, Alec G.

2014-01-01

Designing climate-related research so that study results will be useful to natural resource managers is a unique challenge. While decision makers increasingly recognize the need to consider climate change in their resource management plans, and climate scientists recognize the importance of providing locally-relevant climate data and projections, there often remains a gap between management needs and the information that is available or is being collected. We used decision analysis concepts to bring decision-maker and stakeholder perspectives into the applied research planning process. In 2009 we initiated a series of studies on the impacts of climate change in the Yakima River Basin (YRB) with a four-day stakeholder workshop, bringing together managers, stakeholders, and scientists to develop an integrated conceptual model of climate change and climate change impacts in the YRB. The conceptual model development highlighted areas of uncertainty that limit the understanding of the potential impacts of climate change and decision alternatives by those who will be most directly affected by those changes, and pointed to areas where additional study and engagement of stakeholders would be beneficial. The workshop and resulting conceptual model highlighted the importance of numerous different outcomes to stakeholders in the basin, including social and economic outcomes that go beyond the physical and biological outcomes typically reported in climate impacts studies. Subsequent studies addressed several of those areas of uncertainty, including changes in water temperatures, habitat quality, and bioenergetics of salmonid populations.

Evaluation of near surface ozone and particulate matter in air ...

EPA Pesticide Factsheets

In this study, techniques typically used for future air quality projections are applied to a historical 11-year period to assess the performance of the modeling system when the driving meteorological conditions are obtained using dynamical downscaling of coarse-scale fields without correcting toward higher-resolution observations. The Weather Research and Forecasting model and the Community Multiscale Air Quality model are used to simulate regional climate and air quality over the contiguous United States for 2000–2010. The air quality simulations for that historical period are then compared to observations from four national networks. Comparisons are drawn between defined performance metrics and other published modeling results for predicted ozone, fine particulate matter, and speciated fine particulate matter. The results indicate that the historical air quality simulations driven by dynamically downscaled meteorology are typically within defined modeling performance benchmarks and are consistent with results from other published modeling studies using finer-resolution meteorology. This indicates that the regional climate and air quality modeling framework utilized here does not introduce substantial bias, which provides confidence in the method’s use for future air quality projections. This paper shows that if emissions inputs and coarse-scale meteorological inputs are reasonably accurate, then air quality can be simulated with acceptable accuracy even wi

Earlier occurrence and increased explanatory power of climate for the first incidence of potato late blight caused by Phytophthora infestans in Fennoscandia

PubMed Central

Wiik, Lars; Hannukkala, Asko; Andreasson, Erik; Chen, Deliang; Ou, Tinghai; Liljeroth, Erland; Lankinen, Åsa

2017-01-01

Background Late blight (caused by Phytophthora infestans) is a devastating potato disease that has been found to occur earlier in the season over the last decades in Fennoscandia. Up until now the reasons for this change have not been investigated. Possible explanations for this change are climate alterations, changes in potato production or changes in pathogen biology, such as increased fitness or changes in gene flow within P. infestans populations. The first incidence of late blight is of high economic importance since fungicidal applications should be typically applied two weeks before the first signs of late blight and are repeated on average once a week. Methods We use field observations of first incidence of late blight in experimental potato fields from five sites in Sweden and Finland covering a total of 30 years and investigate whether the earlier incidence of late blight can be related to the climate. Results We linked the field data to meteorological data and found that the previous assumption, used in common late blight models, that the disease only develops at relative humidity levels above 90% had to be rejected. Rather than the typically assumed threshold relationship between late blight disease development and relative humidity we found a linear relationship. Our model furthermore showed two distinct responses of late blight to climate. At the beginning of the observation time (in Sweden until the early 90s and in Finland until the 2000s) the link between climate and first incidence was very weak. However, for the remainder of the time period the link was highly significant, indicating a change in the biological properties of the pathogen which could for example be a change in the dominating reproduction mode or a physiological change in the response of the pathogen to climate. Conclusions The study shows that models used in decision support systems need to be checked and re-parametrized regularly to be able to capture changes in pathogen biology. While this study was performed with data from Fennoscandia this new pathogen biology and late blight might spread to (or already be present at) other parts of the world as well. The strong link between climate and first incidence together with the presented model offers a tool to assess late blight incidence in future climates. PMID:28558041

Earlier occurrence and increased explanatory power of climate for the first incidence of potato late blight caused by Phytophthora infestans in Fennoscandia.

PubMed

Lehsten, Veiko; Wiik, Lars; Hannukkala, Asko; Andreasson, Erik; Chen, Deliang; Ou, Tinghai; Liljeroth, Erland; Lankinen, Åsa; Grenville-Briggs, Laura

2017-01-01

Late blight (caused by Phytophthora infestans) is a devastating potato disease that has been found to occur earlier in the season over the last decades in Fennoscandia. Up until now the reasons for this change have not been investigated. Possible explanations for this change are climate alterations, changes in potato production or changes in pathogen biology, such as increased fitness or changes in gene flow within P. infestans populations. The first incidence of late blight is of high economic importance since fungicidal applications should be typically applied two weeks before the first signs of late blight and are repeated on average once a week. We use field observations of first incidence of late blight in experimental potato fields from five sites in Sweden and Finland covering a total of 30 years and investigate whether the earlier incidence of late blight can be related to the climate. We linked the field data to meteorological data and found that the previous assumption, used in common late blight models, that the disease only develops at relative humidity levels above 90% had to be rejected. Rather than the typically assumed threshold relationship between late blight disease development and relative humidity we found a linear relationship. Our model furthermore showed two distinct responses of late blight to climate. At the beginning of the observation time (in Sweden until the early 90s and in Finland until the 2000s) the link between climate and first incidence was very weak. However, for the remainder of the time period the link was highly significant, indicating a change in the biological properties of the pathogen which could for example be a change in the dominating reproduction mode or a physiological change in the response of the pathogen to climate. The study shows that models used in decision support systems need to be checked and re-parametrized regularly to be able to capture changes in pathogen biology. While this study was performed with data from Fennoscandia this new pathogen biology and late blight might spread to (or already be present at) other parts of the world as well. The strong link between climate and first incidence together with the presented model offers a tool to assess late blight incidence in future climates.

Climate Sensitivity to Realistic Solar Heating of Snow and Ice

NASA Astrophysics Data System (ADS)

Flanner, M.; Zender, C. S.

2004-12-01

Snow and ice-covered surfaces are highly reflective and play an integral role in the planetary radiation budget. However, GCMs typically prescribe snow reflection and absorption based on minimal knowledge of snow physical characteristics. We performed climate sensitivity simulations with the NCAR CCSM including a new physically-based multi-layer snow radiative transfer model. The model predicts the effects of vertically resolved heating, absorbing aerosol, and snowpack transparency on snowpack evolution and climate. These processes significantly reduce the model's near-infrared albedo bias over deep snowpacks. While the current CCSM implementation prescribes all solar radiative absorption to occur in the top 2 cm of snow, we estimate that about 65% occurs beneath this level. Accounting for the vertical distribution of snowpack heating and more realistic reflectance significantly alters snowpack depth, surface albedo, and surface air temperature over Northern Hemisphere regions. Implications for the strength of the ice-albedo feedback will be discussed.

Impact of climate change and seasonal trends on the fate of Arctic oil spills.

PubMed

Nordam, Tor; Dunnebier, Dorien A E; Beegle-Krause, C J; Reed, Mark; Slagstad, Dag

2017-12-01

We investigated the effects of a warmer climate, and seasonal trends, on the fate of oil spilled in the Arctic. Three well blowout scenarios, two shipping accidents and a pipeline rupture were considered. We used ensembles of numerical simulations, using the OSCAR oil spill model, with environmental data for the periods 2009-2012 and 2050-2053 (representing a warmer future) as inputs to the model. Future atmospheric forcing was based on the IPCC's A1B scenario, with the ocean data generated by the hydrodynamic model SINMOD. We found differences in "typical" outcome of a spill in a warmer future compared to the present, mainly due to a longer season of open water. We have demonstrated that ice cover is extremely important for predicting the fate of an Arctic oil spill, and find that oil spills in a warming climate will in some cases result in greater areal coverage and shoreline exposure.

Simulation of future stream alkalinity under changing deposition and climate scenarios.

PubMed

Welsch, Daniel L; Cosby, B Jack; Hornberger, George M

2006-08-31

Models of soil and stream water acidification have typically been applied under scenarios of changing acidic deposition, however, climate change is usually ignored. Soil air CO2 concentrations have potential to increase as climate warms and becomes wetter, thus affecting soil and stream water chemistry by initially increasing stream alkalinity at the expense of reducing base saturation levels on soil exchange sites. We simulate this change by applying a series of physically based coupled models capable of predicting soil air CO2 and stream water chemistry. We predict daily stream water alkalinity for a small catchment in the Virginia Blue Ridge for 60 years into the future given stochastically generated daily climate values. This is done for nine different combinations of climate and deposition. The scenarios for both climate and deposition include a static scenario, a scenario of gradual change, and a scenario of abrupt change. We find that stream water alkalinity continues to decline for all scenarios (average decrease of 14.4 microeq L-1) except where climate is gradually warming and becoming more moist (average increase of 13 microeq L-1). In all other scenarios, base cation removal from catchment soils is responsible for limited alkalinity increase resulting from climate change. This has implications given the extent that acidification models are used to establish policy and legislation concerning deposition and emissions.

Application of Multi-Model CMIP5 Analysis in Future Drought Adaptation Strategies

NASA Astrophysics Data System (ADS)

Casey, M.; Luo, L.; Lang, Y.

2014-12-01

Drought influences the efficacy of numerous natural and artificial systems including species diversity, agriculture, and infrastructure. Global climate change raises concerns that extend well beyond atmospheric and hydrological disciplines - as climate changes with time, the need for system adaptation becomes apparent. Drought, as a natural phenomenon, is typically defined relative to the climate in which it occurs. Typically a 30-year reference time frame (RTF) is used to determine the severity of a drought event. This study investigates the projected future droughts over North America with different RTFs. Confidence in future hydroclimate projection is characterized by the agreement of long term (2005-2100) multi-model precipitation (P) and temperature (T) projections within the Coupled model Intercomparison Project Phase 5 (CMIP5). Drought severity and the propensity of extreme conditions are measured by the multi-scalar, probabilistic, RTF-based Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI). SPI considers only P while SPEI incorporates Evapotranspiration (E) via T; comparing the two reveals the role of temperature change in future hydroclimate change. Future hydroclimate conditions, hydroclimate extremity, and CMIP5 model agreement are assessed for each Representative Concentration Pathway (RCP 2.6, 4.5, 6.0, 8.5) in regions throughout North America for the entire year and for the boreal seasons. In addition, multiple time scales of SPI and SPEI are calculated to characterize drought at time scales ranging from short to long term. The study explores a simple, standardized method for considering adaptation in future drought assessment, which provides a novel perspective to incorporate adaptation with climate change. The result of the analysis is a multi-dimension, probabilistic summary of the hydrological (P, E) environment a natural or artificial system must adapt to over time. Studies similar to this with specified criteria (SPI/SPEI value, time scale, RCP, etc.) can provide professionals in a variety of disciplines with necessary climatic insight to develop adaptation strategies.

Understanding Climate Uncertainty with an Ocean Focus

NASA Astrophysics Data System (ADS)

Tokmakian, R. T.

2009-12-01

Uncertainty in climate simulations arises from various aspects of the end-to-end process of modeling the Earth’s climate. First, there is uncertainty from the structure of the climate model components (e.g. ocean/ice/atmosphere). Even the most complex models are deficient, not only in the complexity of the processes they represent, but in which processes are included in a particular model. Next, uncertainties arise from the inherent error in the initial and boundary conditions of a simulation. Initial conditions are the state of the weather or climate at the beginning of the simulation and other such things, and typically come from observations. Finally, there is the uncertainty associated with the values of parameters in the model. These parameters may represent physical constants or effects, such as ocean mixing, or non-physical aspects of modeling and computation. The uncertainty in these input parameters propagates through the non-linear model to give uncertainty in the outputs. The models in 2020 will no doubt be better than today’s models, but they will still be imperfect, and development of uncertainty analysis technology is a critical aspect of understanding model realism and prediction capability. Smith [2002] and Cox and Stephenson [2007] discuss the need for methods to quantify the uncertainties within complicated systems so that limitations or weaknesses of the climate model can be understood. In making climate predictions, we need to have available both the most reliable model or simulation and a methods to quantify the reliability of a simulation. If quantitative uncertainty questions of the internal model dynamics are to be answered with complex simulations such as AOGCMs, then the only known path forward is based on model ensembles that characterize behavior with alternative parameter settings [e.g. Rougier, 2007]. The relevance and feasibility of using "Statistical Analysis of Computer Code Output" (SACCO) methods for examining uncertainty in ocean circulation due to parameter specification will be described and early results using the ocean/ice components of the CCSM climate model in a designed experiment framework will be shown. Cox, P. and D. Stephenson, Climate Change: A Changing Climate for Prediction, 2007, Science 317 (5835), 207, DOI: 10.1126/science.1145956. Rougier, J. C., 2007: Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations, Climatic Change, 81, 247-264. Smith L., 2002, What might we learn from climate forecasts? Proc. Nat’l Academy of Sciences, Vol. 99, suppl. 1, 2487-2492 doi:10.1073/pnas.012580599.

Potential breeding distributions of U.S. birds predicted with both short-term variability and long-term average climate data.

PubMed

Bateman, Brooke L; Pidgeon, Anna M; Radeloff, Volker C; Flather, Curtis H; VanDerWal, Jeremy; Akçakaya, H Resit; Thogmartin, Wayne E; Albright, Thomas P; Vavrus, Stephen J; Heglund, Patricia J

2016-12-01

Climate conditions, such as temperature or precipitation, averaged over several decades strongly affect species distributions, as evidenced by experimental results and a plethora of models demonstrating statistical relations between species occurrences and long-term climate averages. However, long-term averages can conceal climate changes that have occurred in recent decades and may not capture actual species occurrence well because the distributions of species, especially at the edges of their range, are typically dynamic and may respond strongly to short-term climate variability. Our goal here was to test whether bird occurrence models can be predicted by either covariates based on short-term climate variability or on long-term climate averages. We parameterized species distribution models (SDMs) based on either short-term variability or long-term average climate covariates for 320 bird species in the conterminous USA and tested whether any life-history trait-based guilds were particularly sensitive to short-term conditions. Models including short-term climate variability performed well based on their cross-validated area-under-the-curve AUC score (0.85), as did models based on long-term climate averages (0.84). Similarly, both models performed well compared to independent presence/absence data from the North American Breeding Bird Survey (independent AUC of 0.89 and 0.90, respectively). However, models based on short-term variability covariates more accurately classified true absences for most species (73% of true absences classified within the lowest quarter of environmental suitability vs. 68%). In addition, they have the advantage that they can reveal the dynamic relationship between species and their environment because they capture the spatial fluctuations of species potential breeding distributions. With this information, we can identify which species and guilds are sensitive to climate variability, identify sites of high conservation value where climate variability is low, and assess how species' potential distributions may have already shifted due recent climate change. However, long-term climate averages require less data and processing time and may be more readily available for some areas of interest. Where data on short-term climate variability are not available, long-term climate information is a sufficient predictor of species distributions in many cases. However, short-term climate variability data may provide information not captured with long-term climate data for use in SDMs. © 2016 by the Ecological Society of America.

Assessment of Energy Savings Potential from the Use of Demand Control Ventilation Systems in General Office Spaces in California

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

Hong, Tianzhen; Fisk, William J.

2009-07-08

Demand controlled ventilation (DCV) was evaluated for general office spaces in California. A medium size office building meeting the prescriptive requirements of the 2008 California building energy efficiency standards (CEC 2008) was assumed in the building energy simulations performed with the EnergyPlus program to calculate the DCV energy savings potential in five typical California climates. Three design occupancy densities and two minimum ventilation rates were used as model inputs to cover a broader range of design variations. The assumed values of minimum ventilation rates in offices without DCV, based on two different measurement methods, were 81 and 28 cfm per occupant. These rates are based on the co-author's unpublished analyses of data from EPA's survey of 100 U.S. office buildings. These minimum ventilation rates exceed the 15 to 20 cfm per person required in most ventilation standards for offices. The cost effectiveness of applying DCV in general office spaces was estimated via a life cycle cost analyses that considered system costs and energy cost reductions. The results of the energy modeling indicate that the energy savings potential of DCV is largest in the desert area of California (climate zone 14), followed by Mountains (climate zone 16), Central Valley (climate zone 12), North Coast (climate zone 3), and South Coast (climate zone 6). The results of the life cycle cost analysis show DCV is cost effective for office spaces if the typical minimum ventilation rates without DCV is 81 cfm per person, except at the low design occupancy of 10 people per 1000 ft{sup 2} in climate zones 3 and 6. At the low design occupancy of 10 people per 1000 ft{sup 2}, the greatest DCV life cycle cost savings is a net present value (NPV) ofmore » $$0.52/ft{sup 2} in climate zone 14, followed by $$0.32/ft{sup 2} in climate zone 16 and $$0.19/ft{sup 2} in climate zone 12. At the medium design occupancy of 15 people per 1000 ft{sup 2}, the DCV savings are higher with a NPV $$0.93/ft{sup 2} in climate zone 14, followed by $$0.55/ft{sup 2} in climate zone 16, $$0.46/ft{sup 2} in climate zone 12, $$0.30/ft{sup 2} in climate zone 3, $$0.16/ft{sup 2} in climate zone 3. At the high design occupancy of 20 people per 1000 ft{sup 2}, the DCV savings are even higher with a NPV $$1.37/ft{sup 2} in climate zone 14, followed by $$0.86/ft{sup 2} in climate zone 16, $$0.84/ft{sup 2} in climate zone 3, $$0.82/ft{sup 2} in climate zone 12, and $0.65/ft{sup 2} in climate zone 6. DCV was not found to be cost effective if the typical minimum ventilation rate without DCV is 28 cfm per occupant, except at high design occupancy of 20 people per 1000 ft{sup 2} in climate zones 14 and 16. Until the large uncertainties about the base case ventilation rates in offices without DCV are reduced, the case for requiring DCV in general office spaces will be a weak case.« less

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.

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.

Climate change and Ixodes tick-borne diseases of humans

PubMed Central

Ostfeld, Richard S.; Brunner, Jesse L.

2015-01-01

The evidence that climate warming is changing the distribution of Ixodes ticks and the pathogens they transmit is reviewed and evaluated. The primary approaches are either phenomenological, which typically assume that climate alone limits current and future distributions, or mechanistic, asking which tick-demographic parameters are affected by specific abiotic conditions. Both approaches have promise but are severely limited when applied separately. For instance, phenomenological approaches (e.g. climate envelope models) often select abiotic variables arbitrarily and produce results that can be hard to interpret biologically. On the other hand, although laboratory studies demonstrate strict temperature and humidity thresholds for tick survival, these limits rarely apply to field situations. Similarly, no studies address the influence of abiotic conditions on more than a few life stages, transitions or demographic processes, preventing comprehensive assessments. Nevertheless, despite their divergent approaches, both mechanistic and phenomenological models suggest dramatic range expansions of Ixodes ticks and tick-borne disease as the climate warms. The predicted distributions, however, vary strongly with the models' assumptions, which are rarely tested against reasonable alternatives. These inconsistencies, limited data about key tick-demographic and climatic processes and only limited incorporation of non-climatic processes have weakened the application of this rich area of research to public health policy or actions. We urge further investigation of the influence of climate on vertebrate hosts and tick-borne pathogen dynamics. In addition, testing model assumptions and mechanisms in a range of natural contexts and comparing their relative importance as competing models in a rigorous statistical framework will significantly advance our understanding of how climate change will alter the distribution, dynamics and risk of tick-borne disease. PMID:25688022

Just-in-time Time Data Analytics and Visualization of Climate Simulations using the Bellerophon Framework

NASA Astrophysics Data System (ADS)

Anantharaj, V. G.; Venzke, J.; Lingerfelt, E.; Messer, B.

2015-12-01

Climate model simulations are used to understand the evolution and variability of earth's climate. Unfortunately, high-resolution multi-decadal climate simulations can take days to weeks to complete. Typically, the simulation results are not analyzed until the model runs have ended. During the course of the simulation, the output may be processed periodically to ensure that the model is preforming as expected. However, most of the data analytics and visualization are not performed until the simulation is finished. The lengthy time period needed for the completion of the simulation constrains the productivity of climate scientists. Our implementation of near real-time data visualization analytics capabilities allows scientists to monitor the progress of their simulations while the model is running. Our analytics software executes concurrently in a co-scheduling mode, monitoring data production. When new data are generated by the simulation, a co-scheduled data analytics job is submitted to render visualization artifacts of the latest results. These visualization output are automatically transferred to Bellerophon's data server located at ORNL's Compute and Data Environment for Science (CADES) where they are processed and archived into Bellerophon's database. During the course of the experiment, climate scientists can then use Bellerophon's graphical user interface to view animated plots and their associated metadata. The quick turnaround from the start of the simulation until the data are analyzed permits research decisions and projections to be made days or sometimes even weeks sooner than otherwise possible! The supercomputer resources used to run the simulation are unaffected by co-scheduling the data visualization jobs, so the model runs continuously while the data are visualized. Our just-in-time data visualization software looks to increase climate scientists' productivity as climate modeling moves into exascale era of computing.

Role of carbon and climate in forming the Páramo, an Andean evolutionary hotspot

NASA Astrophysics Data System (ADS)

Hill, Daniel

2015-04-01

According to a number of genetic diversification measures the Páramo grasslands of the high equatorial Andes show the greatest rates of speciation on the planet. This is probably driven by contrasting ranges of the ecosystem between glacial and interglacial periods of the Pleistocene. During the warm interglacial periods the treeline is high in the Andes restricting the Páramos to the highest regions of the Andean mountain chain, while in the cool glacial periods the Páramo areas expand and probably coalesce, bringing isolated populations into contact with each other. The origin of the Páramo ecosystem is placed close to the end of the Pliocene and has been related to the finale of regional Andean mountain building. However, this formation date is also coincident with the global cooling at the end of the Pliocene, as Northern Hemisphere glaciation and the bipolar Pleistocene ice ages begin. Furthermore, it is estimated that atmospheric CO2 concentrations dropped from the 400 ppmv typical of the Pliocene to values more typical of the Pleistocene at around this time. Global climate model simulations, coupled with a high resolution biome model, give us the opportunity to test these competing hypotheses for the formation of the Páramo ecosystem. A series of HadCM3 climate model simulations are presented here varying the height of the highest altitude Andes and the global climate from its pre-industrial state to the Pliocene. The climate are topographic changes are varied both independently and together. These climatologies are then used to drive a high-resolution biome model, BIOME4, and simulate the impact on Andean vegetation. These models seem to reproduce the observed changes in high altitude grassland biomes during the Pliocene. The climate and biome modelling presented here show that the climate changes associated with the Plio-Pleistocene boundary are the primary cause of the initial formation of this unique and important ecosystem. Although the reduction of the highest altitude Andean regions reduces the available area for grassland biomes, the effect is significantly smaller than the impact of Plio-Pleistocene climate change. By individually introducing the Pliocene changes (atmospheric carbon dioxide levels, mean temperature, minimum temperatures, precipitation and direct sunlight) to the BIOME4 simulations, it is shown that the primary driver of these changes is atmospheric carbon dioxide levels. The higher Pliocene levels favour the expansion of the forest biomes, increasing the altitude of the treeline and replacing grassland biomes. This suggests that in such areas prediction of future changes and plans to preserve these important ecosystems must consider the impact of both climate change and CO2 fertilization.

Examining the Performance of Statistical Downscaling Methods: Toward Matching Applications to Data Products

NASA Astrophysics Data System (ADS)

Dixon, K. W.; Lanzante, J. R.; Adams-Smith, D.

2017-12-01

Several challenges exist when seeking to use future climate model projections in a climate impacts study. A not uncommon approach is to utilize climate projection data sets derived from more than one future emissions scenario and from multiple global climate models (GCMs). The range of future climate responses represented in the set is sometimes taken to be indicative of levels of uncertainty in the projections. Yet, GCM outputs are deemed to be unsuitable for direct use in many climate impacts applications. GCM grids typically are viewed as being too coarse. Additionally, regional or local-scale biases in a GCM's simulation of the contemporary climate that may not be problematic from a global climate modeling perspective may be unacceptably large for a climate impacts application. Statistical downscaling (SD) of climate projections - a type of post-processing that uses observations to inform the refinement of GCM projections - is often used in an attempt to account for GCM biases and to provide additional spatial detail. "What downscaled climate projection is the best one to use" is a frequently asked question, but one that is not always easy to answer, as it can be dependent on stakeholder needs and expectations. Here we present results from a perfect model experimental design illustrating how SD method performance can vary not only by SD method, but how performance can also vary by location, season, climate variable of interest, amount of projected climate change, SD configuration choices, and whether one is interested in central tendencies or the tails of the distribution. Awareness of these factors can be helpful when seeking to determine the suitability of downscaled climate projections for specific climate impacts applications. It also points to the potential value of considering more than one SD data product in a study, so as to acknowledge uncertainties associated with the strengths and weaknesses of different downscaling methods.

Incorporating variability in simulations of seasonally forced phenology using integral projection models

DOE PAGES

Goodsman, Devin W.; Aukema, Brian H.; McDowell, Nate G.; ...

2017-11-26

Phenology models are becoming increasingly important tools to accurately predict how climate change will impact the life histories of organisms. We propose a class of integral projection phenology models derived from stochastic individual-based models of insect development and demography. Our derivation, which is based on the rate summation concept, produces integral projection models that capture the effect of phenotypic rate variability on insect phenology, but which are typically more computationally frugal than equivalent individual-based phenology models. We demonstrate our approach using a temperature-dependent model of the demography of the mountain pine beetle (Dendroctonus ponderosae Hopkins), an insect that kills maturemore » pine trees. This work illustrates how a wide range of stochastic phenology models can be reformulated as integral projection models. Due to their computational efficiency, these integral projection models are suitable for deployment in large-scale simulations, such as studies of altered pest distributions under climate change.« less

Incorporating variability in simulations of seasonally forced phenology using integral projection models

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

Goodsman, Devin W.; Aukema, Brian H.; McDowell, Nate G.

Phenology models are becoming increasingly important tools to accurately predict how climate change will impact the life histories of organisms. We propose a class of integral projection phenology models derived from stochastic individual-based models of insect development and demography. Our derivation, which is based on the rate summation concept, produces integral projection models that capture the effect of phenotypic rate variability on insect phenology, but which are typically more computationally frugal than equivalent individual-based phenology models. We demonstrate our approach using a temperature-dependent model of the demography of the mountain pine beetle (Dendroctonus ponderosae Hopkins), an insect that kills maturemore » pine trees. This work illustrates how a wide range of stochastic phenology models can be reformulated as integral projection models. Due to their computational efficiency, these integral projection models are suitable for deployment in large-scale simulations, such as studies of altered pest distributions under climate change.« less

Eco-hydrological Modeling in the Framework of Climate Change

NASA Astrophysics Data System (ADS)

Fatichi, Simone; Ivanov, Valeriy Y.; Caporali, Enrica

2010-05-01

A blueprint methodology for studying climate change impacts, as inferred from climate models, on eco-hydrological dynamics at the plot and small catchment scale is presented. Input hydro-meteorological variables for hydrological and eco-hydrological models for present and future climates are reproduced using a stochastic downscaling technique and a weather generator, "AWE-GEN". The generated time series of meteorological variables for the present climate and an ensemble of possible future climates serve as input to a newly developed physically-based eco-hydrological model "Tethys-Chloris". An application of the proposed methodology is realized reproducing the current (1961-2000) and multiple future (2081-2100) climates for the location of Tucson (Arizona). A general reduction of precipitation and a significant increase of air temperature are inferred. The eco-hydrological model is successively applied to detect changes in water recharge and vegetation dynamics for a desert shrub ecosystem, typical of the semi-arid climate of south Arizona. Results for the future climate account for uncertainties in the downscaling and are produced in terms of probability density functions. A comparison of control and future scenarios is discussed in terms of changes in the hydrological balance components, energy fluxes, and indices of vegetation productivity. An appreciable effect of climate change can be observed in metrics of vegetation performance. The negative impact on vegetation due to amplification of water stress in a warmer and dryer climate is offset by a positive effect of carbon dioxide augment. This implies a positive shift in plant capabilities to exploit water. Consequently, the plant water use efficiency and rain use efficiency are expected to increase. Interesting differences in the long-term vegetation productivity are also observed for the ensemble of future climates. The reduction of precipitation and the substantial maintenance of vegetation cover ultimately leads to the depletion of soil moisture and recharge to deeper layers. Such an outcome can affect the long-tem water availability in semi-arid systems and expose plants to more severe and frequent periods of stress.

Development of ALARO-Climate regional climate model for a very high resolution

NASA Astrophysics Data System (ADS)

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

2013-04-01

ALARO-Climate is a new regional climate model (RCM) derived from the ALADIN LAM model family. It is based on the numerical weather prediction model ALARO and developed at the Czech Hydrometeorological Institute. The model is expected to able to work in the so called "grey zone" physics (horizontal resolution of 4 - 7 km) and at the same time retain its ability to be operated in resolutions in between 20 and 50 km, which are typical for contemporary generation of regional climate models. Here we present the main features of the RCM ALARO-Climate and results of the first model simulations on longer time-scales (1961-1990). The model was driven by the ERA-40/Interim re-analyses and run on the large pan-European integration domain ("ENSEMBLES / Euro-Cordex domain") with spatial resolution of 25 km. The simulated model climate was compared with the gridded observation of air temperature (mean, maximum, minimum) and precipitation from the E-OBS version 7 dataset. The validation of the first ERA-40 simulation has revealed significant cold biases in all seasons (between -4 and -2 °C) and overestimation of precipitation on 20% to 60% in the selected Central Europe target area (0° - 30° eastern longitude ; 40° - 60° northern latitude). The consequent adaptations in the model and their effect on the simulated properties of climate variables are illustrated. Acknowledgements: This study was performed within the frame of projects ALARO (project P209/11/2405 sponsored by the Czech Science Foundation) and CzechGlobe Centre (CZ.1.05/1.1.00/02.0073). The partial support was also provided under the projects P209-11-0956 of the Czech Science Foundation and CZ.1.07/2.4.00/31.0056 (Operational Programme of Education for Competitiveness of Ministry of Education, Youth and Sports of the Czech Republic).

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

To transfer or not to transfer? Investigating the combined effects of trainee characteristics, team leader support, and team climate.

PubMed

Smith-Jentsch, K A; Salas, E; Brannick, M T

2001-04-01

Eighty pilots participated in a study of variables influencing the transfer process. Posttraining performance was assessed in a flight simulation under 1 of 2 conditions. Those in the maximum performance condition were made aware of the skill to be assessed and the fact that their teammates were confederates, whereas those in the typical performance condition were not. The results indicated that (a) simulator ratings correlated with a measure of transfer to the cockpit for those in the typical condition only; (b) team leader support, manipulated in a pretask brief, moderated the disparity between maximum and typical performance; (c) team climate mediated the impact of support on performance in the typical condition; (d) those with a stronger predisposition toward the trained skill viewed their climate as more supportive; and (e) perceptions of team climate were better predictors of performance for those with a more external locus of control.

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

Could geoengineering research help answer one of the biggest questions in climate science?: GEOENGINEERING RESEARCH

DOE PAGES

Wood, Robert; Ackerman, Thomas; Rasch, Philip J.; ...

2017-06-22

Anthropogenic aerosol impacts on clouds constitute the largest source of uncertainty in quantifying the radiative forcing of climate, and hinders our ability to determine Earth's climate sensitivity to greenhouse gas increases. Representation of aerosol–cloud interactions in global models is particularly challenging because these interactions occur on typically unresolved scales. Observational studies show influences of aerosol on clouds, but correlations between aerosol and clouds are insufficient to constrain aerosol forcing because of the difficulty in separating aerosol and meteorological impacts. In this commentary, we argue that this current impasse may be overcome with the development of approaches to conduct control experimentsmore » whereby aerosol particle perturbations can be introduced into patches of marine low clouds in a systematic manner. Such cloud perturbation experiments constitute a fresh approach to climate science and would provide unprecedented data to untangle the effects of aerosol particles on cloud microphysics and the resulting reflection of solar radiation by clouds. Here, the control experiments would provide a critical test of high-resolution models that are used to develop an improved representation aerosol–cloud interactions needed to better constrain aerosol forcing in global climate models.« less

Could geoengineering research help answer one of the biggest questions in climate science?: GEOENGINEERING RESEARCH

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

Wood, Robert; Ackerman, Thomas; Rasch, Philip J.

Anthropogenic aerosol impacts on clouds constitute the largest source of uncertainty in quantifying the radiative forcing of climate, and hinders our ability to determine Earth's climate sensitivity to greenhouse gas increases. Representation of aerosol–cloud interactions in global models is particularly challenging because these interactions occur on typically unresolved scales. Observational studies show influences of aerosol on clouds, but correlations between aerosol and clouds are insufficient to constrain aerosol forcing because of the difficulty in separating aerosol and meteorological impacts. In this commentary, we argue that this current impasse may be overcome with the development of approaches to conduct control experimentsmore » whereby aerosol particle perturbations can be introduced into patches of marine low clouds in a systematic manner. Such cloud perturbation experiments constitute a fresh approach to climate science and would provide unprecedented data to untangle the effects of aerosol particles on cloud microphysics and the resulting reflection of solar radiation by clouds. Here, the control experiments would provide a critical test of high-resolution models that are used to develop an improved representation aerosol–cloud interactions needed to better constrain aerosol forcing in global climate models.« less

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.

Does internal climate variability overwhelm climate change signals in streamflow? The upper Po and Rhone basin case studies.

PubMed

Fatichi, S; Rimkus, S; Burlando, P; Bordoy, R

2014-09-15

Projections of climate change effects in streamflow are increasingly required to plan water management strategies. These projections are however largely uncertain due to the spread among climate model realizations, internal climate variability, and difficulties in transferring climate model results at the spatial and temporal scales required by catchment hydrology. A combination of a stochastic downscaling methodology and distributed hydrological modeling was used in the ACQWA project to provide projections of future streamflow (up to year 2050) for the upper Po and Rhone basins, respectively located in northern Italy and south-western Switzerland. Results suggest that internal (stochastic) climate variability is a fundamental source of uncertainty, typically comparable or larger than the projected climate change signal. Therefore, climate change effects in streamflow mean, frequency, and seasonality can be masked by natural climatic fluctuations in large parts of the analyzed regions. An exception to the overwhelming role of stochastic variability is represented by high elevation catchments fed by glaciers where streamflow is expected to be considerably reduced due to glacier retreat, with consequences appreciable in the main downstream rivers in August and September. Simulations also identify regions (west upper Rhone and Toce, Ticino river basins) where a strong precipitation increase in the February to April period projects streamflow beyond the range of natural climate variability during the melting season. This study emphasizes the importance of including internal climate variability in climate change analyses, especially when compared to the limited uncertainty that would be accounted for by few deterministic projections. The presented results could be useful in guiding more specific impact studies, although design or management decisions should be better based on reliability and vulnerability criteria as suggested by recent literature. Copyright © 2013 Elsevier B.V. All rights reserved.

An integrated hydrological modeling approach for detection and attribution of climatic and human impacts on coastal water resources

NASA Astrophysics Data System (ADS)

Feng, Dapeng; Zheng, Yi; Mao, Yixin; Zhang, Aijing; Wu, Bin; Li, Jinguo; Tian, Yong; Wu, Xin

2018-02-01

Water resources in coastal areas can be profoundly influenced by both climate change and human activities. These climatic and human impacts are usually intertwined and difficult to isolate. This study developed an integrated model-based approach for detection and attribution of climatic and human impacts and applied this approach to the Luanhe Plain, a typical coastal area in northern China. An integrated surface water-groundwater model was developed for the study area using GSFLOW (coupled groundwater and surface-water flow). Model calibration and validation were performed for background years between 1975 and 2000. The variation in water resources between the 1980s and 1990s was then quantitatively attributed to climate variability, groundwater pumping and changes in upstream inflow. Climate scenarios for future years (2075-2100) were also developed by downscaling the projections in CMIP5. Potential water resource responses to climate change, as well as their uncertainty, were then investigated through integrated modeling. The study results demonstrated the feasibility and value of the integrated modeling-based analysis for water resource management in areas with complex surface water-groundwater interaction. Specific findings for the Luanhe Plain included the following: (1) During the historical period, upstream inflow had the most significant impact on river outflow to the sea, followed by climate variability, whereas groundwater pumping was the least influential. (2) The increase in groundwater pumping had a dominant influence on the decline in groundwater change, followed by climate variability. (3) Synergetic and counteractive effects among different impacting factors, while identified, were not significant, which implied that the interaction among different factors was not very strong in this case. (4) It is highly probable that future climate change will accelerate groundwater depletion in the study area, implying that strict regulations for groundwater pumping are imperative for adaptation.

Results from the VALUE perfect predictor experiment: process-based evaluation

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Soares, Pedro; Hertig, Elke; Brands, Swen; Huth, Radan; Cardoso, Rita; Kotlarski, Sven; Casado, Maria; Pongracz, Rita; Bartholy, Judit

2016-04-01

Until recently, the evaluation of downscaled climate model simulations has typically been limited to surface climatologies, including long term means, spatial variability and extremes. But these aspects are often, at least partly, tuned in regional climate models to match observed climate. The tuning issue is of course particularly relevant for bias corrected regional climate models. In general, a good performance of a model for these aspects in present climate does therefore not imply a good performance in simulating climate change. It is now widely accepted that, to increase our condidence in climate change simulations, it is necessary to evaluate how climate models simulate relevant underlying processes. In other words, it is important to assess whether downscaling does the right for the right reason. Therefore, VALUE has carried out a broad process-based evaluation study based on its perfect predictor experiment simulations: the downscaling methods are driven by ERA-Interim data over the period 1979-2008, reference observations are given by a network of 85 meteorological stations covering all European climates. More than 30 methods participated in the evaluation. In order to compare statistical and dynamical methods, only variables provided by both types of approaches could be considered. This limited the analysis to conditioning local surface variables on variables from driving processes that are simulated by ERA-Interim. We considered the following types of processes: at the continental scale, we evaluated the performance of downscaling methods for positive and negative North Atlantic Oscillation, Atlantic ridge and blocking situations. At synoptic scales, we considered Lamb weather types for selected European regions such as Scandinavia, the United Kingdom, the Iberian Pensinsula or the Alps. At regional scales we considered phenomena such as the Mistral, the Bora or the Iberian coastal jet. Such process-based evaluation helps to attribute biases in surface variables to underlying processes and ultimately to improve climate models.

Addressing potential local adaptation in species distribution models: implications for conservation under climate change

USGS Publications Warehouse

Hällfors, Maria Helena; Liao, Jishan; Dzurisin, Jason D. K.; Grundel, Ralph; Hyvärinen, Marko; Towle, Kevin; Wu, Grace C.; Hellmann, Jessica J.

2016-01-01

Species distribution models (SDMs) have been criticized for involving assumptions that ignore or categorize many ecologically relevant factors such as dispersal ability and biotic interactions. Another potential source of model error is the assumption that species are ecologically uniform in their climatic tolerances across their range. Typically, SDMs to treat a species as a single entity, although populations of many species differ due to local adaptation or other genetic differentiation. Not taking local adaptation into account, may lead to incorrect range prediction and therefore misplaced conservation efforts. A constraint is that we often do not know the degree to which populations are locally adapted, however. Lacking experimental evidence, we still can evaluate niche differentiation within a species' range to promote better conservation decisions. We explore possible conservation implications of making type I or type II errors in this context. For each of two species, we construct three separate MaxEnt models, one considering the species as a single population and two of disjunct populations. PCA analyses and response curves indicate different climate characteristics in the current environments of the populations. Model projections into future climates indicate minimal overlap between areas predicted to be climatically suitable by the whole species versus population-based models. We present a workflow for addressing uncertainty surrounding local adaptation in SDM application and illustrate the value of conducting population-based models to compare with whole-species models. These comparisons might result in more cautious management actions when alternative range outcomes are considered.

Cloud-System Resolving Models: Status and Prospects

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Moncreiff, Mitch

2008-01-01

Cloud-system resolving models (CRM), which are based on the nonhydrostatic equations of motion and typically have a grid-spacing of about a kilometer, originated as cloud-process models in the 1970s. This paper reviews the status and prospects of CRMs across a wide range of issues, such as microphysics and precipitation; interaction between clouds and radiation; and the effects of boundary-layer and surface-processes on cloud systems. Since CRMs resolve organized convection, tropical waves and the large-scale circulation, there is the prospect for several advances in both basic knowledge of scale-interaction requisite to parameterizing mesoscale processes in climate models. In superparameterization, CRMs represent convection, explicitly replacing many of the assumptions necessary in contemporary parameterization. Global CRMs have been run on an experimental basis, giving prospect to a new generation of climate weather prediction in a decade, and climate models due course. CRMs play a major role in the retrieval of surface-rain and latent heating from satellite measurements. Finally, enormous wide dynamic ranges of CRM simulations present new challenges for model validation against observations.

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.

An assessment of irrigation needs and crop yield for the United States under potential climate changes

USGS Publications Warehouse

Brumbelow, Kelly; Georgakakos, Aris P.

2000-01-01

Past assessments of climate change on U.S. agriculture have mostly focused on changes in crop yield. Few studies have included the entire conterminous U.S., and few studies have assessed changing irrigation requirements. None have included the effects of changing soil moisture characteristics as determined by changing climatic forcing. This study assesses changes in irrigation requirements and crop yields for five crops in the areas of the U.S. where they have traditionally been grown. Physiologically-based crop models are used to incorporate inputs of climate, soils, agricultural management, and drought stress tolerance. Soil moisture values from a macroscale hydrologic model run under a future climate scenario are used to initialize soil moisture content at the beginning of each growing season. Historical crop yield data is used to calibrate model parameters and determine locally acceptable drought stress as a management parameter. Changes in irrigation demand and crop yield are assessed for both means and extremes by comparing results for atmospheric forcing close to the present climate with those for a future climate scenario. Assessments using the Canadian Center for Climate Modeling and Analysis General Circulation Model (CGCM1) indicate greater irrigation demands in the southern U.S. and decreased irrigation demands in the northern and western U.S. Crop yields typically increase except for winter wheat in the southern U.S. and corn. Variability in both irrigation demands and crop yields increases in most cases. Assessment results for the CGCM1 climate scenario are compared to those for the Hadley Centre for Climate Prediction and Research GCM (HadCM2) scenario for southwestern Georgia. The comparison shows significant differences in irrigation and yield trends, both in magnitude and direction. The differences reflect the high forecast uncertainty of current GCMs. Nonetheless, both GCMs indicate higher variability in future climatic forcing and, consequently, in the response of agricultural systems.

Effect of Climate Change on Mediterranean Winter Ranges of Two Migratory Passerines.

PubMed

Tellería, José L; Fernández-López, Javier; Fandos, Guillermo

2016-01-01

We studied the effect of climate change on the distribution of two insectivorous passerines (the meadow pipit Anthus pratensis and the chiffchaff Phylloscopus collybita) in wintering grounds of the Western Mediterranean basin. In this region, precipitation and temperature can affect the distribution of these birds through direct (thermoregulation costs) or indirect effects (primary productivity). Thus, it can be postulated that projected climate changes in the region will affect the extent and suitability of their wintering grounds. We studied pipit and chiffchaff abundance in several hundred localities along a belt crossing Spain and Morocco and assessed the effects of climate and other geographical and habitat predictors on bird distribution. Multivariate analyses reported a positive effect of temperature on the present distribution of the two species, with an additional effect of precipitation on the meadow pipit. These climate variables were used with Maxent to model the occurrence probabilities of species using ring recoveries as presence data. Abundance and occupancy of the two species in the study localities adjusted to the distribution models, with more birds in sectors of high climate suitability. After validation, these models were used to forecast the distribution of climate suitability according to climate projections for 2050-2070 (temperature increase and precipitation reduction). Results show an expansion of climatically suitable sectors into the highlands by the effect of warming on the two species, and a retreat of the meadow pipit from southern sectors related to rain reduction. The predicted patterns show a mean increase in climate suitability for the two species due to the warming of the large highland expanses typical of the western Mediterranean.

Bayesian hierarchical models for regional climate reconstructions of the last glacial maximum

NASA Astrophysics Data System (ADS)

Weitzel, Nils; Hense, Andreas; Ohlwein, Christian

2017-04-01

Spatio-temporal reconstructions of past climate are important for the understanding of the long term behavior of the climate system and the sensitivity to forcing changes. Unfortunately, they are subject to large uncertainties, have to deal with a complex proxy-climate structure, and a physically reasonable interpolation between the sparse proxy observations is difficult. Bayesian Hierarchical Models (BHMs) are a class of statistical models that is well suited for spatio-temporal reconstructions of past climate because they permit the inclusion of multiple sources of information (e.g. records from different proxy types, uncertain age information, output from climate simulations) and quantify uncertainties in a statistically rigorous way. BHMs in paleoclimatology typically consist of three stages which are modeled individually and are combined using Bayesian inference techniques. The data stage models the proxy-climate relation (often named transfer function), the process stage models the spatio-temporal distribution of the climate variables of interest, and the prior stage consists of prior distributions of the model parameters. For our BHMs, we translate well-known proxy-climate transfer functions for pollen to a Bayesian framework. In addition, we can include Gaussian distributed local climate information from preprocessed proxy records. The process stage combines physically reasonable spatial structures from prior distributions with proxy records which leads to a multivariate posterior probability distribution for the reconstructed climate variables. The prior distributions that constrain the possible spatial structure of the climate variables are calculated from climate simulation output. We present results from pseudoproxy tests as well as new regional reconstructions of temperatures for the last glacial maximum (LGM, ˜ 21,000 years BP). These reconstructions combine proxy data syntheses with information from climate simulations for the LGM that were performed in the PMIP3 project. The proxy data syntheses consist either of raw pollen data or of normally distributed climate data from preprocessed proxy records. Future extensions of our method contain the inclusion of other proxy types (transfer functions), the implementation of other spatial interpolation techniques, the use of age uncertainties, and the extension to spatio-temporal reconstructions of the last deglaciation. Our work is part of the PalMod project funded by the German Federal Ministry of Education and Science (BMBF).

Comparison of modeled and typical meteorological year. Diffuse, direct, and tilted solar radiation values with measured data in a cloudy climate: Seattle-Tacoma data

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

Straub, D.; Baylon, D.; Smith, O.

1980-01-01

Four commonly used solar radiation models that determine the diffuse and direct components of the solar radiation on a horizontal surface are compared against measured data to determine their predictive and modeling applicability. The John Hay model is determined to underpredict the diffuse and the Pereira/Rabl model to overpredict the diffuse radiation. The daily Liu and Jordan correlation and the hourly Boes correlation are shown to be better predictors.

COSEE-AK Ocean Science Fairs: A Science Fair Model That Grounds Student Projects in Both Western Science and Traditional Native Knowledge

ERIC Educational Resources Information Center

Dublin, Robin; Sigman, Marilyn; Anderson, Andrea; Barnhardt, Ray; Topkok, Sean Asiqluq

2014-01-01

We have developed the traditional science fair format into an ocean science fair model that promoted the integration of Western science and Alaska Native traditional knowledge in student projects focused on the ocean, aquatic environments, and climate change. The typical science fair judging criteria for the validity and presentation of the…

Data-based discharge extrapolation: estimating annual discharge for a partially gauged large river basin from its small sub-basins

NASA Astrophysics Data System (ADS)

Gong, L.

2013-12-01

Large-scale hydrological models and land surface models are by far the only tools for accessing future water resources in climate change impact studies. Those models estimate discharge with large uncertainties, due to the complex interaction between climate and hydrology, the limited quality and availability of data, as well as model uncertainties. A new purely data-based scale-extrapolation method is proposed, to estimate water resources for a large basin solely from selected small sub-basins, which are typically two-orders-of-magnitude smaller than the large basin. Those small sub-basins contain sufficient information, not only on climate and land surface, but also on hydrological characteristics for the large basin In the Baltic Sea drainage basin, best discharge estimation for the gauged area was achieved with sub-basins that cover 2-4% of the gauged area. There exist multiple sets of sub-basins that resemble the climate and hydrology of the basin equally well. Those multiple sets estimate annual discharge for gauged area consistently well with 5% average error. The scale-extrapolation method is completely data-based; therefore it does not force any modelling error into the prediction. The multiple predictions are expected to bracket the inherent variations and uncertainties of the climate and hydrology of the basin. The method can be applied in both un-gauged basins and un-gauged periods with uncertainty estimation.

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.

Testing the Joint UK Land Environment Simulator (JULES) for flood forecasting

NASA Astrophysics Data System (ADS)

Batelis, Stamatios-Christos; Rosolem, Rafael; Han, Dawei; Rahman, Mostaquimur

2017-04-01

Land Surface Models (LSM) are based on physics principles and simulate the exchanges of energy, water and biogeochemical cycles between the land surface and lower atmosphere. Such models are typically applied for climate studies or effects of land use changes but as the resolution of LSMs and supporting observations are continuously increasing, its representation of hydrological processes need to be addressed adequately. For example, changes in climate and land use can alter the hydrology of a region, for instance, by altering its flooding regime. LSMs can be a powerful tool because of their ability to spatially represent a region with much finer resolution. However, despite such advantages, its performance has not been extensively assessed for flood forecasting simply because its representation of typical hydrological processes, such as overland flow and river routing, are still either ignored or roughly represented. In this study, we initially test the Joint UK Land Environment Simulator (JULES) as a flood forecast tool focusing on its river routing scheme. In particular, JULES river routing parameterization is based on the Rapid Flow Model (RFM) which relies on six prescribed parameters (two surface and two subsurface wave celerities, and two return flow fractions). Although this routing scheme is simple, the prescription of its six default parameters is still too generalized. Our aim is to understand the importance of each RFM parameter in a series of JULES simulations at a number of catchments in the UK for the 2006-2015 period. This is carried out, for instance, by making a number of assumptions of parameter behaviour (e.g., spatially uniform versus varying and/or temporally constant or time-varying parameters within each catchment). Hourly rainfall radar in combination with the CHESS (Climate, Hydrological and Ecological research Support System) meteorological daily data both at 1 km2 resolution are used. The evaluation of the model is based on hourly runoff data provided by the National River Flood Archive using a number of model performance metrics. We use a calibrated conceptually-based lumped model, more typically applied in flood studies, as a benchmark for our analysis.

Limits to global and Australian temperature change this century based on expert judgment of climate sensitivity

NASA Astrophysics Data System (ADS)

Grose, Michael R.; Colman, Robert; Bhend, Jonas; Moise, Aurel F.

2017-05-01

The projected warming of surface air temperature at the global and regional scale by the end of the century is directly related to emissions and Earth's climate sensitivity. Projections are typically produced using an ensemble of climate models such as CMIP5, however the range of climate sensitivity in models doesn't cover the entire range considered plausible by expert judgment. Of particular interest from a risk-management perspective is the lower impact outcome associated with low climate sensitivity and the low-probability, high-impact outcomes associated with the top of the range. Here we scale climate model output to the limits of expert judgment of climate sensitivity to explore these limits. This scaling indicates an expanded range of projected change for each emissions pathway, including a much higher upper bound for both the globe and Australia. We find the possibility of exceeding a warming of 2 °C since pre-industrial is projected under high emissions for every model even scaled to the lowest estimate of sensitivity, and is possible under low emissions under most estimates of sensitivity. Although these are not quantitative projections, the results may be useful to inform thinking about the limits to change until the sensitivity can be more reliably constrained, or this expanded range of possibilities can be explored in a more formal way. When viewing climate projections, accounting for these low-probability but high-impact outcomes in a risk management approach can complement the focus on the likely range of projections. They can also highlight the scale of the potential reduction in range of projections, should tight constraints on climate sensitivity be established by future research.

Determining the importance of model calibration for forecasting absolute/relative changes in streamflow from LULC and climate changes

USGS Publications Warehouse

Niraula, Rewati; Meixner, Thomas; Norman, Laura M.

2015-01-01

Land use/land cover (LULC) and climate changes are important drivers of change in streamflow. Assessing the impact of LULC and climate changes on streamflow is typically done with a calibrated and validated watershed model. However, there is a debate on the degree of calibration required. The objective of this study was to quantify the variation in estimated relative and absolute changes in streamflow associated with LULC and climate changes with different calibration approaches. The Soil and Water Assessment Tool (SWAT) was applied in an uncalibrated (UC), single outlet calibrated (OC), and spatially-calibrated (SC) mode to compare the relative and absolute changes in streamflow at 14 gaging stations within the Santa Cruz River Watershed in southern Arizona, USA. For this purpose, the effect of 3 LULC, 3 precipitation (P), and 3 temperature (T) scenarios were tested individually. For the validation period, Percent Bias (PBIAS) values were >100% with the UC model for all gages, the values were between 0% and 100% with the OC model and within 20% with the SC model. Changes in streamflow predicted with the UC and OC models were compared with those of the SC model. This approach implicitly assumes that the SC model is “ideal”. Results indicated that the magnitude of both absolute and relative changes in streamflow due to LULC predicted with the UC and OC results were different than those of the SC model. The magnitude of absolute changes predicted with the UC and SC models due to climate change (both P and T) were also significantly different, but were not different for OC and SC models. Results clearly indicated that relative changes due to climate change predicted with the UC and OC were not significantly different than that predicted with the SC models. This result suggests that it is important to calibrate the model spatially to analyze the effect of LULC change but not as important for analyzing the relative change in streamflow due to climate change. This study also indicated that model calibration in not necessary to determine the direction of change in streamflow due to LULC and climate change.

Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part II: climate variability

NASA Astrophysics Data System (ADS)

Rackow, T.; Goessling, H. F.; Jung, T.; Sidorenko, D.; Semmler, T.; Barbi, D.; Handorf, D.

2018-04-01

This study forms part II of two papers describing ECHAM6-FESOM, a newly established global climate model with a unique multi-resolution sea ice-ocean component. While part I deals with the model description and the mean climate state, here we examine the internal climate variability of the model under constant present-day (1990) conditions. We (1) assess the internal variations in the model in terms of objective variability performance indices, (2) analyze variations in global mean surface temperature and put them in context to variations in the observed record, with particular emphasis on the recent warming slowdown, (3) analyze and validate the most common atmospheric and oceanic variability patterns, (4) diagnose the potential predictability of various climate indices, and (5) put the multi-resolution approach to the test by comparing two setups that differ only in oceanic resolution in the equatorial belt, where one ocean mesh keeps the coarse 1° resolution applied in the adjacent open-ocean regions and the other mesh is gradually refined to 0.25°. Objective variability performance indices show that, in the considered setups, ECHAM6-FESOM performs overall favourably compared to five well-established climate models. Internal variations of the global mean surface temperature in the model are consistent with observed fluctuations and suggest that the recent warming slowdown can be explained as a once-in-one-hundred-years event caused by internal climate variability; periods of strong cooling in the model (`hiatus' analogs) are mainly associated with ENSO-related variability and to a lesser degree also to PDO shifts, with the AMO playing a minor role. Common atmospheric and oceanic variability patterns are simulated largely consistent with their real counterparts. Typical deficits also found in other models at similar resolutions remain, in particular too weak non-seasonal variability of SSTs over large parts of the ocean and episodic periods of almost absent deep-water formation in the Labrador Sea, resulting in overestimated North Atlantic SST variability. Concerning the influence of locally (isotropically) increased resolution, the ENSO pattern and index statistics improve significantly with higher resolution around the equator, illustrating the potential of the novel unstructured-mesh method for global climate modeling.

On the appropriate definition of soil profile configuration and initial conditions for land surface-hydrology models in cold regions

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

A Self-Organizing Map-Based Approach to Generating Reduced-Size, Statistically Similar Climate Datasets

NASA Astrophysics Data System (ADS)

Cabell, R.; Delle Monache, L.; Alessandrini, S.; Rodriguez, L.

2015-12-01

Climate-based studies require large amounts of data in order to produce accurate and reliable results. Many of these studies have used 30-plus year data sets in order to produce stable and high-quality results, and as a result, many such data sets are available, generally in the form of global reanalyses. While the analysis of these data lead to high-fidelity results, its processing can be very computationally expensive. This computational burden prevents the utilization of these data sets for certain applications, e.g., when rapid response is needed in crisis management and disaster planning scenarios resulting from release of toxic material in the atmosphere. We have developed a methodology to reduce large climate datasets to more manageable sizes while retaining statistically similar results when used to produce ensembles of possible outcomes. We do this by employing a Self-Organizing Map (SOM) algorithm to analyze general patterns of meteorological fields over a regional domain of interest to produce a small set of "typical days" with which to generate the model ensemble. The SOM algorithm takes as input a set of vectors and generates a 2D map of representative vectors deemed most similar to the input set and to each other. Input predictors are selected that are correlated with the model output, which in our case is an Atmospheric Transport and Dispersion (T&D) model that is highly dependent on surface winds and boundary layer depth. To choose a subset of "typical days," each input day is assigned to its closest SOM map node vector and then ranked by distance. Each node vector is treated as a distribution and days are sampled from them by percentile. Using a 30-node SOM, with sampling every 20th percentile, we have been able to reduce 30 years of the Climate Forecast System Reanalysis (CFSR) data for the month of October to 150 "typical days." To estimate the skill of this approach, the "Measure of Effectiveness" (MOE) metric is used to compare area and overlap of statistical exceedance between the reduced data set and the full 30-year CFSR dataset. Using the MOE, we find that our SOM-derived climate subset produces statistics that fall within 85-90% overlap with the full set while using only 15% of the total data length, and consequently, 15% of the computational time required to run the T&D model for the full period.

Objective calibration of regional climate models

NASA Astrophysics Data System (ADS)

Bellprat, O.; Kotlarski, S.; Lüthi, D.; SchäR, C.

2012-12-01

Climate models are subject to high parametric uncertainty induced by poorly confined model parameters of parameterized physical processes. Uncertain model parameters are typically calibrated in order to increase the agreement of the model with available observations. The common practice is to adjust uncertain model parameters manually, often referred to as expert tuning, which lacks objectivity and transparency in the use of observations. These shortcomings often haze model inter-comparisons and hinder the implementation of new model parameterizations. Methods which would allow to systematically calibrate model parameters are unfortunately often not applicable to state-of-the-art climate models, due to computational constraints facing the high dimensionality and non-linearity of the problem. Here we present an approach to objectively calibrate a regional climate model, using reanalysis driven simulations and building upon a quadratic metamodel presented by Neelin et al. (2010) that serves as a computationally cheap surrogate of the model. Five model parameters originating from different parameterizations are selected for the optimization according to their influence on the model performance. The metamodel accurately estimates spatial averages of 2 m temperature, precipitation and total cloud cover, with an uncertainty of similar magnitude as the internal variability of the regional climate model. The non-linearities of the parameter perturbations are well captured, such that only a limited number of 20-50 simulations are needed to estimate optimal parameter settings. Parameter interactions are small, which allows to further reduce the number of simulations. In comparison to an ensemble of the same model which has undergone expert tuning, the calibration yields similar optimal model configurations, but leading to an additional reduction of the model error. The performance range captured is much wider than sampled with the expert-tuned ensemble and the presented methodology is effective and objective. It is argued that objective calibration is an attractive tool and could become standard procedure after introducing new model implementations, or after a spatial transfer of a regional climate model. Objective calibration of parameterizations with regional models could also serve as a strategy toward improving parameterization packages of global climate models.

On the role of ozone feedback in the ENSO amplitude response under global warming

NASA Astrophysics Data System (ADS)

Nowack, P. J.; Braesicke, P.; Abraham, N. L.; Pyle, J. A.

2017-12-01

The El Niño-Southern Oscillation (ENSO) in the tropical Pacific is of key importance to global climate and weather. However, climate models still disagree on the ENSO's response under climate change. Here we show that typical model representations of ozone can have a first-order impact on ENSO amplitude projections in climate sensitivity simulations (i.e. standard abrupt 4xCO2). We mainly explain this effect by the lapse rate adjustment of the tropical troposphere to ozone changes in the upper troposphere and lower stratosphere (UTLS) under 4xCO2. The ozone-induced lapse rate changes modify the Walker circulation response to the CO2 forcing and consequently tropical Pacific surface temperature gradients. Therefore, not including ozone feedbacks increases the number of extreme ENSO events in our model. In addition, we demonstrate that even if ozone changes in the tropical UTLS are included in the simulations, the neglect of the ozone response in the middle-upper stratosphere still leads to significantly larger ENSO amplitudes (compared to simulations run with a fully interactive atmospheric chemistry scheme). Climate modeling studies of the ENSO often neglect changes in ozone. Our results imply that this could affect the inter-model spread found in ENSO projections and, more generally, surface climate change simulations. We discuss the additional complexity in quantifying such ozone-related effects that arises from the apparent model dependency of chemistry-climate feedbacks and, possibly, their range of surface climate impacts. In conclusion, we highlight the need to understand better the coupling between ozone, the tropospheric circulation, and climate variability. Reference: Nowack PJ, Braesicke P, Abraham NL, and Pyle JA (2017), On the role of ozone feedback in the ENSO amplitude response under global warming, Geophys. Res. Lett. 44, 3858-3866, doi:10.1002/2016GL072418.

Assessing the Assessment Methods: Climate Change and Hydrologic Impacts

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Accounting for multiple sources of uncertainty in impact assessments: The example of the BRACE study

NASA Astrophysics Data System (ADS)

O'Neill, B. C.

2015-12-01

Assessing climate change impacts often requires the use of multiple scenarios, types of models, and data sources, leading to a large number of potential sources of uncertainty. For example, a single study might require a choice of a forcing scenario, climate model, bias correction and/or downscaling method, societal development scenario, model (typically several) for quantifying elements of societal development such as economic and population growth, biophysical model (such as for crop yields or hydrology), and societal impact model (e.g. economic or health model). Some sources of uncertainty are reduced or eliminated by the framing of the question. For example, it may be useful to ask what an impact outcome would be conditional on a given societal development pathway, forcing scenario, or policy. However many sources of uncertainty remain, and it is rare for all or even most of these sources to be accounted for. I use the example of a recent integrated project on the Benefits of Reduced Anthropogenic Climate changE (BRACE) to explore useful approaches to uncertainty across multiple components of an impact assessment. BRACE comprises 23 papers that assess the differences in impacts between two alternative climate futures: those associated with Representative Concentration Pathways (RCPs) 4.5 and 8.5. It quantifies difference in impacts in terms of extreme events, health, agriculture, tropical cyclones, and sea level rise. Methodologically, it includes climate modeling, statistical analysis, integrated assessment modeling, and sector-specific impact modeling. It employs alternative scenarios of both radiative forcing and societal development, but generally uses a single climate model (CESM), partially accounting for climate uncertainty by drawing heavily on large initial condition ensembles. Strengths and weaknesses of the approach to uncertainty in BRACE are assessed. Options under consideration for improving the approach include the use of perturbed physics ensembles of CESM, employing results from multiple climate models, and combining the results from single impact models with statistical representations of uncertainty across multiple models. A key consideration is the relationship between the question being addressed and the uncertainty approach.

Climate and air pollution impacts on habitat suitability of Austrian forest ecosystems

PubMed Central

Djukic, Ika; Kitzler, Barbara; Kobler, Johannes; Mol-Dijkstra, Janet P.; Posch, Max; Reinds, Gert Jan; Schlutow, Angela; Starlinger, Franz; Wamelink, Wieger G. W.

2017-01-01

Climate change and excess deposition of airborne nitrogen (N) are among the main stressors to floristic biodiversity. One particular concern is the deterioration of valuable habitats such as those protected under the European Habitat Directive. In future, climate-driven shifts (and losses) in the species potential distribution, but also N driven nutrient enrichment may threaten these habitats. We applied a dynamic geochemical soil model (VSD+) together with a novel niche-based plant response model (PROPS) to 5 forest habitat types (18 forest sites) protected under the EU Directive in Austria. We assessed how future climate change and N deposition might affect habitat suitability, defined as the capacity of a site to host its typical plant species. Our evaluation indicates that climate change will be the main driver of a decrease in habitat suitability in the future in Austria. The expected climate change will increase the occurrence of thermophilic plant species while decreasing cold-tolerant species. In addition to these direct impacts, climate change scenarios caused an increase of the occurrence probability of oligotrophic species due to a higher N immobilisation in woody biomass leading to soil N depletion. As a consequence, climate change did offset eutrophication from N deposition, even when no further reduction in N emissions was assumed. Our results show that climate change may have positive side-effects in forest habitats when multiple drivers of change are considered. PMID:28898262

Climate and air pollution impacts on habitat suitability of Austrian forest ecosystems.

PubMed

Dirnböck, Thomas; Djukic, Ika; Kitzler, Barbara; Kobler, Johannes; Mol-Dijkstra, Janet P; Posch, Max; Reinds, Gert Jan; Schlutow, Angela; Starlinger, Franz; Wamelink, Wieger G W

2017-01-01

Climate change and excess deposition of airborne nitrogen (N) are among the main stressors to floristic biodiversity. One particular concern is the deterioration of valuable habitats such as those protected under the European Habitat Directive. In future, climate-driven shifts (and losses) in the species potential distribution, but also N driven nutrient enrichment may threaten these habitats. We applied a dynamic geochemical soil model (VSD+) together with a novel niche-based plant response model (PROPS) to 5 forest habitat types (18 forest sites) protected under the EU Directive in Austria. We assessed how future climate change and N deposition might affect habitat suitability, defined as the capacity of a site to host its typical plant species. Our evaluation indicates that climate change will be the main driver of a decrease in habitat suitability in the future in Austria. The expected climate change will increase the occurrence of thermophilic plant species while decreasing cold-tolerant species. In addition to these direct impacts, climate change scenarios caused an increase of the occurrence probability of oligotrophic species due to a higher N immobilisation in woody biomass leading to soil N depletion. As a consequence, climate change did offset eutrophication from N deposition, even when no further reduction in N emissions was assumed. Our results show that climate change may have positive side-effects in forest habitats when multiple drivers of change are considered.

Do we know how to reconcile preservation of landscapes with adaptation of agriculture to climate change? A case-study in a hilly area in Southern Italy

NASA Astrophysics Data System (ADS)

Menenti, Massimo; Alfieri, Silvia; Basile, Angelo; Bonfante, Antonello; Monaco, Eugenia; Riccardi, Maria; De Lorenzi, Francesca

2013-04-01

Limited impacts of climate change on agricultural yields are unlikely to induce any significant changes in current landscapes. Larger impacts, unacceptable on economic or social ground, are likely to trigger interventions towards adaptation of agricultural production systems by reducing or removing vulnerabilities to climate variability and change. Such interventions may require a transition to a different production system, i.e. complete substitution of current crops, or displacement of current crops at their current location towards other locations, e.g. at higher elevations within the landscape. We have assessed the impacts of climate change and evaluated options for adaptation of a valley in Southern Italy, dominated by vine and olive orchards with a significant presence of wheat. We have first estimated the climatic requirements of several varieties for each dominant species. Next, to identify options for adaptation we have evaluated the compatibility of such requirements with indicators of a reference (current) climate and of future climate. This climate - compatibility assessment was done for each soil unit within the valley, leading to maps of locations where each crop is expected to be compatible with climate. This leads to identify both potential crop substitutions within the entire valley and crop displacements from one location to another within the valley. Two climate scenarios were considered: reference (1961-90) and future (2021-2050) climate, the former from climatic statistics, and the latter from statistical downscaling of general circulation models (AOGCM). Climatic data consists of daily time series of maximum and minimum temperature, and daily rainfall on a grid with a spatial resolution of 35 km. We evaluated the adaptive capacity of the "Valle Telesina" (Campania Region, Southern Italy). A mechanistic model of water flow in the soil-plant-atmosphere system (SWAP) was used to describe the hydrological conditions in response to climate for each soil unit. Crop-specific input data and model parameters were estimated on the basis of local experiments and of scientific literature and assumed to be generically representative of the species. Time series of MODIS TIR data were used to downscale gridded climate data on air temperature for both the reference and the future climate. The results indicate that no complete crop substitution will be required within this time frame, i.e. the Valle Telesina will preserve its typical landscape features of a vine - olive orchards dominated production system, typical of many regions in Mediterranean Europe. On the other hand very significant crop displacements will be necessary to grow each variety under optimal hydrothermal conditions, from the point of view of both quantity and quality of yield. The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008)

Dynamically downscaling predictions for deciduous tree leaf emergence in California under current and future climate.

PubMed

Medvigy, David; Kim, Seung Hee; Kim, Jinwon; Kafatos, Menas C

2016-07-01

Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical downscaling of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at ~200-km resolution for 1981-2000 and 2031-2050 conditions. GCM simulations are then dynamically downscaled to 32- and 8-km resolution, and leaf emergence is again simulated. For 1981-2000, the regional average leaf emergence date is 30.8 days earlier in 32-km simulations than in ~200-km simulations. Differences between the 32 and 8 km simulations are small and mostly local. The impact of downscaling from 200 to 8 km is ~15 % smaller in 2031-2050 than in 1981-2000, indicating that the impacts of downscaling are unlikely to be stationary.

LES ARM Symbiotic Simulation and Observation (LASSO) Implementation Strategy

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

Gustafson Jr., WI; Vogelmann, AM

2015-09-01

This document illustrates the design of the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) workflow to provide a routine, high-resolution modeling capability to augment the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s high-density observations. LASSO will create a powerful new capability for furthering ARM’s mission to advance understanding of cloud, radiation, aerosol, and land-surface processes. The combined observational and modeling elements will enable a new level of scientific inquiry by connecting processes and context to observations and providing needed statistics for details that cannot be measured. The result will be improved process understandingmore » that facilitates concomitant improvements in climate model parameterizations. The initial LASSO implementation will be for ARM’s Southern Great Plains site in Oklahoma and will focus on shallow convection, which is poorly simulated by climate models due in part to clouds’ typically small spatial scale compared to model grid spacing, and because the convection involves complicated interactions of microphysical and boundary layer processes.« less

Hydrological Impacts of Land Use Change and Climate Variability in the Headwater Region of the Heihe River Basin, Northwest China

PubMed Central

Zhang, Ling; Nan, Zhuotong; Xu, Yi; Li, Shuo

2016-01-01

Land use change and climate variability are two key factors impacting watershed hydrology, which is strongly related to the availability of water resources and the sustainability of local ecosystems. This study assessed separate and combined hydrological impacts of land use change and climate variability in the headwater region of a typical arid inland river basin, known as the Heihe River Basin, northwest China, in the recent past (1995–2014) and near future (2015–2024), by combining two land use models (i.e., Markov chain model and Dyna-CLUE) with a hydrological model (i.e., SWAT). The potential impacts in the near future were explored using projected land use patterns and hypothetical climate scenarios established on the basis of analyzing long-term climatic observations. Land use changes in the recent past are dominated by the expansion of grassland and a decrease in farmland; meanwhile the climate develops with a wetting and warming trend. Land use changes in this period induce slight reductions in surface runoff, groundwater discharge and streamflow whereas climate changes produce pronounced increases in them. The joint hydrological impacts are similar to those solely induced by climate changes. Spatially, both the effects of land use change and climate variability vary with the sub-basin. The influences of land use changes are more identifiable in some sub-basins, compared with the basin-wide impacts. In the near future, climate changes tend to affect the hydrological regimes much more prominently than land use changes, leading to significant increases in all hydrological components. Nevertheless, the role of land use change should not be overlooked, especially if the climate becomes drier in the future, as in this case it may magnify the hydrological responses. PMID:27348224

Sensitivity of Alpine Snow and Streamflow Regimes to Climate Changes

NASA Astrophysics Data System (ADS)

Rasouli, K.; Pomeroy, J. W.; Marks, D. G.; Bernhardt, M.

2014-12-01

Understanding the sensitivity of hydrological processes to climate change in alpine areas with snow dominated regimes is of paramount importance as alpine basins show both high runoff efficiency associated with the melt of the seasonal snowpack and great sensitivity of snow processes to temperature change. In this study, meteorological data measured in a selection of alpine headwaters basins including Reynolds Mountain East, Idaho, USA, Wolf Creek, Yukon in Canada, and Zugspitze Mountain, Germany with climates ranging from arctic to continental temperate were used to study the snow and streamflow sensitivity to climate change. All research sites have detailed multi-decadal meteorological and snow measurements. The Cold Regions Hydrological Modelling platform (CRHM) was used to create a model representing a typical alpine headwater basin discretized into hydrological response units with physically based representations of snow redistribution by wind, complex terrain snowmelt energetics and runoff processes in alpine tundra. The sensitivity of snow hydrology to climate change was investigated by changing air temperature and precipitation using weather generating methods based on the change factors obtained from different climate model projections for future and current periods. The basin mean and spatial variability of peak snow water equivalent, sublimation loss, duration of snow season, snowmelt rates, streamflow peak, and basin discharge were assessed under varying climate scenarios and the most sensitive hydrological mechanisms to the changes in the different alpine climates were detected. The results show that snow hydrology in colder alpine climates is more resilient to warming than that in warmer climates, but that compensatory factors to warming such as reduced blowing snow sublimation loss and reduced melt rate should also be assessed when considering climate change impacts on alpine hydrology.

Hydrological Impacts of Land Use Change and Climate Variability in the Headwater Region of the Heihe River Basin, Northwest China.

PubMed

Zhang, Ling; Nan, Zhuotong; Xu, Yi; Li, Shuo

2016-01-01

Land use change and climate variability are two key factors impacting watershed hydrology, which is strongly related to the availability of water resources and the sustainability of local ecosystems. This study assessed separate and combined hydrological impacts of land use change and climate variability in the headwater region of a typical arid inland river basin, known as the Heihe River Basin, northwest China, in the recent past (1995-2014) and near future (2015-2024), by combining two land use models (i.e., Markov chain model and Dyna-CLUE) with a hydrological model (i.e., SWAT). The potential impacts in the near future were explored using projected land use patterns and hypothetical climate scenarios established on the basis of analyzing long-term climatic observations. Land use changes in the recent past are dominated by the expansion of grassland and a decrease in farmland; meanwhile the climate develops with a wetting and warming trend. Land use changes in this period induce slight reductions in surface runoff, groundwater discharge and streamflow whereas climate changes produce pronounced increases in them. The joint hydrological impacts are similar to those solely induced by climate changes. Spatially, both the effects of land use change and climate variability vary with the sub-basin. The influences of land use changes are more identifiable in some sub-basins, compared with the basin-wide impacts. In the near future, climate changes tend to affect the hydrological regimes much more prominently than land use changes, leading to significant increases in all hydrological components. Nevertheless, the role of land use change should not be overlooked, especially if the climate becomes drier in the future, as in this case it may magnify the hydrological responses.

An Integrated Systems Approach to Designing Climate Change Adaptation Policy in Water Resources

NASA Astrophysics Data System (ADS)

Ryu, D.; Malano, H. M.; Davidson, B.; George, B.

2014-12-01

Climate change projections are characterised by large uncertainties with rainfall variability being the key challenge in designing adaptation policies. Climate change adaptation in water resources shows all the typical characteristics of 'wicked' problems typified by cognitive uncertainty as new scientific knowledge becomes available, problem instability, knowledge imperfection and strategic uncertainty due to institutional changes that inevitably occur over time. Planning that is characterised by uncertainties and instability requires an approach that can accommodate flexibility and adaptive capacity for decision-making. An ability to take corrective measures in the event that scenarios and responses envisaged initially derive into forms at some future stage. We present an integrated-multidisciplinary and comprehensive framework designed to interface and inform science and decision making in the formulation of water resource management strategies to deal with climate change in the Musi Catchment of Andhra Pradesh, India. At the core of this framework is a dialogue between stakeholders, decision makers and scientists to define a set of plausible responses to an ensemble of climate change scenarios derived from global climate modelling. The modelling framework used to evaluate the resulting combination of climate scenarios and adaptation responses includes the surface and groundwater assessment models (SWAT & MODFLOW) and the water allocation modelling (REALM) to determine the water security of each adaptation strategy. Three climate scenarios extracted from downscaled climate models were selected for evaluation together with four agreed responses—changing cropping patterns, increasing watershed development, changing the volume of groundwater extraction and improving irrigation efficiency. Water security in this context is represented by the combination of level of water availability and its associated security of supply for three economic activities (agriculture, urban, industrial) on a spatially distributed basis. The resulting combinations of climate scenarios and adaptation responses were subjected to a combined hydro-economic assessment based on the degree of water security together with its cost-effectiveness against the Business-as-usual scenario.

An iceberg model implementation in ACME.

NASA Astrophysics Data System (ADS)

Comeau, D.; Turner, A. K.; Hunke, E. C.

2017-12-01

Icebergs represent approximately half of the mass flux from the Antarctic ice sheet, transporting freshwater and nutrients away from the coast to the Southern Ocean. Icebergs impact the surrounding ocean and sea ice environment, and serve as nutrient sources for biogeochemical activity, yet these processes are typically not resolved in current climate models. We have implemented a parameterization for iceberg drift and decay into the Department of Energy's Accelerated Climate Model for Energy (ACME), where the ocean, sea ice, and land ice components are based on the unstructured grid modeling framework Multiple Prediction Across Scales (MPAS), to improve the representation of Antarctic mass flux to the Southern Ocean and its impacts on ocean stratification and circulation, sea ice, and biogeochemical processes in a fully coupled global climate model. The iceberg model is implemented in two frameworks: Lagrangian and Eulerian. The Lagrangian framework embeds individual icebergs into the ocean and sea ice grids, and will be useful in modeling `giant' (>10 nautical miles) iceberg events, which may have highly localized impacts on ocean and sea ice. The Eulerian framework allows us to model a realistic population of Antarctic icebergs without the computational expense of individual particle tracking to simulate the aggregate impact on the Southern Ocean climate system. This capability, together with under ice-shelf ocean cavities and dynamic ice-shelf fronts, will allow for extremely high fidelity simulation of the southern cryosphere within ACME.

Characterizing bias correction uncertainty in wheat yield predictions

NASA Astrophysics Data System (ADS)

Ortiz, Andrea Monica; Jones, Julie; Freckleton, Robert; Scaife, Adam

2017-04-01

Farming systems are under increased pressure due to current and future climate change, variability and extremes. Research on the impacts of climate change on crop production typically rely on the output of complex Global and Regional Climate Models, which are used as input to crop impact models. Yield predictions from these top-down approaches can have high uncertainty for several reasons, including diverse model construction and parameterization, future emissions scenarios, and inherent or response uncertainty. These uncertainties propagate down each step of the 'cascade of uncertainty' that flows from climate input to impact predictions, leading to yield predictions that may be too complex for their intended use in practical adaptation options. In addition to uncertainty from impact models, uncertainty can also stem from the intermediate steps that are used in impact studies to adjust climate model simulations to become more realistic when compared to observations, or to correct the spatial or temporal resolution of climate simulations, which are often not directly applicable as input into impact models. These important steps of bias correction or calibration also add uncertainty to final yield predictions, given the various approaches that exist to correct climate model simulations. In order to address how much uncertainty the choice of bias correction method can add to yield predictions, we use several evaluation runs from Regional Climate Models from the Coordinated Regional Downscaling Experiment over Europe (EURO-CORDEX) at different resolutions together with different bias correction methods (linear and variance scaling, power transformation, quantile-quantile mapping) as input to a statistical crop model for wheat, a staple European food crop. The objective of our work is to compare the resulting simulation-driven hindcasted wheat yields to climate observation-driven wheat yield hindcasts from the UK and Germany in order to determine ranges of yield uncertainty that result from different climate model simulation input and bias correction methods. We simulate wheat yields using a General Linear Model that includes the effects of seasonal maximum temperatures and precipitation, since wheat is sensitive to heat stress during important developmental stages. We use the same statistical model to predict future wheat yields using the recently available bias-corrected simulations of EURO-CORDEX-Adjust. While statistical models are often criticized for their lack of complexity, an advantage is that we are here able to consider only the effect of the choice of climate model, resolution or bias correction method on yield. Initial results using both past and future bias-corrected climate simulations with a process-based model will also be presented. Through these methods, we make recommendations in preparing climate model output for crop models.

Estimating the numerical diapycnal mixing in an eddy-permitting ocean model

NASA Astrophysics Data System (ADS)

Megann, Alex

2018-01-01

Constant-depth (or "z-coordinate") ocean models such as MOM4 and NEMO have become the de facto workhorse in climate applications, having attained a mature stage in their development and are well understood. A generic shortcoming of this model type, however, is a tendency for the advection scheme to produce unphysical numerical diapycnal mixing, which in some cases may exceed the explicitly parameterised mixing based on observed physical processes, and this is likely to have effects on the long-timescale evolution of the simulated climate system. Despite this, few quantitative estimates have been made of the typical magnitude of the effective diapycnal diffusivity due to numerical mixing in these models. GO5.0 is a recent ocean model configuration developed jointly by the UK Met Office and the National Oceanography Centre. It forms the ocean component of the GC2 climate model, and is closely related to the ocean component of the UKESM1 Earth System Model, the UK's contribution to the CMIP6 model intercomparison. GO5.0 uses version 3.4 of the NEMO model, on the ORCA025 global tripolar grid. An approach to quantifying the numerical diapycnal mixing in this model, based on the isopycnal watermass analysis of Lee et al. (2002), is described, and the estimates thereby obtained of the effective diapycnal diffusivity in GO5.0 are compared with the values of the explicit diffusivity used by the model. It is shown that the effective mixing in this model configuration is up to an order of magnitude higher than the explicit mixing in much of the ocean interior, implying that mixing in the model below the mixed layer is largely dominated by numerical mixing. This is likely to have adverse consequences for the representation of heat uptake in climate models intended for decadal climate projections, and in particular is highly relevant to the interpretation of the CMIP6 class of climate models, many of which use constant-depth ocean models at ¼° resolution

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

van der Zwaan, Bob; Kober, Tom; Calderon, Silvia

In this paper we investigate opportunities for energy technology deployment under climate change mitigation efforts in Latin America. Through several carbon tax and CO 2 abatement scenarios until 2050 we analyze what resources and technologies, notably for electricity generation, could be cost-optimal in the energy sector to significantly reduce CO 2 emissions in the region. By way of sensitivity test we perform a cross-model comparison study and inspect whether robust conclusions can be drawn across results from different models as well as different types of models (general versus partial equilibrium). Given the abundance of biomass resources in Latin America, theymore » play a large role in energy supply in all scenarios we inspect. This is especially true for stringent climate policy scenarios, for instance because the use of biomass in power plants in combination with CCS can yield negative CO 2 emissions. We find that hydropower, which today contributes about 800 TWh to overall power production in Latin America, could be significantly expanded to meet the climate policies we investigate, typically by about 50%, but potentially by as much as 75%. According to all models, electricity generation increases exponentially with a two- to three-fold expansion between 2010 and 2050.Wefind that in our climate policy scenarios renewable energy overall expands typically at double-digit growth rates annually, but there is substantial spread in model results for specific options such as wind and solar power: the climate policies that we simulate raise wind power in 2050 on average to half the production level that hydropower provides today, while they raise solar power to either a substantially higher or a much lower level than hydropower supplies at present, depending on which model is used. Also for CCS we observe large diversity in model outcomes, which reflects the uncertainties with regard to its future implementation potential as a result of the challenges this CO 2 abatement technology experiences. The extent to which different mitigation options can be used in practice varies greatly between countries within Latin America, depending on factors such as resource potentials, economic performance, environmental impacts, and availability of technical expertise. We provide concise assessments of possible deployment opportunities for some low-carbon energy options, for the region at large and with occasional country-level detail in specific cases.« less

Downscaling Pest Risk Analyses: Identifying Current and Future Potentially Suitable Habitats for Parthenium hysterophorus with Particular Reference to Europe and North Africa

PubMed Central

Kriticos, Darren J.; Brunel, Sarah; Ota, Noboru; Fried, Guillaume; Oude Lansink, Alfons G. J. M.; Panetta, F. Dane; Prasad, T. V. Ramachandra; Shabbir, Asad; Yaacoby, Tuvia

2015-01-01

Pest Risk Assessments (PRAs) routinely employ climatic niche models to identify endangered areas. Typically, these models consider only climatic factors, ignoring the ‘Swiss Cheese’ nature of species ranges due to the interplay of climatic and habitat factors. As part of a PRA conducted for the European and Mediterranean Plant Protection Organization, we developed a climatic niche model for Parthenium hysterophorus, explicitly including the effects of irrigation where it was known to be practiced. We then downscaled the climatic risk model using two different methods to identify the suitable habitat types: expert opinion (following the EPPO PRA guidelines) and inferred from the global spatial distribution. The PRA revealed a substantial risk to the EPPO region and Central and Western Africa, highlighting the desirability of avoiding an invasion by P. hysterophorus. We also consider the effects of climate change on the modelled risks. The climate change scenario indicated the risk of substantial further spread of P. hysterophorus in temperate northern hemisphere regions (North America, Europe and the northern Middle East), and also high elevation equatorial regions (Western Brazil, Central Africa, and South East Asia) if minimum temperatures increase substantially. Downscaling the climate model using habitat factors resulted in substantial (approximately 22–53%) reductions in the areas estimated to be endangered. Applying expert assessments as to suitable habitat classes resulted in the greatest reduction in the estimated endangered area, whereas inferring suitable habitats factors from distribution data identified more land use classes and a larger endangered area. Despite some scaling issues with using a globally conformal Land Use Systems dataset, the inferential downscaling method shows promise as a routine addition to the PRA toolkit, as either a direct model component, or simply as a means of better informing an expert assessment of the suitable habitat types. PMID:26325680

Climate Change Risk Management Consulting: The opportunity for an independent business practice

NASA Astrophysics Data System (ADS)

Ciccozzi, R.

2009-04-01

The Paper outlines the main questions to be addressed with reference to the actual demand of climate change risk management consulting, in the financial services. Moreover, the Project shall also try to investigate if the Catastrophe Modelling Industry can start and manage a business practice specialised on climate change risk exposures. In this context, the Paper aims at testing the possibility to build a sound business case, based upon typical MBA course analysis tools, such as PEST(LE), SWOT, etc. Specific references to the tools to be used and to other contribution from academic literature and general documentation are also discussed in the body of the Paper and listed at the end. The analysis shall also focus on the core competencies required for an independent climate change risk management consulting business practice, with the purpose to outline a valid definition of how to achieve competitive advantage in climate change risk management consulting.

Thermohaline circulation and its box models simulation

NASA Astrophysics Data System (ADS)

Bazyura, Kateryna; Polonsky, Alexander; Sannikov, Viktor

2014-05-01

Ocean Thermochaline circulation (THC) is the part of large-scale World Ocean circulation and one of the main climate system components. It is generated by global meridional density gradients, which are controlled by surface heat and freshwater fluxes. THC regulates climate variability on different timescales (from decades to thousands years) [Stocker (2000), Clark (2002)]. Study of paleoclimatic evidences of abrupt and dramatic changes in ocean-atmosphere system in the past (such as, Dansgaard-Oeschger and Heinrich events or Younger Dryas, see e.g., [Rahmstorf (2002), Alley & Clark(1999)]) shows that these events are connected with THC regimes. At different times during last 120,000 years, three THC modes have prevailed in the Atlantic. They can be labeled as stadial, interstadial and Heinrich modes or as cold, warm and off mode. THC collapse (or thermohaline catastrophe) can be one of the consequences of global warming (including modern anthropogenic climate changes occurring at the moment). The ideas underlying different box-model studies, possibility of thermochaline catastrophe in present and past are discussed in this presentation. Response of generalized four box model of North Atlantic thermohaline circulation [developing the model of Griffies & Tzippermann (1995)] on periodic, stochastic and linear forcing is studied in details. To estimate climatic parameters of the box model we used monthly salinity and temperature data of ECMWF operational Ocean Reanalysis System 3 (ORA-S3) and data from atmospheric NCEP/NCAR reanalysis on precipitation, and heat fluxes for 1959-2011. Mean values, amplitude of seasonal cycle, amplitudes and periods of typical interdecadal oscillations, white noise level, linear trend coefficients and their significance level were estimated for every hydrophysical parameter. In response to intense freshwater or heat forcing, THC regime can change resulting in thermohaline catastrophe. We analyze relevant thresholds of external forcing in cases of using linear and nonlinear seawater state equation. In the frame of four-box model it is shown that: 1) The occurrence of the thermohaline catastrophe, which is likely happened at Younger Dryas period or developed as Heinrich events in the past, is improbable in modern climate epoch. 2) Choice of nonlinear seawater equitation of state leads to stabilization of warm mode of THC, which corresponds to modern climate state. 3) Typical white noise in heat and freshwater fluxes leads to generation of multidecadal oscillations of volume transport. Time-scale of these oscillations coincides with Atlantic Multidecadal oscillation periodicity. So, it is shown that that recent climate is characterized by quasi-periodical stable multidecadal THC warm regime. Stocker, T. F., 2000: Past and future reorganisations in the climate system. Quat. Sci.Rev, Vol. 19, P.301-319. Clark U., 2002: The role of the thermohaline circulation in abrupt climate change. Nature. Vol. 415, P.863-869. Rahmstorf S., 2002: Ocean circulation and climate during the past 120000 years. Nature. Vol. 419, P.207-214. Alley, R. B. & Clark, P. U., 1999: The deglaciation of the Northern Hemisphere: a global perspective. Annu.Rev. Earth Planet. Sci. Vol. 27, P.149-182. Griffies S.M., Tziperman E., 1995: A linear thermohaline oscillator driven by stochastic atmospheric forcing. Journal of Climate. Vol. 8. P. 2440-2453.

The contribution of natural variability to GCM bias: Can we effectively bias-correct climate projections?

NASA Astrophysics Data System (ADS)

McAfee, S. A.; DeLaFrance, A.

2017-12-01

Investigating the impacts of climate change often entails using projections from inherently imperfect general circulation models (GCMs) to drive models that simulate biophysical or societal systems in great detail. Error or bias in the GCM output is often assessed in relation to observations, and the projections are adjusted so that the output from impacts models can be compared to historical or observed conditions. Uncertainty in the projections is typically accommodated by running more than one future climate trajectory to account for differing emissions scenarios, model simulations, and natural variability. The current methods for dealing with error and uncertainty treat them as separate problems. In places where observed and/or simulated natural variability is large, however, it may not be possible to identify a consistent degree of bias in mean climate, blurring the lines between model error and projection uncertainty. Here we demonstrate substantial instability in mean monthly temperature bias across a suite of GCMs used in CMIP5. This instability is greatest in the highest latitudes during the cool season, where shifts from average temperatures below to above freezing could have profound impacts. In models with the greatest degree of bias instability, the timing of regional shifts from below to above average normal temperatures in a single climate projection can vary by about three decades, depending solely on the degree of bias assessed. This suggests that current bias correction methods based on comparison to 20- or 30-year normals may be inappropriate, particularly in the polar regions.

Streamflow Bias Correction for Climate Change Impact Studies: Harmless Correction or Wrecking Ball?

NASA Astrophysics Data System (ADS)

Nijssen, B.; Chegwidden, O.

2017-12-01

Projections of the hydrologic impacts of climate change rely on a modeling chain that includes estimates of future greenhouse gas emissions, global climate models, and hydrologic models. The resulting streamflow time series are used in turn as input to impact studies. While these flows can sometimes be used directly in these impact studies, many applications require additional post-processing to remove model errors. Water resources models and regulation studies are a prime example of this type of application. These models rely on specific flows and reservoir levels to trigger reservoir releases and diversions and do not function well if the unregulated streamflow inputs are significantly biased in time and/or amount. This post-processing step is typically referred to as bias-correction, even though this step corrects not just the mean but the entire distribution of flows. Various quantile-mapping approaches have been developed that adjust the modeled flows to match a reference distribution for some historic period. Simulations of future flows are then post-processed using this same mapping to remove hydrologic model errors. These streamflow bias-correction methods have received far less scrutiny than the downscaling and bias-correction methods that are used for climate model output, mostly because they are less widely used. However, some of these methods introduce large artifacts in the resulting flow series, in some cases severely distorting the climate change signal that is present in future flows. In this presentation, we discuss our experience with streamflow bias-correction methods as part of a climate change impact study in the Columbia River basin in the Pacific Northwest region of the United States. To support this discussion, we present a novel way to assess whether a streamflow bias-correction method is merely a harmless correction or is more akin to taking a wrecking ball to the climate change signal.

Feasible Electricity Infrastructure Pathways in the Context of Climate-Water Change Constraints

NASA Astrophysics Data System (ADS)

Miara, A.; Vorosmarty, C. J.; Macknick, J.; Cohen, S. M.; Tidwell, V. C.; Newmark, R. L.; Fekete, B. M.; Corsi, F.; Sun, Y.; Proussevitch, A. A.; Glidden, S.

2017-12-01

The carbon and water intensity of US electricity generation has recently decreased due to the natural gas revolution and deployment of renewable technologies. Yet, power plants that require water for cooling still provide 80% of electricity generation and projected climate-water conditions may limit their power output and affect reliability. Understanding the connections and tradeoffs across water, electricity and climate systems is timely, as the nation tries to mitigate and adapt to a changing climate. Electricity expansion models are used to provide insight on power sector pathways given certain policy goals and economic conditions, but do not typically account for productivity limitations due to physical climate-water constraints. Here, we account for such constraints by coupling an electricity expansion model (Regional Energy Deployment System - ReEDS) with the combined Water Balance and Thermoelectric Power and Thermal Pollution Models (WBM-TP2M), which calculate the available capacity at power plants as a function of hydrologic flows, climate conditions, power plant technology and environmental regulations. To fully capture and incorporate climate-water impacts into ReEDS, a specific rule-set was designed for the temporal and spatial downscaling and up-scaling of ReEDS results into WBM-TP2M inputs and visa versa - required to achieve a modeling `loop' that will enable convergence on a feasible solution in the context of economic and geophysical constraints and opportunities. This novel modeling approach is the next phase of research for understanding electricity system vulnerabilities and adaptation measures using energy-water-climate modeling, which to-date has been limited by a focus on individual generators without analyzing power generation as a collective regional system. This study considers four energy policy/economic pathways under future climate-water resource conditions, designed under the National Energy Water System assessment framework. Results highlight the importance of linking Earth-system and economic modeling tools and provide insight on potential electricity infrastructure pathways that are sustainable, in terms lowering both water use and carbon emissions, and reliable in the face of future climate-water resource constraints.

Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models

NASA Astrophysics Data System (ADS)

Drijfhout, Sybren; Bathiany, Sebastian; Beaulieu, Claudie; Brovkin, Victor; Claussen, Martin; Huntingford, Chris; Scheffer, Marten; Sgubin, Giovanni; Swingedouw, Didier

2015-10-01

Abrupt transitions of regional climate in response to the gradual rise in atmospheric greenhouse gas concentrations are notoriously difficult to foresee. However, such events could be particularly challenging in view of the capacity required for society and ecosystems to adapt to them. We present, to our knowledge, the first systematic screening of the massive climate model ensemble informing the recent Intergovernmental Panel on Climate Change report, and reveal evidence of 37 forced regional abrupt changes in the ocean, sea ice, snow cover, permafrost, and terrestrial biosphere that arise after a certain global temperature increase. Eighteen out of 37 events occur for global warming levels of less than 2°, a threshold sometimes presented as a safe limit. Although most models predict one or more such events, any specific occurrence typically appears in only a few models. We find no compelling evidence for a general relation between the overall number of abrupt shifts and the level of global warming. However, we do note that abrupt changes in ocean circulation occur more often for moderate warming (less than 2°), whereas over land they occur more often for warming larger than 2°. Using a basic proportion test, however, we find that the number of abrupt shifts identified in Representative Concentration Pathway (RCP) 8.5 scenarios is significantly larger than in other scenarios of lower radiative forcing. This suggests the potential for a gradual trend of destabilization of the climate with respect to such shifts, due to increasing global mean temperature change.

Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models.

PubMed

Drijfhout, Sybren; Bathiany, Sebastian; Beaulieu, Claudie; Brovkin, Victor; Claussen, Martin; Huntingford, Chris; Scheffer, Marten; Sgubin, Giovanni; Swingedouw, Didier

2015-10-27

Abrupt transitions of regional climate in response to the gradual rise in atmospheric greenhouse gas concentrations are notoriously difficult to foresee. However, such events could be particularly challenging in view of the capacity required for society and ecosystems to adapt to them. We present, to our knowledge, the first systematic screening of the massive climate model ensemble informing the recent Intergovernmental Panel on Climate Change report, and reveal evidence of 37 forced regional abrupt changes in the ocean, sea ice, snow cover, permafrost, and terrestrial biosphere that arise after a certain global temperature increase. Eighteen out of 37 events occur for global warming levels of less than 2°, a threshold sometimes presented as a safe limit. Although most models predict one or more such events, any specific occurrence typically appears in only a few models. We find no compelling evidence for a general relation between the overall number of abrupt shifts and the level of global warming. However, we do note that abrupt changes in ocean circulation occur more often for moderate warming (less than 2°), whereas over land they occur more often for warming larger than 2°. Using a basic proportion test, however, we find that the number of abrupt shifts identified in Representative Concentration Pathway (RCP) 8.5 scenarios is significantly larger than in other scenarios of lower radiative forcing. This suggests the potential for a gradual trend of destabilization of the climate with respect to such shifts, due to increasing global mean temperature change.

Relating health and climate impacts to grid-scale emissions using adjoint sensitivity modeling for the Climate and Clean Air Coalition

NASA Astrophysics Data System (ADS)

Henze, D. K.; Lacey, F.; Seltzer, M.; Vallack, H.; Kuylenstierna, J.; Bowman, K. W.; Anenberg, S.; Sasser, E.; Lee, C. J.; Martin, R.

2013-12-01

The Climate and Clean Air Coalition (CCAC) was initiated in 2012 to develop, understand and promote measures to reduce short lived climate forcers such as aerosol, ozone and methane. The Coalition now includes over 30 nations, and as a service to these nations is committed to providing a decision support toolkit that allows member nations to explore the benefits of a range of emissions mitigation measures in terms of the combined impacts on air quality and climate and so help in the development of their National Action Plans. Here we will present recent modeling work to support the development of the CCAC National Action Plans toolkit. Adjoint sensitivity analysis is presented as a means of efficiently relating air quality, climate and crop impacts back to changes in emissions from each species, sector and location at the grid-scale resolution of typical global air quality model applications. The GEOS-Chem adjoint model is used to estimate the damages per ton of emissions of PM2.5 related mortality, the impacts of ozone precursors on crops and ozone-related health effects, and the combined impacts of these species on regional surface temperature changes. We show how the benefits-per-emission vary spatially as a function of the surrounding environment, and how this impacts the overall benefit of sector-specific control strategies. We present initial findings for Bangladesh, as well as Mexico, Ghana and Colombia, some of the first countries to join the CCAC, and discuss general issues related to adjoint-based metrics for quantifying air quality and climate co-benefits.

A probabilistic method for streamflow projection and associated uncertainty analysis in a data sparse alpine region

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Confronting species distribution model predictions with species functional traits.

PubMed

Wittmann, Marion E; Barnes, Matthew A; Jerde, Christopher L; Jones, Lisa A; Lodge, David M

2016-02-01

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day(-1). Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = -0.26, 95% CI (-0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.

The effects of climate downscaling technique and observational data set on modeled ecological responses.

PubMed

Pourmokhtarian, Afshin; Driscoll, Charles T; Campbell, John L; Hayhoe, Katharine; Stoner, Anne M K

2016-07-01

Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training observations used at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three downscaling methods: the delta method (or the change factor method), monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD), and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8º resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (eight ensembles of six runs).The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chemistry. All three downscaling methods were highly sensitive to the observations used, resulting in projections that were significantly different between station-based and grid-based observations. The choice of downscaling method also slightly affected the results, however not as much as the choice of observations. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of carefully considering field observations used for training, as well as the downscaling method used to generate climate change projections, for smaller-scale modeling studies. Different sources of variability including selection of AOGCM, emissions scenario, downscaling technique, and data used for training downscaling models, result in a wide range of projected forest ecosystem responses to future climate change. © 2016 by the Ecological Society of America.

Morphology and mixing state of individual freshly emitted wildfire carbonaceous particles.

PubMed

China, Swarup; Mazzoleni, Claudio; Gorkowski, Kyle; Aiken, Allison C; Dubey, Manvendra K

2013-01-01

Biomass burning is one of the largest sources of carbonaceous aerosols in the atmosphere, significantly affecting earth's radiation budget and climate. Tar balls, abundant in biomass burning smoke, absorb sunlight and have highly variable optical properties, typically not accounted for in climate models. Here we analyse single biomass burning particles from the Las Conchas fire (New Mexico, 2011) using electron microscopy. We show that the relative abundance of tar balls (80%) is 10 times greater than soot particles (8%). We also report two distinct types of tar balls; one less oxidized than the other. Furthermore, the mixing of soot particles with other material affects their optical, chemical and physical properties. We quantify the morphology of soot particles and classify them into four categories: ~50% are embedded (heavily coated), ~34% are partly coated, ~12% have inclusions and~4% are bare. Inclusion of these observations should improve climate model performances.

Research on Relation between El Nino Climate and Summer Electricity Consumption

NASA Astrophysics Data System (ADS)

Miao, B.; Lin, J. Y.; Liu, C.

2018-01-01

El Nino is a typical climate phenomena. Such phenomena would have influence on climate in China and furthermore impact the electricity condition. This paper is purposed to explore how El Nino phenomena affecting electricity and make prediction on summer electricity consumption. Since meteorological characteristics are complex and multiplex, a variety of meteorological factors should be considered and the paper used Body Feeling Temperature to measure it. Furthermore, to make prediction on summer electricity, the paper used the Pearson Analysis to measure the correlation between weather and electricity and then extracted the weather-used electricity from the whole society electricity using least square method. Finally, the paper built the model on relation between weather-used electricity and body feeling temperature, and took Beijing as an example to make electricity prediction. The prediction idea and model the paper put forward is reliable and practicable.

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

Temporal Considerations of Carbon Sequestration in LCA

Treesearch

James Salazar; Richard Bergman

2013-01-01

Accounting for carbon sequestration in LCA illustrates the limitations of a single global warming characterization factor. Typical cradle-to-grave LCA models all emissions from end-of-life processes and then characterizes these flows by IPCC GWP (100-yr) factors. A novel method estimates climate change impact by characterizing annual emissions with the IPCC GHG forcing...

Systems Approach to Climate, Water, and Diarrhea in Hubli-Dharwad, India.

PubMed

Mellor, Jonathan; Kumpel, Emily; Ercumen, Ayse; Zimmerman, Julie

2016-12-06

Anthropogenic climate change will likely increase diarrhea rates for communities with inadequate water, sanitation, or hygiene facilities including those with intermittent water supplies. Current approaches to study these impacts typically focus on the effect of temperature on all-cause diarrhea while excluding precipitation and diarrhea etiology while not providing actionable adaptation strategies. We develop a partially mechanistic, systems approach to estimate future diarrhea prevalence and design adaptation strategies. The model incorporates downscaled global climate models, water quality data, quantitative microbial risk assessment, and pathogen prevalence in an agent-based modeling framework incorporating precipitation and diarrhea etiology. It is informed using water quality and diarrhea data from Hubli-Dharwad, India-a city with an intermittent piped water supply exhibiting seasonal water quality variability vulnerable to climate change. We predict all-cause diarrhea prevalence to increase by 4.9% (Range: 1.5-9.0%) by 2011-2030, 11.9% (Range: 7.1-18.2%) by 2046-2065, and 18.2% (Range: 9.1-26.2%) by 2080-2099. Rainfall is an important modifying factor. Rotavirus prevalence is estimated to decline by 10.5% with Cryptosporidium and E. coli prevalence increasing by 9.9% and 6.3%, respectively, by 2080-2099 in this setting. These results suggest that ceramic water filters would be recommended as a climate adaptation strategy over chlorination. This work highlights the vulnerability of intermittent water supplies to climate change and the urgent need for improvements.

Effect of Climate Change on Mediterranean Winter Ranges of Two Migratory Passerines

PubMed Central

Tellería, José L.; Fernández-López, Javier; Fandos, Guillermo

2016-01-01

We studied the effect of climate change on the distribution of two insectivorous passerines (the meadow pipit Anthus pratensis and the chiffchaff Phylloscopus collybita) in wintering grounds of the Western Mediterranean basin. In this region, precipitation and temperature can affect the distribution of these birds through direct (thermoregulation costs) or indirect effects (primary productivity). Thus, it can be postulated that projected climate changes in the region will affect the extent and suitability of their wintering grounds. We studied pipit and chiffchaff abundance in several hundred localities along a belt crossing Spain and Morocco and assessed the effects of climate and other geographical and habitat predictors on bird distribution. Multivariate analyses reported a positive effect of temperature on the present distribution of the two species, with an additional effect of precipitation on the meadow pipit. These climate variables were used with Maxent to model the occurrence probabilities of species using ring recoveries as presence data. Abundance and occupancy of the two species in the study localities adjusted to the distribution models, with more birds in sectors of high climate suitability. After validation, these models were used to forecast the distribution of climate suitability according to climate projections for 2050–2070 (temperature increase and precipitation reduction). Results show an expansion of climatically suitable sectors into the highlands by the effect of warming on the two species, and a retreat of the meadow pipit from southern sectors related to rain reduction. The predicted patterns show a mean increase in climate suitability for the two species due to the warming of the large highland expanses typical of the western Mediterranean. PMID:26761791

Socioemotional self-perceptions, family climate, and hopeful thinking among students with learning disabilities and typically achieving students from the same classes.

PubMed

Idan, Orly; Margalit, Malka

2014-01-01

This study aimed at examining the adjustment of students with learning disabilities (LD) and at exploring the mediating role of hope. By means of a multidimensional approach, the interactions between risk and protective factors emerging from internal and external resources among 856 high school students (10th to 12th grades) were analyzed. A total of 529 typically achieving students and 327 students with LD attending general education classes in seven high schools completed seven instruments measuring sense of coherence, basic psychological needs, loneliness, family climate, hope, academic self-efficacy, and effort. The students' achievements in English, history, and mathematics were collected. The analysis used structural equation modeling, and the results emphasized the significant role of hope as a mediator between risk and protective factors and academic self-efficacy and its significance for students with and without LD in explaining achievements and effort investment.

The Detection and Attribution Model Intercomparison Project (DAMIP v1.0)contribution to CMIP6

DOE PAGES

Gillett, Nathan P.; Shiogama, Hideo; Funke, Bernd; ...

2016-10-18

Detection and attribution (D&A) simulations were important components of CMIP5 and underpinned the climate change detection and attribution assessments of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. The primary goals of the Detection and Attribution Model Intercomparison Project (DAMIP) are to facilitate improved estimation of the contributions of anthropogenic and natural forcing changes to observed global warming as well as to observed global and regional changes in other climate variables; to contribute to the estimation of how historical emissions have altered and are altering contemporary climate risk; and to facilitate improved observationally constrained projections of futuremore » climate change. D&A studies typically require unforced control simulations and historical simulations including all major anthropogenic and natural forcings. Such simulations will be carried out as part of the DECK and the CMIP6 historical simulation. In addition D&A studies require simulations covering the historical period driven by individual forcings or subsets of forcings only: such simulations are proposed here. Key novel features of the experimental design presented here include firstly new historical simulations with aerosols-only, stratospheric-ozone-only, CO2-only, solar-only, and volcanic-only forcing, facilitating an improved estimation of the climate response to individual forcing, secondly future single forcing experiments, allowing observationally constrained projections of future climate change, and thirdly an experimental design which allows models with and without coupled atmospheric chemistry to be compared on an equal footing.« less

The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6

NASA Astrophysics Data System (ADS)

Gillett, Nathan P.; Shiogama, Hideo; Funke, Bernd; Hegerl, Gabriele; Knutti, Reto; Matthes, Katja; Santer, Benjamin D.; Stone, Daithi; Tebaldi, Claudia

2016-10-01

Detection and attribution (D&A) simulations were important components of CMIP5 and underpinned the climate change detection and attribution assessments of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. The primary goals of the Detection and Attribution Model Intercomparison Project (DAMIP) are to facilitate improved estimation of the contributions of anthropogenic and natural forcing changes to observed global warming as well as to observed global and regional changes in other climate variables; to contribute to the estimation of how historical emissions have altered and are altering contemporary climate risk; and to facilitate improved observationally constrained projections of future climate change. D&A studies typically require unforced control simulations and historical simulations including all major anthropogenic and natural forcings. Such simulations will be carried out as part of the DECK and the CMIP6 historical simulation. In addition D&A studies require simulations covering the historical period driven by individual forcings or subsets of forcings only: such simulations are proposed here. Key novel features of the experimental design presented here include firstly new historical simulations with aerosols-only, stratospheric-ozone-only, CO2-only, solar-only, and volcanic-only forcing, facilitating an improved estimation of the climate response to individual forcing, secondly future single forcing experiments, allowing observationally constrained projections of future climate change, and thirdly an experimental design which allows models with and without coupled atmospheric chemistry to be compared on an equal footing.

Paleodynamics of large closed lakes as a standard for climate modeling data verification

NASA Astrophysics Data System (ADS)

Kislov, Alexander

2015-04-01

Observed and reconstructed variations of large lakes can serve as a standard for assessing the quality of the model run off simulated by climate models. It provides the opportunity to assess whether models designed for future scenarios are skillful in 'out-of sample' climate change experiments. Based on general ideas about the laws of temporal dynamics relating to massive inertial objects, slow changes of the lake level under the semi-steady climate state can be represented as resulting from the accumulation of small anomalies in the water regime; it appears like a kind of "self-developing" system. To test this hypothesis, the water balance model of the Caspian Sea (CS) was used. Time scale for the CS is estimated as ~20 years. Model is interpreted as stochastic, and from this perspective, it is a Langevin equation that incorporates the action of precipitation and evaporation like random white noise, so that the whole can be thought of as an analogue of Brownian motion. Under these conditions, the CS palaeostages during the Holocene is represented by a system undergoing random walk. It should be emphasized that modeling results are interpreted from the probabilistic point of view, despite the fact that the model is deterministically based on the physical law of conservation of water mass. Despite the CS, another candidate to be as a potential evaluation tool for climate model simulations is the Black Sea (BS) until its merger with the Mediterranean. Therefore, although the image of the CS, BS and other lakes within the climate models is very simplified (or absent), changes in the levels could be used to assess the ability of climate models to reproduce the water budget over the catchment areas. For the CS or the BS, they are the large parts of the East European Plane and can be as indicators of climate model quality. However, the use of reconstructed data of other closed lakes is problematic. It is due to its water budget components cannot be simulated with needed accuracy because they are either too small (the size of the largest closed Siberian lake (the Chany) is less than the typical grid box of climate model) or they are located in mountain region (like the Issyk-Kul Lake, located in the northern Tian Shan mountains) where the lake variability is determined by badly reproduced glacier melting.

Contrasting growth forecasts across the geographical range of Scots pine due to altitudinal and latitudinal differences in climatic sensitivity.

PubMed

Matías, Luis; Linares, Juan C; Sánchez-Miranda, Ángela; Jump, Alistair S

2017-10-01

Ongoing changes in global climate are altering ecological conditions for many species. The consequences of such changes are typically most evident at the edge of a species' geographical distribution, where differences in growth or population dynamics may result in range expansions or contractions. Understanding population responses to different climatic drivers along wide latitudinal and altitudinal gradients is necessary in order to gain a better understanding of plant responses to ongoing increases in global temperature and drought severity. We selected Scots pine (Pinus sylvestris L.) as a model species to explore growth responses to climatic variability (seasonal temperature and precipitation) over the last century through dendrochronological methods. We developed linear models based on age, climate and previous growth to forecast growth trends up to year 2100 using climatic predictions. Populations were located at the treeline across a latitudinal gradient covering the northern, central and southernmost populations and across an altitudinal gradient at the southern edge of the distribution (treeline, medium and lower elevations). Radial growth was maximal at medium altitude and treeline of the southernmost populations. Temperature was the main factor controlling growth variability along the gradients, although the timing and strength of climatic variables affecting growth shifted with latitude and altitude. Predictive models forecast a general increase in Scots pine growth at treeline across the latitudinal distribution, with southern populations increasing growth up to year 2050, when it stabilizes. The highest responsiveness appeared at central latitude, and moderate growth increase is projected at the northern limit. Contrastingly, the model forecasted growth declines at lowland-southern populations, suggesting an upslope range displacement over the coming decades. Our results give insight into the geographical responses of tree species to climate change and demonstrate the importance of incorporating biogeographical variability into predictive models for an accurate prediction of species dynamics as climate changes. © 2017 John Wiley & Sons Ltd.

Recent advances in understanding secondary organic aerosols: implications for global climate forcing

NASA Astrophysics Data System (ADS)

Shrivastava, Manish

2017-04-01

Anthropogenic emissions and land-use changes have modified atmospheric aerosol concentrations and size distributions over time. Understanding pre-industrial conditions and changes in organic aerosol due to anthropogenic activities is important because these features 1) influence estimates of aerosol radiative forcing and 2) can confound estimates of the historical response of climate to increases in greenhouse gases (e.g. the 'climate sensitivity'). Secondary organic aerosol (SOA), formed in the atmosphere by oxidation of organic gases, often represents a major fraction of global submicron-sized atmospheric organic aerosol. Over the past decade, significant advances in understanding SOA properties and formation mechanisms have occurred through measurements, yet current climate models typically do not comprehensively include all important processes. This presentation is based on a US Department of Energy Atmospheric Systems Research sponsored workshop, which highlighted key SOA processes overlooked in climate models that could greatly affect climate forcing estimates. We will highlight the importance of processes that influence the growth of SOA particles to sizes relevant for clouds and radiative forcing, including: formation of extremely low-volatility organics in the gas-phase; isoprene epoxydiols (IEPOX) multi-phase chemistry; particle-phase oligomerization; and physical properties such as viscosity. We also highlight some of the recently discovered important processes that involve interactions between natural biogenic emissions and anthropogenic emissions such as effects of sulfur and NOx emissions on SOA. We will present examples of integrated model-measurement studies that relate the observed evolution of organic aerosol mass and number with knowledge of particle properties such as volatility and viscosity. We will also highlight the importance of continuing efforts to rank the most influential SOA processes that affect climate forcing, but are often missing in climate models. Ultimately, gas- and particle-phase chemistry processes that capture the dynamic evolution of number and mass concentrations of SOA particles need to be accurately and efficiently represented in regional and global atmospheric chemistry-climate models.

Evaluation of global horizontal irradiance to plane-of-array irradiance models at locations across the United States

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

Lave, Matthew; Hayes, William; Pohl, Andrew

2015-02-02

We report an evaluation of the accuracy of combinations of models that estimate plane-of-array (POA) irradiance from measured global horizontal irradiance (GHI). This estimation involves two steps: 1) decomposition of GHI into direct and diffuse horizontal components and 2) transposition of direct and diffuse horizontal irradiance (DHI) to POA irradiance. Measured GHI and coincident measured POA irradiance from a variety of climates within the United States were used to evaluate combinations of decomposition and transposition models. A few locations also had DHI measurements, allowing for decoupled analysis of either the decomposition or the transposition models alone. Results suggest that decompositionmore » models had mean bias differences (modeled versus measured) that vary with climate. Transposition model mean bias differences depended more on the model than the location. Lastly, when only GHI measurements were available and combinations of decomposition and transposition models were considered, the smallest mean bias differences were typically found for combinations which included the Hay/Davies transposition model.« less

Incorporating variability in simulations of seasonally forced phenology using integral projection models

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

Goodsman, Devin W.; Aukema, Brian H.; McDowell, Nate G.

Phenology models are becoming increasingly important tools to accurately predict how climate change will impact the life histories of organisms. We propose a class of integral projection phenology models derived from stochastic individual-based models of insect development and demography.Our derivation, which is based on the rate-summation concept, produces integral projection models that capture the effect of phenotypic rate variability on insect phenology, but which are typically more computationally frugal than equivalent individual-based phenology models. We demonstrate our approach using a temperature-dependent model of the demography of the mountain pine beetle (Dendroctonus ponderosae Hopkins), an insect that kills mature pine trees.more » This work illustrates how a wide range of stochastic phenology models can be reformulated as integral projection models. Due to their computational efficiency, these integral projection models are suitable for deployment in large-scale simulations, such as studies of altered pest distributions under climate change.« less

Estimating thermal performance curves from repeated field observations

USGS Publications Warehouse

Childress, Evan; Letcher, Benjamin H.

2017-01-01

Estimating thermal performance of organisms is critical for understanding population distributions and dynamics and predicting responses to climate change. Typically, performance curves are estimated using laboratory studies to isolate temperature effects, but other abiotic and biotic factors influence temperature-performance relationships in nature reducing these models' predictive ability. We present a model for estimating thermal performance curves from repeated field observations that includes environmental and individual variation. We fit the model in a Bayesian framework using MCMC sampling, which allowed for estimation of unobserved latent growth while propagating uncertainty. Fitting the model to simulated data varying in sampling design and parameter values demonstrated that the parameter estimates were accurate, precise, and unbiased. Fitting the model to individual growth data from wild trout revealed high out-of-sample predictive ability relative to laboratory-derived models, which produced more biased predictions for field performance. The field-based estimates of thermal maxima were lower than those based on laboratory studies. Under warming temperature scenarios, field-derived performance models predicted stronger declines in body size than laboratory-derived models, suggesting that laboratory-based models may underestimate climate change effects. The presented model estimates true, realized field performance, avoiding assumptions required for applying laboratory-based models to field performance, which should improve estimates of performance under climate change and advance thermal ecology.

The effects of Antarctic iceberg calving-size distribution in a global climate model

NASA Astrophysics Data System (ADS)

Stern, A. A.; Adcroft, A.; Sergienko, O.

2016-08-01

Icebergs calved from the Antarctic continent act as moving sources of freshwater while drifting in the Southern Ocean. The lifespan of these icebergs strongly depends on their original size during calving. In order to investigate the effects (if any) of the calving size of icebergs on the Southern Ocean, we use a coupled general circulation model with an iceberg component. Iceberg calving length is varied from 62 m up to 2.3 km, which is the typical range used in climate models. Results show that increasing the size of calving icebergs leads to an increase in the westward iceberg freshwater transport around Antarctica. In simulations using larger icebergs, the reduced availability of meltwater in the Amundsen and Bellingshausen Seas suppresses the sea-ice growth in the region. In contrast, the increased iceberg freshwater transport leads to increased sea-ice growth around much of the East Antarctic coastline. These results suggest that the absence of large tabular icebergs with horizontal extent of tens of kilometers in climate models may introduces systematic biases in sea-ice formation, ocean temperatures, and salinities around Antarctica.

Development of ALARO-Climate regional climate model for a very high resolution

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Using multiple climate projections for assessing hydrological response to climate change in the Thukela River Basin, South Africa

NASA Astrophysics Data System (ADS)

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

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

Spatial heterogeneity in the timing of birch budburst in response to future climate warming in Ireland

NASA Astrophysics Data System (ADS)

Caffarra, Amelia; Zottele, Fabio; Gleeson, Emily; Donnelly, Alison

2014-05-01

In order to predict the impact of future climate warming on trees it is important to quantify the effect climate has on their development. Our understanding of the phenological response to environmental drivers has given rise to various mathematical models of the annual growth cycle of plants. These models simulate the timing of phenophases by quantifying the relationship between development and its triggers, typically temperature. In addition, other environmental variables have an important role in determining the timing of budburst. For example, photoperiod has been shown to have a strong influence on phenological events of a number of tree species, including Betula pubescens (birch). A recently developed model for birch (DORMPHOT), which integrates the effects of temperature and photoperiod on budburst, was applied to future temperature projections from a 19-member ensemble of regional climate simulations (on a 25 km grid) generated as part of the ENSEMBLES project, to simulate the timing of birch budburst in Ireland each year up to the end of the present century. Gridded temperature time series data from the climate simulations were used as input to the DORMPHOT model to simulate future budburst timing. The results showed an advancing trend in the timing of birch budburst over most regions in Ireland up to 2100. Interestingly, this trend appeared greater in the northeast of the country than in the southwest, where budburst is currently relatively early. These results could have implications for future forest planning, species distribution modeling, and the birch allergy season.

How does bias correction of regional climate model precipitation affect modelled runoff?

NASA Astrophysics Data System (ADS)

Teng, J.; Potter, N. J.; Chiew, F. H. S.; Zhang, L.; Wang, B.; Vaze, J.; Evans, J. P.

2015-02-01

Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the differences between the methods are small in the modelling experiments here (and as reported in the literature), mainly due to the substantial corrections required and inconsistent errors over time (non-stationarity). The errors in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitations of the RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces.

Identifying ontogenetic, environmental and individual components of forest tree growth

PubMed Central

Chaubert-Pereira, Florence; Caraglio, Yves; Lavergne, Christian; Guédon, Yann

2009-01-01

Background and Aims This study aimed to identify and characterize the ontogenetic, environmental and individual components of forest tree growth. In the proposed approach, the tree growth data typically correspond to the retrospective measurement of annual shoot characteristics (e.g. length) along the trunk. Methods Dedicated statistical models (semi-Markov switching linear mixed models) were applied to data sets of Corsican pine and sessile oak. In the semi-Markov switching linear mixed models estimated from these data sets, the underlying semi-Markov chain represents both the succession of growth phases and their lengths, while the linear mixed models represent both the influence of climatic factors and the inter-individual heterogeneity within each growth phase. Key Results On the basis of these integrative statistical models, it is shown that growth phases are not only defined by average growth level but also by growth fluctuation amplitudes in response to climatic factors and inter-individual heterogeneity and that the individual tree status within the population may change between phases. Species plasticity affected the response to climatic factors while tree origin, sampling strategy and silvicultural interventions impacted inter-individual heterogeneity. Conclusions The transposition of the proposed integrative statistical modelling approach to cambial growth in relation to climatic factors and the study of the relationship between apical growth and cambial growth constitute the next steps in this research. PMID:19684021

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.

Accelerating Climate and Weather Simulations through Hybrid Computing

NASA Technical Reports Server (NTRS)

Zhou, Shujia; Cruz, Carlos; Duffy, Daniel; Tucker, Robert; Purcell, Mark

2011-01-01

Unconventional multi- and many-core processors (e.g. IBM (R) Cell B.E.(TM) and NVIDIA (R) GPU) have emerged as effective accelerators in trial climate and weather simulations. Yet these climate and weather models typically run on parallel computers with conventional processors (e.g. Intel, AMD, and IBM) using Message Passing Interface. To address challenges involved in efficiently and easily connecting accelerators to parallel computers, we investigated using IBM's Dynamic Application Virtualization (TM) (IBM DAV) software in a prototype hybrid computing system with representative climate and weather model components. The hybrid system comprises two Intel blades and two IBM QS22 Cell B.E. blades, connected with both InfiniBand(R) (IB) and 1-Gigabit Ethernet. The system significantly accelerates a solar radiation model component by offloading compute-intensive calculations to the Cell blades. Systematic tests show that IBM DAV can seamlessly offload compute-intensive calculations from Intel blades to Cell B.E. blades in a scalable, load-balanced manner. However, noticeable communication overhead was observed, mainly due to IP over the IB protocol. Full utilization of IB Sockets Direct Protocol and the lower latency production version of IBM DAV will reduce this overhead.

Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

NASA Astrophysics Data System (ADS)

Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

2016-12-01

Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

Future climate risk from compound events

NASA Astrophysics Data System (ADS)

Zscheischler, Jakob; Westra, Seth; van den Hurk, Bart J. J. M.; Seneviratne, Sonia I.; Ward, Philip J.; Pitman, Andy; AghaKouchak, Amir; Bresch, David N.; Leonard, Michael; Wahl, Thomas; Zhang, Xuebin

2018-06-01

Floods, wildfires, heatwaves and droughts often result from a combination of interacting physical processes across multiple spatial and temporal scales. The combination of processes (climate drivers and hazards) leading to a significant impact is referred to as a `compound event'. Traditional risk assessment methods typically only consider one driver and/or hazard at a time, potentially leading to underestimation of risk, as the processes that cause extreme events often interact and are spatially and/or temporally dependent. Here we show how a better understanding of compound events may improve projections of potential high-impact events, and can provide a bridge between climate scientists, engineers, social scientists, impact modellers and decision-makers, who need to work closely together to understand these complex events.

A Simple Climate Model Program for High School Education

NASA Astrophysics Data System (ADS)

Dommenget, D.

2012-04-01

The future climate change projections of the IPCC AR4 are based on GCM simulations, which give a distinct global warming pattern, with an arctic winter amplification, an equilibrium land sea contrast and an inter-hemispheric warming gradient. While these simulations are the most important tool of the IPCC predictions, the conceptual understanding of these predicted structures of climate change are very difficult to reach if only based on these highly complex GCM simulations and they are not accessible for ordinary people. In this study presented here we will introduce a very simple gridded globally resolved energy balance model based on strongly simplified physical processes, which is capable of simulating the main characteristics of global warming. The model shall give a bridge between the 1-dimensional energy balance models and the fully coupled 4-dimensional complex GCMs. It runs on standard PC computers computing globally resolved climate simulation with 2yrs per second or 100,000yrs per day. The program can compute typical global warming scenarios in a few minutes on a standard PC. The computer code is only 730 line long with very simple formulations that high school students should be able to understand. The simple model's climate sensitivity and the spatial structure of the warming pattern is within the uncertainties of the IPCC AR4 models simulations. It is capable of simulating the arctic winter amplification, the equilibrium land sea contrast and the inter-hemispheric warming gradient with good agreement to the IPCC AR4 models in amplitude and structure. The program can be used to do sensitivity studies in which students can change something (e.g. reduce the solar radiation, take away the clouds or make snow black) and see how it effects the climate or the climate response to changes in greenhouse gases. This program is available for every one and could be the basis for high school education. Partners for a high school project are wanted!

Climate Change-Induced Range Expansion of a Subterranean Rodent: Implications for Rangeland Management in Qinghai-Tibetan Plateau

PubMed Central

Su, Junhu; Aryal, Achyut; Nan, Zhibiao; Ji, Weihong

2015-01-01

Disturbances, both human-induced and natural, may re-shape ecosystems by influencing their composition, structure, and functional processes. Plateau zokor (Eospalax baileyi) is a typical subterranean rodent endemic to Qinghai-Tibetan Plateau (QTP), which are considered ecosystem engineers influencing the alpine ecosystem function. It is also regarded as a pest aggravating the degradation of overgrazed grassland and subject to regular control in QTP since 1950s. Climate change has been predicted in this region but little research exists exploring its impact on such subterranean rodent populations. Using plateau zokor as a model, through maximum entropy niche-based modeling (Maxent) and sustainable habitat models, we investigate zokor habitat dynamics driven by the future climate scenarios. Our models project that zokor suitable habitat will increase by 6.25% in 2050 in QTP. The predication indicated more threats in terms of grassland degradation as zokor suitable habitat will increase in 2050. Distribution of zokors will shift much more in their southern range with lower elevation compare to northern range with higher elevation. The estimated distance of shift ranges from 1 km to 94 km from current distribution. Grassland management should take into account such predictions in order to design mitigation measures to prevent further grassland degradation in QTP under climate change scenarios. PMID:26406891

Coupling Agent-Based and Groundwater Modeling to Explore Demand Management Strategies for Shared Resources

NASA Astrophysics Data System (ADS)

Al-Amin, S.

2015-12-01

Municipal water demands in growing population centers in the arid southwest US are typically met through increased groundwater withdrawals. Hydro-climatic uncertainties attributed to climate change and land use conversions may also alter demands and impact the replenishment of groundwater supply. Groundwater aquifers are not necessarily confined within municipal and management boundaries, and multiple diverse agencies may manage a shared resource in a decentralized approach, based on individual concerns and resources. The interactions among water managers, consumers, and the environment influence the performance of local management strategies and regional groundwater resources. This research couples an agent-based modeling (ABM) framework and a groundwater model to analyze the effects of different management approaches on shared groundwater resources. The ABM captures the dynamic interactions between household-level consumers and policy makers to simulate water demands under climate change and population growth uncertainties. The groundwater model is used to analyze the relative effects of management approaches on reducing demands and replenishing groundwater resources. The framework is applied for municipalities located in the Verde River Basin, Arizona that withdraw groundwater from the Verde Formation-Basin Fill-Carbonate aquifer system. Insights gained through this simulation study can be used to guide groundwater policy-making under changing hydro-climatic scenarios for a long-term planning horizon.

Assessing climate change impacts on wetlands in a flow regulated catchment: A case study in the Macquarie Marshes, Australia.

PubMed

Fu, Baihua; Pollino, Carmel A; Cuddy, Susan M; Andrews, Felix

2015-07-01

Globally wetlands are increasingly under threat due to changes in water regimes as a result of river regulation and climate change. We developed the Exploring CLimAte Impacts on Management (EXCLAIM) decision support system (DSS), which simulates flow-driven habitat condition for 16 vegetation species, 13 waterbird species and 4 fish groups in the Macquarie catchment, Australia. The EXCLAIM DSS estimates impacts to habitat condition, considering scenarios of climate change and water management. The model framework underlying the DSS is a probabilistic Bayesian network, and this approach was chosen to explicitly represent uncertainties in climate change scenarios and predicted ecological outcomes. The results suggest that the scenario with no climate change and no water resource development (i.e. flow condition without dams, weirs or water license entitlements, often regarded as a surrogate for 'natural' flow) consistently has the most beneficial outcomes for vegetation, waterbird and native fish. The 2030 dry climate change scenario delivers the poorest ecological outcomes overall, whereas the 2030 wet climate change scenario has beneficial outcomes for waterbird breeding, but delivers poor outcomes for river red gum and black box woodlands, and fish that prefer river channels as habitats. A formal evaluation of the waterbird breeding model showed that higher numbers of observed nest counts are typically associated with higher modelled average breeding habitat conditions. The EXCLAIM DSS provides a generic framework to link hydrology and ecological habitats for a large number of species, based on best available knowledge of their flood requirements. It is a starting point towards developing an integrated tool for assessing climate change impacts on wetland ecosystems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Species-free species distribution models describe macroecological properties of protected area networks.

PubMed

Robinson, Jason L; Fordyce, James A

2017-01-01

Among the greatest challenges facing the conservation of plants and animal species in protected areas are threats from a rapidly changing climate. An altered climate creates both challenges and opportunities for improving the management of protected areas in networks. Increasingly, quantitative tools like species distribution modeling are used to assess the performance of protected areas and predict potential responses to changing climates for groups of species, within a predictive framework. At larger geographic domains and scales, protected area network units have spatial geoclimatic properties that can be described in the gap analysis typically used to measure or aggregate the geographic distributions of species (stacked species distribution models, or S-SDM). We extend the use of species distribution modeling techniques in order to model the climate envelope (or "footprint") of individual protected areas within a network of protected areas distributed across the 48 conterminous United States and managed by the US National Park System. In our approach we treat each protected area as the geographic range of a hypothetical endemic species, then use MaxEnt and 5 uncorrelated BioClim variables to model the geographic distribution of the climatic envelope associated with each protected area unit (modeling the geographic area of park units as the range of a species). We describe the individual and aggregated climate envelopes predicted by a large network of 163 protected areas and briefly illustrate how macroecological measures of geodiversity can be derived from our analysis of the landscape ecological context of protected areas. To estimate trajectories of change in the temporal distribution of climatic features within a protected area network, we projected the climate envelopes of protected areas in current conditions onto a dataset of predicted future climatic conditions. Our results suggest that the climate envelopes of some parks may be locally unique or have narrow geographic distributions, and are thus prone to future shifts away from the climatic conditions in these parks in current climates. In other cases, some parks are broadly similar to large geographic regions surrounding the park or have climatic envelopes that may persist into near-term climate change. Larger parks predict larger climatic envelopes, in current conditions, but on average the predicted area of climate envelopes are smaller in our single future conditions scenario. Individual units in a protected area network may vary in the potential for climate adaptation, and adaptive management strategies for the network should account for the landscape contexts of the geodiversity or climate diversity within individual units. Conservation strategies, including maintaining connectivity, assessing the feasibility of assisted migration and other landscape restoration or enhancements can be optimized using analysis methods to assess the spatial properties of protected area networks in biogeographic and macroecological contexts.

Critical appraisal of assumptions in chains of model calculations used to project local climate impacts for adaptation decision support—the case of Baakse Beek

NASA Astrophysics Data System (ADS)

van der Sluijs, Jeroen P.; Arjan Wardekker, J.

2015-04-01

In order to enable anticipation and proactive adaptation, local decision makers increasingly seek detailed foresight about regional and local impacts of climate change. To this end, the Netherlands Models and Data-Centre implemented a pilot chain of sequentially linked models to project local climate impacts on hydrology, agriculture and nature under different national climate scenarios for a small region in the east of the Netherlands named Baakse Beek. The chain of models sequentially linked in that pilot includes a (future) weather generator and models of respectively subsurface hydrogeology, ground water stocks and flows, soil chemistry, vegetation development, crop yield and nature quality. These models typically have mismatching time step sizes and grid cell sizes. The linking of these models unavoidably involves the making of model assumptions that can hardly be validated, such as those needed to bridge the mismatches in spatial and temporal scales. Here we present and apply a method for the systematic critical appraisal of model assumptions that seeks to identify and characterize the weakest assumptions in a model chain. The critical appraisal of assumptions presented in this paper has been carried out ex-post. For the case of the climate impact model chain for Baakse Beek, the three most problematic assumptions were found to be: land use and land management kept constant over time; model linking of (daily) ground water model output to the (yearly) vegetation model around the root zone; and aggregation of daily output of the soil hydrology model into yearly input of a so called ‘mineralization reduction factor’ (calculated from annual average soil pH and daily soil hydrology) in the soil chemistry model. Overall, the method for critical appraisal of model assumptions presented and tested in this paper yields a rich qualitative insight in model uncertainty and model quality. It promotes reflectivity and learning in the modelling community, and leads to well informed recommendations for model improvement.

Assessing the response of runoff to climate change and human activities for a typical basin in the Northern Taihang Mountain, China

NASA Astrophysics Data System (ADS)

Wang, Jinfeng; Gao, Yanchuan; Wang, Sheng

2018-04-01

Climate change and human activities are the two main factors on runoff change. Quantifying the contribution of climate change and human activities on runoff change is important for water resources planning and management. In this study, the variation trend and abrupt change point of hydro-meteorological factors during 1960-2012 were detected by using the Mann-Kendall test and Pettitt change-point statistics. Then the runoff was simulated by SWAT model. The contribution of climate change and human activities on runoff change was calculated based on the SWAT model and the elasticity coefficient method. The results showed that in contrast to the increasing trend for annual temperature, the significant decreasing trends were detected for annual runoff and precipitation, with an abrupt change point in 1982. The simulated results of SWAT had good consistency with observed ones, and the values of R2 and E_{NS} all exceeded 0.75. The two methods used for assessing the contribution of climate change and human activities on runoff reduction yielded consistent results. The contribution of climate change (precipitation reduction and temperature rise) was {˜ }37.5%, while the contribution of human activities (the increase of economic forest and built-up land, hydrologic projects) was {˜ }62.5%.

Leaf-trait plasticity and species vulnerability to climate change in a Mongolian steppe.

PubMed

Liancourt, Pierre; Boldgiv, Bazartseren; Song, Daniel S; Spence, Laura A; Helliker, Brent R; Petraitis, Peter S; Casper, Brenda B

2015-09-01

Climate change is expected to modify plant assemblages in ways that will have major consequences for ecosystem functions. How climate change will affect community composition will depend on how individual species respond, which is likely related to interspecific differences in functional traits. The extraordinary plasticity of some plant traits is typically neglected in assessing how climate change will affect different species. In the Mongolian steppe, we examined whether leaf functional traits under ambient conditions and whether plasticity in these traits under altered climate could explain climate-induced biomass responses in 12 co-occurring plant species. We experimentally created three probable climate change scenarios and used a model selection procedure to determine the set of baseline traits or plasticity values that best explained biomass response. Under all climate change scenarios, plasticity for at least one leaf trait correlated with change in species performance, while functional leaf-trait values in ambient conditions did not. We demonstrate that trait plasticity could play a critical role in vulnerability of species to a rapidly changing environment. Plasticity should be considered when examining how climate change will affect plant performance, species' niche spaces, and ecological processes that depend on plant community composition. © 2015 John Wiley & Sons Ltd.

Demand Shifting with Thermal Mass in Large Commercial Buildings in a California Hot Climate Zone

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

Xu, Peng; Yin, Rongxin; Brown, Carrie

2009-06-01

The potential for using building thermal mass for load shifting and peak energy demand reduction has been demonstrated in a number of simulation, laboratory, and field studies. Previous Lawrence Berkeley National Laboratory research has demonstrated that the approach is very effective in cool and moderately warm climate conditions (California Climate Zones 2-4). However, this method had not been tested in hotter climate zones. This project studied the potential of pre-cooling the building early in the morning and increasing temperature setpoints during peak hours to reduce cooling-related demand in two typical office buildings in hotter California climates ? one in Visaliamore » (CEC Climate Zone 13) and the other in San Bernardino (CEC Climate Zone 10). The conclusion of the work to date is that pre-cooling in hotter climates has similar potential to that seen previously in cool and moderate climates. All other factors being equal, results to date indicate that pre-cooling increases the depth (kW) and duration (kWh) of the possible demand shed of a given building. The effectiveness of night pre-cooling in typical office building under hot weather conditions is very limited. However, night pre-cooling is helpful for office buildings with an undersized HVAC system. Further work is required to duplicate the tests in other typical buildings and in other hot climate zones and prove that pre-cooling is truly effective.« less

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.

Impacts of boundary condition changes on regional climate projections over West Africa

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

Coupling records of fluvial activity from the last interglacial-glacial cycle with climate forcing using both geochronology and numerical modelling

NASA Astrophysics Data System (ADS)

Briant, Rebecca; Mottram, Gareth; Wainwright, John

2010-05-01

River systems are a critical component of the landscape. An understanding of their response to variations in the Earth's climate is vital in light of the expected changes in global climate (e.g. 1.8 to 4.8°C temperature rise) that are forecast to occur over the next c. 100 years. Over the longer term, it becomes increasingly likely that the changes we will see may even be of a magnitude for which the most appropriate analogue we have is the glacial-interglacial scale (c. 10°C temperature change) and other climate changes typical of the Quaternary period (last 2 million years). Therefore it is crucial to apply our understanding of climate-driven changes during the Quaternary to future projections of both climate and landscape change, especially since landscape instability is a key characteristic of the Quaternary. Linking river activity to climate requires both the recognition of potentially climate-driven changes within the fluvial sedimentary record and the linkage of these to external climate records using various geochronological techniques. To this end, this paper firstly presents results from the Welland catchment, Fenland Basin where climatically-driven phases of river activity have been identified using detailed sedimentological analysis and palaeontological environmental reconstruction. Dating of these using radiocarbon and optically-stimulated luminescence dating has shown broad correspondence to external climate fluctuations at a marine isotope substage scale over the last interglacial-glacial cycle (MIS 5d onwards). The precision and accuracy of the two different age techniques varies in different parts of this time period and this will be discussed. Limitations in the precision of these geochronological techniques have prompted the use of a further, complementary to improve understanding of these sequences, i.e. ensemble numerical modeling. The rationale behind this approach is that river response to climate can be traced within the model and validated against the known geological record. If the known geological record can be replicated, then the detailed linkages between climate and river activity shown in the model can be used understand to the relationships between climate change and river activity more clearly. This paper will present the results of three-dimensional cellular automata modeling of the Welland catchment, compare them to the geological record, and draw out what this means for our understanding of earth surface processes.

Models of reforestation productivity and carbon sequestration for land use and climate change adaptation planning in South Australia.

PubMed

Hobbs, Trevor J; Neumann, Craig R; Meyer, Wayne S; Moon, Travis; Bryan, Brett A

2016-10-01

Environmental management and regional land use planning has become more complex in recent years as growing world population, climate change, carbon markets and government policies for sustainability have emerged. Reforestation and agroforestry options for environmental benefits, carbon sequestration, economic development and biodiversity conservation are now important considerations of land use planners. New information has been collected and regionally-calibrated models have been developed to facilitate better regional land use planning decisions and counter the limitations of currently available models of reforestation productivity and carbon sequestration. Surveys of above-ground biomass of 264 reforestation sites (132 woodlots, 132 environmental plantings) within the agricultural regions of South Australia were conducted, and combined with spatial information on climate and soils, to develop new spatial and temporal models of plant density and above-ground biomass productivity from reforestation. The models can be used to estimate productivity and total carbon sequestration (i.e. above-ground + below-ground biomass) under a continuous range of planting designs (e.g. variable proportions of trees and shrubs or plant densities), timeframes and future climate scenarios. Representative spatial models (1 ha resolution) for 3 reforestation designs (i.e. woodlots, typical environmental planting, biodiverse environmental plantings) × 3 timeframes (i.e. 25, 45, 65 years) × 4 possible climates (i.e. no change, mild, moderate, severe warming and drying) were generated (i.e. 36 scenarios) for use within land use planning tools. Copyright © 2016 Elsevier Ltd. All rights reserved.

From climate-change spaghetti to climate-change distributions for 21st Century California

USGS Publications Warehouse

Dettinger, M.D.

2005-01-01

The uncertainties associated with climate-change projections for California are unlikely to disappear any time soon, and yet important long-term decisions will be needed to accommodate those potential changes. Projection uncertainties have typically been addressed by analysis of a few scenarios, chosen based on availability or to capture the extreme cases among available projections. However, by focusing on more common projections rather than the most extreme projections (using a new resampling method), new insights into current projections emerge: (1) uncertainties associated with future greenhouse-gas emissions are comparable with the differences among climate models, so that neither source of uncertainties should be neglected or underrepresented; (2) twenty-first century temperature projections spread more, overall, than do precipitation scenarios; (3) projections of extremely wet futures for California are true outliers among current projections; and (4) current projections that are warmest tend, overall, to yield a moderately drier California, while the cooler projections yield a somewhat wetter future. The resampling approach applied in this paper also provides a natural opportunity to objectively incorporate measures of model skill and the likelihoods of various emission scenarios into future assessments.

Global Warming in Geologic Time

ScienceCinema

Archer, David

2018-01-01

The notion is pervasive in the climate science community and in the public at large that the climate impacts of fossil fuel CO2 release will only persist for a few centuries. This conclusion has no basis in theory or models of the atmosphere / ocean carbon cycle, which we review here. The largest fraction of the CO2 recovery will take place on time scales of centuries, as CO2 invades the ocean, but a significant fraction of the fossil fuel CO2, ranging in published models in the literature from 20-60%, remains airborne for a thousand years or longer. Ultimate recovery takes place on time scales of hundreds of thousands of years, a geologic longevity typically associated in public perceptions with nuclear waste. The glacial / interglacial climate cycles demonstrate that ice sheets and sea level respond dramatically to millennial-timescale changes in climate forcing. There are also potential positive feedbacks in the carbon cycle, including methane hydrates in the ocean, and peat frozen in permafrost, that are most sensitive to the long tail of the fossil fuel CO2 in the atmosphere.

An Assessment of Actual and Potential Building Climate Zone Change and Variability From the Last 30 Years Through 2100 Using NASA's MERRA and CMIP5 Simulations

NASA Technical Reports Server (NTRS)

Stackhouse, Paul W., Jr.; Chandler, William S.; Hoell, James M.; Westberg, David; Zhang, Taiping

2015-01-01

Background: In the US, residential and commercial building infrastructure combined consumes about 40% of total energy usage and emits about 39% of total CO2 emission (DOE/EIA "Annual Energy Outlook 2013"). Building codes, as used by local and state enforcement entities are typically tied to the dominant climate within an enforcement jurisdiction classified according to various climate zones. These climate zones are based upon a 30-year average of local surface observations and are developed by DOE and ASHRAE. Establishing the current variability and potential changes to future building climate zones is very important for increasing the energy efficiency of buildings and reducing energy costs and emissions in the future. Objectives: This paper demonstrates the usefulness of using NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA) atmospheric data assimilation to derive the DOE/ASHRAE building climate zone maps and then using MERRA to define the last 30 years of variability in climate zones for the Continental US. An atmospheric assimilation is a global atmospheric model optimized to satellite, atmospheric and surface in situ measurements. Using MERRA as a baseline, we then evaluate the latest Climate Model Inter-comparison Project (CMIP) climate model Version 5 runs to assess potential variability in future climate zones under various assumptions. Methods: We derive DOE/ASHRAE building climate zones using surface and temperature data products from MERRA. We assess these zones using the uncertainties derived by comparison to surface measurements. Using statistical tests, we evaluate variability of the climate zones in time and assess areas in the continental US for statistically significant trends by region. CMIP 5 produced a data base of over two dozen detailed climate model runs under various greenhouse gas forcing assumptions. We evaluate the variation in building climate zones for 3 different decades using an ensemble and quartile statistics to provide an assessment of potential building climate zone changes relative to the uncertainties demonstrated using MERRA. Findings and Conclusions: These results show that there is a statistically significant increase in the area covered by warmer climate zones and a tendency for a reduction of area in colder climate zones in some limited regions. The CMIP analysis shows that models vary from relatively little building climate zone change for the least sensitive and conservation assumptions to a warming of at most 3 zones for certain areas, particularly the north central US by the end of the 21st century.

Evaluating simulated functional trait patterns and quantifying modelled trait diversity effects on simulated ecosystem fluxes

NASA Astrophysics Data System (ADS)

Pavlick, R.; Schimel, D.

2014-12-01

Dynamic Global Vegetation Models (DGVMs) typically employ only a small set of Plant Functional Types (PFTs) to represent the vast diversity of observed vegetation forms and functioning. There is growing evidence, however, that this abstraction may not adequately represent the observed variation in plant functional traits, which is thought to play an important role for many ecosystem functions and for ecosystem resilience to environmental change. The geographic distribution of PFTs in these models is also often based on empirical relationships between present-day climate and vegetation patterns. Projections of future climate change, however, point toward the possibility of novel regional climates, which could lead to no-analog vegetation compositions incompatible with the PFT paradigm. Here, we present results from the Jena Diversity-DGVM (JeDi-DGVM), a novel traits-based vegetation model, which simulates a large number of hypothetical plant growth strategies constrained by functional tradeoffs, thereby allowing for a more flexible temporal and spatial representation of the terrestrial biosphere. First, we compare simulated present-day geographical patterns of functional traits with empirical trait observations (in-situ and from airborne imaging spectroscopy). The observed trait patterns are then used to improve the tradeoff parameterizations of JeDi-DGVM. Finally, focusing primarily on the simulated leaf traits, we run the model with various amounts of trait diversity. We quantify the effects of these modeled biodiversity manipulations on simulated ecosystem fluxes and stocks for both present-day conditions and transient climate change scenarios. The simulation results reveal that the coarse treatment of plant functional traits by current PFT-based vegetation models may contribute substantial uncertainty regarding carbon-climate feedbacks. Further development of trait-based models and further investment in global in-situ and spectroscopic plant trait observations are needed.

The Impact of Climate Projection Method on the Analysis of Climate Change in Semi-arid Basins

NASA Astrophysics Data System (ADS)

Halper, E.; Shamir, E.

2016-12-01

In small basins with arid climates, rainfall characteristics are highly variable and stream flow is tightly coupled with the nuances of rainfall events (e.g. hourly precipitation patterns Climate change assessments in these basins typically employ CMIP5 projections downscaled with Bias Corrected Statistical Downscaling and Bias Correction/Constructed Analogs (BCSD-BCCA) methods, but these products have drawbacks. Specifically, BCSD-BCCA these projections do not explicitly account for localized physical precipitation mechanisms (e.g. monsoon and snowfall) that are essential to many hydrological systems in the U. S. Southwest. An investigation of the impact of different types of precipitation projections for two kinds of hydrologic studies is being conducted under the U.S. Bureau of Reclamation's Science and Technology Grant Program. An innovative modeling framework consisting of a weather generator of likely hourly precipitation scenarios, coupled with rainfall-runoff, river routing and groundwater models, has been developed in the Nogales, Arizona area. This framework can simulate the impact of future climate on municipal water operations. This framework allows the rigorous comparison of the BCSD-BCCA methods with alternative approaches including rainfall output from dynamical downscaled Regional Climate Models (RCM), a stochastic rainfall generator forced by either Global Climate Models (GCM) or RCM, and projections using historical records conditioned on either GCM or RCM. The results will provide guide for the use of climate change projections into hydrologic studies of semi-arid areas. The project extends this comparison to analyses of flood control. Large flows on the Bill Williams River are a concern for the operation of dams along the Lower Colorado River. After adapting the weather generator for this region, we will evaluate the model performance for rainfall and stream flow, with emphasis on statistical features important to the specific needs of flood management. The end product of the research is to develop a test to guide selection of a precipitation projection method (including downscaling procedure) for a given region and objective.

Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models

PubMed Central

Drijfhout, Sybren; Bathiany, Sebastian; Beaulieu, Claudie; Brovkin, Victor; Claussen, Martin; Huntingford, Chris; Scheffer, Marten; Sgubin, Giovanni; Swingedouw, Didier

2015-01-01

Abrupt transitions of regional climate in response to the gradual rise in atmospheric greenhouse gas concentrations are notoriously difficult to foresee. However, such events could be particularly challenging in view of the capacity required for society and ecosystems to adapt to them. We present, to our knowledge, the first systematic screening of the massive climate model ensemble informing the recent Intergovernmental Panel on Climate Change report, and reveal evidence of 37 forced regional abrupt changes in the ocean, sea ice, snow cover, permafrost, and terrestrial biosphere that arise after a certain global temperature increase. Eighteen out of 37 events occur for global warming levels of less than 2°, a threshold sometimes presented as a safe limit. Although most models predict one or more such events, any specific occurrence typically appears in only a few models. We find no compelling evidence for a general relation between the overall number of abrupt shifts and the level of global warming. However, we do note that abrupt changes in ocean circulation occur more often for moderate warming (less than 2°), whereas over land they occur more often for warming larger than 2°. Using a basic proportion test, however, we find that the number of abrupt shifts identified in Representative Concentration Pathway (RCP) 8.5 scenarios is significantly larger than in other scenarios of lower radiative forcing. This suggests the potential for a gradual trend of destabilization of the climate with respect to such shifts, due to increasing global mean temperature change. PMID:26460042

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.

Extending the Confrontation of Weather and Climate Models from Soil Moisture to Surface Flux Data

NASA Astrophysics Data System (ADS)

Dirmeyer, P.; Chen, L.; Wu, J.

2016-12-01

The atmosphere and land components of weather and climate models are typically developed separately and coupled as a last step before new model versions are released. Separate testing of land surface models (LSMs) and atmospheric models is often quite extensive in the development phase, but validation of coupled land-atmosphere behavior is often minimal if performed at all. This is partly because of this piecemeal model development approach and partly because the necessary in situ data to confront coupled land-atmosphere models (LAMs) has been meager until quite recently. Over the past 10-20 years there has been a growing number of networks of measurements of land surface states, surface fluxes, radiation and near-surface meteorology, although they have been largely uncoordinated and frequently incomplete across the range of variables necessary to validate LAMs. We extend recent work "confronting" a variety of LSMs and LAMs with in situ observations of soil moisture from cross-standardized networks to comparisons with measurements of surface latent and sensible heat fluxes at FLUXNET sites in a variety of climate regimes around the world. The motivation is to determine how well LSMs represent observed statistics of variability and co-variability, how much models differ from one another, and how those statistics change when the LSMs are coupled to atmospheric models. Furthermore, comparisons are made to several LAMs in both open-loop (free running) and reanalysis configurations. This shows to what extent data assimilation can constrain the processes involved in flux variability, and helps illuminate model development pathways to improve coupled land-atmosphere interactions in weather and climate models.

Permafrost Favourability Index: Spatial modelling in the French Alps using a Rock Glacier Inventory

NASA Astrophysics Data System (ADS)

Marcer, Marco; Bodin, Xavier; Brenning, Alexander; Schoeneich, Philippe; Charvet, Raphaële; Gottardi, Frédéric

2017-12-01

In the present study we used the first rock glacier inventory for the entire French Alps to model spatial permafrost distribution in the region. The inventory, which does not originally belong to this study, was revised by the authors in order to obtain a database suitable for statistical modelling. Climatic and topographic data evaluated at the rock glacier locations were used as predictor variables in a Generalized Linear Model. Model performances are strong, suggesting that, in agreement with several previous studies, this methodology is able to model accurately rock glacier distribution. A methodology to estimate model uncertainties is proposed, revealing that the subjectivity in the interpretation of rock glacier activity and contours may substantially bias the model. The model highlights a North-South trend in the regional pattern of permafrost distribution which is attributed to the climatic influences of the Atlantic and Mediterranean climates. Further analysis suggest that lower amounts of precipitation in the early winter and a thinner snow cover, as typically found in the Mediterranean area, could contribute to the existence of permafrost at higher temperatures compared to the Northern Alps. A comparison with the Alpine Permafrost Index Map (APIM) shows no major differences with our model, highlighting the very good predictive power of the APIM despite its tendency to slightly overestimate permafrost extension with respect to our database. The use of rock glaciers as indicators of permafrost existence despite their time response to climate change is discussed and an interpretation key is proposed in order to ensure the proper use of the model for research as well as for operational purposes.

Projected Changes in Persistent Extreme Warm-Season Weather Events: The Role of Quasi-Resonant Amplification

NASA Astrophysics Data System (ADS)

Mann, M. E.; Rahmstorf, S.; Kornhuber, K.; Steinman, B. A.; Miller, S. K.; Coumou, D.

2017-12-01

Persistent episodes of extreme weather in the Northern Hemisphere summer are typically associated with high-amplitude quasi-stationary atmospheric Rossby waves with zonal wavenumbers. Such disturbances are favoured by the phenomenon of Quasi-Resonant Amplification (QRA). A fingerprint for the occurrence of QRA can be defined in terms of the zonally-averaged surface temperature field. Examining future state-of-the-art (CMIP5) climate model projections we find that such events are likely to increase by 50% over the next century under business-as-usual carbon emissions, but there is considerable variation among climate models, with some models predicting a near tripling of QRA events by the end of the century. These results are strongly dependent on assumptions regarding the prominence of changes in radiative forcing associated with anthropogenic aerosols over the next century.

Towards a climate-dependent paradigm of ammonia emission and deposition

PubMed Central

Sutton, Mark A.; Reis, Stefan; Riddick, Stuart N.; Dragosits, Ulrike; Nemitz, Eiko; Theobald, Mark R.; Tang, Y. Sim; Braban, Christine F.; Vieno, Massimo; Dore, Anthony J.; Mitchell, Robert F.; Wanless, Sarah; Daunt, Francis; Fowler, David; Blackall, Trevor D.; Milford, Celia; Flechard, Chris R.; Loubet, Benjamin; Massad, Raia; Cellier, Pierre; Personne, Erwan; Coheur, Pierre F.; Clarisse, Lieven; Van Damme, Martin; Ngadi, Yasmine; Clerbaux, Cathy; Skjøth, Carsten Ambelas; Geels, Camilla; Hertel, Ole; Wichink Kruit, Roy J.; Pinder, Robert W.; Bash, Jesse O.; Walker, John T.; Simpson, David; Horváth, László; Misselbrook, Tom H.; Bleeker, Albert; Dentener, Frank; de Vries, Wim

2013-01-01

Existing descriptions of bi-directional ammonia (NH3) land–atmosphere exchange incorporate temperature and moisture controls, and are beginning to be used in regional chemical transport models. However, such models have typically applied simpler emission factors to upscale the main NH3 emission terms. While this approach has successfully simulated the main spatial patterns on local to global scales, it fails to address the environment- and climate-dependence of emissions. To handle these issues, we outline the basis for a new modelling paradigm where both NH3 emissions and deposition are calculated online according to diurnal, seasonal and spatial differences in meteorology. We show how measurements reveal a strong, but complex pattern of climatic dependence, which is increasingly being characterized using ground-based NH3 monitoring and satellite observations, while advances in process-based modelling are illustrated for agricultural and natural sources, including a global application for seabird colonies. A future architecture for NH3 emission–deposition modelling is proposed that integrates the spatio-temporal interactions, and provides the necessary foundation to assess the consequences of climate change. Based on available measurements, a first empirical estimate suggests that 5°C warming would increase emissions by 42 per cent (28–67%). Together with increased anthropogenic activity, global NH3 emissions may increase from 65 (45–85) Tg N in 2008 to reach 132 (89–179) Tg by 2100. PMID:23713128

Towards a climate-dependent paradigm of ammonia emission and deposition.

PubMed

Sutton, Mark A; Reis, Stefan; Riddick, Stuart N; Dragosits, Ulrike; Nemitz, Eiko; Theobald, Mark R; Tang, Y Sim; Braban, Christine F; Vieno, Massimo; Dore, Anthony J; Mitchell, Robert F; Wanless, Sarah; Daunt, Francis; Fowler, David; Blackall, Trevor D; Milford, Celia; Flechard, Chris R; Loubet, Benjamin; Massad, Raia; Cellier, Pierre; Personne, Erwan; Coheur, Pierre F; Clarisse, Lieven; Van Damme, Martin; Ngadi, Yasmine; Clerbaux, Cathy; Skjøth, Carsten Ambelas; Geels, Camilla; Hertel, Ole; Wichink Kruit, Roy J; Pinder, Robert W; Bash, Jesse O; Walker, John T; Simpson, David; Horváth, László; Misselbrook, Tom H; Bleeker, Albert; Dentener, Frank; de Vries, Wim

2013-07-05

Existing descriptions of bi-directional ammonia (NH3) land-atmosphere exchange incorporate temperature and moisture controls, and are beginning to be used in regional chemical transport models. However, such models have typically applied simpler emission factors to upscale the main NH3 emission terms. While this approach has successfully simulated the main spatial patterns on local to global scales, it fails to address the environment- and climate-dependence of emissions. To handle these issues, we outline the basis for a new modelling paradigm where both NH3 emissions and deposition are calculated online according to diurnal, seasonal and spatial differences in meteorology. We show how measurements reveal a strong, but complex pattern of climatic dependence, which is increasingly being characterized using ground-based NH3 monitoring and satellite observations, while advances in process-based modelling are illustrated for agricultural and natural sources, including a global application for seabird colonies. A future architecture for NH3 emission-deposition modelling is proposed that integrates the spatio-temporal interactions, and provides the necessary foundation to assess the consequences of climate change. Based on available measurements, a first empirical estimate suggests that 5°C warming would increase emissions by 42 per cent (28-67%). Together with increased anthropogenic activity, global NH3 emissions may increase from 65 (45-85) Tg N in 2008 to reach 132 (89-179) Tg by 2100.

iGen: An automated generator of simplified models with provable error bounds.

NASA Astrophysics Data System (ADS)

Tang, D.; Dobbie, S.

2009-04-01

Climate models employ various simplifying assumptions and parameterisations in order to increase execution speed. However, in order to draw conclusions about the Earths climate from the results of a climate simulation it is necessary to have information about the error that these assumptions and parameterisations introduce. A novel computer program, called iGen, is being developed which automatically generates fast, simplified models by analysing the source code of a slower, high resolution model. The resulting simplified models have provable bounds on error compared to the high resolution model and execute at speeds that are typically orders of magnitude faster. iGen's input is a definition of the prognostic variables of the simplified model, a set of bounds on acceptable error and the source code of a model that captures the behaviour of interest. In the case of an atmospheric model, for example, this would be a global cloud resolving model with very high resolution. Although such a model would execute far too slowly to be used directly in a climate model, iGen never executes it. Instead, it converts the code of the resolving model into a mathematical expression which is then symbolically manipulated and approximated to form a simplified expression. This expression is then converted back into a computer program and output as a simplified model. iGen also derives and reports formal bounds on the error of the simplified model compared to the resolving model. These error bounds are always maintained below the user-specified acceptable error. Results will be presented illustrating the success of iGen's analysis of a number of example models. These extremely encouraging results have lead on to work which is currently underway to analyse a cloud resolving model and so produce an efficient parameterisation of moist convection with formally bounded error.

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.

Predicting tree biomass growth in the temperate-boreal ecotone: is tree size, age, competition or climate response most important?

USGS Publications Warehouse

Foster, Jane R.; Finley, Andrew O.; D'Amato, Anthony W.; Bradford, John B.; Banerjee, Sudipto

2016-01-01

As global temperatures rise, variation in annual climate is also changing, with unknown consequences for forest biomes. Growing forests have the ability to capture atmospheric CO2and thereby slow rising CO2 concentrations. Forests’ ongoing ability to sequester C depends on how tree communities respond to changes in climate variation. Much of what we know about tree and forest response to climate variation comes from tree-ring records. Yet typical tree-ring datasets and models do not capture the diversity of climate responses that exist within and among trees and species. We address this issue using a model that estimates individual tree response to climate variables while accounting for variation in individuals’ size, age, competitive status, and spatially structured latent covariates. Our model allows for inference about variance within and among species. We quantify how variables influence aboveground biomass growth of individual trees from a representative sample of 15 northern or southern tree species growing in a transition zone between boreal and temperate biomes. Individual trees varied in their growth response to fluctuating mean annual temperature and summer moisture stress. The variation among individuals within a species was wider than mean differences among species. The effects of mean temperature and summer moisture stress interacted, such that warm years produced positive responses to summer moisture availability and cool years produced negative responses. As climate models project significant increases in annual temperatures, growth of species likeAcer saccharum, Quercus rubra, and Picea glauca will vary more in response to summer moisture stress than in the past. The magnitude of biomass growth variation in response to annual climate was 92–95% smaller than responses to tree size and age. This means that measuring or predicting the physical structure of current and future forests could tell us more about future C dynamics than growth responses related to climate change alone.

Predicting tree biomass growth in the temperate-boreal ecotone: Is tree size, age, competition, or climate response most important?

PubMed

Foster, Jane R; Finley, Andrew O; D'Amato, Anthony W; Bradford, John B; Banerjee, Sudipto

2016-06-01

As global temperatures rise, variation in annual climate is also changing, with unknown consequences for forest biomes. Growing forests have the ability to capture atmospheric CO2 and thereby slow rising CO2 concentrations. Forests' ongoing ability to sequester C depends on how tree communities respond to changes in climate variation. Much of what we know about tree and forest response to climate variation comes from tree-ring records. Yet typical tree-ring datasets and models do not capture the diversity of climate responses that exist within and among trees and species. We address this issue using a model that estimates individual tree response to climate variables while accounting for variation in individuals' size, age, competitive status, and spatially structured latent covariates. Our model allows for inference about variance within and among species. We quantify how variables influence aboveground biomass growth of individual trees from a representative sample of 15 northern or southern tree species growing in a transition zone between boreal and temperate biomes. Individual trees varied in their growth response to fluctuating mean annual temperature and summer moisture stress. The variation among individuals within a species was wider than mean differences among species. The effects of mean temperature and summer moisture stress interacted, such that warm years produced positive responses to summer moisture availability and cool years produced negative responses. As climate models project significant increases in annual temperatures, growth of species like Acer saccharum, Quercus rubra, and Picea glauca will vary more in response to summer moisture stress than in the past. The magnitude of biomass growth variation in response to annual climate was 92-95% smaller than responses to tree size and age. This means that measuring or predicting the physical structure of current and future forests could tell us more about future C dynamics than growth responses related to climate change alone. © 2015 John Wiley & Sons Ltd.

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

Water-Constrained Electric Sector Capacity Expansion Modeling Under Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Cohen, S. M.; Macknick, J.; Miara, A.; Vorosmarty, C. J.; Averyt, K.; Meldrum, J.; Corsi, F.; Prousevitch, A.; Rangwala, I.

2015-12-01

Over 80% of U.S. electricity generation uses a thermoelectric process, which requires significant quantities of water for power plant cooling. This water requirement exposes the electric sector to vulnerabilities related to shifts in water availability driven by climate change as well as reductions in power plant efficiencies. Electricity demand is also sensitive to climate change, which in most of the United States leads to warming temperatures that increase total cooling-degree days. The resulting demand increase is typically greater for peak demand periods. This work examines the sensitivity of the development and operations of the U.S. electric sector to the impacts of climate change using an electric sector capacity expansion model that endogenously represents seasonal and local water resource availability as well as climate impacts on water availability, electricity demand, and electricity system performance. Capacity expansion portfolios and water resource implications from 2010 to 2050 are shown at high spatial resolution under a series of climate scenarios. Results demonstrate the importance of water availability for future electric sector capacity planning and operations, especially under more extreme hotter and drier climate scenarios. In addition, region-specific changes in electricity demand and water resources require region-specific responses that depend on local renewable resource availability and electricity market conditions. Climate change and the associated impacts on water availability and temperature can affect the types of power plants that are built, their location, and their impact on regional water resources.

Model tropical Atlantic biases underpin diminished Pacific decadal variability

NASA Astrophysics Data System (ADS)

McGregor, Shayne; Stuecker, Malte F.; Kajtar, Jules B.; England, Matthew H.; Collins, Mat

2018-06-01

Pacific trade winds have displayed unprecedented strengthening in recent decades1. This strengthening has been associated with east Pacific sea surface cooling2 and the early twenty-first-century slowdown in global surface warming2,3, amongst a host of other substantial impacts4-9. Although some climate models produce the timing of these recently observed trends10, they all fail to produce the trend magnitude2,11,12. This may in part be related to the apparent model underrepresentation of low-frequency Pacific Ocean variability and decadal wind trends2,11-13 or be due to a misrepresentation of a forced response1,14-16 or a combination of both. An increasingly prominent connection between the Pacific and Atlantic basins has been identified as a key driver of this strengthening of the Pacific trade winds12,17-20. Here we use targeted climate model experiments to show that combining the recent Atlantic warming trend with the typical climate model bias leads to a substantially underestimated response for the Pacific Ocean wind and surface temperature. The underestimation largely stems from a reduction and eastward shift of the atmospheric heating response to the tropical Atlantic warming trend. This result suggests that the recent Pacific trends and model decadal variability may be better captured by models with improved mean-state climatologies.

Applying "Climate" system to teaching basic climatology and raising public awareness of climate change issues

NASA Astrophysics Data System (ADS)

Gordova, Yulia; Okladnikov, Igor; Titov, Alexander; Gordov, Evgeny

2016-04-01

While there is a strong demand for innovation in digital learning, available training programs in the environmental sciences have no time to adapt to rapid changes in the domain content. A joint group of scientists and university teachers develops and implements an educational environment for new learning experiences in basics of climatic science and its applications. This so-called virtual learning laboratory "Climate" contains educational materials and interactive training courses developed to provide undergraduate and graduate students with profound understanding of changes in regional climate and environment. The main feature of this Laboratory is that students perform their computational tasks on climate modeling and evaluation and assessment of climate change using the typical tools of the "Climate" information-computational system, which are usually used by real-life practitioners performing such kind of research. Students have an opportunity to perform computational laboratory works using information-computational tools of the system and improve skills of their usage simultaneously with mastering the subject. We did not create an artificial learning environment to pass the trainings. On the contrary, the main purpose of association of the educational block and computational information system was to familiarize students with the real existing technologies for monitoring and analysis of data on the state of the climate. Trainings are based on technologies and procedures which are typical for Earth system sciences. Educational courses are designed to permit students to conduct their own investigations of ongoing and future climate changes in a manner that is essentially identical to the techniques used by national and international climate research organizations. All trainings are supported by lectures, devoted to the basic aspects of modern climatology, including analysis of current climate change and its possible impacts ensuring effective links between theory and practice. Along with its usage in graduate and postgraduate education, "Climate" is used as a framework for a developed basic information course on climate change for common public. In this course basic concepts and problems of modern climate change and its possible consequences are described for non-specialists. The course will also include links to relevant information resources on topical issues of Earth Sciences and a number of case studies, which are carried out for a selected region to consolidate the received knowledge.

Climate warming feedback from mountain birch forest expansion: reduced albedo dominates carbon uptake.

PubMed

de Wit, Heleen A; Bryn, Anders; Hofgaard, Annika; Karstensen, Jonas; Kvalevåg, Maria M; Peters, Glen P

2014-07-01

Expanding high-elevation and high-latitude forest has contrasting climate feedbacks through carbon sequestration (cooling) and reduced surface reflectance (warming), which are yet poorly quantified. Here, we present an empirically based projection of mountain birch forest expansion in south-central Norway under climate change and absence of land use. Climate effects of carbon sequestration and albedo change are compared using four emission metrics. Forest expansion was modeled for a projected 2.6 °C increase in summer temperature in 2100, with associated reduced snow cover. We find that the current (year 2000) forest line of the region is circa 100 m lower than its climatic potential due to land-use history. In the future scenarios, forest cover increased from 12% to 27% between 2000 and 2100, resulting in a 59% increase in biomass carbon storage and an albedo change from 0.46 to 0.30. Forest expansion in 2100 was behind its climatic potential, forest migration rates being the primary limiting factor. In 2100, the warming caused by lower albedo from expanding forest was 10 to 17 times stronger than the cooling effect from carbon sequestration for all emission metrics considered. Reduced snow cover further exacerbated the net warming feedback. The warming effect is considerably stronger than previously reported for boreal forest cover, because of the typically low biomass density in mountain forests and the large changes in albedo of snow-covered tundra areas. The positive climate feedback of high-latitude and high-elevation expanding forests with seasonal snow cover exceeds those of afforestation at lower elevation, and calls for further attention of both modelers and empiricists. The inclusion and upscaling of these climate feedbacks from mountain forests into global models is warranted to assess the potential global impacts. © 2013 John Wiley & Sons Ltd.

The evaluation of the climate change effects on maize and fennel cultivation by means of an hydrological physically based model: the case study of an irrigated district of southern Italy

NASA Astrophysics Data System (ADS)

Bonfante, A.; Alfieri, M. S.; Basile, A.; De Lorenzi, F.; Fiorentino, N.; Menenti, M.

2012-04-01

The effect of climate change on irrigated agricultural systems will be different from area to area depending on some factors as: (i) water availability, (ii) crop water demand (iii) soil hydrological behavior and (iv) irrigation management strategy. The adaptation of irrigated crop systems to future climate change can be supported by physically based model which simulate the water and heat fluxes in the soil-vegetation-atmosphere system. The aim of this work is to evaluate the effects of climate change on the heat and water balance of a maize-fennel rotation. This was applied to a on-demand irrigation district of Southern Italy ("Destra Sele", Campania Region, 22.645 ha). Two climate scenarios were considered, current climate (1961-1990) and future climate (2021-2050), the latter constructed by applying statistical downscaling to GCMs scenarios. For each climate scenario the soil moisture regime of the selected study area was calculated by means of a simulation model of the soil-water-atmosphere system (SWAP). Synthetic indicators of the soil water regimes (e.g., crop water stress index - CWSI, available water content) have been calculated and impacts evaluated taking into account the yield response functions to water availability of different cultivars. Different irrigation delivering strategies were also simulated. The hydrological model SWAP was applied to the representative soils of the whole area (20 soil units) for which the soil hydraulic properties were derived by means of pedo-transfer function (HYPRES) tested and validated on the typical soils in the study area. Upper boundary conditions were derived from two climate scenarios, i.e. current and future. Unit gradient in soil water potential was set as lower boundary condition. Crop-specific input data and model parameters were derived from field experiments, in the same area, where the SWAP model was calibrated and validated. The results obtained have shown a significant increase of CWSI in the future climate scenario, and some spatial patterns strongly influenced by the soils characteristics. Adaptability of different maize cultivars has been evaluated. The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008) Keywords: Plant Adaptative capacity, SWAP, Climate changes, Maize, Fennel

Visualization and Analysis of Climate Simulation Performance Data

NASA Astrophysics Data System (ADS)

Röber, Niklas; Adamidis, Panagiotis; Behrens, Jörg

2015-04-01

Visualization is the key process of transforming abstract (scientific) data into a graphical representation, to aid in the understanding of the information hidden within the data. Climate simulation data sets are typically quite large, time varying, and consist of many different variables sampled on an underlying grid. A large variety of climate models - and sub models - exist to simulate various aspects of the climate system. Generally, one is mainly interested in the physical variables produced by the simulation runs, but model developers are also interested in performance data measured along with these simulations. Climate simulation models are carefully developed complex software systems, designed to run in parallel on large HPC systems. An important goal thereby is to utilize the entire hardware as efficiently as possible, that is, to distribute the workload as even as possible among the individual components. This is a very challenging task, and detailed performance data, such as timings, cache misses etc. have to be used to locate and understand performance problems in order to optimize the model implementation. Furthermore, the correlation of performance data to the processes of the application and the sub-domains of the decomposed underlying grid is vital when addressing communication and load imbalance issues. High resolution climate simulations are carried out on tens to hundreds of thousands of cores, thus yielding a vast amount of profiling data, which cannot be analyzed without appropriate visualization techniques. This PICO presentation displays and discusses the ICON simulation model, which is jointly developed by the Max Planck Institute for Meteorology and the German Weather Service and in partnership with DKRZ. The visualization and analysis of the models performance data allows us to optimize and fine tune the model, as well as to understand its execution on the HPC system. We show and discuss our workflow, as well as present new ideas and solutions that greatly aided our understanding. The software employed is based on Avizo Green, ParaView and SimVis, as well as own developed software extensions.

Unlocking the climate riddle in forested ecosystems

Treesearch

Greg C. Liknes; Christopher W. Woodall; Brian F. Walters; Sara A. Goeking

2012-01-01

Climate information is often used as a predictor in ecological studies, where temporal averages are typically based on climate normals (30-year means) or seasonal averages. While ensemble projections of future climate forecast a higher global average annual temperature, they also predict increased climate variability. It remains to be seen whether forest ecosystems...

Simulating adaptive wood harvest in a changing climate

NASA Astrophysics Data System (ADS)

Yousefpour, Rasoul; Nabel, Julia; Pongratz, Julia

2016-04-01

The world's forest experience substantial carbon exchange fluxes between land and atmosphere. Large carbon sinks occur in response to changes in environmental conditions (such as climate change and increased atmospheric CO2 concentrations), removing about one quarter of current anthropogenic CO2-emissions. Large sinks also occur due to regrowth of forest on areas of agricultural abandonment or forest management. Forest management, on the other hand, also leads to substantial amounts of carbon being eventually released to the atmosphere. Both sinks and sources attributable to forests are therefore dependent on the intensity of management. Forest management in turn depends on the availability of resources, which is influenced by environmental conditions and sustainability of management systems applied. Estimating future carbon fluxes therefore requires accounting for the interaction of environmental conditions, forest growth, and management. However, this interaction is not fully captured by current modeling approaches: Earth system models depict in detail interactions between climate, the carbon cycle, and vegetation growth, but use prescribed information on management. Resource needs and land management, however, are simulated by Integrated Assessment Models that typically only have coarse representations of the influence of environmental changes on vegetation growth and are typically based on the demand for wood driven by regional population growth and energy needs. Here we present a study that provides the link between environmental conditions, forest growth and management. We extend the land component JSBACH of the Max Planck Institute's Earth system model (MPI-ESM) to simulate potential wood harvest in response to altered growth conditions and thus as adaptive to changing climate and CO2 conditions. We apply the altered model to estimate potential wood harvest for future climates (representative concentration pathways, RCPs) for the management scenario of "sustained yields" (SY), i.e. that wood harvest is not allowed to reduce wood carbon stocks below their present-day average state. We find that the potentials for SY range from about 420 to 610 PgC cumulatively until 2100 depending on assumed future climate (RCPs 2.6, 4.5 or 8.5). They are thus substantially higher than the harvest prescribed in the context of the same RCPs for the coupled model intercomparison project (CMIP5), which ranged from about 130 to 210 PgC. The underlying drivers of the higher potentials of SY as compared to the RCP harvest are in all scenarios foremost avoided natural mortality, followed by avoided losses due to fire and windbreak. Further, usage of the increase in forest carbon stocks simulated with time under RCP harvest plays a large role in the first decades of the 21st century. The potential wood harvest that we simulate accounting for environmental changes does not include considerations on biodiversity and other ecosystem services or technical feasibility. However, the substantially higher simulated harvest from SY as compared to that prescribed from the RCPs and the difference found between climate scenarios highlights the need to account for effects of environmental changes on vegetation growth also in socio-economic models and thus the need for a consistent representation of climate-landuse interactions.

Snow in Earth System Models: Recent Progress and Future Challenges

NASA Astrophysics Data System (ADS)

Clark, M. P.; Slater, A. G.

2016-12-01

Snow is the most variable of terrestrial boundary conditions. Some 50 million km^2 of the Northern Hemisphere typically sees periods of accumulation and ablation in the annual cycle. The wonderous properties of snow, such as high albedo, thermal insulation and its ability to act as a water store make it an integral part of the global climate system. Earliest inclusions of snow within climate models were simple adjustments to albedo and a moisture store. Modern Earth Syetem Models now represent snow through a myriad of model architectures and parameterizations that span a broad range of complexity. Understanding the impacts of modeling decisions upon simulation of snow and other Earth System components (either directly or via feedbacks) is an ongoing area of research. Snow models are progressing with multi-layer representations and capabilities such as complex albedo schemes that include contaminants. While considerable advances have been made, numerous challenges also remain. Simply getting a grasp on the mass of snow (seasonal or permanent) has proved more difficult than expected over the past 30 years. Snow interactions with vegetation has improved but the details of vegetation masking and emergence are still limited. Inclusion of blowing snow processes, in terms of transport and sublimation, is typically rare and sublimation remains a difficult quantity to measure. Contemplation of snow crystal form within models and integration with radiative transfer schemes for better understanding of full spectrum interations (from UV to long microwave) may simultaneously advance simulation and remote sensing. A series of international modeling experiments and directed field campaigns are planned in the near future with the aim of pushing our knowledge forward.

Multi-model inference for incorporating trophic and climate uncertainty into stock assessments

NASA Astrophysics Data System (ADS)

Ianelli, James; Holsman, Kirstin K.; Punt, André E.; Aydin, Kerim

2016-12-01

Ecosystem-based fisheries management (EBFM) approaches allow a broader and more extensive consideration of objectives than is typically possible with conventional single-species approaches. Ecosystem linkages may include trophic interactions and climate change effects on productivity for the relevant species within the system. Presently, models are evolving to include a comprehensive set of fishery and ecosystem information to address these broader management considerations. The increased scope of EBFM approaches is accompanied with a greater number of plausible models to describe the systems. This can lead to harvest recommendations and biological reference points that differ considerably among models. Model selection for projections (and specific catch recommendations) often occurs through a process that tends to adopt familiar, often simpler, models without considering those that incorporate more complex ecosystem information. Multi-model inference provides a framework that resolves this dilemma by providing a means of including information from alternative, often divergent models to inform biological reference points and possible catch consequences. We apply an example of this approach to data for three species of groundfish in the Bering Sea: walleye pollock, Pacific cod, and arrowtooth flounder using three models: 1) an age-structured "conventional" single-species model, 2) an age-structured single-species model with temperature-specific weight at age, and 3) a temperature-specific multi-species stock assessment model. The latter two approaches also include consideration of alternative future climate scenarios, adding another dimension to evaluate model projection uncertainty. We show how Bayesian model-averaging methods can be used to incorporate such trophic and climate information to broaden single-species stock assessments by using an EBFM approach that may better characterize uncertainty.

Air-quality bioindication in the greater central valley of California, with epiphytic macrolichen communities.

Treesearch

Sarah Jovan; Bruce McCune

2005-01-01

Air-quality monitoring in the United States is typically focused on urban areas even though the detrimental effects of pollution often extend into surrounding ecosystems. The purpose of this study was to construct a model, based upon epiphytic macrolichen community data, to indicate air-quality and climate in forested areas throughout the greater Central Valley of...

Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

NASA Technical Reports Server (NTRS)

Mueller, Thomas; Tucker, Compton J.; Dressler, Gunnar; Pinzon, Jorge E.; Leimgruber, Peter; Dubayah, Ralph O.; Hurtt, George C.; Boehning-Gaese, Katrin; Fagan, William F.

2014-01-01

Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth's human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects.

The role of country-to-region assignments in global integrated modeling of energy, agriculture, land use, and climate

NASA Astrophysics Data System (ADS)

Kyle, P.; Patel, P.; Calvin, K. V.

2014-12-01

Global integrated assessment models used for understanding the linkages between the future energy, agriculture, and climate systems typically represent between 8 and 30 geopolitical macro-regions, balancing the benefits of geographic resolution with the costs of additional data collection, processing, analysis, and computing resources. As these models are continually being improved and updated in order to address new questions for the research and policy communities, it is worth examining the consequences of the country-to-region mapping schemes used for model results. This study presents an application of a data processing system built for the GCAM integrated assessment model that allows any country-to-region assignments, with a minimum of four geopolitical regions and a maximum of 185. We test ten different mapping schemes, including the specific mappings used in existing major integrated assessment models. We also explore the impacts of clustering nations into regions according to the similarity of the structure of each nation's energy and agricultural sectors, as indicated by multivariate analysis. Scenarios examined include a reference scenario, a low-emissions scenario, and scenarios with agricultural and buildings sector climate change impacts. We find that at the global level, the major output variables (primary energy, agricultural land use) are surprisingly similar regardless of regional assignments, but at finer geographic scales, differences are pronounced. We suggest that enhancing geographic resolution is advantageous for analysis of climate impacts on the buildings and agricultural sectors, due to the spatial heterogeneity of these drivers.

Recent improvement and projected worsening of weather in the United States.

PubMed

Egan, Patrick J; Mullin, Megan

2016-04-21

As climate change unfolds, weather systems in the United States have been shifting in patterns that vary across regions and seasons. Climate science research typically assesses these changes by examining individual weather indicators, such as temperature or precipitation, in isolation, and averaging their values across the spatial surface. As a result, little is known about population exposure to changes in weather and how people experience and evaluate these changes considered together. Here we show that in the United States from 1974 to 2013, the weather conditions experienced by the vast majority of the population improved. Using previous research on how weather affects local population growth to develop an index of people’s weather preferences, we find that 80% of Americans live in counties that are experiencing more pleasant weather than they did four decades ago. Virtually all Americans are now experiencing the much milder winters that they typically prefer, and these mild winters have not been offset by markedly more uncomfortable summers or other negative changes. Climate change models predict that this trend is temporary, however, because US summers will eventually warm more than winters. Under a scenario in which greenhouse gas emissions proceed at an unabated rate (Representative Concentration Pathway 8.5), we estimate that 88% of the US public will experience weather at the end of the century that is less preferable than weather in the recent past. Our results have implications for the public’s understanding of the climate change problem, which is shaped in part by experiences with local weather. Whereas weather patterns in recent decades have served as a poor source of motivation for Americans to demand a policy response to climate change, public concern may rise once people’s everyday experiences of climate change effects start to become less pleasant.

CO2 forcing induces semi-direct effects with consequences for climate feedback interpretations

NASA Astrophysics Data System (ADS)

Andrews, Timothy; Forster, Piers M.

2008-02-01

Climate forcing and feedbacks are diagnosed from seven slab-ocean GCMs for 2 × CO2 using a regression method. Results are compared to those using conventional methodologies to derive a semi-direct forcing due to tropospheric adjustment, analogous to the semi-direct effect of absorbing aerosols. All models show a cloud semi-direct effect, indicating a rapid cloud response to CO2; cloud typically decreases, enhancing the warming. Similarly there is evidence of semi-direct effects from water-vapour, lapse-rate, ice and snow. Previous estimates of climate feedbacks are unlikely to have taken these semi-direct effects into account and so misinterpret processes as feedbacks that depend only on the forcing, but not the global surface temperature. We show that the actual cloud feedback is smaller than what previous methods suggest and that a significant part of the cloud response and the large spread between previous model estimates of cloud feedback is due to the semi-direct forcing.

Impact of tropical cyclones on modeled extreme wind-wave climate

DOE PAGES

Timmermans, Ben; Stone, Daithi; Wehner, Michael; ...

2017-02-16

Here, the effect of forcing wind resolution on the extremes of global wind-wave climate are investigated in numerical simulations. Forcing winds from the Community Atmosphere Model at horizontal resolutions of ~1.0° and ~0.25° are used to drive Wavewatch III. Differences in extreme wave height are found to manifest most strongly in tropical cyclone (TC) regions, emphasizing the need for high-resolution forcing in those areas. Comparison with observations typically show improvement in performance with increased forcing resolution, with a strong influence in the tail of the distribution, although simulated extremes can exceed observations. A simulation for the end of the 21stmore » century under a RCP 8.5 type emission scenario suggests further increases in extreme wave height in TC regions.« less

Impact of tropical cyclones on modeled extreme wind-wave climate

NASA Astrophysics Data System (ADS)

Timmermans, Ben; Stone, Dáithí; Wehner, Michael; Krishnan, Harinarayan

2017-02-01

The effect of forcing wind resolution on the extremes of global wind-wave climate are investigated in numerical simulations. Forcing winds from the Community Atmosphere Model at horizontal resolutions of ˜1.0° and ˜0.25° are used to drive Wavewatch III. Differences in extreme wave height are found to manifest most strongly in tropical cyclone (TC) regions, emphasizing the need for high-resolution forcing in those areas. Comparison with observations typically show improvement in performance with increased forcing resolution, with a strong influence in the tail of the distribution, although simulated extremes can exceed observations. A simulation for the end of the 21st century under a RCP 8.5 type emission scenario suggests further increases in extreme wave height in TC regions.

Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas

PubMed Central

Kesorn, Kraisak; Ongruk, Phatsavee; Chompoosri, Jakkrawarn; Phumee, Atchara; Thavara, Usavadee; Tawatsin, Apiwat; Siriyasatien, Padet

2015-01-01

Background In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings Areas with high incidence of dengue outbreaks in central Thailand were studied. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Ae. aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. In this research, we applied the SVM with the radial basis function (RBF) kernel to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions The infection rates of the Ae. aegypti female mosquitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. We demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE). PMID:25961289

A large-scale integrated karst-vegetation recharge model to understand the impact of climate and land cover change

NASA Astrophysics Data System (ADS)

Sarrazin, Fanny; Hartmann, Andreas; Pianosi, Francesca; Wagener, Thorsten

2017-04-01

Karst aquifers are an important source of drinking water in many regions of the world, but their resources are likely to be affected by changes in climate and land cover. Karst areas are highly permeable and produce large amounts of groundwater recharge, while surface runoff is typically negligible. As a result, recharge in karst systems may be particularly sensitive to environmental changes compared to other less permeable systems. However, current large-scale hydrological models poorly represent karst specificities. They tend to provide an erroneous water balance and to underestimate groundwater recharge over karst areas. A better understanding of karst hydrology and estimating karst groundwater resources at a large-scale is therefore needed for guiding water management in a changing world. The first objective of the present study is to introduce explicit vegetation processes into a previously developed karst recharge model (VarKarst) to better estimate evapotranspiration losses depending on the land cover characteristics. The novelty of the approach for large-scale modelling lies in the assessment of model output uncertainty, and parameter sensitivity to avoid over-parameterisation. We find that the model so modified is able to produce simulations consistent with observations of evapotranspiration and soil moisture at Fluxnet sites located in carbonate rock areas. Secondly, we aim to determine the model sensitivities to climate and land cover characteristics, and to assess the relative influence of changes in climate and land cover on aquifer recharge. We perform virtual experiments using synthetic climate inputs, and varying the value of land cover parameters. In this way, we can control for variations in climate input characteristics (e.g. precipitation intensity, precipitation frequency) and vegetation characteristics (e.g. canopy water storage capacity, rooting depth), and we can isolate the effect that each of these quantities has on recharge. Our results show that these factors are strongly interacting and are generating non-linear responses in recharge.

Dynamic climate emulators for solar geoengineering

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

MacMartin, Douglas G.; Kravitz, Ben

2016-12-22

Climate emulators trained on existing simulations can be used to project project the climate effects that result from different possible future pathways of anthropogenic forcing, without further relying on general circulation model (GCM) simulations. We extend this idea to include different amounts of solar geoengineering in addition to different pathways of greenhouse gas concentrations, by training emulators from a multi-model ensemble of simulations from the Geoengineering Model Intercomparison Project (GeoMIP). The emulator is trained on the abrupt 4 × CO 2 and a compensating solar reduction simulation (G1), and evaluated by comparing predictions against a simulated 1 % per yearmore » CO 2 increase and a similarly smaller solar reduction (G2). We find reasonable agreement in most models for predicting changes in temperature and precipitation (including regional effects), and annual-mean Northern Hemisphere sea ice extent, with the difference between simulation and prediction typically being smaller than natural variability. This verifies that the linearity assumption used in constructing the emulator is sufficient for these variables over the range of forcing considered. Annual-minimum Northern Hemisphere sea ice extent is less well predicted, indicating a limit to the linearity assumption.« less

How will the future warming climate impact the river discharge in the alpine mountain region of upper Heihe River Basin?

NASA Astrophysics Data System (ADS)

Wang, Y.; Yang, H.; Yang, D.; Gao, B.; Qin, Y.

2017-12-01

The Tibetan Plateau is more sensitive to the global climate change than other areas due to its special geography. Previous studies have shown that, besides the changes of temperature and precipitation, the changes in the cryosphere such as glacier and frozen ground also have important and far-reaching effects on the ecological and hydrological processes in the basin. In order to reliably predict the future runoff changing trend in the future, it's important to estimate the responses of cryosphere to the future climate change, as well as its impacts on the hydrological processes. Based on typical future climate scenarios (under emission scenario RCP4.5) from five general circulation models (GCMs) and one regional climate model (RCM), as well as a distributed eco-hydrological model (GBEHM), this study analyzes the possible future climate change (from 2011 to 2060) and its impacts on cryospheric and hydrological processes in upper Heihe River Basin, a typical cold mountain region located in the Northeast Tibetan Plateau. The results suggest that air temperature is expected to rise in the future by approximately 0.32 °C/10a, and precipitation is expected to rise slightly by about 3 mm/10a. Under the rising air temperature, the maximum frozen depth of seasonally frozen ground will decrease by about 4.1 cm/10a and the active layer depth of the frozen ground will increase by about 6.2 cm/10a. The runoff is expected to reduce by approximately 6 mm/10a and the evapotranspiration is expected to increase by approximately 9 mm/10a. These changes in hydrological processes are mainly caused by the air temperature rise. The impacts of air temperature change on the hydrological processes are mainly due to the changes of frozen ground. The thickening of active layer of the frozen ground increases the soil storage capacity, leading to the decrease of runoff and increase of evapotranspiration. Results show that, when the active layer depth increase by 1 cm, the runoff will decrease by about 1 2 mm and the evapotranspiration will increase by about 0.7 2 mm. Additionally, the changes from permafrost to seasonal frozen ground increase the groundwater infiltration, which also leads to the decrease of surface runoff.

Revisiting concepts of thermal physiology: Predicting responses of mammals to climate change.

PubMed

Mitchell, Duncan; Snelling, Edward P; Hetem, Robyn S; Maloney, Shane K; Strauss, Willem Maartin; Fuller, Andrea

2018-02-26

The accuracy of predictive models (also known as mechanistic or causal models) of animal responses to climate change depends on properly incorporating the principles of heat transfer and thermoregulation into those models. Regrettably, proper incorporation of these principles is not always evident. We have revisited the relevant principles of thermal physiology and analysed how they have been applied in predictive models of large mammals, which are particularly vulnerable, to climate change. We considered dry heat exchange, evaporative heat transfer, the thermoneutral zone and homeothermy, and we examined the roles of size and shape in the thermal physiology of large mammals. We report on the following misconceptions in influential predictive models: underestimation of the role of radiant heat transfer, misassignment of the role and misunderstanding of the sustainability of evaporative cooling, misinterpretation of the thermoneutral zone as a zone of thermal tolerance or as a zone of sustainable energetics, confusion of upper critical temperature and critical thermal maximum, overestimation of the metabolic energy cost of evaporative cooling, failure to appreciate that the current advantages of size and shape will become disadvantageous as climate change advances, misassumptions about skin temperature and, lastly, misconceptions about the relationship between body core temperature and its variability with body mass in large mammals. Not all misconceptions invalidate the models, but we believe that preventing inappropriate assumptions from propagating will improve model accuracy, especially as models progress beyond their current typically static format to include genetic and epigenetic adaptation that can result in phenotypic plasticity. © 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.

Selecting a climate model subset to optimise key ensemble properties

NASA Astrophysics Data System (ADS)

Herger, Nadja; Abramowitz, Gab; Knutti, Reto; Angélil, Oliver; Lehmann, Karsten; Sanderson, Benjamin M.

2018-02-01

End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

Changes in Benefits of Flood Protection Standard under Climate Change

NASA Astrophysics Data System (ADS)

Lim, W. H.; Koirala, S.; Yamazaki, D.; Hirabayashi, Y.; Kanae, S.

2014-12-01

Understanding potential risk of river flooding under future climate scenarios might be helpful for developing risk management strategies (including mitigation, adaptation). Such analyses are typically performed at the macro scales (e.g., regional, global) where the climate model output could support (e.g., Hirabayashi et al., 2013, Arnell and Gosling, 2014). To understand the potential benefits of infrastructure upgrading as part of climate adaptation strategies, it is also informative to understand the potential impact of different flood protection standards (in terms of return periods) on global river flooding under climate change. In this study, we use a baseline period (forced by observed hydroclimate conditions) and CMIP5 model output (historic and future periods) to drive a global river routing model called CaMa-Flood (Yamazaki et al., 2011) and simulate the river water depth at a spatial resolution of 15 min x 15 min. From the simulated results of baseline period, we use the annual maxima river water depth to fit the Gumbel distribution and prepare the return period-flood risk relationship (involving population and GDP). From the simulated results of CMIP5 model, we also used the annual maxima river water depth to obtain the Gumbel distribution and then estimate the exceedance probability (historic and future periods). We apply the return period-flood risk relationship (above) to the exceedance probability and evaluate the potential risk of river flooding and changes in the benefits of flood protection standard (e.g., 100-year flood of the baseline period) from the past into the future (represented by the representative concentration pathways). In this presentation, we show our preliminary results. References: Arnell, N.W, Gosling, S., N., 2014. The impact of climate change on river flood risk at the global scale. Climatic Change 122: 127-140, doi: 10.1007/s10584-014-1084-5. Hirabayashi et al., 2013. Global flood risk under climate change. Nature Climate Change 3: 816-821, doi: 10.1038/nclimate1911. Yamazaki et al., 2011. A physically based description of floodplain inundation dynamics in a global river routing model. Water Resources Research 47, W04501, doi: 10.1029/2010wr009726.

Eliciting climate experts' knowledge to address model uncertainties in regional climate projections: a case study of Guanacaste, Northwest Costa Rica

NASA Astrophysics Data System (ADS)

Grossmann, I.; Steyn, D. G.

2014-12-01

Global general circulation models typically cannot provide the detailed and accurate regional climate information required by stakeholders for climate adaptation efforts, given their limited capacity to resolve the regional topography and changes in local sea surface temperature, wind and circulation patterns. The study region in Northwest Costa Rica has a tropical wet-dry climate with a double-peak wet season. During the dry season the central Costa Rican mountains prevent tropical Atlantic moisture from reaching the region. Most of the annual precipitation is received following the northward migration of the ITCZ in May that allows the region to benefit from moist southwesterly flow from the tropical Pacific. The wet season begins with a short period of "early rains" and is interrupted by the mid-summer drought associated with the intensification and westward expansion of the North Atlantic subtropical high in late June. Model projections for the 21st century indicate a lengthening and intensification of the mid-summer drought and a weakening of the early rains on which current crop cultivation practices rely. We developed an expert elicitation to systematically address uncertainties in the available model projections of changes in the seasonal precipitation pattern. Our approach extends an elicitation approach developed previously at Carnegie Mellon University. Experts in the climate of the study region or Central American climate were asked to assess the mechanisms driving precipitation during each part of the season, uncertainties regarding these mechanisms, expected changes in each mechanism in a warming climate, and the capacity of current models to reproduce these processes. To avoid overconfidence bias, a step-by-step procedure was followed to estimate changes in the timing and intensity of precipitation during each part of the season. The questions drew upon interviews conducted with the regions stakeholders to assess their climate information needs. This study is part of the FuturAgua project funded by the Belmont Freshwater Security call. The expert opinions on expected changes in the seasonal precipitation pattern are being used to inform regional efforts to build drought resilience and to create and compare alternative water management strategies with the region's stakeholders.

Research on Potential Induced Degradation (PID) of PV Modules in Different Typical Climate Regions

NASA Astrophysics Data System (ADS)

Daoren, Gong; Yingnan, Chen; Gang, Sun; Wenjing, Wang; Zhenshuang, Ji

2018-03-01

Potential Induced Degradation (PID) is one of the most important factors effecting the performances of Photovoltaic (PV) modules and PV systems in recent years. In this paper the PID phenomena of the PV power plant in different typical climate regions were studied and some experimental PID simulations were carried out in order to find out the factors effecting the performance by PID. The results show that the typical PID phenomena are easy to occur in cells close to the border of the PV module. PID phenomena can appear in PV power plants under different climate conditions, but the effecting degrees on module performance are different depending on temperature, humidity and other parameters. We also find the maximum power would recover in some degree after positive-bias voltage duration.

Intrinsic ethics regarding integrated assessment models for climate management.

PubMed

Schienke, Erich W; Baum, Seth D; Tuana, Nancy; Davis, Kenneth J; Keller, Klaus

2011-09-01

In this essay we develop and argue for the adoption of a more comprehensive model of research ethics than is included within current conceptions of responsible conduct of research (RCR). We argue that our model, which we label the ethical dimensions of scientific research (EDSR), is a more comprehensive approach to encouraging ethically responsible scientific research compared to the currently typically adopted approach in RCR training. This essay focuses on developing a pedagogical approach that enables scientists to better understand and appreciate one important component of this model, what we call intrinsic ethics. Intrinsic ethical issues arise when values and ethical assumptions are embedded within scientific findings and analytical methods. Through a close examination of a case study and its application in teaching, namely, evaluation of climate change integrated assessment models, this paper develops a method and case for including intrinsic ethics within research ethics training to provide scientists with a comprehensive understanding and appreciation of the critical role of values and ethical choices in the production of research outcomes.

Energy Performance and Optimal Control of Air-conditioned Buildings Integrated with Phase Change Materials

NASA Astrophysics Data System (ADS)

Zhu, Na

This thesis presents an overview of the previous research work on dynamic characteristics and energy performance of buildings due to the integration of PCMs. The research work on dynamic characteristics and energy performance of buildings using PCMs both with and without air-conditioning is reviewed. Since the particular interest in using PCMs for free cooling and peak load shifting, specific research efforts on both subjects are reviewed separately. A simplified physical dynamic model of building structures integrated with SSPCM (shaped-stabilized phase change material) is developed and validated in this study. The simplified physical model represents the wall by 3 resistances and 2 capacitances and the PCM layer by 4 resistances and 2 capacitances respectively while the key issue is the parameter identification of the model. This thesis also presents the studies on the thermodynamic characteristics of buildings enhanced by PCM and on the investigation of the impacts of PCM on the building cooling load and peak cooling demand at different climates and seasons as well as the optimal operation and control strategies to reduce the energy consumption and energy cost by reducing the air-conditioning energy consumption and peak load. An office building floor with typical variable air volume (VAV) air-conditioning system is used and simulated as the reference building in the comparison study. The envelopes of the studied building are further enhanced by integrating the PCM layers. The building system is tested in two selected cities of typical climates in China including Hong Kong and Beijing. The cold charge and discharge processes, the operation and control strategies of night ventilation and the air temperature set-point reset strategy for minimizing the energy consumption and electricity cost are studied. This thesis presents the simulation test platform, the test results on the cold storage and discharge processes, the air-conditioning energy consumption and demand reduction potentials in typical air-conditioning seasons in typical China cites as well as the impacts of operation and control strategies.

Technical Note: Linking climate change and downed woody debris decomposition across forests of the eastern United States

USGS Publications Warehouse

Russell, Matthew B.; Woodall, Christopher W.; D'Amato, Anthony W.; Fraver, Shawn; Bradford, John B.

2014-01-01

Forest ecosystems play a critical role in mitigating greenhouse gas emissions. Forest carbon (C) is stored through photosynthesis and released via decomposition and combustion. Relative to C fixation in biomass, much less is known about C depletion through decomposition of woody debris, particularly under a changing climate. It is assumed that the increased temperatures and longer growing seasons associated with projected climate change will increase the decomposition rates (i.e., more rapid C cycling) of downed woody debris (DWD); however, the magnitude of this increase has not been previously addressed. Using DWD measurements collected from a national forest inventory of the eastern United States, we show that the residence time of DWD may decrease (i.e., more rapid decomposition) by as much as 13% over the next 200 years, depending on various future climate change scenarios and forest types. Although existing dynamic global vegetation models account for the decomposition process, they typically do not include the effect of a changing climate on DWD decomposition rates. We expect that an increased understanding of decomposition rates, as presented in this current work, will be needed to adequately quantify the fate of woody detritus in future forests. Furthermore, we hope these results will lead to improved models that incorporate climate change scenarios for depicting future dead wood dynamics in addition to a traditional emphasis on live-tree demographics.

Predicting Decade-to-Century Climate Change: Prospects for Improving Models

NASA Technical Reports Server (NTRS)

Somerville, Richard C. J.

1999-01-01

Recent research has led to a greatly increased understanding of the uncertainties in today's climate models. In attempting to predict the climate of the 21st century, we must confront not only computer limitations on the affordable resolution of global models, but also a lack of physical realism in attempting to model key processes. Until we are able to incorporate adequate treatments of critical elements of the entire biogeophysical climate system, our models will remain subject to these uncertainties, and our scenarios of future climate change, both anthropogenic and natural, will not fully meet the requirements of either policymakers or the public. The areas of most-needed model improvements are thought to include air-sea exchanges, land surface processes, ice and snow physics, hydrologic cycle elements, and especially the role of aerosols and cloud-radiation interactions. Of these areas, cloud-radiation interactions are known to be responsible for much of the inter-model differences in sensitivity to greenhouse gases. Recently, we have diagnostically evaluated several current and proposed model cloud-radiation treatments against extensive field observations. Satellite remote sensing provides an indispensable component of the observational resources. Cloud-radiation parameterizations display a strong sensitivity to vertical resolution, and we find that vertical resolutions typically used in global models are far from convergence. We also find that newly developed advanced parameterization schemes with explicit cloud water budgets and interactive cloud radiative properties are potentially capable of matching observational data closely. However, it is difficult to evaluate the realism of model-produced fields of cloud extinction, cloud emittance, cloud liquid water content and effective cloud droplet radius until high-quality measurements of these quantities become more widely available. Thus, further progress will require a combination of theoretical and modeling research, together with intensified emphasis on both in situ and space-based remote sensing observations.

Statistical and dynamical assessment of land-ocean-atmosphere interactions across North Africa

NASA Astrophysics Data System (ADS)

Yu, Yan

North Africa is highly vulnerable to hydrologic variability and extremes, including impacts of climate change. The current understanding of oceanic versus terrestrial drivers of North African droughts and pluvials is largely model-based, with vast disagreement among models in terms of the simulated oceanic impacts and vegetation feedbacks. Regarding oceanic impacts, the relative importance of the tropical Pacific, tropical Indian, and tropical Atlantic Oceans in regulating the North African rainfall variability, as well as the underlying mechanism, remains debated among different modeling studies. Classic theory of land-atmosphere interactions across the Sahel ecotone, largely based on climate modeling experiments, has promoted positive vegetation-rainfall feedbacks associated with a dominant surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback with its underlying albedo mechanism, nor its relative importance compared with oceanic drivers, has been convincingly demonstrated up to now using observational data. Here, the multivariate Generalized Equilibrium Feedback Assessment (GEFA) is applied in order to identify the observed oceanic and terrestrial drivers of North African climate and quantify their impacts. The reliability of the statistical GEFA method is first evaluated against dynamical experiments within the Community Earth System Model (CESM). In order to reduce the sampling error caused by short data records, the traditional GEFA approach is refined through stepwise GEFA, in which unimportant forcings are dropped through stepwise selection. In order to evaluate GEFA's reliability in capturing oceanic impacts, the atmospheric response to a sea-surface temperature (SST) forcing across the tropical Pacific, tropical Indian, and tropical Atlantic Ocean is estimated independently through ensembles of dynamical experiments and compared with GEFA-based assessments. Furthermore, GEFA's performance in capturing terrestrial impacts is evaluated through ensembles of fully coupled CESM dynamical experiments, with modified leaf area index (LAI) and soil moisture across the Sahel or West African Monsoon (WAM) region. The atmospheric responses to oceanic and terrestrial forcings are generally consistent between the dynamical experiments and statistical GEFA, confirming GEFA's capability of isolating the individual impacts of oceanic and terrestrial forcings on North African climate. Furthermore, with the incorporation of stepwise selection, GEFA can now provide reliable estimates of the oceanic and terrestrial impacts on the North African climate with the typical length of observational datasets, thereby enhancing the method's applicability. After the successful validation of GEFA, the key observed oceanic and terrestrial drivers of North African climate are identified through the application of GEFA to gridded observations, remote sensing products, and reanalyses. According to GEFA, oceanic drivers dominate over terrestrial drivers in terms of their observed impacts on North African climate in most seasons. Terrestrial impacts are comparable to, or more important than, oceanic impacts on rainfall during the post-monsoon across the Sahel and WAM region, and after the short rain across the Horn of Africa (HOA). The key ocean basins that regulate North African rainfall are typically located in the tropics. While the observed impacts of SST variability across the tropical Pacific and tropical Atlantic Oceans on the Sahel rainfall are largely consistent with previous model-based findings, minimal impacts from tropical Indian Ocean variability on Sahel rainfall are identified in observations, in contrast to previous modeling studies. The current observational analysis verifies model-hypothesized positive vegetation-rainfall feedback across the Sahel and HOA, which is confined to the post-monsoon and post-short rains season, respectively. However, the observed positive vegetation feedback to rainfall in the semi-arid Sahel and HOA is largely due to moisture recycling, rather than the classic albedo mechanism. Future projections of Sahel rainfall remain highly uncertain in terms of both sign and magnitude within phases three and five of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). The GEFA-based observational analyses will provide a benchmark for evaluating climate models, which will facilitate effective process-based model weighting for more reliable projections of regional climate, as well as model development.

WSN system design by using an innovative neural network model to perform thermals forecasting in a urban canyon scenario

NASA Astrophysics Data System (ADS)

Giuseppina, Nicolosi; Salvatore, Tirrito

2015-12-01

Wireless Sensor Networks (WSNs) were studied by researchers in order to manage Heating, Ventilating and Air-Conditioning (HVAC) indoor systems. WSN can be useful specially to regulate indoor confort in a urban canyon scenario, where the thermal parameters vary rapidly, influenced by outdoor climate changing. This paper shows an innovative neural network approach, by using WSN data collected, in order to forecast the indoor temperature to varying the outdoor conditions based on climate parameters and boundary conditions typically of urban canyon. In this work more attention will be done to influence of traffic jam and number of vehicles in queue.

A Regional Analysis of Non-Methane Hydrocarbons And Meteorology of The Rural Southeast United States

DTIC Science & Technology

1996-01-01

Zt is an ARIMA time series. This is a typical regression model , except that it allows for autocorrelation in the error term Z. In this work, an ARMA...data=folder; var residual; run; II Statistical output of 1992 regression model on 1993 ozone data ARIMA Procedure Maximum Likelihood Estimation Approx...at each of the sites, and to show the effect of synoptic meteorology on high ozone by examining NOAA daily weather maps and climatic data

The long-term effects of planting and harvesting on secondary forest dynamics under climate change in northeastern China

PubMed Central

Yao, Jing; He, Xingyuan; He, Hongshi; Chen, Wei; Dai, Limin; Lewis, Bernard J.; Yu, Lizhong

2016-01-01

Unlike the virgin forest in the Changbaishan Nature Reserve in northeastern China, little research on a landscape scale has been conducted on secondary forests in the region under conditions of a warming climate. This research was undertaken in the upper Hun River region where the vegetation is representative of the typical secondary forest of northeastern China. The spatially explicit forest landscape model LANDIS was utilized to simulate the responses of forest restoration dynamics to anthropogenic disturbance (planting and harvesting) and evaluate the difference of the restoration process under continuation of current climatic conditions and climate warming. The results showed that: (1) The interaction of planting and harvesting has organizational scale effects on the forest. The combination of planting and harvesting policies has significant effects on the overall forest but not on individual species. (2) The area expansion of the historically dominant species Pinus koraiensis is less under climate warming than under continuation of current climatic conditions. These suggests that we should carefully take historically dominant species as the main focus for forest restoration, especially when they are near their natural distribution boundary, because they are probably less capable of successfully adapting to climate change. PMID:26725308

The long-term effects of planting and harvesting on secondary forest dynamics under climate change in northeastern China.

PubMed

Yao, Jing; He, Xingyuan; He, Hongshi; Chen, Wei; Dai, Limin; Lewis, Bernard J; Yu, Lizhong

2016-01-04

Unlike the virgin forest in the Changbaishan Nature Reserve in northeastern China, little research on a landscape scale has been conducted on secondary forests in the region under conditions of a warming climate. This research was undertaken in the upper Hun River region where the vegetation is representative of the typical secondary forest of northeastern China. The spatially explicit forest landscape model LANDIS was utilized to simulate the responses of forest restoration dynamics to anthropogenic disturbance (planting and harvesting) and evaluate the difference of the restoration process under continuation of current climatic conditions and climate warming. The results showed that: (1) The interaction of planting and harvesting has organizational scale effects on the forest. The combination of planting and harvesting policies has significant effects on the overall forest but not on individual species. (2) The area expansion of the historically dominant species Pinus koraiensis is less under climate warming than under continuation of current climatic conditions. These suggests that we should carefully take historically dominant species as the main focus for forest restoration, especially when they are near their natural distribution boundary, because they are probably less capable of successfully adapting to climate change.

Integration of ENSO Signal Power Through Hydrological Processes in the Little River Watershed

NASA Astrophysics Data System (ADS)

Keener, V. W.; Jones, J. W.; Bosch, D. D.; Cho, J.

2011-12-01

The relationship of the El-Nino/Southern Oscillation (ENSO) to hydrology is typically discussed in terms of the ability to separate significantly different hydrologic variable responses versus the anomaly that has taken place. Most of the work relating ENSO trends to proxy variables had been done on precipitation records until the mid 1990s, at which point increasing numbers of studies started to focus on ENSO relationships with streamflow as well as other environmental variables. The signals in streamflow are typically complex, representing the integration of both climatic, landscape, and anthropological responses that are able to strengthen the inherent ENSO signal in chaotic regional precipitation data. There is a need to identify climate non-stationarities related to ENSO and their links to watershed-scale outcomes. For risk-management in particular, inter-annual modes of climate variability and their seasonal expression are of interest. In this study, we analyze 36 years of historical monthly streamflow data from the Little River Watershed (LWR), a coastal plain ecosystem in Georgia, in conjunction with wavelet spectral analysis and modeling via the Soil & Water Assessment Tool (SWAT). Using both spectral and physical models allows us to identify the mechanism by which the ENSO signal power in surface and simulated groundwater flow is strengthened as compared to precipitation. The clear increase in the power of the inter-annual climate signal is demonstrated by shared patterns in water budget and exceedance curves, as well as in high ENSO related energy in the 95% significant wavelet spectra for each variable and the NINO 3.4 index. In the LRW, the power of the ENSO teleconnection is increased in both the observed and simulated stream flow through the mechanisms of groundwater flow and interflow, through confinement by a geological layer, the Hawthorn Formation. This non-intuitive relationship between ENSO signal strength and streamflow could prove to be helpful for making seasonal climate predictions in a geographic area with a weaker than desirable ENSO signal, as a predictive relationship could be found between streamflow or other proxy hydro-climatic variables.

GEOSS AIP-2 Climate Change and Biodiversity Use Scenarios: Interoperability Infrastructures (Invited)

NASA Astrophysics Data System (ADS)

Nativi, S.; Santoro, M.

2009-12-01

Currently, one of the major challenges for scientific community is the study of climate change effects on life on Earth. To achieve this, it is crucial to understand how climate change will impact on biodiversity and, in this context, several application scenarios require modeling the impact of climate change on distribution of individual species. In the context of GEOSS AIP-2 (Global Earth Observation System of Systems, Architecture Implementation Pilot- Phase 2), the Climate Change & Biodiversity thematic Working Group developed three significant user scenarios. A couple of them make use of a GEOSS-based framework to study the impact of climate change factors on regional species distribution. The presentation introduces and discusses this framework which provides an interoperability infrastructures to loosely couple standard services and components to discover and access climate and biodiversity data, and run forecast and processing models. The framework is comprised of the following main components and services: a)GEO Portal: through this component end user is able to search, find and access the needed services for the scenario execution; b)Graphical User Interface (GUI): this component provides user interaction functionalities. It controls the workflow manager to perform the required operations for the scenario implementation; c)Use Scenario controller: this component acts as a workflow controller implementing the scenario business process -i.e. a typical climate change & biodiversity projection scenario; d)Service Broker implementing Mediation Services: this component realizes a distributed catalogue which federates several discovery and access components (exposing them through a unique CSW standard interface). Federated components publish climate, environmental and biodiversity datasets; e)Ecological Niche Model Server: this component is able to run one or more Ecological Niche Models (ENM) on selected biodiversity and climate datasets; f)Data Access Transaction server: this component publishes the model outputs. The framework was successfully tested in two use scenarios of the GEOSS AIP-2 Climate Change and Biodiversity WG aiming to predict species distribution changes due to Climate Change factors, with the scientific patronage of the University of Colorado and the University of Alaska. The first scenario dealt with the Pikas specie regional distribution in the Great Basin area (North America). While, the second one concerned the modeling of the Arctic Food Chain species in the North Pole area -the relationships between different environmental parameters and Polar Bears distribution was analyzed. Results are published in the GEOSS AIP-2 web site: http://www.ogcnetwork.net/AIP2develop .

Past exposure to climate extremes can inform future projections and guide management: coral reefs as a model system

NASA Astrophysics Data System (ADS)

Donner, S. D.

2016-12-01

Coral reefs are thought to be more sensitive to climate change than any other marine ecosystem. Episodes of mass coral bleaching, due to anomalously warm water temperatures, have led to coral mortality, declines in coral cover and shifts in the population of other reef-dwelling organisms. The onset of mass bleaching is typically predicted using accumulated heat stress, specifically when the SST exceeds a local climatological maximum by 1-2 °C for a month or more. However, recent evidence suggests that the threshold at which bleaching occurs depends on the past thermal experience of the coral reef and the composition of the coral community. This presentation describes the results of a long-term field and modelling research program evaluating the influence of climate experience on the susceptibility of coral reef ecosystems to future climate extremes. Modeling work identified Kiribati's equatorial Gilbert Islands, where the El Niño / Southern Oscillation drives year-to-year shifts in current strength, current direction and consequently ocean temperatures, as an ideal natural laboratory for studying ocean climate extremes. The field program then tracked changes in the coral communities over multiple heat stress events (e.g. 2004-5, 2009-10 El Niño) at a matrix of sites exposed to different levels of historical climate variability and human disturbance. Among the results is evidence that coral bleaching patterns are best predicted by the coefficient of variation of past SST, light exposure, and the presence of particular resilient coral taxa, rather than the standard heat stress metrics. The lessons of this research can be applicable other systems where past experience influences the response to climate extremes

Heterogeneous Sensitivity of Tropical Precipitation Extremes during Growth and Mature Phases of Atmospheric Warming

NASA Astrophysics Data System (ADS)

Parhi, P.; Giannini, A.; Lall, U.; Gentine, P.

2016-12-01

Assessing and managing risks posed by climate variability and change is challenging in the tropics, from both a socio-economic and a scientific perspective. Most of the vulnerable countries with a limited climate adaptation capability are in the tropics. However, climate projections, particularly of extreme precipitation, are highly uncertain there. The CMIP5 (Coupled Model Inter- comparison Project - Phase 5) inter-model range of extreme precipitation sensitivity to the global temperature under climate change is much larger in the tropics as compared to the extra-tropics. It ranges from nearly 0% to greater than 30% across models (O'Gorman 2012). The uncertainty is also large in historical gauge or satellite based observational records. These large uncertainties in the sensitivity of tropical precipitation extremes highlight the need to better understand how tropical precipitation extremes respond to warming. We hypothesize that one of the factors explaining the large uncertainty is due to differing sensitivities during different phases of warming. We consider the `growth' and `mature' phases of warming under climate variability case- typically associated with an El Niño event. In the remote tropics (away from tropical Pacific Ocean), the response of the precipitation extremes during the two phases can be through different pathways: i) a direct and fast changing radiative forcing in an atmospheric column, acting top-down due to the tropospheric warming, and/or ii) an indirect effect via changes in surface temperatures, acting bottom-up through surface water and energy fluxes. We also speculate that the insights gained here might be useful in interpreting the large sensitivity under climate change scenarios, since the physical mechanisms during the two warming phases under climate variability case, have some correspondence with an increasing and stabilized green house gas emission scenarios.

Mountain Climates on the Move: Implications for Past and Future Vegetation Shifts in the Northeastern United States

NASA Astrophysics Data System (ADS)

Wason, J. W., III; Dovciak, M.; Bevilacqua, E.

2015-12-01

Climate change in the northeastern United States is expected to shift climatic (temperature) envelopes for spruce-fir forests upslope and northward decreasing their area in the region by 2100. Coarse scale landscape models however, may not incorporate heterogeneity in climatic conditions in mountains that can create climatic refugia for species in high-elevation spruce-fir forests. To determine spatial and temporal trends in climate of mountain spruce-fir forests we measured microclimate at 98 forest plots in 2012 and 2013 on 12 mountains in New York, Vermont, New Hampshire, and Maine. By linking regional climate trends with our spatial climate data we calculated elevational shifts in temperature envelopes during the last 50 years. Additionally we linked our spatial dataset to a range of future climate conditions for 2100 based on Representative Concentration Pathways (1 to 5°C warming). We hypothesized that climates have already changed to an extent that spruce-fir forests should begin to respond and that future climate conditions may shift suitable habitat for spruce-fir forests beyond their current range. We found that regional climate change over the last 50 years has resulted in warming of 0.66 and 1.62°C for average annual daily maximum (Tmax) and minimum (Tmin) temperatures in the region. When linked to our spatial microclimate model, this warming results in a 100 (Tmax) and 312m (Tmin) upslope shift in temperature envelopes. Future climate projections suggest that by 2100 Tmax may shift upslope between 152 and 758m for the 1 and 5°C scenarios respectively, while Tmin may shift upslope between 192 and 962m. Spruce-fir forests typically occupy an elevation range of ~500m suggesting that the climate experienced in these forests 50 years ago may not be found within their elevation range by 2100. These results are discussed in the context of responses of tree populations and growth rates observed along the elevation gradients of northeastern United States.

A Mixed Phase Tale: New Ways of using in-situ cloud observations to reduce climate model biases in Southern Ocean

NASA Astrophysics Data System (ADS)

Gettelman, A.; Stith, J. L.

2014-12-01

Southern ocean clouds are a critical part of the earth's energy budget, and significant biases in the climatology of these clouds exist in models used to predict climate change. We compare in situ measurements of cloud microphysical properties of ice and liquid over the S. Ocean with constrained output from the atmospheric component of an Earth System Model. Observations taken during the HIAPER (the NSF/NCAR G-V aircraft) Pole-to-Pole Observations (HIPPO) multi-year field campaign are compared with simulations from the atmospheric component of the Community Earth System Model (CESM). Remarkably, CESM is able to accurately simulate the locations of cloud formation, and even cloud microphysical properties are comparable between the model and observations. Significantly, the simulations do not predict sufficient supercooled liquid. Altering the model cloud and aerosol processes to better reproduce the observations of supercooled liquid acts to reduce long-standing biases in S. Ocean clouds in CESM, which are typical of other models. Furthermore, sensitivity tests show where better observational constraints on aerosols and cloud microphysics can reduce uncertainty and biases in global models. These results are intended to show how we can connect large scale simulations with field observations in the S. Ocean to better understand Southern Ocean cloud processes and reduce biases in global climate simulations.

How does bias correction of RCM precipitation affect modelled runoff?

NASA Astrophysics Data System (ADS)

Teng, J.; Potter, N. J.; Chiew, F. H. S.; Zhang, L.; Vaze, J.; Evans, J. P.

2014-09-01

Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the difference between the tested methods is small in the modelling experiments here (and as reported in the literature), mainly because of the substantial corrections required and inconsistent errors over time (non-stationarity). The errors remaining in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitation of RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces.

Simulating polar bear energetics during a seasonal fast using a mechanistic model.

PubMed

Mathewson, Paul D; Porter, Warren P

2013-01-01

In this study we tested the ability of a mechanistic model (Niche Mapper™) to accurately model adult, non-denning polar bear (Ursus maritimus) energetics while fasting during the ice-free season in the western Hudson Bay. The model uses a steady state heat balance approach, which calculates the metabolic rate that will allow an animal to maintain its core temperature in its particular microclimate conditions. Predicted weight loss for a 120 day fast typical of the 1990s was comparable to empirical studies of the population, and the model was able to reach a heat balance at the target metabolic rate for the entire fast, supporting use of the model to explore the impacts of climate change on polar bears. Niche Mapper predicted that all but the poorest condition bears would survive a 120 day fast under current climate conditions. When the fast extended to 180 days, Niche Mapper predicted mortality of up to 18% for males. Our results illustrate how environmental conditions, variation in animal properties, and thermoregulation processes may impact survival during extended fasts because polar bears were predicted to require additional energetic expenditure for thermoregulation during a 180 day fast. A uniform 3°C temperature increase reduced male mortality during a 180 day fast from 18% to 15%. Niche Mapper explicitly links an animal's energetics to environmental conditions and thus can be a valuable tool to help inform predictions of climate-related population changes. Since Niche Mapper is a generic model, it can make energetic predictions for other species threatened by climate change.

Simulating Polar Bear Energetics during a Seasonal Fast Using a Mechanistic Model

PubMed Central

Mathewson, Paul D.; Porter, Warren P.

2013-01-01

In this study we tested the ability of a mechanistic model (Niche Mapper™) to accurately model adult, non-denning polar bear (Ursus maritimus) energetics while fasting during the ice-free season in the western Hudson Bay. The model uses a steady state heat balance approach, which calculates the metabolic rate that will allow an animal to maintain its core temperature in its particular microclimate conditions. Predicted weight loss for a 120 day fast typical of the 1990s was comparable to empirical studies of the population, and the model was able to reach a heat balance at the target metabolic rate for the entire fast, supporting use of the model to explore the impacts of climate change on polar bears. Niche Mapper predicted that all but the poorest condition bears would survive a 120 day fast under current climate conditions. When the fast extended to 180 days, Niche Mapper predicted mortality of up to 18% for males. Our results illustrate how environmental conditions, variation in animal properties, and thermoregulation processes may impact survival during extended fasts because polar bears were predicted to require additional energetic expenditure for thermoregulation during a 180 day fast. A uniform 3°C temperature increase reduced male mortality during a 180 day fast from 18% to 15%. Niche Mapper explicitly links an animal’s energetics to environmental conditions and thus can be a valuable tool to help inform predictions of climate-related population changes. Since Niche Mapper is a generic model, it can make energetic predictions for other species threatened by climate change. PMID:24019883

Recent advances in understanding secondary organic aerosol: Implications for global climate forcing

NASA Astrophysics Data System (ADS)

Shrivastava, Manish; Cappa, Christopher D.; Fan, Jiwen; Goldstein, Allen H.; Guenther, Alex B.; Jimenez, Jose L.; Kuang, Chongai; Laskin, Alexander; Martin, Scot T.; Ng, Nga Lee; Petaja, Tuukka; Pierce, Jeffrey R.; Rasch, Philip J.; Roldin, Pontus; Seinfeld, John H.; Shilling, John; Smith, James N.; Thornton, Joel A.; Volkamer, Rainer; Wang, Jian; Worsnop, Douglas R.; Zaveri, Rahul A.; Zelenyuk, Alla; Zhang, Qi

2017-06-01

Anthropogenic emissions and land use changes have modified atmospheric aerosol concentrations and size distributions over time. Understanding preindustrial conditions and changes in organic aerosol due to anthropogenic activities is important because these features (1) influence estimates of aerosol radiative forcing and (2) can confound estimates of the historical response of climate to increases in greenhouse gases. Secondary organic aerosol (SOA), formed in the atmosphere by oxidation of organic gases, represents a major fraction of global submicron-sized atmospheric organic aerosol. Over the past decade, significant advances in understanding SOA properties and formation mechanisms have occurred through measurements, yet current climate models typically do not comprehensively include all important processes. This review summarizes some of the important developments during the past decade in understanding SOA formation. We highlight the importance of some processes that influence the growth of SOA particles to sizes relevant for clouds and radiative forcing, including formation of extremely low volatility organics in the gas phase, acid-catalyzed multiphase chemistry of isoprene epoxydiols, particle-phase oligomerization, and physical properties such as volatility and viscosity. Several SOA processes highlighted in this review are complex and interdependent and have nonlinear effects on the properties, formation, and evolution of SOA. Current global models neglect this complexity and nonlinearity and thus are less likely to accurately predict the climate forcing of SOA and project future climate sensitivity to greenhouse gases. Efforts are also needed to rank the most influential processes and nonlinear process-related interactions, so that these processes can be accurately represented in atmospheric chemistry-climate models.

Impact of Land Model Depth on Long Term Climate Variability and Change.

NASA Astrophysics Data System (ADS)

Gonzalez-Rouco, J. F.; García-Bustamante, E.; Hagemann, S.; Lorentz, S.; Jungclaus, J.; de Vrese, P.; Melo, C.; Navarro, J.; Steinert, N.

2017-12-01

The available evidence indicates that the simulation of subsurface thermodynamics in current General Circulation Models (GCMs) is not accurate enough due to the land-surface model imposing a zero heat flux boundary condition that is too close to the surface. Shallow land model components distort the amplitude and phase of the heat propagation in the subsurface with implications for energy storage and land-air interactions. Off line land surface model experiments forced with GCM climate change simulations and comparison with borehole temperature profiles indicate there is a large reduction of the energy storage of the soil using the typical shallow land models included in most GCMs. However, the impact of increasing the depth of the soil model in `on-line' GCM simulations of climate variability or climate change has not yet been systematically explored. The JSBACH land surface model has been used in stand alone mode, driven by outputs of the MPIESM to assess the impacts of progressively increasing the depth of the soil model. In a first stage, preindustrial control simulations are developed increasing the lower depth of the zero flux bottom boundary condition placed for temperature at the base of the fifth model layer (9.83 m) down to 294.6 m (layer 9), thus allowing for the bottom layers to reach equilibrium. Starting from piControl conditions, historical and scenario simulations have been performed since 1850 yr. The impact of increasing depths on the subsurface layer temperatures is analysed as well as the amounts of energy involved. This is done also considering permafrost processes (freezing and thawing). An evaluation on the influence of deepening the bottom boundary on the simulation of low frequency variability and temperature trends is provided.

A framework for global river flood risk assessment

NASA Astrophysics Data System (ADS)

Winsemius, H. C.; Van Beek, L. P. H.; Bouwman, A.; Ward, P. J.; Jongman, B.

2012-04-01

There is an increasing need for strategic global assessments of flood risks. Such assessments may be required by: (a) International Financing Institutes and Disaster Management Agencies to evaluate where, when, and which investments in flood risk mitigation are most required; (b) (re-)insurers, who need to determine their required coverage capital; and (c) large companies to account for risks of regional investments. In this contribution, we propose a framework for global river flood risk assessment. The framework combines coarse scale resolution hazard probability distributions, derived from global hydrological model runs (typical scale about 0.5 degree resolution) with high resolution estimates of exposure indicators. The high resolution is required because floods typically occur at a much smaller scale than the typical resolution of global hydrological models, and exposure indicators such as population, land use and economic value generally are strongly variable in space and time. The framework therefore estimates hazard at a high resolution ( 1 km2) by using a) global forcing data sets of the current (or in scenario mode, future) climate; b) a global hydrological model; c) a global flood routing model, and d) importantly, a flood spatial downscaling routine. This results in probability distributions of annual flood extremes as an indicator of flood hazard, at the appropriate resolution. A second component of the framework combines the hazard probability distribution with classical flood impact models (e.g. damage, affected GDP, affected population) to establish indicators for flood risk. The framework can be applied with a large number of datasets and models and sensitivities of such choices can be evaluated by the user. The framework is applied using the global hydrological model PCR-GLOBWB, combined with a global flood routing model. Downscaling of the hazard probability distributions to 1 km2 resolution is performed with a new downscaling algorithm, applied on a number of target regions. We demonstrate the use of impact models in these regions based on global GDP, population, and land use maps. In this application, we show sensitivities of the estimated risks with regard to the use of different climate input datasets, decisions made in the downscaling algorithm, and different approaches to establish distributed estimates of GDP and asset exposure to flooding.

Assessing simulated ecosystem processes for climate variability research at Glacier National Park, USA

USGS Publications Warehouse

White, J.D.; Running, S.W.; Thornton, P.E.; Keane, R.E.; Ryan, K.C.; Fagre, D.B.; Key, C.H.

1998-01-01

Glacier National Park served as a test site for ecosystem analyses than involved a suite of integrated models embedded within a geographic information system. The goal of the exercise was to provide managers with maps that could illustrate probable shifts in vegetation, net primary production (NPP), and hydrologic responses associated with two selected climatic scenarios. The climatic scenarios were (a) a recent 12-yr record of weather data, and (b) a reconstituted set that sequentially introduced in repeated 3-yr intervals wetter-cooler, drier-warmer, and typical conditions. To extrapolate the implications of changes in ecosystem processes and resulting growth and distribution of vegetation and snowpack, the model incorporated geographic data. With underlying digital elevation maps, soil depth and texture, extrapolated climate, and current information on vegetation types and satellite-derived estimates of a leaf area indices, simulations were extended to envision how the park might look after 120 yr. The predictions of change included underlying processes affecting the availability of water and nitrogen. Considerable field data were acquired to compare with model predictions under current climatic conditions. In general, the integrated landscape models of ecosystem processes had good agreement with measured NPP, snowpack, and streamflow, but the exercise revealed the difficulty and necessity of averaging point measurements across landscapes to achieve comparable results with modeled values. Under the extremely variable climate scenario significant changes in vegetation composition and growth as well as hydrologic responses were predicted across the park. In particular, a general rise in both the upper and lower limits of treeline was predicted. These shifts would probably occur along with a variety of disturbances (fire, insect, and disease outbreaks) as predictions of physiological stress (water, nutrients, light) altered competitive relations and hydrologic responses. The use of integrated landscape models applied in this exercise should provide managers with insights into the underlying processes important in maintaining community structure, and at the same time, locate where changes on the landscape are most likely to occur.

Predicting the Spatial Distribution of Aspen Growth Potential in the Upper Great Lakes Region

Treesearch

Eric J. Gustafson; Sue M. Lietz; John L. Wright

2003-01-01

One way to increase aspen yields is to produce aspen on sites where aspen growth potential is highest. Aspen growth rates are typically predicted using site index, but this is impractical for landscape-level assessments. We tested the hypothesis that aspen growth can be predicted from site and climate variables and generated a model to map the spatial variability of...

Influence of climate variability, fire and phosphorus limitation on vegetation structure and dynamics of the Amazon-Cerrado border

NASA Astrophysics Data System (ADS)

Ane Dionizio, Emily; Heil Costa, Marcos; de Almeida Castanho, Andrea D.; Ferreira Pires, Gabrielle; Schwantes Marimon, Beatriz; Hur Marimon-Junior, Ben; Lenza, Eddie; Martins Pimenta, Fernando; Yang, Xiaojuan; Jain, Atul K.

2018-02-01

Climate, fire and soil nutrient limitation are important elements that affect vegetation dynamics in areas of the forest-savanna transition. In this paper, we use the dynamic vegetation model INLAND to evaluate the influence of interannual climate variability, fire and phosphorus (P) limitation on Amazon-Cerrado transitional vegetation structure and dynamics. We assess how each environmental factor affects net primary production, leaf area index and aboveground biomass (AGB), and compare the AGB simulations to an observed AGB map. We used two climate data sets (monthly average climate for 1961-1990 and interannual climate variability for 1948-2008), two data sets of total soil P content (one based on regional field measurements and one based on global data), and the INLAND fire module. Our results show that the inclusion of interannual climate variability, P limitation and fire occurrence each contribute to simulating vegetation types that more closely match observations. These effects are spatially heterogeneous and synergistic. In terms of magnitude, the effect of fire is strongest and is the main driver of vegetation changes along the transition. Phosphorus limitation, in turn, has a stronger effect on transitional ecosystem dynamics than interannual climate variability does. Overall, INLAND typically simulates more than 80 % of the AGB variability in the transition zone. However, the AGB in many places is clearly not well simulated, indicating that important soil and physiological factors in the Amazon-Cerrado border region, such as lithology, water table depth, carbon allocation strategies and mortality rates, still need to be included in the model.

Increased seasonality in the Western Mediterranean during the last glacial from limpet shell geochemistry

NASA Astrophysics Data System (ADS)

Ferguson, Julie E.; Henderson, Gideon M.; Fa, Darren A.; Finlayson, J. Clive; Charnley, Norman R.

2011-08-01

The seasonal cycle is a fundamental aspect of climate, with a significant influence on mean climate and on human societies. Assessing seasonality in different climate states is therefore important but, outside the tropics, very few palaeoclimate records with seasonal resolution exist and there are currently no glacial-age seasonal-resolution sea-surface-temperature (SST) records at mid to high latitudes. Here we show that both Mg/Ca and oxygen isotope (δ 18O) ratios in modern limpet ( Patella) shells record the seasonal range of SST in the western Mediterranean — a region particularly susceptible to seasonal change. Analysis of a suite of fossil limpet shells from Gibraltar shows that SST seasonality was greater during the last glacial by ~ 2 °C as a result of greater winter cooling. These extra-tropical seasonal-resolution SST records for the last glacial suggest that the presence of large ice-sheets in the northern hemisphere enhances winter cooling. This result also indicates that seasonality in the Mediterranean is not well-represented in most palaeoclimate models, which typically show little change in seasonal amplitude, and provides a new test for the accuracy of climate models.

Gap formation following climatic events in spatially structured plant communities

PubMed Central

Liao, Jinbao; De Boeck, Hans J.; Li, Zhenqing; Nijs, Ivan

2015-01-01

Gaps play a crucial role in maintaining species diversity, yet how community structure and composition influence gap formation is still poorly understood. We apply a spatially structured community model to predict how species diversity and intraspecific aggregation shape gap patterns emerging after climatic events, based on species-specific mortality responses. In multispecies communities, average gap size and gap-size diversity increased rapidly with increasing mean mortality once a mortality threshold was exceeded, greatly promoting gap recolonization opportunity. This result was observed at all levels of species richness. Increasing interspecific difference likewise enhanced these metrics, which may promote not only diversity maintenance but also community invasibility, since more diverse niches for both local and exotic species are provided. The richness effects on gap size and gap-size diversity were positive, but only expressed when species were sufficiently different. Surprisingly, while intraspecific clumping strongly promoted gap-size diversity, it hardly influenced average gap size. Species evenness generally reduced gap metrics induced by climatic events, so the typical assumption of maximum evenness in many experiments and models may underestimate community diversity and invasibility. Overall, understanding the factors driving gap formation in spatially structured assemblages can help predict community secondary succession after climatic events. PMID:26114803

Impact of Dust Radiative Forcing upon Climate. Chapter 13

NASA Technical Reports Server (NTRS)

Miller, Ronald L.; Knippertz, Peter; Perez Garcia-Pando, Carlos; Perlwitz, Jan P.; Tegan, Ina

2014-01-01

Dust aerosols perturb the atmospheric radiative flux at both solar and thermal wavelengths, altering the energy and water cycles. The climate adjusts by redistributing energy and moisture, so that local temperature perturbations, for example, depend upon the forcing over the entire extent of the perturbed circulation. Within regions frequently mixed by deep convection, including the deep tropics, dust particles perturb the surface air temperature primarily through radiative forcing at the top of the atmosphere (TOA). Many models predict that dust reduces global precipitation. This reduction is typically attributed to the decrease of surface evaporation in response to dimming of the surface. A counterexample is presented, where greater shortwave absorption by dust increases evaporation and precipitation despite greater dimming of the surface. This is attributed to the dependence of surface evaporation upon TOA forcing through its influence upon surface temperature and humidity. Perturbations by dust to the surface wind speed and vegetation (through precipitation anomalies) feed back upon the dust aerosol concentration. The current uncertainty of radiative forcing attributed to dust and the resulting range of climate perturbations calculated by models remain a useful test of our understanding of the mechanisms relating dust radiative forcing to the climate response.

Development of thermal models of footwear using finite element analysis.

PubMed

Covill, D; Guan, Z W; Bailey, M; Raval, H

2011-03-01

Thermal comfort is increasingly becoming a crucial factor to be considered in footwear design. The climate inside a shoe is controlled by thermal and moisture conditions and is crucial to attain comfort. Research undertaken has shown that thermal conditions play a dominant role in shoe climate. Development of thermal models that are capable of predicting in-shoe temperature distributions is an effective way forward to undertake extensive parametric studies to assist optimized design. In this paper, two-dimensional and three-dimensional thermal models of in-shoe climate were developed using finite element analysis through commercial code Abaqus. The thermal material properties of the upper shoe, sole, and air were considered. Dry heat flux from the foot was calculated on the basis of typical blood flow in the arteries on the foot. Using the thermal models developed, in-shoe temperatures were predicted to cover various locations for controlled ambient temperatures of 15, 25, and 35 degrees C respectively. The predicted temperatures were compared with multipoint measured temperatures through microsensor technology. Reasonably good correlation was obtained, with averaged errors of 6, 2, and 1.5 per cent, based on the averaged in-shoe temperature for the above three ambient temperatures. The models can be further used to help design shoes with optimized thermal comfort.

A modelling study of long term green roof retention performance.

PubMed

Stovin, Virginia; Poë, Simon; Berretta, Christian

2013-12-15

This paper outlines the development of a conceptual hydrological flux model for the long term continuous simulation of runoff and drought risk for green roof systems. A green roof's retention capacity depends upon its physical configuration, but it is also strongly influenced by local climatic controls, including the rainfall characteristics and the restoration of retention capacity associated with evapotranspiration during dry weather periods. The model includes a function that links evapotranspiration rates to substrate moisture content, and is validated against observed runoff data. The model's application to typical extensive green roof configurations is demonstrated with reference to four UK locations characterised by contrasting climatic regimes, using 30-year rainfall time-series inputs at hourly simulation time steps. It is shown that retention performance is dependent upon local climatic conditions. Volumetric retention ranges from 0.19 (cool, wet climate) to 0.59 (warm, dry climate). Per event retention is also considered, and it is demonstrated that retention performance decreases significantly when high return period events are considered in isolation. For example, in Sheffield the median per-event retention is 1.00 (many small events), but the median retention for events exceeding a 1 in 1 yr return period threshold is only 0.10. The simulation tool also provides useful information about the likelihood of drought periods, for which irrigation may be required. A sensitivity study suggests that green roofs with reduced moisture-holding capacity and/or low evapotranspiration rates will tend to offer reduced levels of retention, whilst high moisture-holding capacity and low evapotranspiration rates offer the strongest drought resistance. Copyright © 2013 Elsevier Ltd. All rights reserved.

A hierarchical perspective of plant diversity

USGS Publications Warehouse

Sarr, Daniel; Hibbs, D.E.; Huston, M.

2005-01-01

Predictive models of plant diversity have typically focused on either a landscapea??s capacity for richness (equilibrium models), or on the processes that regulate competitive exclusion, and thus allow species to coexist (nonequilibrium models). Here, we review the concepts and purposes of a hierarchical, multiscale model of the controls of plant diversity that incorporates the equilibrium model of climatic favorability at macroscales, nonequilibrium models of competition at microscales, and a mixed model emphasizing environmental heterogeneity at mesoscales. We evaluate the conceptual model using published data from three spatially nested datasets: (1) a macroscale analysis of ecoregions in the continental and western U.S.; (2) a mesoscale study in California; and (3) a microscale study in the Siskiyou Mountains of Oregon and California. At the macroscale (areas from 3889 km2 to 638,300 km2), climate (actual evaporation) was a strong predictor of tree diversity (R2 = 0.80), as predicted by the conceptual model, but area was a better predictor for vascular plant diversity overall (R2 = 0.38), which suggests different types of plants differ in their sensitivity to climatic controls. At mesoscales (areas from 1111 km2 to 15,833 km2 ), climate was still an important predictor of richness (R2 = 0.52), but, as expected, topographic heterogeneity explained an important share of the variance (R2 = 0.19), showed positive correlations with diversity of trees, shrubs, and annual and perennial herbs, and was the primary predictor of shrub and annual plant species richness. At microscales (0.1 ha plots), spatial patterns of diversity showed a clear unimodal pattern along a climatea??driven productivity gradient and a negative relationship with soil fertility. The strong decline in understory and total diversity at the most productive sites suggests that competitive controls, as predicted, can override climatic controls at this scale. We conclude that this hierarchical, multiscale model provides a sound basis to understand and analyze plant species diversity. Specifically, future research should employ the principles in this paper to explore climatic controls on species richness of different life forms, better quantify environmental heterogeneity in landscapes, and analyze how these largea??scale factors interact with local nonequilibrium dynamics to maintain plant diversity.

Climate sensitivity and meridional overturning circulation in the late Eocene using GFDL CM2.1

NASA Astrophysics Data System (ADS)

Hutchinson, David K.; de Boer, Agatha M.; Coxall, Helen K.; Caballero, Rodrigo; Nilsson, Johan; Baatsen, Michiel

2018-06-01

The Eocene-Oligocene transition (EOT), which took place approximately 34 Ma ago, is an interval of great interest in Earth's climate history, due to the inception of the Antarctic ice sheet and major global cooling. Climate simulations of the transition are needed to help interpret proxy data, test mechanistic hypotheses for the transition and determine the climate sensitivity at the time. However, model studies of the EOT thus far typically employ control states designed for a different time period, or ocean resolution on the order of 3°. Here we developed a new higher resolution palaeoclimate model configuration based on the GFDL CM2.1 climate model adapted to a late Eocene (38 Ma) palaeogeography reconstruction. The ocean and atmosphere horizontal resolutions are 1° × 1.5° and 3° × 3.75° respectively. This represents a significant step forward in resolving the ocean geography, gateways and circulation in a coupled climate model of this period. We run the model under three different levels of atmospheric CO2: 400, 800 and 1600 ppm. The model exhibits relatively high sensitivity to CO2 compared with other recent model studies, and thus can capture the expected Eocene high latitude warmth within observed estimates of atmospheric CO2. However, the model does not capture the low meridional temperature gradient seen in proxies. Equatorial sea surface temperatures are too high in the model (30-37 °C) compared with observations (max 32 °C), although observations are lacking in the warmest regions of the western Pacific. The model exhibits bipolar sinking in the North Pacific and Southern Ocean, which persists under all levels of CO2. North Atlantic surface salinities are too fresh to permit sinking (25-30 psu), due to surface transport from the very fresh Arctic ( ˜ 20 psu), where surface salinities approximately agree with Eocene proxy estimates. North Atlantic salinity increases by 1-2 psu when CO2 is halved, and similarly freshens when CO2 is doubled, due to changes in the hydrological cycle.

Climate and Vegetation Effects on Temperate Mountain Forest Evapotranspiration

EPA Science Inventory

Current forest composition may be resilient to typical climatic variability; however, climate trends, combined with projected changes in species composition, may increase tree vulnerability to water stress. A shift in forest composition toward tree species with higher water use h...

Will changes in phenology track climate change? A study of growth initiation timing in coast Douglas-fir.

PubMed

Ford, Kevin R; Harrington, Constance A; Bansal, Sheel; Gould, Peter J; St Clair, J Bradley

2016-11-01

Under climate change, the reduction of frost risk, onset of warm temperatures and depletion of soil moisture are all likely to occur earlier in the year in many temperate regions. The resilience of tree species will depend on their ability to track these changes in climate with shifts in phenology that lead to earlier growth initiation in the spring. Exposure to warm temperatures ('forcing') typically triggers growth initiation, but many trees also require exposure to cool temperatures ('chilling') while dormant to readily initiate growth in the spring. If warming increases forcing and decreases chilling, climate change could maintain, advance or delay growth initiation phenology relative to the onset of favorable conditions. We modeled the timing of height- and diameter-growth initiation in coast Douglas-fir (an ecologically and economically vital tree in western North America) to determine whether changes in phenology are likely to track changes in climate using data from field-based and controlled-environment studies, which included conditions warmer than those currently experienced in the tree's range. For high latitude and elevation portions of the tree's range, our models predicted that warming will lead to earlier growth initiation and allow trees to track changes in the onset of the warm but still moist conditions that favor growth, generally without substantially greater exposure to frost. In contrast, toward lower latitude and elevation range limits, the models predicted that warming will lead to delayed growth initiation relative to changes in climate due to reduced chilling, with trees failing to capture favorable conditions in the earlier parts of the spring. This maladaptive response to climate change was more prevalent for diameter-growth initiation than height-growth initiation. The decoupling of growth initiation with the onset of favorable climatic conditions could reduce the resilience of coast Douglas-fir to climate change at the warm edges of its distribution. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Will changes in phenology track climate change? A study of growth initiation timing in coast Douglas-fir

USGS Publications Warehouse

Ford, Kevin R.; Harrington, Constance A.; Bansal, Sheel; Gould, Petter J.; St. Clair, Bradley

2016-01-01

Under climate change, the reduction of frost risk, onset of warm temperatures and depletion of soil moisture are all likely to occur earlier in the year in many temperate regions. The resilience of tree species will depend on their ability to track these changes in climate with shifts in phenology that lead to earlier growth initiation in the spring. Exposure to warm temperatures (“forcing”) typically triggers growth initiation, but many trees also require exposure to cool temperatures (“chilling”) while dormant to readily initiate growth in the spring. If warming increases forcing and decreases chilling, climate change could maintain, advance or delay growth initiation phenology relative to the onset of favorable conditions. We modeled the timing of height- and diameter-growth initiation in coast Douglas-fir (an ecologically and economically vital tree in western North America) to determine whether changes in phenology are likely to track changes in climate using data from field-based and controlled-environment studies, which included conditions warmer than those currently experienced in the tree's range. For high latitude and elevation portions of the tree's range, our models predicted that warming will lead to earlier growth initiation and allow trees to track changes in the onset of the warm but still moist conditions that favor growth, generally without substantially greater exposure to frost. In contrast, towards lower latitude and elevation range limits, the models predicted that warming will lead to delayed growth initiation relative to changes in climate due to reduced chilling, with trees failing to capture favorable conditions in the earlier parts of the spring. This maladaptive response to climate change was more prevalent for diameter-growth initiation than height-growth initiation. The decoupling of growth initiation with the onset of favorable climatic conditions could reduce the resilience of coast Douglas-fir to climate change at the warm edges of its distribution.

Game Theoretic Modeling of Water Resources Allocation Under Hydro-Climatic Uncertainty

NASA Astrophysics Data System (ADS)

Brown, C.; Lall, U.; Siegfried, T.

2005-12-01

Typical hydrologic and economic modeling approaches rely on assumptions of climate stationarity and economic conditions of ideal markets and rational decision-makers. In this study, we incorporate hydroclimatic variability with a game theoretic approach to simulate and evaluate common water allocation paradigms. Game Theory may be particularly appropriate for modeling water allocation decisions. First, a game theoretic approach allows economic analysis in situations where price theory doesn't apply, which is typically the case in water resources where markets are thin, players are few, and rules of exchange are highly constrained by legal or cultural traditions. Previous studies confirm that game theory is applicable to water resources decision problems, yet applications and modeling based on these principles is only rarely observed in the literature. Second, there are numerous existing theoretical and empirical studies of specific games and human behavior that may be applied in the development of predictive water allocation models. With this framework, one can evaluate alternative orderings and rules regarding the fraction of available water that one is allowed to appropriate. Specific attributes of the players involved in water resources management complicate the determination of solutions to game theory models. While an analytical approach will be useful for providing general insights, the variety of preference structures of individual players in a realistic water scenario will likely require a simulation approach. We propose a simulation approach incorporating the rationality, self-interest and equilibrium concepts of game theory with an agent-based modeling framework that allows the distinct properties of each player to be expressed and allows the performance of the system to manifest the integrative effect of these factors. Underlying this framework, we apply a realistic representation of spatio-temporal hydrologic variability and incorporate the impact of decision-making a priori to hydrologic realizations and those made a posteriori on alternative allocation mechanisms. Outcomes are evaluated in terms of water productivity, net social benefit and equity. The performance of hydro-climate prediction modeling in each allocation mechanism will be assessed. Finally, year-to-year system performance and feedback pathways are explored. In this way, the system can be adaptively managed toward equitable and efficient water use.

Using MERRA, AMIP II, CMIP5 Outputs to Assess Actual and Potential Building Climate Zone Change and Variability From the Last 30 Years Through 2100

NASA Astrophysics Data System (ADS)

Stackhouse, P. W.; Westberg, D. J.; Hoell, J. M., Jr.; Chandler, W.; Zhang, T.

2014-12-01

In the US, residential and commercial building infrastructure combined consumes about 40% of total energy usage and emits about 39% of total CO2emission (DOE/EIA "Annual Energy Outlook 2013"). Thus, increasing the energy efficiency of buildings is paramount to reducing energy costs and emissions. Building codes, as used by local and state enforcement entities are typically tied to the dominant climate within an enforcement jurisdiction classified according to various climate zones. These climates zones are based upon a 30-year average of local surface observations and are developed by DOE and ASHRAE (formerly known as the American Society of Hearting, Refrigeration and Air-Conditioning Engineers). A significant shortcoming of the methodology used in constructing such maps is the use of surface observations (located mainly near airports) that are unequally distributed and frequently have periods of missing data that need to be filled by various approximation schemes. This paper demonstrates the usefulness of using NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA) atmospheric data assimilation to derive the ASHRAE climate zone maps and then using MERRA to define the last 30 years of variability in climate zones. These results show that there is a statistically significant increase in the area covered by warmer climate zones and some tendency for a reduction of area in colder climate zones that require longer time series to confirm. Using the uncertainties of the basic surface temperature and precipitation parameters from MERRA as determined by comparison to surface measurements, we first compare patterns and variability of ASHRAE climate zones from MERRA relative to present day climate model runs from AMIP simulations to establish baseline sensitivity. Based upon these results, we assess the variability of the ASHRAE climate zones according to CMIP runs through 2100 using an ensemble analysis that classifies model output changes by percentiles. Estimates of statistical significance are then compared to original model variability during the AMIP period. This work quantifies and tests for significance the changes seen in the various US regions that represent a potential contribution by NASA to the ongoing National Climate Assessment.

A modelling approach to assessing the timescale uncertainties in proxy series with chronological errors

NASA Astrophysics Data System (ADS)

Divine, D.; Godtliebsen, F.; Rue, H.

2012-04-01

Detailed knowledge of past climate variations is of high importance for gaining a better insight into the possible future climate scenarios. The relative shortness of available high quality instrumental climate data conditions the use of various climate proxy archives in making inference about past climate evolution. It, however, requires an accurate assessment of timescale errors in proxy-based paleoclimatic reconstructions. We here propose an approach to assessment of timescale errors in proxy-based series with chronological uncertainties. The method relies on approximation of the physical process(es) forming a proxy archive by a random Gamma process. Parameters of the process are partly data-driven and partly determined from prior assumptions. For a particular case of a linear accumulation model and absolutely dated tie points an analytical solution is found suggesting the Beta-distributed probability density on age estimates along the length of a proxy archive. In a general situation of uncertainties in the ages of the tie points the proposed method employs MCMC simulations of age-depth profiles yielding empirical confidence intervals on the constructed piecewise linear best guess timescale. It is suggested that the approach can be further extended to a more general case of a time-varying expected accumulation between the tie points. The approach is illustrated by using two ice and two lake/marine sediment cores representing the typical examples of paleoproxy archives with age models constructed using tie points of mixed origin.

Did 250 years of forest management in Europe cool the climate?

NASA Astrophysics Data System (ADS)

Naudts, Kim; Chen, Yiying; McGrath, Matthew; Ryder, James; Valade, Aude; Otto, Juliane; Luyssaert, Sebastiaan

2016-04-01

Over the past two centuries European forest has evolved from being an over-exploited source of timber to a sustainably managed provider of diverse ecosystem services. Although this transition is often perceived as exemplary in resources management, the loss of unmanaged forest, the progressive shift from traditional coppice forestry to the current production-oriented management and the massive conversion of broadleaved to coniferous species are typically overlooked when assessing the impact of land-use change on climate. Here we present a study that addressed this gap by: (1) developing and reparameterizing the ORCHIDEE land surface model to simulate the biogeochemical and biophysical effects of forest management, (2) reconstructing the land-use history of Europe, accounting for changes in forest management and land cover. The model was coupled to the atmospheric model LMDz in a factorial simulation experiment to attribute climate change to global anthropogenic greenhouse gas emission and European land-use change since 1750 (i.e., afforestation, wood extraction and species conversion). We find that, despite considerable afforestation, Europe's forests failed to realize a net removal of CO2 from the atmosphere due to wood extraction. Moreover, biophysical changes due to the conversion of deciduous forest into coniferous forest have offset mitigation through the carbon cycle. Thus, two and a half centuries of forest management in Europe did not mitigate climate warming (Naudts et al., 2016). Naudts, K., Chen, Y., McGrath, M.J., Ryder, J., Valade, A., Otto, J., Luyssaert, S, Europe's forest management did not mitigate climate warming, Science, Accepted.

The Role of Temporal Evolution in Modeling Atmospheric Emissions from Tropical Fires

NASA Technical Reports Server (NTRS)

Marlier, Miriam E.; Voulgarakis, Apostolos; Shindell, Drew T.; Faluvegi, Gregory S.; Henry, Candise L.; Randerson, James T.

2014-01-01

Fire emissions associated with tropical land use change and maintenance influence atmospheric composition, air quality, and climate. In this study, we explore the effects of representing fire emissions at daily versus monthly resolution in a global composition-climate model. We find that simulations of aerosols are impacted more by the temporal resolution of fire emissions than trace gases such as carbon monoxide or ozone. Daily-resolved datasets concentrate emissions from fire events over shorter time periods and allow them to more realistically interact with model meteorology, reducing how often emissions are concurrently released with precipitation events and in turn increasing peak aerosol concentrations. The magnitude of this effect varies across tropical ecosystem types, ranging from smaller changes in modeling the low intensity, frequent burning typical of savanna ecosystems to larger differences when modeling the short-term, intense fires that characterize deforestation events. The utility of modeling fire emissions at a daily resolution also depends on the application, such as modeling exceedances of particulate matter concentrations over air quality guidelines or simulating regional atmospheric heating patterns.

The influence of oceanic basins on drought and ecosystem dynamics in Northeast Brazil

NASA Astrophysics Data System (ADS)

Santos Pereira, Marcos Paulo; Justino, Flavio; Mendes Malhado, Ana Claudia; Barbosa, Humberto; Marengo, José

2014-12-01

The 2012 drought in Northeast Brazil was the harshest in decades, with potentially significant impacts on the vegetation of the unique semi-arid caatinga biome and on local livelihoods. Here, we use a coupled climate-vegetation model (CCM3-IBIS) to: (1) investigate the role of the Pacific and Atlantic oceans in the 2012 drought, and; (2) evaluate the response of the caatinga vegetation to the 2012 climate extreme. Our results indicate that anomalous sea surface temperatures (SSTs) in the Atlantic Ocean were the primary factor forcing the 2012 drought, with Pacific Ocean SST having a larger role in sustaining typical climatic conditions in the region. The drought strongly influenced net primary production in the caatinga, causing a reduction in annual net ecosystem exchange indicating a reduction in amount of CO2 released to the atmosphere.

Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: Statistically downscaled forcing data and hydrologic models

USGS Publications Warehouse

Mizukami, Naoki; Clark, Martyn P.; Gutmann, Ethan D.; Mendoza, Pablo A.; Newman, Andrew J.; Nijssen, Bart; Livneh, Ben; Hay, Lauren E.; Arnold, Jeffrey R.; Brekke, Levi D.

2016-01-01

Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.

Relating adaptive genetic traits to climate for Sandberg bluegrass from the intermountain western United States.

PubMed

Johnson, Richard C; Horning, Matthew E; Espeland, Erin K; Vance-Borland, Ken

2015-02-01

Genetic variation for potentially adaptive traits of the key restoration species Sandberg bluegrass (Poa secunda J. Presl) was assessed over the intermountain western United States in relation to source population climate. Common gardens were established at two intermountain west sites with progeny from two maternal parents from each of 130 wild populations. Data were collected over 2 years at each site on fifteen plant traits associated with production, phenology, and morphology. Analyses of variance revealed strong population differences for all plant traits (P < 0.0001), indicating genetic variation. Both the canonical correlation and linear correlation established associations between source populations and climate variability. Populations from warmer, more arid climates had generally lower dry weight, earlier phenology, and smaller, narrower leaves than those from cooler, moister climates. The first three canonical variates were regressed with climate variables resulting in significant models (P < 0.0001) used to map 12 seed zones. Of the 700 981 km(2) mapped, four seed zones represented 92% of the area in typically semi-arid and arid regions. The association of genetic variation with source climates in the intermountain west suggested climate driven natural selection and evolution. We recommend seed transfer zones and population movement guidelines to enhance adaptation and diversity for large-scale restoration projects.

AO/NAO Response to Climate Change. 1; Respective Influences of Stratospheric and Tropospheric Climate Changes

NASA Technical Reports Server (NTRS)

Rind, D.; Perlwitz, J.; Lonergan, P.

2005-01-01

We utilize the GISS Global Climate Middle Atmosphere Model and 8 different climate change experiments, many of them focused on stratospheric climate forcings, to assess the relative influence of tropospheric and stratospheric climate change on the extratropical circulation indices (Arctic Oscillation, AO; North Atlantic Oscillation, NAO). The experiments are run in two different ways: with variable sea surface temperatures (SSTs) to allow for a full tropospheric climate response, and with specified SSTs to minimize the tropospheric change. The results show that tropospheric warming (cooling) experiments and stratospheric cooling (warming) experiments produce more positive (negative) AO/NAO indices. For the typical magnitudes of tropospheric and stratospheric climate changes, the tropospheric response dominates; results are strongest when the tropospheric and stratospheric influences are producing similar phase changes. Both regions produce their effect primarily by altering wave propagation and angular momentum transports, but planetary wave energy changes accompanying tropospheric climate change are also important. Stratospheric forcing has a larger impact on the NAO than on the AO, and the angular momentum transport changes associated with it peak in the upper troposphere, affecting all wavenumbers. Tropospheric climate changes influence both the A0 and NAO with effects that extend throughout the troposphere. For both forcings there is often vertical consistency in the sign of the momentum transport changes, obscuring the difference between direct and indirect mechanisms for influencing the surface circulation.

Improving models to assess impacts of climate change on Mediterranean water resources

NASA Astrophysics Data System (ADS)

Rocha, João; Carvalho Santos, Cláudia; Keizer, Jan Jacob; Alexandre Diogo, Paulo; Nunes, João Pedro

2016-04-01

In recent decades, water availability for human consumption has faced major constraints due to increasing pollution and reduced water availability. Water resources availability can gain additional stresses and pressures in the context of potential climate change scenarios. For the last decades, the climate change paradigm has been the scope of many researchers and the focus of decision makers, policies and environmental/climate legislation. Decision-makers face a wide range of constrains, as they are forced to define new strategies that merge planning, management and climate change adaptations. In turn, decision-makers must create integrated strategies aiming at the sustainable use of resources. There are multiple uncertainties associated with climate change impact assessment and water resources. Typically, most studies have dealt with uncertainties in emission scenarios and resulting socio-economic conditions, including land-use and water use. Less frequently, studies have address the disparities between the future climates generated by climate models for the same greenhouse gas concentrations; and the uncertainties related with the limited knowledge of how watersheds work, which also limits the capacity to simulate them with models. Therefore, the objective of this study is to apply the SWAT (Soil and Water Assessment Tool) hydrological model to a catchment in Alentejo, southern Portugal; and to evaluate the uncertainty associated both to the calibration of hydrological models and the use of different climate change scenarios and models (a combination of 4 GCM (General Circulation Models) and 1 RCM (Regional Circulation Models) for the scenarios RCP 4.5 and 8.5. The Alentejo region is highly vulnerable to the effects of potential climate changes with particular focus on water resources availability, despite several reservoirs used for freshwater supply and agriculture irrigation (e.g. the Alqueva reservoir - the largest artificial lake of the Iberian Peninsula). Here the SWAT2012 model was applied to the catchment of Monte Novo and Vigia. The Monte Novo and Vigia reservoirs were selected due to their importance for the district of Évora, respectively for urban water supply and irrigation. The catchment is a multipurpose reservoir system that covers an area of about 81473 ha and drains into the Alqueva reservoir (25.000 ha). The SWAT2012 model was run for 1973-2012. The calibration routines were conducted on a monthly basis using the SWATCUP. The calibration performance rating is expressed by: NSE 0.89, bR² 0.89, Pbias 7.29 (Vigia) and NSE 0.84, bR² 0.83, Pbias 6.29 (Monte Novo). Expected results are a generalized decrease of water availability in the basin, more intense under the scenario RCP 8.5. However the uncertainty related to the use of different climate change models show different outcomes, which may be considered for the strategies to be adopted. We will take advantage of SWAT's automatic calibration capacities to explore how multiple interpretations of present-day hydrological processes could lead to different outputs in future climate scenarios, and compare this uncertainty with other sources of uncertainty related with future scenarios or different outputs from climate models.

Examination of Daily Weather in the NCAR CCM

NASA Astrophysics Data System (ADS)

Cocke, S. D.

2006-05-01

The NCAR CCM is one of the most extensively studied climate models in the scientific community. However, most studies focus primarily on the long term mean behavior, typically monthly or longer time scales. In this study we examine the daily weather in the GCM by performing a series of daily or weekly 10 day forecasts for one year at moderate (T63) and high (T126) resolution. The model is initialized with operational "AVN" and ECMWF analyses, and model performance is compared to that of major operational centers, using conventional skill scores used by the major centers. Such a detailed look at the CCM at shorter time scales may lead to improvements in physical parameterizations, which may in turn lead to improved climate simulations. One finding from this study is that the CCM has a significant drying tendency in the lower troposphere compared to the operational analyses. Another is that the large scale predictability of the GCM is competitive with most of the operational models, particularly in the southern hemisphere.

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.

Uncertainty of a hydrological climate change impact assessment - Is it really all about climate uncertainty?

NASA Astrophysics Data System (ADS)

Honti, Mark; Reichert, Peter; Scheidegger, Andreas; Stamm, Christian

2013-04-01

Climate change impact assessments have become more and more popular in hydrology since the middle 1980's with another boost after the publication of the IPCC AR4 report. During hundreds of impact studies a quasi-standard methodology emerged, which is mainly shaped by the growing public demand for predicting how water resources management or flood protection should change in the close future. The ``standard'' workflow considers future climate under a specific IPCC emission scenario simulated by global circulation models (GCMs), possibly downscaled by a regional climate model (RCM) and/or a stochastic weather generator. The output from the climate models is typically corrected for bias before feeding it into a calibrated hydrological model, which is run on the past and future meteorological data to analyse the impacts of climate change on the hydrological indicators of interest. The impact predictions are as uncertain as any forecast that tries to describe the behaviour of an extremely complex system decades into the future. Future climate predictions are uncertain due to the scenario uncertainty and the GCM model uncertainty that is obvious on finer resolution than continental scale. Like in any hierarchical model system, uncertainty propagates through the descendant components. Downscaling increases uncertainty with the deficiencies of RCMs and/or weather generators. Bias correction adds a strong deterministic shift to the input data. Finally the predictive uncertainty of the hydrological model ends the cascade that leads to the total uncertainty of the hydrological impact assessment. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. There are only few studies, which found that the predictive uncertainty of hydrological models can be in the same range or even larger than climatic uncertainty. We carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on 2 small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment method with 2 different likelihood functions. One was a time-series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was a likelihood function for the flow quantiles directly. Due to the better data coverage and smaller hydrological complexity in one of our test catchments we had better performance from the hydrological model and thus could observe that the relative importance of different uncertainty sources varied between sites, boundary conditions and flow indicators. The uncertainty of future climate was important, but not dominant. The deficiencies of the hydrological model were on the same scale, especially for the sites and flow components where model performance for the past observations was further from optimal (Nash-Sutcliffe index = 0.5 - 0.7). The overall uncertainty of predictions was well beyond the expected change signal even for the best performing site and flow indicator.

Addressing spatial scales and new mechanisms in climate impact ecosystem modeling

NASA Astrophysics Data System (ADS)

Poulter, B.; Joetzjer, E.; Renwick, K.; Ogunkoya, G.; Emmett, K.

2015-12-01

Climate change impacts on vegetation distributions are typically addressed using either an empirical approach, such as a species distribution model (SDM), or with process-based methods, for example, dynamic global vegetation models (DGVMs). Each approach has its own benefits and disadvantages. For example, an SDM is constrained by data and few parameters, but does not include adaptation or acclimation processes or other ecosystem feedbacks that may act to mitigate or enhance climate effects. Alternatively, a DGVM model includes many mechanisms relating plant growth and disturbance to climate, but simulations are costly to perform at high-spatial resolution and there remains large uncertainty on a variety of fundamental physical processes. To address these issues, here, we present two DGVM-based case studies where i) high-resolution (1 km) simulations are being performed for vegetation in the Greater Yellowstone Ecosystem using a biogeochemical, forest gap model, LPJ-GUESS, and ii) where new mechanisms for simulating tropical tree-mortality are being introduced. High-resolution DGVM model simulations require not only computing and reorganizing code but also a consideration of scaling issues on vegetation dynamics and stochasticity and also on disturbance and migration. New mechanisms for simulating forest mortality must consider hydraulic limitations and carbon reserves and their interactions on source-sink dynamics and in controlling water potentials. Improving DGVM approaches by addressing spatial scale challenges and integrating new approaches for estimating forest mortality will provide new insights more relevant for land management and possibly reduce uncertainty by physical processes more directly comparable to experimental and observational evidence.

Anticipating Future Extreme Climate Events for Alaska Using Dynamical Downscaling and Quantile Mapping

NASA Astrophysics Data System (ADS)

Lader, R.; Walsh, J. E.

2016-12-01

Alaska is projected to experience major changes in extreme climate during the 21st century, due to greenhouse warming and exacerbated by polar amplification, wherein the Arctic is warming at twice the rate compared to the Northern Hemisphere. Given its complex topography, Alaska displays extreme gradients of temperature and precipitation. However, global climate models (GCMs), which typically have a spatial resolution on the order of 100km, struggle to replicate these extremes. To help resolve this issue, this study employs dynamically downscaled regional climate simulations and quantile-mapping methodologies to provide a full suite of daily model variables at 20 km spatial resolution for Alaska, from 1970 to 2100. These data include downscaled products of the: ERA-Interim reanalysis from 1979 to 2015, GFDL-CM3 historical from 1970 to 2005, and GFDL-CM3 RCP 8.5 from 2006 to 2100. Due to the limited nature of long-term observations and high-resolution modeling in Alaska, these data enable a broad expansion of extremes analysis. This study uses these data to highlight a subset of the 27 climate extremes indices, previously defined by the Expert Team on Climate Change Detection and Indices, as they pertain to climate change in Alaska. These indices are based on the statistical distributions of daily surface temperature and precipitation and focus on threshold exceedance, and percentiles. For example, the annual number of days with a daily maximum temperature greater than 25°C is anticipated to triple in many locations in Alaska by the end of the century. Climate extremes can also refer to long duration events, such as the record-setting warmth that defined the 2015-16 cold season in Alaska. The downscaled climate model simulations indicate that this past winter will be considered normal by as early as the mid-2040s, if we continue to warm according to the business-as-usual RCP 8.5 emissions scenario. This represents an accelerated warming as compared to projections form the coarse scale GCMs, and this greater rate of change in the downscaled products is noted with other extremes indices as well.

Precipitation response to solar geoengineering in a high-resolution tropical-cyclone permitting coupled general circulation model

NASA Astrophysics Data System (ADS)

Irvine, P. J.; Keith, D.; Dykema, J. A.; Vecchi, G. A.; Horowitz, L. W.

2016-12-01

Solar geoengineering may limit or even halt the rise in global-average surface temperatures. Evidence from the geoMIP model intercomparison project shows that idealized geoengineering can greatly reduce temperature changes on a region-by-region basis. If solar geoengineering is used to hold radiative forcing or surface temperatures constant in the face of rising CO2, then the global evaporation and precipitation rates will be reduced below pre-industrial. The spartial and frequency distribution of the precipitation response is, however, much less well understood. There is limited evidence that solar geoengineering may reduce extreme precipitation events more that it reduces mean precipitation, but that evidence is based on relatively course resolution models that may to a poor job representing the distribution of extreme precipitation in the current climate. The response of global and regional climate, as well as tropical cyclone (TC) activity, to increasing solar geoengineering is explored through experiments with climate models spanning a broad range of atmospheric resolutions. Solar geoengineering is represented by an idealized adjustment of the solar constant that roughly halves the rate of increase in radiative forcing in a scenario with increasing CO2 concentration. The coarsest resolution model has approximately a 2-degree global resolution, representative of the typical resolution of past GCMs used to explore global response to CO2 increase, and its response is compared to that of two tropical cyclone permitting GCMs of approximately 0.5 and 0.25 degree resolution (FLOR and HiFLOR). The models have exactly the same ocean and sea-ice components, as well as the same parameterizations and parameter settings. These high-resolution models are used for real-time seasonal prediction, providing a unified framework for seasonal-to-multidecadal climate modeling. We assess the extreme precipitation response, comparing the frequency distribution of extreme events with and without solar geoengineering. We compare our results to two prior studies of the response of climate extremes to solar geoengineering.

The Transformation of Climate Models to Earth System Models and their Role in Policy Development and Decision Support

NASA Astrophysics Data System (ADS)

Washington, W. M.

2012-12-01

We have seen over the last few decades continued improvement in climate models such that they are becoming Earth system models (ESMs). Usually climate models use specified concentrations of greenhouse gases whereas ESMs allow carbon, water, biochemical and other cycles to be fully interactive between various model components. Typically ESMs have atmospheric, ocean, land/vegetation, sea ice, urbanization components and some are starting to include glacier change which can directly affect sea level change. Steve Schneider, for whom this lecture is named after, strongly encouraged the development of such models and he went further to strongly suggest that these tools be developed beyond just the climate science questions. The modeling community needs to be interacting with the social, behavioral, and economic science communities. This would allow for realistic humankind interactions with the Earth system. In 2012, the federal government with advice from the National Academies developed a new strategic plan for the U. S. Global Change Research Program entitled The National Global Change Research Plan 2012-2021. This new plan has added the social, behavioral, and economic sciences to the mix of research expertise. It should be pointed out that the Global Change Research Act of 1990 passed by Congress specified strategic goals: advance science, inform decisions, conduct assessments, and communicate and educate. In order to carry out these goals an implementation plan is being put together by the 13 federal agencies and departments. Throughout Steve's professional life, he knew that to make global change understood required this broad community of sciences to work together to answer the questions that the public and policymakers had about environmental change. This talk will not only be about the historical developments in the field but also about the future research challenges. As part of the talk I will show several unpublished video segments of Steve explaining what mankind should do about climate and global change.

Stochastic or statistic? Comparing flow duration curve models in ungauged basins and changing climates

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2015-09-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drives of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by a strong wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are strongly favored over statistical models.

A multi-model framework for simulating wildlife population response to land-use and climate change

USGS Publications Warehouse

McRae, B.H.; Schumaker, N.H.; McKane, R.B.; Busing, R.T.; Solomon, A.M.; Burdick, C.A.

2008-01-01

Reliable assessments of how human activities will affect wildlife populations are essential for making scientifically defensible resource management decisions. A principle challenge of predicting effects of proposed management, development, or conservation actions is the need to incorporate multiple biotic and abiotic factors, including land-use and climate change, that interact to affect wildlife habitat and populations through time. Here we demonstrate how models of land-use, climate change, and other dynamic factors can be integrated into a coherent framework for predicting wildlife population trends. Our framework starts with land-use and climate change models developed for a region of interest. Vegetation changes through time under alternative future scenarios are predicted using an individual-based plant community model. These predictions are combined with spatially explicit animal habitat models to map changes in the distribution and quality of wildlife habitat expected under the various scenarios. Animal population responses to habitat changes and other factors are then projected using a flexible, individual-based animal population model. As an example application, we simulated animal population trends under three future land-use scenarios and four climate change scenarios in the Cascade Range of western Oregon. We chose two birds with contrasting habitat preferences for our simulations: winter wrens (Troglodytes troglodytes), which are most abundant in mature conifer forests, and song sparrows (Melospiza melodia), which prefer more open, shrubby habitats. We used climate and land-use predictions from previously published studies, as well as previously published predictions of vegetation responses using FORCLIM, an individual-based forest dynamics simulator. Vegetation predictions were integrated with other factors in PATCH, a spatially explicit, individual-based animal population simulator. Through incorporating effects of landscape history and limited dispersal, our framework predicted population changes that typically exceeded those expected based on changes in mean habitat suitability alone. Although land-use had greater impacts on habitat quality than did climate change in our simulations, we found that small changes in vital rates resulting from climate change or other stressors can have large consequences for population trajectories. The ability to integrate bottom-up demographic processes like these with top-down constraints imposed by climate and land-use in a dynamic modeling environment is a key advantage of our approach. The resulting framework should allow researchers to synthesize existing empirical evidence, and to explore complex interactions that are difficult or impossible to capture through piecemeal modeling approaches. ?? 2008 Elsevier B.V.

Soil carbon management in large-scale Earth system modelling: implications for crop yields and nitrogen leaching

NASA Astrophysics Data System (ADS)

Olin, S.; Lindeskog, M.; Pugh, T. A. M.; Schurgers, G.; Wårlind, D.; Mishurov, M.; Zaehle, S.; Stocker, B. D.; Smith, B.; Arneth, A.

2015-06-01

We explore cropland management alternatives and the effect these can have on future C and N pools and fluxes using the land use-enabled dynamic vegetation model LPJ-GUESS. Simulated crop production, cropland carbon storage, carbon sequestration and nitrogen leaching from croplands are evaluated and discussed. Compared to the version of LPJ-GUESS that does not include land use dynamics, estimates of soil carbon stocks and nitrogen leaching from terrestrial to aquatic ecosystems were improved. We explore trade-offs between important ecosystem services that can be provided from agricultural fields such as crop yields, retention of nitrogen and carbon storage. These trade-offs are evaluated for current land use and climate and further explored for future conditions within the two future climate change scenarios, RCP 2.6 and 8.5. Our results show that the potential for carbon sequestration due to typical cropland management practices such as no-till and cover-crops proposed in literature is not realised, globally or over larger climatic regions. Our results highlight important considerations to be made when modelling C-N interactions in agricultural ecosystems under future environmental change, and the effects these have on terrestrial biogeochemical cycles.

Impact Assessment of Salinization Affected Soil on Greenhouse Crops using SALTMED

NASA Astrophysics Data System (ADS)

Pappa, Polyxeni; Daliakopoulos, Ioannis; Tsanis, Ioannis; Varouchakis, Emmanouil

2015-04-01

Here we assess the effects of soil salinization on greenhouse crops and the potential benefits of rainwater harvesting as a soil amelioration technology. The study deals with the following scenarios: (a) variation of irrigation water salinity from 3,000 μS/cm to 500 μS/cm through mixing with rainwater, (b) crop substitution for increased tolerance and (c) climatic variability to account for the impact of climate change. In order to draw meaningful conclusions, a model that takes into account vegetation interaction, soil, irrigation water and climate variables is required. The SALTMED model is a reliable and tested physical process model that simulates evapotranspiration, plant water uptake, water and solute transport to estimate crop yield and biomass production under all irrigation systems. SALTMED is tested with the above scenarios in the RECARE FP7 Project Case Study of Timpaki, in the Island of Crete, Greece. Simulations are conducted for typical cultivations of Solanum lycopersicum, Capsicum anuumm and Solanum melongena. Preliminary results indicate the optimal combination from a set of solutions concerning the soil and water parameters can be beneficial against the salinization threat. Future research includes the validation of the results with field experiments. Keywords: salinization, greenhouse, tomato, SALTMED, rainwater, RECARE

Fire Influences on Atmospheric Composition, Air Quality, and Climate

NASA Technical Reports Server (NTRS)

Voulgarakis, Apostolos; Field, Robert D.

2015-01-01

Fires impact atmospheric composition through their emissions, which range from long-lived gases to short-lived gases and aerosols. Effects are typically larger in the tropics and boreal regions but can also be substantial in highly populated areas in the northern mid-latitudes. In all regions, fire can impact air quality and health. Similarly, its effect on large-scale atmospheric processes, including regional and global atmospheric chemistry and climate forcing, can be substantial, but this remains largely unexplored. The impacts are primarily realised in the boundary layer and lower free troposphere but can also be noticeable in upper troposphere/lower stratosphere (UT/LS) region, for the most intense fires. In this review, we summarise the recent literature on findings related to fire impact on atmospheric composition, air quality and climate. We explore both observational and modelling approaches and present information on key regions and on the globe as a whole. We also discuss the current and future directions in this area of research, focusing on the major advances in emission estimates, the emerging efforts to include fire as a component in Earth system modelling and the use of modelling to assess health impacts of fire emissions.

Influence of sky view factor on outdoor thermal environment and physiological equivalent temperature

NASA Astrophysics Data System (ADS)

He, Xiaodong; Miao, Shiguang; Shen, Shuanghe; Li, Ju; Zhang, Benzhi; Zhang, Ziyue; Chen, Xiujie

2015-03-01

Sky view factor (SVF), which is an indicator of urban canyon geometry, affects the surface energy balance, local air circulation, and outdoor thermal comfort. This study focused on a continuous and long-term meteorological observation system to investigate the effects of SVF on outdoor thermal conditions and physiological equivalent temperature (PET) in the central business district (CBD) of Beijing (which is located within Chaoyang District), specifically addressed current knowledge gaps for SVF-PET relationships in cities with typical continental/microthermal climates. An urban sub-domain scale model and the RayMan model were used to diagnose wind fields and to calculate SVF and long-term PET, respectively. Analytical results show that the extent of shading contributes to variations in thermal perception distribution. Highly shaded areas (SVF <0.3) typically exhibit less frequent hot conditions during summer, while enduring longer periods of cold discomfort in winter than moderately shaded areas (0.3< SVF <0.5) and slightly shaded areas (SVF >0.5), and vice versa. Because Beijing has a monsoon-influenced humid continental climate with hot summers and long, cold, windy, and dry winters, a design project that ideally provides moderate shading should be planned to balance hot discomfort in summer and cold discomfort in winter, which effectively prolongs the comfort periods in outdoor spaces throughout the entire year. This research indicate that climate zone characteristics, urban environmental conditions, and thermal comfort requirements of residents must be accounted for in local-scale scientific planning and design, i.e., for urban canyon streets and residential estates.

Regional climate service in Southern Germany

NASA Astrophysics Data System (ADS)

Schipper, Janus; Hackenbruch, Julia

2013-04-01

Climate change challenges science, politics, business and society at the international, national and regional level. The South German Climate Office at the Karlsruhe Institute of Technology (KIT) is a contact for the structuring and dissemination of information on climate and climate change in the South German region. It provides scientifically based and user-oriented climate information. Thereby it builds a bridge between the climate sciences and society and provides scientific information on climate change in an understandable way. The expertise of KIT, in which several institutions operate on fundamental and applied climate research, and of partner institutions is the basis for the work in the climate office. The regional focus is on the south of Germany. Thematic focuses are e.g. regional climate modeling, trends in extreme weather events such as heavy rain and hail event, and issues for energy and water management. The South German Climate Office is one of four Regional Helmholtz Climate Offices, of which each has a regional and thematic focus. The users of the Climate Office can be summarized into three categories. First, there is the general public. This category consists mainly of non-professionals. Here, special attention is on an understandable translation of climate information. Attention is paid to application-related aspects, because each individual is affected in a different way by climate change. Typical examples of this category are school groups, citizens and the media. The second category consists of experts of other disciplines. Unlike the first category they are mainly interested in the exchange of results and data. It is important to the climate office to provide support for the use of climatological results. Typical representatives of this category are ministries, state offices, and companies. In the third and final category are scientists. In addition to the climatologists, this category also holds representatives from other scientific disciplines, which are directly or indirectly cope with climate change. This category encompasses for example hydrologists (estimation of future flood events) and engineers (housing in a changing climate). For these three categories, different approaches are needed. First, the South German Climate Office reaches a wide audience through regular appearance in the media (newspapers, radio, and television). Because for such appearances the information content needs to be simplified quite strongly, experts will be better addressed through workshops and conferences. For example, the Climate Office has carried out a few events on "Climate and Constructions' in recent years. Several collaborations that led to project work between different scientific disciplines resulted from these events. The experience at the South German Climate Office has shown that the demand for information about climate change and its consequences is very diverse. Therefore, part of the activities is to carry out a categorized view on the requests in order to allow such a user-oriented answering. An additional role of the climate office is to enhance the general visibility of climatological results by workshops and conferences.

Forecasted Impact of Climate Change on Infectious Disease and Health Security in Hawaii by 2050.

PubMed

Canyon, Deon V; Speare, Rick; Burkle, Frederick M

2016-12-01

Climate change is expected to cause extensive shifts in the epidemiology of infectious and vector-borne diseases. Scenarios on the effects of climate change typically attribute altered distribution of communicable diseases to a rise in average temperature and altered incidence of infectious diseases to weather extremes. Recent evaluations of the effects of climate change on Hawaii have not explored this link. It may be expected that Hawaii's natural geography and robust water, sanitation, and health care infrastructure renders residents less vulnerable to many threats that are the focus on smaller, lesser developed, and more vulnerable Pacific islands. In addition, Hawaii's communicable disease surveillance and response system can act rapidly to counter increases in any disease above baseline and to redirect resources to deal with changes, particularly outbreaks due to exotic pathogens. The evidence base examined in this article consistently revealed very low climate sensitivity with respect to infectious and mosquito-borne diseases. A community resilience model is recommended to increase adaptive capacity for all possible climate change impacts rather an approach that focuses specifically on communicable diseases. (Disaster Med Public Health Preparedness. 2016;10:797-804).

"The Effect of Alternative Representations of Lake ...

EPA Pesticide Factsheets

Lakes can play a significant role in regional climate, modulating inland extremes in temperature and enhancing precipitation. Representing these effects becomes more important as regional climate modeling (RCM) efforts focus on simulating smaller scales. When using the Weather Research and Forecasting (WRF) model to downscale future global climate model (GCM) projections into RCM simulations, model users typically must rely on the GCM to represent temperatures at all water points. However, GCMs have insufficient resolution to adequately represent even large inland lakes, such as the Great Lakes. Some interpolation methods, such as setting lake surface temperatures (LSTs) equal to the nearest water point, can result in inland lake temperatures being set from sea surface temperatures (SSTs) that are hundreds of km away. In other cases, a single point is tasked with representing multiple large, heterogeneous lakes. Similar consequences can result from interpolating ice from GCMs to inland lake points, resulting in lakes as large as Lake Superior freezing completely in the space of a single timestep. The use of a computationally-efficient inland lake model can improve RCM simulations where the input data is too coarse to adequately represent inland lake temperatures and ice (Gula and Peltier 2012). This study examines three scenarios under which ice and LSTs can be set within the WRF model when applied as an RCM to produce 2-year simulations at 12 km gri

Toward a west-wide model of Armillaria root disease: New surveys needed in western Oregon, western Washington, and Alaska

Treesearch

J. W. Hanna; M. -S. Kim; N. B. Klopfenstein; A. C. Ramsey; D. W. Omdal; R. L. Mulvey; B. A. Goodrich; B. A. Ferguson; L. M. Winton; E. M. Goheen; J. J. Bronson; H. S. J. Kearns; K. L. Chadwick; M. Murray; D. C. Shaw; G. I. McDonald; E. W. I. Pitman; M. V. Warwell

2017-01-01

Currently, Armillaria root disease causes large growth/volume losses (e.g., 16-55%) in areas of western North America (Filip and Goheen 1984; Cruickshank 2011; Lockman and Kearns 2016). Armillaria root disease is typically more severe in trees that are maladapted to climate-induced stress (Ayres and Lombardero 2000; Kliejunas et al. 2009; Sturrock 2011). Thus, it is...

Reciprocating and Screw Compressor semi-empirical models for establishing minimum energy performance standards

NASA Astrophysics Data System (ADS)

Javed, Hassan; Armstrong, Peter

2015-08-01

The efficiency bar for a Minimum Equipment Performance Standard (MEPS) generally aims to minimize energy consumption and life cycle cost of a given chiller type and size category serving a typical load profile. Compressor type has a significant chiller performance impact. Performance of screw and reciprocating compressors is expressed in terms of pressure ratio and speed for a given refrigerant and suction density. Isentropic efficiency for a screw compressor is strongly affected by under- and over-compression (UOC) processes. The theoretical simple physical UOC model involves a compressor-specific (but sometimes unknown) volume index parameter and the real gas properties of the refrigerant used. Isentropic efficiency is estimated by the UOC model and a bi-cubic, used to account for flow, friction and electrical losses. The unknown volume index, a smoothing parameter (to flatten the UOC model peak) and bi-cubic coefficients are identified by curve fitting to minimize an appropriate residual norm. Chiller performance maps are produced for each compressor type by selecting optimized sub-cooling and condenser fan speed options in a generic component-based chiller model. SEER is the sum of hourly load (from a typical building in the climate of interest) and specific power for the same hourly conditions. An empirical UAE cooling load model, scalable to any equipment capacity, is used to establish proposed UAE MEPS. Annual electricity use and cost, determined from SEER and annual cooling load, and chiller component cost data are used to find optimal chiller designs and perform life-cycle cost comparison between screw and reciprocating compressor-based chillers. This process may be applied to any climate/load model in order to establish optimized MEPS for any country and/or region.

Detecting potential anomalies in projections of rainfall trends and patterns using human observations

NASA Astrophysics Data System (ADS)

Kohfeld, K. E.; Savo, V.; Sillmann, J.; Morton, C.; Lepofsky, D.

2016-12-01

Shifting precipitation patterns are a well-documented consequence of climate change, but their spatial variability is particularly difficult to assess. While the accuracy of global models has increased, specific regional changes in precipitation regimes are not well captured by these models. Typically, researchers who wish to detect trends and patterns in climatic variables, such as precipitation, use instrumental observations. In our study, we combined observations of rainfall by subsistence-oriented communities with several metrics of rainfall estimated from global instrumental records for comparable time periods (1955 - 2005). This comparison was aimed at identifying: 1) which rainfall metrics best match human observations of changes in precipitation; 2) areas where local communities observe changes not detected by global models. The collated observations ( 3800) made by subsistence-oriented communities covered 129 countries ( 1830 localities). For comparable time periods, we saw a substantial correspondence between instrumental records and human observations (66-77%) at the same locations, regardless of whether we considered trends in general rainfall, drought, or extreme rainfall. We observed a clustering of mismatches in two specific regions, possibly indicating some climatic phenomena not completely captured by the currently available global models. Many human observations also indicated an increased unpredictability in the start, end, duration, and continuity of the rainy seasons, all of which may hamper the performance of subsistence activities. We suggest that future instrumental metrics should capture this unpredictability of rainfall. This information would be important for thousands of subsistence-oriented communities in planning, coping, and adapting to climate change.

The urban impact on the regional climate of Dresden

NASA Astrophysics Data System (ADS)

Sändig, B.; Renner, E.

2010-09-01

The principal objective of this research is to clarify the impact of urban elements such as buildings and streets on the regional climate and air quality in the framework of the BMBF-project "Regionales Klimaanpassungsprogramm f¨ur die Modellregion Dresden" (REGKLAM). Drawing on the example of Dresden this work explores how the presence of cities influences the atmospheric flow and the characteristics of the boundary layer. Persuing this target, an urban surface exchange parameterisation module (Martilli et al., 2002) was implemented in a high resolution version of the COSMO model, the forecast model of the German Weather Service (DWD). Using a mesoscale model for this regional climate study implies the advantage of embedding the focused area in a realistic large scale situation via downscaling by means of one way nesting and allows to simulate the urban impact for different IPCC-szenarios. The urban module is based on the assumption that a city could be represented by a bunch of "urban classes". Each urban class is characterised by specific properties such as typical street directions or probability of finding a building in a special height. Based on urban structure data of Dresden (vector shape-files containing the outlines of all buildings and the respective heights) an automated method of extracting the relevant geometrical input parameters for the urban module was developed. By means of this model setup we performed case studies, in which we investigate the interactions between the city structure and the meteorological variables with regard to special synoptical situations such as the Bohemian wind, a typical flow pattern of cold air, sourced from the Bohemian Basin, in the Elbe Valley, which acts then like a wind channel. Another focal point is formed by the investigation of different types of artificial cities ranging from densely builtup areas to suburban areas in order to illuminating the impact of the city type on the dynamical and thermal properties of the atmosphere.

Process-based evaluation of the ÖKS15 Austrian climate scenarios: First results

NASA Astrophysics Data System (ADS)

Mendlik, Thomas; Truhetz, Heimo; Jury, Martin; Maraun, Douglas

2017-04-01

The climate scenarios for Austria from the ÖKS15 project consists of 13 downscaled and bias-corrected RCMs from the EURO-CORDEX project. This dataset is meant for the broad public and is now available at the central national archive for climate data (CCCA Data Center). Because of this huge public outreach it is absolutely necessary to objectively discuss the limitations of this dataset and to publish these limitations, which should also be understood by a non-scientific audience. Even though systematical climatological biases have been accounted for by the Scaled-Distribution-Mapping (SDM) bias-correction method, it is not guaranteed that the model biases have been removed for the right reasons. If climate scenarios do not get the patterns of synoptic variability right, biases will still prevail in certain weather patterns. Ultimately this will have consequences for the projected climate change signals. In this study we derive typical weather types in the Alpine Region based on patterns from mean sea level pressure from ERA-INTERIM data and check the occurrence of these synoptic phenomena in EURO-CORDEX data and their corresponding driving GCMs. Based on these weather patterns we analyze the remaining biases of the downscaled and bias-corrected scenarios. We argue that such a process-based evaluation is not only necessary from a scientific point of view, but can also help the broader public to understand the limitations of downscaled climate scenarios, as model errors can be interpreted in terms of everyday observable weather.

Technical Note: Linking climate change and downed woody debris decomposition across forests of the eastern United States

NASA Astrophysics Data System (ADS)

Russell, M. B.; Woodall, C. W.; D'Amato, A. W.; Fraver, S.; Bradford, J. B.

2014-06-01

Forest ecosystems play a critical role in mitigating greenhouse gas emissions. Long-term forest carbon (C) storage is determined by the balance between C fixation into biomass through photosynthesis and C release via decomposition and combustion. Relative to C fixation in biomass, much less is known about C depletion through decomposition of woody debris, particularly under a changing climate. It is assumed that the increased temperatures and longer growing seasons associated with projected climate change will increase the decomposition rates (i.e., more rapid C cycling) of downed woody debris (DWD); however, the magnitude of this increase has not been previously addressed. Using DWD measurements collected from a national forest inventory of the eastern United States, we show that the residence time of DWD may decrease (i.e., more rapid decomposition) by as much as 13% over the next 200 years depending on various future climate change scenarios and forest types. Although existing dynamic global vegetation models account for the decomposition process, they typically do not include the effect of a changing climate on DWD decomposition rates. We expect that an increased understanding of decomposition rates, as presented in this current work, will be needed to adequately quantify the fate of woody detritus in future forests. Furthermore, we hope these results will lead to improved models that incorporate climate change scenarios for depicting future dead wood dynamics, in addition to a traditional emphasis on live tree demographics.

What spatial scales are believable for climate model projections of sea surface temperature?

NASA Astrophysics Data System (ADS)

Kwiatkowski, Lester; Halloran, Paul R.; Mumby, Peter J.; Stephenson, David B.

2014-09-01

Earth system models (ESMs) provide high resolution simulations of variables such as sea surface temperature (SST) that are often used in off-line biological impact models. Coral reef modellers have used such model outputs extensively to project both regional and global changes to coral growth and bleaching frequency. We assess model skill at capturing sub-regional climatologies and patterns of historical warming. This study uses an established wavelet-based spatial comparison technique to assess the skill of the coupled model intercomparison project phase 5 models to capture spatial SST patterns in coral regions. We show that models typically have medium to high skill at capturing climatological spatial patterns of SSTs within key coral regions, with model skill typically improving at larger spatial scales (≥4°). However models have much lower skill at modelling historical warming patters and are shown to often perform no better than chance at regional scales (e.g. Southeast Asian) and worse than chance at finer scales (<8°). Our findings suggest that output from current generation ESMs is not yet suitable for making sub-regional projections of change in coral bleaching frequency and other marine processes linked to SST warming.

VOC Reactivity and the Ozone Climate Penalty: Modeled Impacts of Updated Aromatic and Monoterpene Chemistry on the Ozone-temperature Connection

NASA Astrophysics Data System (ADS)

Porter, W. C.; Heald, C. L.; Safieddine, S.

2016-12-01

Rising temperatures associated with global warming can increase concentrations of tropospheric ozone (O3) in many regions worldwide, a correlation often described as the "ozone climate penalty". This effect is driven by a variety of underlying chemical, physical, and biological mechanisms, including temperature-dependent reaction rates, emissions of volatile organic compounds (VOCs) from trees and other plant life, and correlations with other meteorological variables. While many of the most important O3-producing VOCs, such as isoprene, are represented in typical chemical transport models such as GEOS-Chem, others - including aromatics from fires and human activity and monoterpenes from natural sources - are not always included in gas-phase chemistry. Here we examine the impact of increased VOC reactivity on the ozone climate penalty due to a more comprehensive treatment of aromatics and monoterpenes in the chemical transport model GEOS-Chem, finding regional impacts not only on daily O3 levels themselves, but also on the O3/temperature relationship. While many uncertainties related to the emissions and chemistry of these species remain, the impact of their inclusion on both current simulations and future projections indicates their importance towards the overall goal of more accurately modeled surface O3.

Comparing regional precipitation and temperature extremes in climate model and reanalysis products

DOE PAGES

Angélil, Oliver; Perkins-Kirkpatrick, Sarah; Alexander, Lisa V.; ...

2016-07-12

A growing field of research aims to characterise the contribution of anthropogenic emissions to the likelihood of extreme weather and climate events. These analyses can be sensitive to the shapes of the tails of simulated distributions. If tails are found to be unrealistically short or long, the anthropogenic signal emerges more or less clearly, respectively, from the noise of possible weather. Here we compare the chance of daily land-surface precipitation and near-surface temperature extremes generated by three Atmospheric Global Climate Models typically used for event attribution, with distributions from six reanalysis products. The likelihoods of extremes are compared for area-averagesmore » over grid cell and regional sized spatial domains. Results suggest a bias favouring overly strong attribution estimates for hot and cold events over many regions of Africa and Australia, and a bias favouring overly weak attribution estimates over regions of North America and Asia. For rainfall, results are more sensitive to geographic location. Although the three models show similar results over many regions, they do disagree over others. Equally, results highlight the discrepancy amongst reanalyses products. This emphasises the importance of using multiple reanalysis and/or observation products, as well as multiple models in event attribution studies.« less

Ocean modelling on the CYBER 205 at GFDL

NASA Technical Reports Server (NTRS)

Cox, M.

1984-01-01

At the Geophysical Fluid Dynamics Laboratory, research is carried out for the purpose of understanding various aspects of climate, such as its variability, predictability, stability and sensitivity. The atmosphere and oceans are modelled mathematically and their phenomenology studied by computer simulation methods. The present state-of-the-art in the computer simulation of large scale oceans on the CYBER 205 is discussed. While atmospheric modelling differs in some aspects, the basic approach used is similar. The equations of the ocean model are presented along with a short description of the numerical techniques used to find their solution. Computational considerations and a typical solution are presented in section 4.

Climate Change Threatens Coexistence within Communities of Mediterranean Forested Wetlands

PubMed Central

Di Paola, Arianna; Valentini, Riccardo; Paparella, Francesco

2012-01-01

The Mediterranean region is one of the hot spots of climate change. This study aims at understanding what are the conditions sustaining tree diversity in Mediterranean wet forests under future scenarios of altered hydrological regimes. The core of the work is a quantitative, dynamic model describing the coexistence of different Mediterranean tree species, typical of arid or semi-arid wetlands. Two kind of species, i.e. Hygrophilous (drought sensitive, flood resistant) and Non-hygrophilous (drought resistant, flood sensitive), are broadly defined according to the distinct adaptive strategies of trees against water stress of summer drought and winter flooding. We argue that at intermediate levels of water supply the dual role of water (resource and stress) results in the coexistence of the two kind of species. A bifurcation analysis allows us to assess the effects of climate change on the coexistence of the two species in order to highlight the impacts of predicted climate scenarios on tree diversity. Specifically, the model has been applied to Mediterranean coastal swamp forests of Central Italy located at Castelporziano Estate and Circeo National Park. Our results show that there are distinct rainfall thresholds beyond which stable coexistence becomes impossible. Regional climatic projections show that the lower rainfall threshold may be approached or crossed during the XXI century, calling for an urgent adaptation and mitigation response to prevent biodiversity losses. PMID:23077484

Moisture availability constraints on the leaf area to sapwood area ratio: analysis of measurements on Australian evergreen angiosperm trees

NASA Astrophysics Data System (ADS)

Togashi, Henrique; Prentice, Colin; Evans, Bradley; Forrester, David; Drake, Paul; Feikema, Paul; Brooksbank, Kim; Eamus, Derek; Taylor, Daniel

2014-05-01

The leaf area to sapwood area ratio (LA:SA) is a key plant trait that links photosynthesis to transpiration. Pipe model theory states that the sapwood cross-sectional area of a stem or branch at any point should scale isometrically with the area of leaves distal to that point. Optimization theory further suggests that LA:SA should decrease towards drier climates. Although acclimation of LA:SA to climate has been reported within species, much less is known about the scaling of this trait with climate among species. We compiled LA:SA measurements from 184 species of Australian evergreen angiosperm trees. The pipe model was broadly confirmed, based on measurements on branches and trunks of trees from one to 27 years old. We found considerable scatter in LA:SA among species. However quantile regression showed strong (0.2

Morphological and moisture availability controls of the leaf area-to-sapwood area ratio: analysis of measurements on Australian trees.

PubMed

Togashi, Henrique Furstenau; Prentice, Iain Colin; Evans, Bradley John; Forrester, David Ian; Drake, Paul; Feikema, Paul; Brooksbank, Kim; Eamus, Derek; Taylor, Daniel

2015-03-01

The leaf area-to-sapwood area ratio (LA:SA) is a key plant trait that links photosynthesis to transpiration. The pipe model theory states that the sapwood cross-sectional area of a stem or branch at any point should scale isometrically with the area of leaves distal to that point. Optimization theory further suggests that LA:SA should decrease toward drier climates. Although acclimation of LA:SA to climate has been reported within species, much less is known about the scaling of this trait with climate among species. We compiled LA:SA measurements from 184 species of Australian evergreen angiosperm trees. The pipe model was broadly confirmed, based on measurements on branches and trunks of trees from one to 27 years old. Despite considerable scatter in LA:SA among species, quantile regression showed strong (0.2 < R1 < 0.65) positive relationships between two climatic moisture indices and the lowermost (5%) and uppermost (5-15%) quantiles of log LA:SA, suggesting that moisture availability constrains the envelope of minimum and maximum values of LA:SA typical for any given climate. Interspecific differences in plant hydraulic conductivity are probably responsible for the large scatter of values in the mid-quantile range and may be an important determinant of tree morphology.

Morphological and moisture availability controls of the leaf area-to-sapwood area ratio: analysis of measurements on Australian trees

PubMed Central

Togashi, Henrique Furstenau; Prentice, Iain Colin; Evans, Bradley John; Forrester, David Ian; Drake, Paul; Feikema, Paul; Brooksbank, Kim; Eamus, Derek; Taylor, Daniel

2015-01-01

The leaf area-to-sapwood area ratio (LA:SA) is a key plant trait that links photosynthesis to transpiration. The pipe model theory states that the sapwood cross-sectional area of a stem or branch at any point should scale isometrically with the area of leaves distal to that point. Optimization theory further suggests that LA:SA should decrease toward drier climates. Although acclimation of LA:SA to climate has been reported within species, much less is known about the scaling of this trait with climate among species. We compiled LA:SA measurements from 184 species of Australian evergreen angiosperm trees. The pipe model was broadly confirmed, based on measurements on branches and trunks of trees from one to 27 years old. Despite considerable scatter in LA:SA among species, quantile regression showed strong (0.2 < R1 < 0.65) positive relationships between two climatic moisture indices and the lowermost (5%) and uppermost (5–15%) quantiles of log LA:SA, suggesting that moisture availability constrains the envelope of minimum and maximum values of LA:SA typical for any given climate. Interspecific differences in plant hydraulic conductivity are probably responsible for the large scatter of values in the mid-quantile range and may be an important determinant of tree morphology. PMID:25859331

The future of climate science analysis in a coming era of exascale computing

NASA Astrophysics Data System (ADS)

Bates, S. C.; Strand, G.

2013-12-01

Projections of Community Earth System Model (CESM) output based on the growth of data archived over 2000-2012 at all of our computing sites (NCAR, NERSC, ORNL) show that we can expect to reach 1,000 PB (1 EB) sometime in the next decade or so. The current paradigms of using site-based archival systems to hold these data that are then accessed via portals or gateways, downloading the data to a local system, and then processing/analyzing the data will be irretrievably broken before then. From a climate modeling perspective, the expertise involved in making climate models themselves efficient on HPC systems will need to be applied to the data as well - providing fast parallel analysis tools co-resident in memory with the data, because the disk I/O bandwidth simply will not keep up with the expected arrival of exaflop systems. The ability of scientists, analysts, stakeholders and others to use climate model output to turn these data into understanding and knowledge will require significant advances in the current typical analysis tools and packages to enable these processes for these vast volumes of data. Allowing data users to enact their own analyses on model output is virtually a requirement as well - climate modelers cannot anticipate all the possibilities for analysis that users may want to do. In addition, the expertise of data scientists, and their knowledge of the model output and their knowledge of best practices in data management (metadata, curation, provenance and so on) will need to be rewarded and exploited to gain the most understanding possible from these volumes of data. In response to growing data size, demand, and future projections, the CESM output has undergone a structure evolution and the data management plan has been reevaluated and updated. The major evolution of the CESM data structure is presented here, along with the CESM experience and role within the CMIP3/CMIP5.

Cloud cover determination in polar regions from satellite imagery

NASA Technical Reports Server (NTRS)

Barry, R. G.; Key, J. R.; Maslanik, J. A.

1988-01-01

The principal objectives of this project are: (1) to develop suitable validation data sets to evaluate the effectiveness of the International Satellite Cloud Climatology Project (ISCCP) operational algorithm for cloud retrieval in polar regions and to validate model simulations of polar cloud cover; (2) to identify limitations of current procedures for varying atmospheric surface conditions, and to explore potential means to remedy them using textural classifiers; and (3) to compare synoptic cloud data from a control run experiment of the GISS climate model II with typical observed synoptic cloud patterns.

Research on trend of warm-humid climate in Central Asia

NASA Astrophysics Data System (ADS)

Gong, Zhi; Peng, Dailiang; Wen, Jingyi; Cai, Zhanqing; Wang, Tiantian; Hu, Yuekai; Ma, Yaxin; Xu, Junfeng

2017-07-01

Central Asia is a typical arid area, which is sensitive and vulnerable part of climate changes, at the same time, Central Asia is the Silk Road Economic Belt of the core district, the warm-humid climate change will affect the production and economic development of neighboring countries. The average annual precipitation, average anneal temperature and evapotranspiration are the important indexes to weigh the climate change. In this paper, the annual precipitation, annual average temperature and evapotranspiration data of every pixel point in Central Asia are analyzed by using long-time series remote sensing data to analyze the trend of warm and humid conditions. Finally, using the model to analyzed the distribution of warm-dry trend, the warm-wet trend, the cold-dry trend and the cold-wet trend in Central Asia and Xinjiang area. The results showed that most of the regions of Central Asia were warm-humid and warm-dry trends, but only a small number of regions showed warm-dry and cold-dry trends. It is of great significance to study the climatic change discipline and guarantee the ecological safety and improve the ability to cope with climate change in the region. It also provide scientific basis for the formulation of regional climate change program. The first section in your paper

A Pilot Study Assesing Climate Change Impacts on Cereals

NASA Astrophysics Data System (ADS)

Topcu, Sevilay; Sen, Burak; Turkes, Murat

2010-05-01

The spatial and temporal impacts of climate change on the growth and yield of major cereals (first and second-crop corn) as well as wheat grown in Cukurova Region in the southern Turkey have been assessed, by combining the outputs from a regional climate model with a crop growth simulation model. With its 1.1 million ha of agricultural land, the Cukurova Region is one of the major agricultural production regions in Turkey. Wheat dominates in rain-fed areas while corn crops are grown in more than 50 % of the irrigated land in the region. Thus, the Region is providing half of the country's total cereal production. Since the region has a typical Mediterranean climate with almost no rain and high temperatures during the summer months, agricultural production is vulnerable to changes in climate in terms of decreasing rainfall and increasing temperatures and consequently shortage of water resources. To predict the future climate for the period 2070-2100, the regional climate model RegCM3 conditions was performed using IPCC's SRESS-A2 scenario, and climatic parameter such as daily mean, maximum and minimum temperatures, radiation as well as total annual precipitation were selected for the simulation study. Data for the period 1961 to 1990 were used as historical reference. The WOFOST model was used to simulate cereal growths and yields for two different water availability senarios: 1) potential production and 2) water-limited production conditions. Potential growth represents the conditions where no limiting factor such as water and nutrients is present, however due to the water-limited production situation, water for irrigation is limited as a consequence of water shortage. The detailed results of previous field experiments carried out with three cereal crops in different locations with different regional soil and climate conditions were used for the verification of the WOFOST model. According to the verification results, the model simulated the yield with less than 5% deviation for all three cereal crops. According to projections of the regional climate model RegCM3, the annual average temperature will likely increase by 3.4 to 4.8 °C, while approximately a 25% decrease in rainfall amounts is expected in the Cukurova Region during the period 2071-2100. Similar results for temperatures were estimated for entire country, however predicted changes in rainfall varies in a wide range for the country. The study showed that with climate change, wheat yield could decrease drastically in rainfed areas, however supplemental irrigation could help to sustain the yield on the current level. Yields of first and second-crop corn are expected to decrease by 58% and 43.4%, respectively, compared to the reference value under water shortages.

Error assessment of biogeochemical models by lower bound methods (NOMMA-1.0)

NASA Astrophysics Data System (ADS)

Sauerland, Volkmar; Löptien, Ulrike; Leonhard, Claudine; Oschlies, Andreas; Srivastav, Anand

2018-03-01

Biogeochemical models, capturing the major feedbacks of the pelagic ecosystem of the world ocean, are today often embedded into Earth system models which are increasingly used for decision making regarding climate policies. These models contain poorly constrained parameters (e.g., maximum phytoplankton growth rate), which are typically adjusted until the model shows reasonable behavior. Systematic approaches determine these parameters by minimizing the misfit between the model and observational data. In most common model approaches, however, the underlying functions mimicking the biogeochemical processes are nonlinear and non-convex. Thus, systematic optimization algorithms are likely to get trapped in local minima and might lead to non-optimal results. To judge the quality of an obtained parameter estimate, we propose determining a preferably large lower bound for the global optimum that is relatively easy to obtain and that will help to assess the quality of an optimum, generated by an optimization algorithm. Due to the unavoidable noise component in all observations, such a lower bound is typically larger than zero. We suggest deriving such lower bounds based on typical properties of biogeochemical models (e.g., a limited number of extremes and a bounded time derivative). We illustrate the applicability of the method with two real-world examples. The first example uses real-world observations of the Baltic Sea in a box model setup. The second example considers a three-dimensional coupled ocean circulation model in combination with satellite chlorophyll a.

Managing fire and fuels in a warmer climate

Treesearch

David L. Peterson

2010-01-01

This historical perspective on fire provides a window into the future of fire in the Pacific Northwest. Although fire will always be more common in the interior portion of the region, a warmer climate could bring more fire to the westside of the Cascade Range where summers are typically dry and will probably become drier. If future climate resembles the climate now...

Climate Change Education in the Southeastern U.S. Through Public Dialogue: Not Just Preaching to the Choir

ERIC Educational Resources Information Center

McNeal, Karen S.; Hammerman, James K. L.; Christiansen, Jonathan A.; Carroll, F. Julian

2014-01-01

Climate change education in the southeastern United States can be challenging. Due to economic factors, as well as the conservative political and faith perspectives typical of the region, high proportions (40%) of the population are not engaged, not convinced, or doubt Earth's climate is changing or that climate change has anthropogenic causes.…

Quantification of uncertainty in flood risk assessment for flood protection planning: a Bayesian approach

NASA Astrophysics Data System (ADS)

Dittes, Beatrice; Špačková, Olga; Ebrahimian, Negin; Kaiser, Maria; Rieger, Wolfgang; Disse, Markus; Straub, Daniel

2017-04-01

Flood risk estimates are subject to significant uncertainties, e.g. due to limited records of historic flood events, uncertainty in flood modeling, uncertain impact of climate change or uncertainty in the exposure and loss estimates. In traditional design of flood protection systems, these uncertainties are typically just accounted for implicitly, based on engineering judgment. In the AdaptRisk project, we develop a fully quantitative framework for planning of flood protection systems under current and future uncertainties using quantitative pre-posterior Bayesian decision analysis. In this contribution, we focus on the quantification of the uncertainties and study their relative influence on the flood risk estimate and on the planning of flood protection systems. The following uncertainty components are included using a Bayesian approach: 1) inherent and statistical (i.e. limited record length) uncertainty; 2) climate uncertainty that can be learned from an ensemble of GCM-RCM models; 3) estimates of climate uncertainty components not covered in 2), such as bias correction, incomplete ensemble, local specifics not captured by the GCM-RCM models; 4) uncertainty in the inundation modelling; 5) uncertainty in damage estimation. We also investigate how these uncertainties are possibly reduced in the future when new evidence - such as new climate models, observed extreme events, and socio-economic data - becomes available. Finally, we look into how this new evidence influences the risk assessment and effectivity of flood protection systems. We demonstrate our methodology for a pre-alpine catchment in southern Germany: the Mangfall catchment in Bavaria that includes the city of Rosenheim, which suffered significant losses during the 2013 flood event.

Generation of Multivariate Surface Weather Series with Use of the Stochastic Weather Generator Linked to Regional Climate Model

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Farda, A.; Huth, R.

2012-12-01

The regional-scale simulations of weather-sensitive processes (e.g. hydrology, agriculture and forestry) for the present and/or future climate often require high resolution meteorological inputs in terms of the time series of selected surface weather characteristics (typically temperature, precipitation, solar radiation, humidity, wind) for a set of stations or on a regular grid. As even the latest Global and Regional Climate Models (GCMs and RCMs) do not provide realistic representation of statistical structure of the surface weather, the model outputs must be postprocessed (downscaled) to achieve the desired statistical structure of the weather data before being used as an input to the follow-up simulation models. One of the downscaling approaches, which is employed also here, is based on a weather generator (WG), which is calibrated using the observed weather series and then modified (in case of simulations for the future climate) according to the GCM- or RCM-based climate change scenarios. The present contribution uses the parametric daily weather generator M&Rfi to follow two aims: (1) Validation of the new simulations of the present climate (1961-1990) made by the ALADIN-Climate/CZ (v.2) Regional Climate Model at 25 km resolution. The WG parameters will be derived from the RCM-simulated surface weather series and compared to those derived from observational data in the Czech meteorological stations. The set of WG parameters will include selected statistics of the surface temperature and precipitation (characteristics of the mean, variability, interdiurnal variability and extremes). (2) Testing a potential of RCM output for calibration of the WG for the ungauged locations. The methodology being examined will consist in using the WG, whose parameters are interpolated from the surrounding stations and then corrected based on a RCM-simulated spatial variability. The quality of the weather series produced by the WG calibrated in this way will be assessed in terms of selected climatic characteristics focusing on extreme precipitation and temperature characteristics (including characteristics of dry/wet/hot/cold spells). Acknowledgements: The present experiment is made within the frame of projects ALARO (project P209/11/2405 sponsored by the Czech Science Foundation), WG4VALUE (project LD12029 sponsored by the Ministry of Education, Youth and Sports) and VALUE (COST ES 1102 action).

Hope for the Forests? Habitat Resiliency Illustrated in the Face of Climate Change Using Fine-Scale Modeling

NASA Astrophysics Data System (ADS)

Flint, L. E.; Flint, A. L.; Weiss, S. B.; Micheli, E. R.

2010-12-01

In the face of rapid climate change, fine-scale predictions of landscape change are of extreme interest to land managers that endeavor to develop long term adaptive strategies for maintaining biodiversity and ecosystem services. Global climate model (GCM) outputs, which generally focus on estimated increases in air temperature, are increasingly applied to species habitat distribution models. For sensitive species subject to climate change, habitat models predict significant migration (either northward or towards higher elevations), or complete extinction. Current studies typically rely on large spatial scale GCM projections (> 10 km) of changes in precipitation and air temperature: at this scale, these models necessarily neglect subtleties of topographic shading, geomorphic expression of the landscape, and fine-scale differences in soil properties - data that is readily available at meaningful local scales. Recent advances in modeling take advantage of available soils, geology, and topographic data to construct watershed-scale scenarios using GCM inputs and result in improved correlations of vegetation distribution with temperature. For this study, future climate projections were downscaled to 270-m and applied to a physically-based hydrologic model to calculate future changes in recharge, runoff, and climatic water deficit (CWD) for basins draining into the northern San Francisco Bay. CWD was analyzed for mapped vegetation types to evaluate the range of CWD for historic time periods in comparison to future time periods. For several forest communities (including blue oak woodlands, montane hardwoods, douglas-fir, and coast redwood) existing landscape area exhibiting suitable CWD diminishes by up 80 percent in the next century, with a trend towards increased CWD throughout the region. However, no forest community loses all suitable habitat, with islands of potential habitat primarily remaining on north facing slopes and deeper soils. Creation of new suitable habitat is also predicted throughout the region. Results have direct application to management issues of habitat connectivity, forest land protection and acquisition, and active management solutions such as transplanting or assisted migration. Although this analysis considers only one driver of forest habitat distribution, consideration of hydrologic derivatives at a fine scale explains current forest community distributions and provides a far more informed perspective on potential future forest distributions. Results demonstrate the utility of fine-scale modeling and provide landscape managers and conservation agencies valuable management tools in fine-scale future forest scenarios and a framework for evaluating forest resiliency in a changing climate.

Climate model assessment of changes in winter-spring streamflow timing over North America

USGS Publications Warehouse

Kam, Jonghun; Knutson, Thomas R.; Milly, Paul C. D.

2018-01-01

Over regions where snow-melt runoff substantially contributes to winter-spring streamflows, warming can accelerate snow melt and reduce dry-season streamflows. However, conclusive detection of changes and attribution to anthropogenic forcing is hindered by brevity of observational records, model uncertainty, and uncertainty concerning internal variability. In this study, a detection/attribution of changes in mid-latitude North American winter-spring streamflow timing is examined using nine global climate models under multiple forcing scenarios. In this study, robustness across models, start/end dates for trends, and assumptions about internal variability is evaluated. Marginal evidence for an emerging detectable anthropogenic influence (according to four or five of nine models) is found in the north-central U.S., where winter-spring streamflows have been coming earlier. Weaker indications of detectable anthropogenic influence (three of nine models) are found in the mountainous western U.S./southwestern Canada and in extreme northeastern U.S./Canadian Maritimes. In the former region, a recent shift toward later streamflows has rendered the full-record trend toward earlier streamflows only marginally significant, with possible implications for previously published climate change detection findings for streamflow timing in this region. In the latter region, no forced model shows as large a shift toward earlier streamflow timing as the detectable observed shift. In other (including warm, snow-free) regions, observed trends are typically not detectable, although in the U.S. central plains we find detectable delays in streamflow, which are inconsistent with forced model experiments.

Land surface energy budget during dry spells: global CMIP5 AMIP simulations vs. satellite observations

NASA Astrophysics Data System (ADS)

Gallego-Elvira, Belen; Taylor, Christopher M.; Harris, Phil P.; Ghent, Darren; Folwell, Sonja S.

2015-04-01

During extended periods without rain (dry spells), the soil can dry out due to vegetation transpiration and soil evaporation. At some point in this drying cycle, land surface conditions change from energy-limited to water-limited evapotranspiration, and this is accompanied by an increase of the ground and overlying air temperatures. Regionally, the characteristics of this transition determine the influence of soil moisture on air temperature and rainfall. Global Climate Models (GCMs) disagree on where and how strongly the surface energy budget is limited by soil moisture. Flux tower observations are improving our understanding of these dry down processes, but typical heterogeneous landscapes are too sparsely sampled to ascertain a representative regional response. Alternatively, satellite observations of land surface temperature (LST) provide indirect information about the surface energy partition at 1km resolution globally. In our study, we analyse how well the dry spell dynamics of LST are represented by GCMs across the globe. We use a spatially and temporally aggregated diagnostic to describe the composite response of LST during surface dry down in rain-free periods in distinct climatic regions. The diagnostic is derived from daytime MODIS-Terra LST observations and bias-corrected meteorological re-analyses, and compared against the outputs of historical climate simulations of seven models running the CMIP5 AMIP experiment. Dry spell events are stratified by antecedent precipitation, land cover type and geographic regions to assess the sensitivity of surface warming rates to soil moisture levels at the onset of a dry spell for different surface and climatic zones. In a number of drought-prone hot spot regions, we find important differences in simulated dry spell behaviour, both between models, and compared to observations. These model biases are likely to compromise seasonal forecasts and future climate projections.

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.

Uncertainty Quantification given Discontinuous Climate Model Response and a Limited Number of Model Runs

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Safta, C.; Debusschere, B.; Najm, H.

2010-12-01

Uncertainty quantification in complex climate models is challenged by the sparsity of available climate model predictions due to the high computational cost of model runs. Another feature that prevents classical uncertainty analysis from being readily applicable is bifurcative behavior in climate model response with respect to certain input parameters. A typical example is the Atlantic Meridional Overturning Circulation. The predicted maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO2 forcing. We outline a methodology for uncertainty quantification given discontinuous model response and a limited number of model runs. Our approach is two-fold. First we detect the discontinuity with Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve shape and location for arbitrarily distributed input parameter values. Then, we construct spectral representations of uncertainty, using Polynomial Chaos (PC) expansions on either side of the discontinuity curve, leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification. The approach is enabled by a Rosenblatt transformation that maps each side of the discontinuity to regular domains where desirable orthogonality properties for the spectral bases hold. We obtain PC modes by either orthogonal projection or Bayesian inference, and argue for a hybrid approach that targets a balance between the accuracy provided by the orthogonal projection and the flexibility provided by the Bayesian inference - where the latter allows obtaining reasonable expansions without extra forward model runs. The model output, and its associated uncertainty at specific design points, are then computed by taking an ensemble average over PC expansions corresponding to possible realizations of the discontinuity curve. The methodology is tested on synthetic examples of discontinuous model data with adjustable sharpness and structure. This work was supported by the Sandia National Laboratories Seniors’ Council LDRD (Laboratory Directed Research and Development) program. Sandia National Laboratories is a multi-program laboratory operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Company, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

Longitudinal thermal heterogeneity in rivers and refugia for coldwater species: effects of scale and climate change

EPA Science Inventory

Climate-change driven increases in water temperature pose multiple challenges for aquatic organisms. Predictions of climate change impacts to biota typically do not account for fine-grained spatiotemporal patterns of stream networks; yet patches of cooler water within rivers c...

Climate Intervention as an Optimization Problem

NASA Astrophysics Data System (ADS)

Caldeira, Ken; Ban-Weiss, George A.

2010-05-01

Typically, climate models simulations of intentional intervention in the climate system have taken the approach of imposing a change (eg, in solar flux, aerosol concentrations, aerosol emissions) and then predicting how that imposed change might affect Earth's climate or chemistry. Computations proceed from cause to effect. However, humans often proceed from "What do I want?" to "How do I get it?" One approach to thinking about intentional intervention in the climate system ("geoengineering") is to ask "What kind of climate do we want?" and then ask "What pattern of radiative forcing would come closest to achieving that desired climate state?" This involves defining climate goals and a cost function that measures how closely those goals are attained. (An important next step is to ask "How would we go about producing these desired patterns of radiative forcing?" However, this question is beyond the scope of our present study.) We performed a variety of climate simulations in NCAR's CAM3.1 atmospheric general circulation model with a slab ocean model and thermodynamic sea ice model. We then evaluated, for a specific set of climate forcing basis functions (ie, aerosol concentration distributions), the extent to which the climate response to a linear combination of those basis functions was similar to a linear combination of the climate response to each basis function taken individually. We then developed several cost functions (eg, relative to the 1xCO2 climate, minimize rms difference in zonal and annual mean land temperature, minimize rms difference in zonal and annual mean runoff, minimize rms difference in a combination of these temperature and runoff indices) and then predicted optimal combinations of our basis functions that would minimize these cost functions. Lastly, we produced forward simulations of the predicted optimal radiative forcing patterns and compared these with our expected results. Obviously, our climate model is much simpler than reality and predictions from individual models do not provide a sound basis for action; nevertheless, our model results indicate that the general approach outlined here can lead to patterns of radiative forcing that make the zonal annual mean climate of a high CO2 world markedly more similar to that of a low CO2 world simultaneously for both temperature and hydrological indices, where degree of similarity is measured using our explicit cost functions. We restricted ourselves to zonally uniform aerosol concentrations distributions that can be defined in terms of a positive-definite quadratic equation on the sine of latitude. Under this constraint, applying an aerosol distribution in a 2xCO2 climate that minimized a combination of rms difference in zonal and annual mean land temperature and runoff relative to the 1xCO2 climate, the rms difference in zonal and annual mean temperatures was reduced by ~90% and the rms difference in zonal and annual mean runoff was reduced by ~80%. This indicates that there may be potential for stratospheric aerosols to diminish simultaneously both temperature and hydrological cycle changes caused by excess CO2 in the atmosphere. Clearly, our model does not include many factors (eg, socio-political consequences, chemical consequences, ocean circulation changes, aerosol transport and microphysics) so we do not argue strongly for our specific climate model results, however, we do argue strongly in favor of our methodological approach. The proposed approach is general, in the sense that cost functions can be developed that represent different valuations. While the choice of appropriate cost functions is inherently a value judgment, evaluating those functions for a specific climate simulation is a quantitative exercise. Thus, the use of explicit cost functions in evaluating model results for climate intervention scenarios is a clear way of separating value judgments from purely scientific and technical issues.

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.

Impact of longer-term modest climate shifts on architecture of high-frequency sequences (Cyclothems), Pennsylvanian of midcontinent U.S.A

USGS Publications Warehouse

Feldman, H.R.; Franseen, E.K.; Joeckel, R.M.; Heckel, P.H.

2005-01-01

Pennsylvanian glacioeustatic cyclothems exposed in Kansas and adjacent areas provide a unique opportunity to test models of the impact of relative sea level and climate on stratal architecture. A succession of eight of these high-frequency sequences, traced along dip for 500 km, reveal that modest climate shifts from relatively dry-seasonal to relatively wet-seasonal with a duration of several sequences (???600,000 to 1 million years) had a dominant impact on facies, sediment dispersal patterns, and sequence architecture. The climate shifts documented herein are intermediate, both in magnitude and duration, between previously documented longer-term climate shifts throughout much of the Pennsylvanian and shorter-term shifts described within individual sequences. Climate indicators are best preserved at sequence boundaries and in incised-valley fills of the lowstand systems tracts (LST). Relatively drier climate indicators include high-chroma paleosols, typically with pedogenic carbonates, and plant assemblages that are dominated by gymnosperms, mostly xerophytic walchian conifers. The associated valleys are small (4 km wide and >20 m deep), and dominated by quartz sandstones derived from distant source areas, reflecting large drainage networks. Transgressive systems tracts (TST) in all eight sequences gen erally are characterized by thin, extensive limestones and thin marine shales, suggesting that the dominant control on TST facies distribution was the sequestration of siliciclastic sediment in updip positions. Highstand systems tracts (HST) were significantly impacted by the intermediate-scale climate cycle in that HSTs from relatively drier climates consist of thin marine shales overlain by extensive, thick regressive limestones, whereas HSTs from relatively wetter climates are dominated by thick marine shales. Previously documented relative sea-level changes do not track the climate cycles, indicating that climate played a role distinct from that of relative sea-level change. These intermediate-scale modest climate shifts had a dominant impact on sequence architecture. This independent measure of climate and relative sea level may allow the testing of models of climate and sediment supply based on modern systems. Copyright ?? 2005, SEPM.

Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

2017-03-01

The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP - Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM - the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the total flow variability in the response of the urban drainage systems to heavy rainfall events.

Unique relation between surface-limited evaporation and relative humidity profiles holds in both field data and climate model simulations

NASA Astrophysics Data System (ADS)

Salvucci, G.; Rigden, A. J.; Gentine, P.; Lintner, B. R.

2013-12-01

A new method was recently proposed for estimating evapotranspiration (ET) from weather station data without requiring measurements of surface limiting factors (e.g. soil moisture, leaf area, canopy conductance) [Salvucci and Gentine, 2013, PNAS, 110(16): 6287-6291]. Required measurements include diurnal air temperature, specific humidity, wind speed, net shortwave radiation, and either measured or estimated incoming longwave radiation and ground heat flux. The approach is built around the idea that the key, rate-limiting, parameter of typical ET models, the land-surface resistance to water vapor transport, can be estimated from an emergent relationship between the diurnal cycle of the relative humidity profile and ET. The emergent relation is that the vertical variance of the relative humidity profile is less than what would occur for increased or decreased evaporation rates, suggesting that land-atmosphere feedback processes minimize this variance. This relation was found to hold over a wide range of climate conditions (arid to humid) and limiting factors (soil moisture, leaf area, energy) at a set of Ameriflux field sites. While the field tests in Salvucci and Gentine (2013) supported the minimum variance hypothesis, the analysis did not reveal the mechanisms responsible for the behavior. Instead the paper suggested, heuristically, that the results were due to an equilibration of the relative humidity between the land surface and the surface layer of the boundary layer. Here we apply this method using surface meteorological fields simulated by a global climate model (GCM), and compare the predicted ET to that simulated by the climate model. Similar to the field tests, the GCM simulated ET is in agreement with that predicted by minimizing the profile relative humidity variance. A reasonable interpretation of these results is that the feedbacks responsible for the minimization of the profile relative humidity variance in nature are represented in the climate model. The climate model components, in particular the land surface model and boundary layer representation, can thus be analyzed in controlled numerical experiments to discern the specific processes leading to the observed behavior. Results of this analysis will be presented.

10. View south of Arctic Observation Room (typical). Natick ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

10. View south of Arctic Observation Room (typical). - Natick Research & Development Laboratories, Climatic Chambers Building, U.S. Army Natick Research, Development & Engineering Center (NRDEC), Natick, Middlesex County, MA

Climate change effects on the geographic distribution of specialist tree species of the Brazilian tropical dry forests.

PubMed

Rodrigues, P M S; Silva, J O; Eisenlohr, P V; Schaefer, C E G R

2015-08-01

The aim of this study was to evaluate the ecological niche models (ENMs) for three specialist trees (Anadenanthera colubrina, Aspidosperma pyrifolium and Myracrodruon urundeuva) in seasonally dry tropical forests (SDTFs) in Brazil, considering present and future pessimist scenarios (2080) of climate change. These three species exhibit typical deciduousness and are widely distributed by SDTF in South America, being important in studies of the historical and evolutionary processes experienced by this ecosystem. The modeling of the potential geographic distribution of species was done by the method of maximum entropy (Maxent).We verified a general expansion of suitable areas for occurrence of the three species in future (c.a., 18%), although there was reduction of areas with high environmental suitability in Caatinga region. Precipitation of wettest quarter and temperature seasonality were the predictor variables that most contributed to our models. Climatic changes can provide more severe and longer dry season with increasing temperature and tree mortality in tropics. On this scenario, areas currently occupied by rainforest and savannas could become more suitable for occurrence of the SDTF specialist trees, whereas regions occupied by Caatinga could not support the future level of unsustainable (e.g., aridity). Long-term multidisciplinary studies are necessary to make reliable predictions of the plant's adaptation strategies and responses to climate changes in dry forest at community level. Based on the high deforestation rate, endemism and threat, public policies to minimize the effects of climate change on the biodiversity found within SDTFs must be undertaken rapidly.

Determining climate effects on US total agricultural productivity.

PubMed

Liang, Xin-Zhong; Wu, You; Chambers, Robert G; Schmoldt, Daniel L; Gao, Wei; Liu, Chaoshun; Liu, Yan-An; Sun, Chao; Kennedy, Jennifer A

2017-03-21

The sensitivity of agricultural productivity to climate has not been sufficiently quantified. The total factor productivity (TFP) of the US agricultural economy has grown continuously for over half a century, with most of the growth typically attributed to technical change. Many studies have examined the effects of local climate on partial productivity measures such as crop yields and economic returns, but these measures cannot account for national-level impacts. Quantifying the relationships between TFP and climate is critical to understanding whether current US agricultural productivity growth will continue into the future. We analyze correlations between regional climate variations and national TFP changes, identify key climate indices, and build a multivariate regression model predicting the growth of agricultural TFP based on a physical understanding of its historical relationship with climate. We show that temperature and precipitation in distinct agricultural regions and seasons explain ∼70% of variations in TFP growth during 1981-2010. To date, the aggregate effects of these regional climate trends on TFP have been outweighed by improvements in technology. Should these relationships continue, however, the projected climate changes could cause TFP to drop by an average 2.84 to 4.34% per year under medium to high emissions scenarios. As a result, TFP could fall to pre-1980 levels by 2050 even when accounting for present rates of innovation. Our analysis provides an empirical foundation for integrated assessment by linking regional climate effects to national economic outcomes, offering a more objective resource for policy making.

Determining climate effects on US total agricultural productivity

PubMed Central

Wu, You; Chambers, Robert G.; Schmoldt, Daniel L.; Gao, Wei; Liu, Chaoshun; Liu, Yan-An; Sun, Chao; Kennedy, Jennifer A.

2017-01-01

The sensitivity of agricultural productivity to climate has not been sufficiently quantified. The total factor productivity (TFP) of the US agricultural economy has grown continuously for over half a century, with most of the growth typically attributed to technical change. Many studies have examined the effects of local climate on partial productivity measures such as crop yields and economic returns, but these measures cannot account for national-level impacts. Quantifying the relationships between TFP and climate is critical to understanding whether current US agricultural productivity growth will continue into the future. We analyze correlations between regional climate variations and national TFP changes, identify key climate indices, and build a multivariate regression model predicting the growth of agricultural TFP based on a physical understanding of its historical relationship with climate. We show that temperature and precipitation in distinct agricultural regions and seasons explain ∼70% of variations in TFP growth during 1981–2010. To date, the aggregate effects of these regional climate trends on TFP have been outweighed by improvements in technology. Should these relationships continue, however, the projected climate changes could cause TFP to drop by an average 2.84 to 4.34% per year under medium to high emissions scenarios. As a result, TFP could fall to pre-1980 levels by 2050 even when accounting for present rates of innovation. Our analysis provides an empirical foundation for integrated assessment by linking regional climate effects to national economic outcomes, offering a more objective resource for policy making. PMID:28265075

Determining climate effects on US total agricultural productivity

NASA Astrophysics Data System (ADS)

Liang, Xin-Zhong; Wu, You; Chambers, Robert G.; Schmoldt, Daniel L.; Gao, Wei; Liu, Chaoshun; Liu, Yan-An; Sun, Chao; Kennedy, Jennifer A.

2017-03-01

The sensitivity of agricultural productivity to climate has not been sufficiently quantified. The total factor productivity (TFP) of the US agricultural economy has grown continuously for over half a century, with most of the growth typically attributed to technical change. Many studies have examined the effects of local climate on partial productivity measures such as crop yields and economic returns, but these measures cannot account for national-level impacts. Quantifying the relationships between TFP and climate is critical to understanding whether current US agricultural productivity growth will continue into the future. We analyze correlations between regional climate variations and national TFP changes, identify key climate indices, and build a multivariate regression model predicting the growth of agricultural TFP based on a physical understanding of its historical relationship with climate. We show that temperature and precipitation in distinct agricultural regions and seasons explain ˜70% of variations in TFP growth during 1981-2010. To date, the aggregate effects of these regional climate trends on TFP have been outweighed by improvements in technology. Should these relationships continue, however, the projected climate changes could cause TFP to drop by an average 2.84 to 4.34% per year under medium to high emissions scenarios. As a result, TFP could fall to pre-1980 levels by 2050 even when accounting for present rates of innovation. Our analysis provides an empirical foundation for integrated assessment by linking regional climate effects to national economic outcomes, offering a more objective resource for policy making.

Integrating Climate Projections into Multi-Level City Planning: A Texas Case Study

NASA Astrophysics Data System (ADS)

Hayhoe, K.; Gelca, R.; Baumer, Z.; Gold, G.

2016-12-01

Climate change impacts on energy and water are a serious concern for many cities across the United States. Regional projections from the National Assessment process, or state-specific efforts as in California and Delaware, are typically used to quantify impacts at the regional scale. However, these are often insufficient to provide information at the scale of decision-making for an individual city. Here, we describe a multi-level approach to developing and integrating usable climate information into planning, using a case study from the City of Austin in Texas, a state where few official climate resources are available. Spearheaded by the Office of Sustainability in collaboration with Austin Water, the first step was to characterize observed trends and future projections of how global climate change might affect Austin's current climate. The City then assembled a team of city experts, consulting engineers, and climate scientists to develop a methodology to assess impacts on regional hydrology as part of its Integrated Water Resource Plan, Austin's 100-year water supply and demand planning effort, an effort which included calculating a range of climate indicators and developing and evaluating a new approach to generating climate inputs - including daily streamflow and evaporation - for existing water availability models. This approach, which brings together a range of public, private, and academic experts to support a stakeholder-initiated planning effort, provides concrete insights into the critical importance of multi-level, long-term engagement for development and application of actionable climate science at the local to regional scale.

Contributions of climate change and human activities to runoff change in seven typical catchments across China.

PubMed

Zhai, Ran; Tao, Fulu

2017-12-15

Climate change and human activities are two major factors affecting water resource change. It is important to understand the roles of the major factors in affecting runoff change in different basins for watershed management. Here, we investigated the trends in climate and runoff in seven typical catchments in seven basins across China from 1961 to 2014. Then we attributed the runoff change to climate change and human activities in each catchment and in three time periods (1980s, 1990s and 2000s), using the VIC model and long-term runoff observation data. During 1961-2014, temperature increased significantly, while the trends in precipitation were insignificant in most of the catchments and inconsistent among the catchments. The runoff in most of the catchments showed a decreasing trend except the Yingluoxia catchment in the northwestern China. The contributions of climate change and human activities to runoff change varied in different catchments and time periods. In the 1980s, climate change contributed more to runoff change than human activities, which was 84%, 59%, -66%, -50%, 59%, 94%, and -59% in the Nianzishan, Yingluoxia, Xiahui, Yangjiaping, Sanjiangkou, Xixian, and Changle catchment, respectively. After that, human activities had played a more essential role in runoff change. In the 1990s and 2000s, human activities contributed more to runoff change than in the 1980s. The contribution by human activities accounted for 84%, -68%, and 67% in the Yingluoxia, Xiahui, and Sanjiangkou catchment, respectively, in the 1990s; and -96%, -67%, -94%, and -142% in the Nianzishan, Yangjiaping, Xixian, and Changle catchment, respectively, in the 2000s. It is also noted that after 2000 human activities caused decrease in runoff in all catchments except the Yingluoxia. Our findings highlight that the effects of human activities, such as increase in water withdrawal, land use/cover change, operation of dams and reservoirs, should be well managed. Copyright © 2017 Elsevier B.V. All rights reserved.

The Impact of ARM on Climate Modeling. Chapter 26

NASA Technical Reports Server (NTRS)

Randall, David A.; Del Genio, Anthony D.; Donner, Leo J.; Collins, William D.; Klein, Stephen A.

2016-01-01

Climate models are among humanity's most ambitious and elaborate creations. They are designed to simulate the interactions of the atmosphere, ocean, land surface, and cryosphere on time scales far beyond the limits of deterministic predictability, and including the effects of time-dependent external forcings. The processes involved include radiative transfer, fluid dynamics, microphysics, and some aspects of geochemistry, biology, and ecology. The models explicitly simulate processes on spatial scales ranging from the circumference of the Earth down to one hundred kilometers or smaller, and implicitly include the effects of processes on even smaller scales down to a micron or so. The atmospheric component of a climate model can be called an atmospheric global circulation model (AGCM). In an AGCM, calculations are done on a three-dimensional grid, which in some of today's climate models consists of several million grid cells. For each grid cell, about a dozen variables are time-stepped as the model integrates forward from its initial conditions. These so-called prognostic variables have special importance because they are the only things that a model remembers from one time step to the next; everything else is recreated on each time step by starting from the prognostic variables and the boundary conditions. The prognostic variables typically include information about the mass of dry air, the temperature, the wind components, water vapor, various condensed-water species, and at least a few chemical species such as ozone. A good way to understand how climate models work is to consider the lengthy and complex process used to develop one. Lets imagine that a new AGCM is to be created, starting from a blank piece of paper. The model may be intended for a particular class of applications, e.g., high-resolution simulations on time scales of a few decades. Before a single line of code is written, the conceptual foundation of the model must be designed through a creative envisioning that starts from the intended application and is based on current understanding of how the atmosphere works and the inventory of mathematical methods available.

Climatological Impact of Atmospheric River Based on NARCCAP and DRI-RCM Datasets

NASA Astrophysics Data System (ADS)

Mejia, J. F.; Perryman, N. M.

2012-12-01

This study evaluates spatial responses of extreme precipitation environments, typically associated with Atmospheric River events, using Regional Climate Model (RCM) output from NARCCAP dataset (50km grid size) and the Desert Research Institute-RCM simulations (36 and 12 km grid size). For this study, a pattern-detection algorithm was developed to characterize Atmospheric Rivers (ARs)-like features from climate models. Topological analysis of the enhanced elongated moisture flux (500-300hPa; daily means) cores is used to objectively characterize such AR features in two distinct groups: (i) zonal, north Pacific ARs, and (ii) subtropical ARs, also known as "Pineapple Express" events. We computed the climatological responses of the different RCMs upon these two AR groups, from which intricate differences among RCMs stand out. This study presents these climatological responses from historical and scenario driven simulations, as well as implications for precipitation extreme-value analyses.

Temperature acclimation of photosynthesis and respiration: A key uncertainty in the carbon cycle-climate feedback

NASA Astrophysics Data System (ADS)

Lombardozzi, Danica L.; Bonan, Gordon B.; Smith, Nicholas G.; Dukes, Jeffrey S.; Fisher, Rosie A.

2015-10-01

Earth System Models typically use static responses to temperature to calculate photosynthesis and respiration, but experimental evidence suggests that many plants acclimate to prevailing temperatures. We incorporated representations of photosynthetic and leaf respiratory temperature acclimation into the Community Land Model, the terrestrial component of the Community Earth System Model. These processes increased terrestrial carbon pools by 20 Pg C (22%) at the end of the 21st century under a business-as-usual (Representative Concentration Pathway 8.5) climate scenario. Including the less certain estimates of stem and root respiration acclimation increased terrestrial carbon pools by an additional 17 Pg C (~40% overall increase). High latitudes gained the most carbon with acclimation, and tropical carbon pools increased least. However, results from both of these regions remain uncertain; few relevant data exist for tropical and boreal plants or for extreme temperatures. Constraining these uncertainties will produce more realistic estimates of land carbon feedbacks throughout the 21st century.

Radiative flux and forcing parameterization error in aerosol-free clear skies

DOE PAGES

Pincus, Robert; Mlawer, Eli J.; Oreopoulos, Lazaros; ...

2015-07-03

This article reports on the accuracy in aerosol- and cloud-free conditions of the radiation parameterizations used in climate models. Accuracy is assessed relative to observationally validated reference models for fluxes under present-day conditions and forcing (flux changes) from quadrupled concentrations of carbon dioxide. Agreement among reference models is typically within 1 W/m 2, while parameterized calculations are roughly half as accurate in the longwave and even less accurate, and more variable, in the shortwave. Absorption of shortwave radiation is underestimated by most parameterizations in the present day and has relatively large errors in forcing. Error in present-day conditions is essentiallymore » unrelated to error in forcing calculations. Recent revisions to parameterizations have reduced error in most cases. As a result, a dependence on atmospheric conditions, including integrated water vapor, means that global estimates of parameterization error relevant for the radiative forcing of climate change will require much more ambitious calculations.« less

9. View to west of Tropic Dressing Room (typical). ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

9. View to west of Tropic Dressing Room (typical). - Natick Research & Development Laboratories, Climatic Chambers Building, U.S. Army Natick Research, Development & Engineering Center (NRDEC), Natick, Middlesex County, MA

Seasonal cycle of precipitation over major river basins in South and Southeast Asia: A review of the CMIP5 climate models data for present climate and future climate projections

NASA Astrophysics Data System (ADS)

Lucarini, Valerio

2017-04-01

We review the skill of thirty coupled climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in terms of reproducing properties of the seasonal cycle of precipitation over the major river basins of South and Southeast Asia (Indus, Ganges, Brahmaputra and Mekong) for the historical period (1961-2000). We also present how these models represent the impact of climate change by the end of century (2061-2100) under the extreme scenario RCP8.5. First, we assess the models' ability to reproduce the observed timings of the monsoon onset and the rate of rapid fractional accumulation (RFA) slope — a measure of seasonality within the active monsoon period. Secondly, we apply a threshold-independent seasonality index (SI) — a multiplicative measure of precipitation (P) and extent of its concentration relative to uniform distribution (relative entropy — RE). We apply SI distinctly over the monsoonal precipitation regime (MPR), westerly precipitation regime (WPR) and annual precipitation. For the present climate, neither any single model nor the multi-model mean performs best in all chosen metrics. Models show overall a modest skill in suggesting right timings of the monsoon onset while the RFA slope is generally underestimated. One third of the models fail to capture the monsoon signal over the Indus basin. Mostly, the estimates for SI during WPR are higher than observed for all basins. When looking at MPR, the models typically simulate an SI higher (lower) than observed for the Ganges and Brahmaputra (Indus and Mekong) basins, following the pattern of overestimation (underestimation) of precipitation. Most of the models are biased negative (positive) for RE estimates over the Brahmaputra and Mekong (Indus and Ganges) basins, implying the extent of precipitation concentration for MPR and number of dry days within WPR lower (higher) than observed for these basins. Such skill of the CMIP5 models in representing the present-day monsoonal hydroclimatology poses some caveats on their ability to represent correctly the climate change signal. Nevertheless, considering the majority-model agreement as a measure of robustness for the qualitative scale projected future changes, we find a slightly delayed onset, and a general increase in the RFA slope and in the extent of precipitation concentration (RE) for MPR. Overall, a modest inter-model agreement suggests an increase in the seasonality of MPR and a less intermittent WPR for all basins and for most of the study domain. The SI-based indicator of change in the monsoonal domain suggests its extension westward over northwest India and Pakistan and northward over China. These findings have serious implications for the food and water security of the region in the future. Reference Ul Hasson, S., Pascale, S., Lucarini, V., & Böhner, J. (2016). Seasonal cycle of precipitation over major river basins in South and Southeast Asia: A review of the CMIP5 climate models data for present climate and future climate projections. Atmospheric Research, 180, 42-63. doi:10.1016/j.atmosres.2016.05.008

Ecohydrologic process modeling of mountain block groundwater recharge.

PubMed

Magruder, Ian A; Woessner, William W; Running, Steve W

2009-01-01

Regional mountain block recharge (MBR) is a key component of alluvial basin aquifer systems typical of the western United States. Yet neither water scientists nor resource managers have a commonly available and reasonably invoked quantitative method to constrain MBR rates. Recent advances in landscape-scale ecohydrologic process modeling offer the possibility that meteorological data and land surface physical and vegetative conditions can be used to generate estimates of MBR. A water balance was generated for a temperate 24,600-ha mountain watershed, elevation 1565 to 3207 m, using the ecosystem process model Biome-BGC (BioGeochemical Cycles) (Running and Hunt 1993). Input data included remotely sensed landscape information and climate data generated with the Mountain Climate Simulator (MT-CLIM) (Running et al. 1987). Estimated mean annual MBR flux into the crystalline bedrock terrain is 99,000 m(3) /d, or approximately 19% of annual precipitation for the 2003 water year. Controls on MBR predictions include evapotranspiration (radiation limited in wet years and moisture limited in dry years), soil properties, vegetative ecotones (significant at lower elevations), and snowmelt (dominant recharge process). The ecohydrologic model is also used to investigate how climatic and vegetative controls influence recharge dynamics within three elevation zones. The ecohydrologic model proves useful for investigating controls on recharge to mountain blocks as a function of climate and vegetation. Future efforts will need to investigate the uncertainty in the modeled water balance by incorporating an advanced understanding of mountain recharge processes, an ability to simulate those processes at varying scales, and independent approaches to calibrating MBR estimates. Copyright © 2009 The Author(s). Journal compilation © 2009 National Ground Water Association.

A method for modeling the effects of climate and land use changes on erosion and sustainability of soil in a Mediterranean watershed (Languedoc, France).

PubMed

Paroissien, Jean-Baptiste; Darboux, Frédéric; Couturier, Alain; Devillers, Benoît; Mouillot, Florent; Raclot, Damien; Le Bissonnais, Yves

2015-03-01

Global climate and land use changes could strongly affect soil erosion and the capability of soils to sustain agriculture and in turn impact regional or global food security. The objective of our study was to develop a method to assess soil sustainability to erosion under changes in land use and climate. The method was applied in a typical mixed Mediterranean landscape in a wine-growing watershed (75 km(2)) within the Languedoc region (La Peyne, France) for two periods: a first period with the current climate and land use and a second period with the climate and land use scenarios at the end of the twenty-first century. The Intergovernmental Panel on Climate Change A1B future rainfall scenarios from the Météo France General circulation model was coupled with four contrasting land use change scenarios that were designed using a spatially-explicit land use change model. Mean annual erosion rate was estimated with an expert-based soil erosion model. Soil life expectancy was assessed using soil depth. Soil erosion rate and soil life expectancy were combined into a sustainability index. The median simulated soil erosion rate for the current period was 3.5 t/ha/year and the soil life expectancy was 273 years, showing a low sustainability of soils. For the future period with the same land use distribution, the median simulated soil erosion rate was 4.2 t/ha/year and the soil life expectancy was 249 years. The results show that soil erosion rate and soil life expectancy are more sensitive to changes in land use than to changes in precipitation. Among the scenarios tested, institution of a mandatory grass cover in vineyards seems to be an efficient means of significantly improving soil sustainability, both in terms of decreased soil erosion rates and increased soil life expectancies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Experiment, monitoring, and gradient methods used to infer climate change effects on plant communities yield consistent patterns

Treesearch

Sarah C. Elmendorf; Gregory H.R. Henry; Robert D. Hollisterd; Anna Maria Fosaa; William A. Gould; Luise Hermanutz; Annika Hofgaard; Ingibjorg I. Jonsdottir; Janet C. Jorgenson; Esther Levesque; Borgbor Magnusson; Ulf Molau; Isla H. Myers-Smith; Steven F. Oberbauer; Christian Rixen; Craig E. Tweedie; Marilyn Walkers

2015-01-01

Inference about future climate change impacts typically relies on one of three approaches: manipulative experiments, historical comparisons (broadly defined to include monitoring the response to ambient climate fluctuations using repeat sampling of plots, dendroecology, and paleoecology techniques), and space-for-time substitutions derived from sampling along...

Climate change vulnerability assessment in Georgia

Treesearch

Binita KC; J. Marshall Shepherd; Cassandra Johnson Gaither

2015-01-01

Climate change is occurring in the Southeastern United States, and one manifestation is changes in frequency and intensity of extreme events. A vulnerability assessment is performed in the state of Georgia (United States) at the county level from 1975 to 2012 in decadal increments. Climate change vulnerability is typically measured as a function of exposure to physical...

Raising Awareness about Climate Change in Pacific Communities

ERIC Educational Resources Information Center

McNamara, Karen Elizabeth

2013-01-01

Community-based climate change projects in the Pacific typically seek to raise the awareness of locals about the consequences of climate change and changing weather patterns. A key concern is that such activities might be done in an ad hoc manner, with little consideration of local relevance, audience and the integration of local experiences and…

Population-level consequences of herbivory, changing climate, and source-sink dynamics on a long-lived invasive shrub.

PubMed

van Klinken, R D; Pichancourt, J B

2015-12-01

Long-lived plant species are highly valued environmentally, economically, and socially, but can also cause substantial harm as invaders. Realistic demographic predictions can guide management decisions, and are particularly valuable for long-lived species where population response times can be long. Long-lived species are also challenging, given population dynamics can be affected by factors as diverse as herbivory, climate, and dispersal. We developed a matrix model to evaluate the effects of herbivory by a leaf-feeding biological control agent released in Australia against a long-lived invasive shrub (mesquite, Leguminoseae: Prosopis spp.). The stage-structured, density-dependent model used an annual time step and 10 climatically diverse years of field data. Mesquite population demography is sensitive to source-sink dynamics as most seeds are consumed and redistributed spatially by livestock. In addition, individual mesquite plants, because they are long lived, experience natural climate variation that cycles over decadal scales, as well as anthropogenic climate change. The model therefore explicitly considered the effects of both net dispersal and climate variation. Herbivory strongly regulated mesquite populations through reduced growth and fertility, but additional mortality of older plants will be required to reach management goals within a reasonable time frame. Growth and survival of seeds and seedlings were correlated with daily soil moisture. As a result, population dynamics were sensitive to rainfall scenario, but population response times were typically slow (20-800 years to reach equilibrium or extinction) due to adult longevity. Equilibrium population densities were expected to remain 5% higher, and be more dynamic, if historical multi-decadal climate patterns persist, the effect being dampened by herbivory suppressing seed production irrespective of preceding rainfall. Dense infestations were unlikely to form under a drier climate, and required net dispersal under the current climate. Seed input wasn't required to form dense infestations under a wetter climate. Each factor we considered (ongoing herbivory, changing climate, and source-sink dynamics) has a strong bearing on how this invasive species should be managed, highlighting the need for considering both ecological context (in this case, source-sink dynamics) and the effect of climate variability at relevant temporal scales (daily, multi-decadal, and anthropogenic) when deriving management recommendations for long-lived species.

Application of CarboSOIL model to predict the effects of climate change on soil organic carbon stocks in agro-silvo-pastoral Mediterranean management systems

NASA Astrophysics Data System (ADS)

Muñoz-Rojas, Miriam; Doro, Luca; Ledda, Luigi; Francaviglia, Rosa

2014-05-01

CarboSOIL is an empirical model based on regression techniques and developed to predict soil organic carbon contents (SOC) at standard soil depths of 0-25, 25-50 and 50-75 cm (Muñoz-Rojas et al., 2013). The model was applied to a study area of north-eastern Sardinia (Italy) (40° 46'N, 9° 10'E, mean altitude 285 m a.s.l.), characterized by extensive agro-silvo-pastoral systems which are typical of similar areas of the Mediterranean basin (e.g. the Iberian peninsula). The area has the same soil type (Haplic Endoleptic Cambisols, Dystric according to WRB), while cork oak forest (Quercus suber L.) is the potential native vegetation which has been converted to managed land with pastures and vineyards in recent years (Lagomarsino et al., 2011; Francaviglia et al., 2012; Bagella et al, 2013; Francaviglia et al., 2014). Six land uses with different levels of cropping intensification were compared: Tilled vineyards (TV); No-tilled grassed vineyards (GV); Hay crop (HC); Pasture (PA); Cork oak forest (CO) and Semi-natural systems (SN). The HC land use includes oats, Italian ryegrass and annual clovers or vetch for 5 years and intercropped by spontaneous herbaceous vegetation in the sixth year. The PA land use is 5 years of spontaneous herbaceous vegetation, and one year of intercropping with oats, Italian ryegrass and annual clovers or vetch cultivated as a hay crop. The SN land use (scrublands, Mediterranean maquis and Helichrysum meadows) arise from the natural re-vegetation of former vineyards which have been set-aside probably due to the low grape yields and the high cost of modern tillage equipment. Both PA and HC are grazed for some months during the year, and include scattered cork-oak trees, which are key components of the 'Dehesa'-type landscape (grazing system with Quercus L.) typical of this area of Sardinia and other areas of southern Mediterranean Europe. Dehesas are often converted to more profitable land uses such as vineyards (Francaviglia et al., 2012; Muñoz-Rojas et al., 2012) or olive groves (Lozano-García and Parras-Alcántara, 2013). The local climate is warm temperate with dry and hot summers, with a mean annual rainfall of 623 mm (range 367-811 mm) and mean annual temperature of 15.0?C (13.8-16.4?C). Climate change scenarios were generated from the baseline climate with two Global Climate Models: GISS (Goddard Institute of Space Studies, USA), and HadCM3 (Met Office, Hadley Centre, UK), for two of the Intergovernmental Panel on Climate Change (IPCC) emission scenarios (A2 and B2). Three time horizons were chosen for climate change projections: 2020, mean climate change for the period 2010-2039; 2050 for the period 2040-2069; and 2080 for the period 2070-2099, providing respectively a very close, an intermediate, and a fully realized climate change scenario. The agreement of model predictions with the measured values of soil organic carbon stocks was tested using the correlation coefficient R2, the root mean square error RMSE and the modelling efficiency EF. For a good model performance, RMSE should have approximately the same order of magnitude of the standard deviation, while EF should be positive and close to 1. With reference to the three soil depths (0-25, 25-50, 50-75 cm), R2, RMSE and EF are in the range 0.76-0.99, 5.07-8.42, and 0.63-0.98 respectively. CarboSOIL predictions are fully acceptable since the linear regression coefficients are always significant at p

A climate game based on a Multi-Actor Dynamic Integrated Assessment Model (MADIAM)

NASA Astrophysics Data System (ADS)

Weber, M.; Hasselmann, K.

2003-04-01

In November 2002 a special exhibition on climate issues opened in the German Museum for Science and Techniques ('Deutsches Museum') in Munich. Within this exposition we present an interactive game in which visitors control future climate policy by adopting the role of either the government, a CEO (Chief Executive Officer) of a global company or a typical private household of an industrialized country. The players endeavor to maintain a sustainable climate in the future (global goal) while pursuing their own individual welfare goals. Task of the exhibition visitor is to combine the personal interests of the actor he is adopting with the global goal. The individual goal of government is maintain economic growth while avoiding conflicts due to inter-regional or societal inequalities. The CEO seeks to maximize total profits (business earnings). The goal of households is to maximize wages and interest earnings. The evolution of the economic system and climate is governed by the decisions of the actors. Government sets economic side conditions in terms of carbon taxes, subsidies for R&D or market infusion support for climate-friendly technologies, and transfers development aid to less advanced regions. The CEOs decide how much to invest in a number of alternative investment options and in which region. Households influences the economy by their purchasing and savings decisions. The model considers four regions, three real actors (mentioned above) and two different goods (climate-adverse and a climate-friendly). We introduce four different kinds of energy (coal, oil/gas, nuclear, renewable). A World Bank handles money flows. At different points in time the actors can cooperate with other actors in order to reach the global goal Stochastic elements regarding future technology and future climate are included. A touch-screen monitor with user friendly interface is used to present animations and videos. An animated climate scientist uses a climate simulator to compute future climate scenarios in response to the actors decisions. The goal of the project is t o to give them a feeling for the problem and demonstrate that that a sustainable future climate can be combined with individual welfare goals if the right decisions are made at the right time.

Prediction of car cabin environment by means of 1D and 3D cabin model

NASA Astrophysics Data System (ADS)

Fišer, J.; Pokorný, J.; Jícha, M.

2012-04-01

Thermal comfort and also reduction of energy requirements of air-conditioning system in vehicle cabins are currently very intensively investigated and up-to-date issues. The article deals with two approaches of modelling of car cabin environment; the first model was created in simulation language Modelica (typical 1D approach without cabin geometry) and the second one was created in specialized software Theseus-FE (3D approach with cabin geometry). Performance and capabilities of this tools are demonstrated on the example of the car cabin and the results from simulations are compared with the results from the real car cabin climate chamber measurements.

Enabling data-driven provenance in NetCDF, via OGC WPS operations. Climate Analysis services use case.

NASA Astrophysics Data System (ADS)

Mihajlovski, A.; Spinuso, A.; Plieger, M.; Som de Cerff, W.

2016-12-01

Modern Climate analysis platforms provide generic and standardized ways of accessing data and processing services. These are typically supported by a wide range of OGC formats and interfaces. However, the problem of instrumentally tracing the lineage of the transformations occurring on a dataset and its provenance remains an open challenge. It requires standard-driven and interoperable solutions to facilitate understanding, sharing of self-describing data products, fostering collaboration among peers. The CLIPC portal provided us real use case, where the need of an instrumented provenance management is fundamental. CLIPC provides a single point of access for scientific information on climate change. The data about the physical environment which is used to inform climate change policy and adaptation measures comes from several categories: satellite measurements, terrestrial observing systems, model projections and simulations and from re-analyses. This is made possible through the Copernicus Earth Observation Programme for Europe. With a backbone combining WPS and OPeNDAP services, CLIPC has two themes: 1. Harmonized access to climate datasets derived from models, observations and re-analyses 2. A climate impact tool kit to evaluate, rank and aggregate indicators The climate impact tool kit is realised with the orchestration of a number of WPS that ingest, normalize and combine NetCDF files. The WPS allowing this specific computation are hosted by the climate4impact portal, which is a more generic climate data-access and processing service. In this context, guaranteeing validation and reproducibility of results, is a clearly stated requirement to improve the quality of the results obtained by the combined analysis Two core contributions made, are the enabling of a provenance wrapper around WPS services and the enabling of provenance tracing within the NetCDF format, which adopts and extends the W3C's PROV model. To disseminate indicator data and create transformed data products, a standardized provenance, metadata and processing infrastructure is researched for CLIPC. These efforts will lead towards the provision of tools for further web service processing development and optimisation, opening up possibilities to scale and administer abstract users and data driven workflows.

Insights from past millennia into climatic impacts on human health and survival

PubMed Central

McMichael, Anthony J.

2012-01-01

Climate change poses threats to human health, safety, and survival via weather extremes and climatic impacts on food yields, fresh water, infectious diseases, conflict, and displacement. Paradoxically, these risks to health are neither widely nor fully recognized. Historical experiences of diverse societies experiencing climatic changes, spanning multicentury to single-year duration, provide insights into population health vulnerability—even though most climatic changes were considerably less than those anticipated this century and beyond. Historical experience indicates the following. (i) Long-term climate changes have often destabilized civilizations, typically via food shortages, consequent hunger, disease, and unrest. (ii) Medium-term climatic adversity has frequently caused similar health, social, and sometimes political consequences. (iii) Infectious disease epidemics have often occurred in association with briefer episodes of temperature shifts, food shortages, impoverishment, and social disruption. (iv) Societies have often learnt to cope (despite hardship for some groups) with recurring shorter-term (decadal to multiyear) regional climatic cycles (e.g., El Niño Southern Oscillation)—except when extreme phases occur. (v) The drought–famine–starvation nexus has been the main, recurring, serious threat to health. Warming this century is not only likely to greatly exceed the Holocene's natural multidecadal temperature fluctuations but to occur faster. Along with greater climatic variability, models project an increased geographic range and severity of droughts. Modern societies, although larger, better resourced, and more interconnected than past societies, are less flexible, more infrastructure-dependent, densely populated, and hence are vulnerable. Adverse historical climate-related health experiences underscore the case for abating human-induced climate change. PMID:22315419

Downscaling future climate projections to the watershed scale: A north San Francisco Bay estuary case study

USGS Publications Warehouse

Micheli, Elisabeth; Flint, Lorraine; Flint, Alan; Weiss, Stuart; Kennedy, Morgan

2012-01-01

We modeled the hydrology of basins draining into the northern portion of the San Francisco Bay Estuary (North San Pablo Bay) using a regional water balance model (Basin Characterization Model; BCM) to estimate potential effects of climate change at the watershed scale. The BCM calculates water balance components, including runoff, recharge, evapotranspiration, soil moisture, and stream flow, based on climate, topography, soils and underlying geology, and the solar-driven energy balance. We downscaled historical and projected precipitation and air temperature values derived from weather stations and global General Circulation Models (GCMs) to a spatial scale of 270 m. We then used the BCM to estimate hydrologic response to climate change for four scenarios spanning this century (2000–2100). Historical climate patterns show that Marin’s coastal regions are typically on the order of 2 °C cooler and receive five percent more precipitation compared to the inland valleys of Sonoma and Napa because of marine influences and local topography. By the last 30 years of this century, North Bay scenarios project average minimum temperatures to increase by 1.0 °C to 3.1 °C and average maximum temperatures to increase by 2.1 °C to 3.4 °C (in comparison to conditions experienced over the last 30 years, 1981–2010). Precipitation projections for the 21st century vary between GCMs (ranging from 2 to 15% wetter than the 20th-century average). Temperature forcing increases the variability of modeled runoff, recharge, and stream discharge, and shifts hydrologic cycle timing. For both high- and low-rainfall scenarios, by the close of this century warming is projected to amplify late-season climatic water deficit (a measure of drought stress on soils) by 8% to 21%. Hydrologic variability within a single river basin demonstrated at the scale of subwatersheds may prove an important consideration for water managers in the face of climate change. Our results suggest that in arid environments characterized by high topo-climatic variability, land and water managers need indicators of local watershed hydrology response to complement regional temperature and precipitation estimates. Our results also suggest that temperature forcing may generate greater drought stress affecting soils and stream flows than can be estimated by variability in precipitation alone.

From cold to hot: Climatic effects and productivity in Wisconsin dairy farms.

PubMed

Qi, L; Bravo-Ureta, B E; Cabrera, V E

2015-12-01

This study examined the effects of climatic conditions on dairy farm productivity using panel data for the state of Wisconsin along with alternative stochastic frontier models. A noteworthy feature of this analysis is that Wisconsin is a major dairy-producing area where winters are typically very cold and snowy and summers are hot and humid. Thus, it is an ideal geographical region for examining the effects of a range of climatic factors on dairy production. We identified the effects of temperature and precipitation, both jointly and separately, on milk output. The analysis showed that increasing temperature in summer or in autumn is harmful for dairy production, whereas warmer winters and warmer springs are beneficial. In contrast, more precipitation had a consistent adverse effect on dairy productivity. Overall, the analysis showed that over the past 17 yr, changes in climatic conditions have had a negative effect on Wisconsin dairy farms. Alternative scenarios predict that climate change would lead to a 5 to 11% reduction in dairy production per year between 2020 and 2039 after controlling for other factors. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Extraordinary range expansion in a common bat: the potential roles of climate change and urbanisation.

PubMed

Ancillotto, L; Santini, L; Ranc, N; Maiorano, L; Russo, D

2016-04-01

Urbanisation and climate change are two global change processes that affect animal distributions, posing critical threats to biodiversity. Due to its versatile ecology and synurbic habits, Kuhl's pipistrelle (Pipistrellus kuhlii) offers a unique opportunity to explore the relative effects of climate change and urbanisation on species distributions. In a climate change scenario, this typically Mediterranean species is expected to expand its range in response to increasing temperatures. We collected 25,132 high-resolution occurrence records from P. kuhlii European range between 1980 and 2013 and modelled the species' distribution with a multi-temporal approach, using three bioclimatic variables and one proxy of urbanisation. Temperature in the coldest quarter of the year was the most important factor predicting the presence of P. kuhlii and showed an increasing trend in the study period; mean annual precipitation and precipitation seasonality were also relevant, but to a lower extent. Although urbanisation increased in recently colonised areas, it had little effect on the species' presence predictability. P. kuhlii expanded its geographical range by about 394 % in the last four decades, a process that can be interpreted as a response to climate change.

Extraordinary range expansion in a common bat: the potential roles of climate change and urbanisation

NASA Astrophysics Data System (ADS)

Ancillotto, L.; Santini, L.; Ranc, N.; Maiorano, L.; Russo, D.

2016-04-01

Urbanisation and climate change are two global change processes that affect animal distributions, posing critical threats to biodiversity. Due to its versatile ecology and synurbic habits, Kuhl's pipistrelle ( Pipistrellus kuhlii) offers a unique opportunity to explore the relative effects of climate change and urbanisation on species distributions. In a climate change scenario, this typically Mediterranean species is expected to expand its range in response to increasing temperatures. We collected 25,132 high-resolution occurrence records from P. kuhlii European range between 1980 and 2013 and modelled the species' distribution with a multi-temporal approach, using three bioclimatic variables and one proxy of urbanisation. Temperature in the coldest quarter of the year was the most important factor predicting the presence of P. kuhlii and showed an increasing trend in the study period; mean annual precipitation and precipitation seasonality were also relevant, but to a lower extent. Although urbanisation increased in recently colonised areas, it had little effect on the species' presence predictability. P. kuhlii expanded its geographical range by about 394 % in the last four decades, a process that can be interpreted as a response to climate change.

Energy savings modelling of re-tuning energy conservation measures in large office buildings

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

Fernandez, Nick; Katipamula, Srinivas; Wang, Weimin

Today, many large commercial buildings use sophisticated building automation systems (BASs) to manage a wide range of building equipment. While the capabilities of BASs have increased over time, many buildings still do not fully use the BAS’s capabilities and are not properly commissioned, operated or maintained, which leads to inefficient operation, increased energy use, and reduced lifetimes of the equipment. This paper investigates the energy savings potential of several common HVAC system re-tuning measures on a typical large office building, using the Department of Energy’s building energy modeling software, EnergyPlus. The baseline prototype model uses roughly as much energy asmore » an average large office building in existing building stock, but does not utilize any re-tuning measures. Individual re-tuning measures simulated against this baseline include automatic schedule adjustments, damper minimum flow adjustments, thermostat adjustments, as well as dynamic resets (set points that change continuously with building and/or outdoor conditions) to static pressure, supply-air temperature, condenser water temperature, chilled and hot water temperature, and chilled and hot water differential pressure set points. Six combinations of these individual measures have been formulated – each designed to conform to limitations to implementation of certain individual measures that might exist in typical buildings. All the individual measures and combinations were simulated in 16 climate locations representative of specific U.S. climate zones. The modeling results suggest that the most effective energy savings measures are those that affect the demand-side of the building (air-systems and schedules). Many of the demand-side individual measures were capable of reducing annual total HVAC system energy consumption by over 20% in most cities that were modeled. Supply side measures affecting HVAC plant conditions were only modestly successful (less than 5% annual HVAC energy savings for most cities for all measures). Combining many of the re-tuning measures revealed deep savings potential. Some of the more aggressive combinations revealed 35-75% reductions in annual HVAC energy consumption, depending on climate and building vintage.« less

Short-lived halocarbons efficient at influencing climate through ozone loss in the upper troposphere-lower stratosphere

NASA Astrophysics Data System (ADS)

Hossaini, Ryan; Chipperfield, Martyn; Montzka, Steven; Rap, Alex; Dhomse, Sandip; Feng, Wuhu

2015-04-01

Halogenated very short-lived substances (VSLS) of both natural and anthropogenic origin are a significant source of atmospheric bromine, chlorine and iodine. Due to relatively short atmospheric lifetimes (typically <6 months), VSLS breakdown in the upper troposphere-lower stratosphere (UTLS), where ozone perturbations drive a disproportionately large climate impact compared to other altitudes. Here we present chemical transport model simulations that quantify VSLS-driven ozone loss in the UTLS and infer the climate relevance of these ozone perturbations using a radiative transfer model. Our results indicate that through their impact on UTLS ozone, VSLS are efficient at influencing climate. We calculate a whole atmosphere global mean radiative effect (RE) of -0.20 (-0.16 to -0.23) Wm-2 from natural and anthropogenic VSLS-driven ozone loss, including a tropospheric contribution of -0.12 Wm-2. In the stratosphere, the RE due to ozone loss from natural bromine-containing VSLS (e.g. CHBr3, CH2Br2) is almost half of that from long-lived anthropogenic compounds (e.g. CFCs) and normalized by equivalent chlorine is ~4 times larger. We show that the anthropogenic chlorine-containing VSLS, not regulated by the Montreal Protocol, also contribute to ozone loss in the UTLS and that the atmospheric concentration of dichloromethane (CH2Cl2), the most abundant of these, is increasing rapidly. Finally, we present evidence that VSLS have made a small yet previously unrecognized contribution to the ozone-driven radiative forcing of climate since pre-industrial times of -0.02 (-0.01 to -0.03) Wm-2. Given the climate leverage that VSLS possess, future increases to their emissions, either through continued industrial or altered natural processes, may be important for future climate forcing.

Effects of multiple stresses hydropower, acid deposition and climate change on water chemistry and salmon populations in the River Otra, Norway.

PubMed

Wright, Richard F; Couture, Raoul-Marie; Christiansen, Anne B; Guerrero, José-Luis; Kaste, Øyvind; Barlaup, Bjørn T

2017-01-01

Many surface waters in Europe suffer from the adverse effects of multiple stresses. The Otra River, southernmost Norway, is impacted by acid deposition, hydropower development and increasingly by climate change. The river holds a unique population of land-locked salmon and anadromous salmon in the lower reaches. Both populations have been severely affected by acidification. The decrease in acid deposition since the 1980s has led to partial recovery of both populations. Climate change with higher temperatures and altered precipitation can potentially further impact fish populations. We used a linked set of process-oriented models to simulate future climate, discharge, and water chemistry at five sub-catchments in the Otra river basin. Projections to year 2100 indicate that future climate change will give a small but measureable improvement in water quality, but that additional reductions in acid deposition are needed to promote full restoration of the fish communities. These results can help guide management decisions to sustain key salmon habitats and carry out effective long-term mitigation strategies such as liming. The Otra River is typical of many rivers in Europe in that it fails to achieve the good ecological status target of the EU Water Framework Directive. The programme of measures needed in the river basin management plan necessarily must consider the multiple stressors of acid deposition, hydropower, and climate change. This is difficult, however, as the synergistic and antagonistic effects are complex and challenging to address with modelling tools currently available. Copyright © 2016 Elsevier B.V. All rights reserved.

Recent advances in understanding secondary organic aerosol: Implications for global climate forcing: Advances in Secondary Organic Aerosol

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

Shrivastava, Manish; Cappa, Christopher D.; Fan, Jiwen

Anthropogenic emissions and land use changes have modified atmospheric aerosol concentrations and size distributions over time. Understanding preindustrial conditions and changes in organic aerosol due to anthropogenic activities is important because these features (1) influence estimates of aerosol radiative forcing and (2) can confound estimates of the historical response of climate to increases in greenhouse gases. Secondary organic aerosol (SOA), formed in the atmosphere by oxidation of organic gases, represents a major fraction of global submicron-sized atmospheric organic aerosol. Over the past decade, significant advances in understanding SOA properties and formation mechanisms have occurred through measurements, yet current climate modelsmore » typically do not comprehensively include all important processes. Our review summarizes some of the important developments during the past decade in understanding SOA formation. We also highlight the importance of some processes that influence the growth of SOA particles to sizes relevant for clouds and radiative forcing, including formation of extremely low volatility organics in the gas phase, acid-catalyzed multiphase chemistry of isoprene epoxydiols, particle-phase oligomerization, and physical properties such as volatility and viscosity. Several SOA processes highlighted in this review are complex and interdependent and have nonlinear effects on the properties, formation, and evolution of SOA. Current global models neglect this complexity and nonlinearity and thus are less likely to accurately predict the climate forcing of SOA and project future climate sensitivity to greenhouse gases. Efforts are also needed to rank the most influential processes and nonlinear process-related interactions, so that these processes can be accurately represented in atmospheric chemistry-climate models.« less

Recent advances in understanding secondary organic aerosol: Implications for global climate forcing: Advances in Secondary Organic Aerosol

DOE PAGES

Shrivastava, Manish; Cappa, Christopher D.; Fan, Jiwen; ...

2017-06-15

Anthropogenic emissions and land use changes have modified atmospheric aerosol concentrations and size distributions over time. Understanding preindustrial conditions and changes in organic aerosol due to anthropogenic activities is important because these features (1) influence estimates of aerosol radiative forcing and (2) can confound estimates of the historical response of climate to increases in greenhouse gases. Secondary organic aerosol (SOA), formed in the atmosphere by oxidation of organic gases, represents a major fraction of global submicron-sized atmospheric organic aerosol. Over the past decade, significant advances in understanding SOA properties and formation mechanisms have occurred through measurements, yet current climate modelsmore » typically do not comprehensively include all important processes. Our review summarizes some of the important developments during the past decade in understanding SOA formation. We also highlight the importance of some processes that influence the growth of SOA particles to sizes relevant for clouds and radiative forcing, including formation of extremely low volatility organics in the gas phase, acid-catalyzed multiphase chemistry of isoprene epoxydiols, particle-phase oligomerization, and physical properties such as volatility and viscosity. Several SOA processes highlighted in this review are complex and interdependent and have nonlinear effects on the properties, formation, and evolution of SOA. Current global models neglect this complexity and nonlinearity and thus are less likely to accurately predict the climate forcing of SOA and project future climate sensitivity to greenhouse gases. Efforts are also needed to rank the most influential processes and nonlinear process-related interactions, so that these processes can be accurately represented in atmospheric chemistry-climate models.« less

Assessing risks of climate variability and climate change for Indonesian rice agriculture.

PubMed

Naylor, Rosamond L; Battisti, David S; Vimont, Daniel J; Falcon, Walter P; Burke, Marshall B

2007-05-08

El Niño events typically lead to delayed rainfall and decreased rice planting in Indonesia's main rice-growing regions, thus prolonging the hungry season and increasing the risk of annual rice deficits. Here we use a risk assessment framework to examine the potential impact of El Niño events and natural variability on rice agriculture in 2050 under conditions of climate change, with a focus on two main rice-producing areas: Java and Bali. We select a 30-day delay in monsoon onset as a threshold beyond which significant impact on the country's rice economy is likely to occur. To project the future probability of monsoon delay and changes in the annual cycle of rainfall, we use output from the Intergovernmental Panel on Climate Change AR4 suite of climate models, forced by increasing greenhouse gases, and scale it to the regional level by using empirical downscaling models. Our results reveal a marked increase in the probability of a 30-day delay in monsoon onset in 2050, as a result of changes in the mean climate, from 9-18% today (depending on the region) to 30-40% at the upper tail of the distribution. Predictions of the annual cycle of precipitation suggest an increase in precipitation later in the crop year (April-June) of approximately 10% but a substantial decrease (up to 75% at the tail) in precipitation later in the dry season (July-September). These results indicate a need for adaptation strategies in Indonesian rice agriculture, including increased investments in water storage, drought-tolerant crops, crop diversification, and early warning 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.

The impact of high-end climate change on agricultural welfare

PubMed Central

Stevanović, Miodrag; Popp, Alexander; Lotze-Campen, Hermann; Dietrich, Jan Philipp; Müller, Christoph; Bonsch, Markus; Schmitz, Christoph; Bodirsky, Benjamin Leon; Humpenöder, Florian; Weindl, Isabelle

2016-01-01

Climate change threatens agricultural productivity worldwide, resulting in higher food prices. Associated economic gains and losses differ not only by region but also between producers and consumers and are affected by market dynamics. On the basis of an impact modeling chain, starting with 19 different climate projections that drive plant biophysical process simulations and ending with agro-economic decisions, this analysis focuses on distributional effects of high-end climate change impacts across geographic regions and across economic agents. By estimating the changes in surpluses of consumers and producers, we find that climate change can have detrimental impacts on global agricultural welfare, especially after 2050, because losses in consumer surplus generally outweigh gains in producer surplus. Damage in agriculture may reach the annual loss of 0.3% of future total gross domestic product at the end of the century globally, assuming further opening of trade in agricultural products, which typically leads to interregional production shifts to higher latitudes. Those estimated global losses could increase substantially if international trade is more restricted. If beneficial effects of atmospheric carbon dioxide fertilization can be realized in agricultural production, much of the damage could be avoided. Although trade policy reforms toward further liberalization help alleviate climate change impacts, additional compensation mechanisms for associated environmental and development concerns have to be considered. PMID:27574700

The impact of high-end climate change on agricultural welfare.

PubMed

Stevanović, Miodrag; Popp, Alexander; Lotze-Campen, Hermann; Dietrich, Jan Philipp; Müller, Christoph; Bonsch, Markus; Schmitz, Christoph; Bodirsky, Benjamin Leon; Humpenöder, Florian; Weindl, Isabelle

2016-08-01

Climate change threatens agricultural productivity worldwide, resulting in higher food prices. Associated economic gains and losses differ not only by region but also between producers and consumers and are affected by market dynamics. On the basis of an impact modeling chain, starting with 19 different climate projections that drive plant biophysical process simulations and ending with agro-economic decisions, this analysis focuses on distributional effects of high-end climate change impacts across geographic regions and across economic agents. By estimating the changes in surpluses of consumers and producers, we find that climate change can have detrimental impacts on global agricultural welfare, especially after 2050, because losses in consumer surplus generally outweigh gains in producer surplus. Damage in agriculture may reach the annual loss of 0.3% of future total gross domestic product at the end of the century globally, assuming further opening of trade in agricultural products, which typically leads to interregional production shifts to higher latitudes. Those estimated global losses could increase substantially if international trade is more restricted. If beneficial effects of atmospheric carbon dioxide fertilization can be realized in agricultural production, much of the damage could be avoided. Although trade policy reforms toward further liberalization help alleviate climate change impacts, additional compensation mechanisms for associated environmental and development concerns have to be considered.

19. View northwest of Tropic Chamber reciprocal compressors (typical), in ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

19. View northwest of Tropic Chamber reciprocal compressors (typical), in machine area. - Natick Research & Development Laboratories, Climatic Chambers Building, U.S. Army Natick Research, Development & Engineering Center (NRDEC), Natick, Middlesex County, MA

Simulation of a semi-permanent wetland basin in the Cottonwood Lake area, east-central North Dakota

USGS Publications Warehouse

Carroll, R.W.H.; Pohll, G.M.; Tracy, J.C.; Winter, T.C.; ,

2001-01-01

A coupled surface/subsurface hydrologic model was developed to examine the effects of climatic conditions on stage fluctuations within a semi-permanent wetland located in the Prairie Pothole region of east-central North Dakota. Model calibration was accomplished using data collected from 1981 to 1996 to encompass extreme climatic conditions. Results show that the processes of precipitation largely control wetland stage. Surface runoff produces short duration, high magnitude flows typically associated with spring thaw. On the other hand, groundwater contribution provides flows smaller in magnitude but higher in duration and these become increasingly important with respect to wetland stage during extended periods of drought and flood. Peak groundwater fluxes lag one-to-two months behind peak recharge rates and therefore occur predominantly during the month of June. Groundwater fluxes then attenuate slowly for the remainder of the year to the point where water may move out of the wetland and into the underlying aquifer during the fall and winter months. Despite an over simplification of the complex groundwater component of the wetland system it was found that this modeling approach was able to predict system response over 15 years, under extreme climatic conditions and with relatively easily attainable data input.

AGCM Biases in Evaporation Regime: Impacts on Soil Moisture Memory and Land-Atmosphere Feedback

NASA Technical Reports Server (NTRS)

Mahanama, Sarith P. P.; Koster, Randal D.

2005-01-01

Because precipitation and net radiation in an atmospheric general circulation model (AGCM) are typically biased relative to observations, the simulated evaporative regime of a region may be biased, with consequent negative effects on the AGCM s ability to translate an initialized soil moisture anomaly into an improved seasonal prediction. These potential problems are investigated through extensive offline analyses with the Mosaic land surface model (LSM). We first forced the LSM globally with a 15-year observations-based dataset. We then repeated the simulation after imposing a representative set of GCM climate biases onto the forcings - the observational forcings were scaled so that their mean seasonal cycles matched those simulated by the NSIPP-1 (NASA Global Modeling and Assimilation Office) AGCM over the same period-The AGCM s climate biases do indeed lead to significant biases in evaporative regime in certain regions, with the expected impacts on soil moisture memory timescales. Furthermore, the offline simulations suggest that the biased forcing in the AGCM should contribute to overestimated feedback in certain parts of North America - parts already identified in previous studies as having excessive feedback. The present study thus supports the notion that the reduction of climate biases in the AGCM will lead to more appropriate translations of soil moisture initialization into seasonal prediction skill.

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.

PM2.5 and tropospheric ozone in China: overview of situation and responses

NASA Astrophysics Data System (ADS)

Zhang, Hua

This work reviewed the observational status of PM2.5 and tropospheric ozone in China. It told us the observational facts on the ratios of typical types of aerosol components to the total PM2.5/PM10, and daily and seasonal change of near surface ozone concentration at different cities of China; the global concentration distribution of tropospheric ozone observed by satellite in 2010-2013 was also given for comparison; the PM2.5 concentration distribution and their seasonal change in China region were simulated by an aerosol chemistry-global climate modeling system. Different contribution from five kinds of aerosols to the simulated PM2.5 was analyzed. Then, it linked the emissions of aerosol and greenhouse gases and their radiative forcing and thus gave their climatic effect by reducing their emissions on the basis of most recently published IPCC AR5. Finally it suggested policies on reducing emissions of short-lived climate pollutants (SLCPs) (such as PM2.5 and tropospheric ozone) in China from protecting both climate and environment.

Maximum rates of climate change are systematically underestimated in the geological record.

PubMed

Kemp, David B; Eichenseer, Kilian; Kiessling, Wolfgang

2015-11-10

Recently observed rates of environmental change are typically much higher than those inferred for the geological past. At the same time, the magnitudes of ancient changes were often substantially greater than those established in recent history. The most pertinent disparity, however, between recent and geological rates is the timespan over which the rates are measured, which typically differ by several orders of magnitude. Here we show that rates of marked temperature changes inferred from proxy data in Earth history scale with measurement timespan as an approximate power law across nearly six orders of magnitude (10(2) to >10(7) years). This scaling reveals how climate signals measured in the geological record alias transient variability, even during the most pronounced climatic perturbations of the Phanerozoic. Our findings indicate that the true attainable pace of climate change on timescales of greatest societal relevance is underestimated in geological archives.

Towards an automatic statistical model for seasonal precipitation prediction and its application to Central and South Asian headwater catchments

NASA Astrophysics Data System (ADS)

Gerlitz, Lars; Gafurov, Abror; Apel, Heiko; Unger-Sayesteh, Katy; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

Statistical climate forecast applications typically utilize a small set of large scale SST or climate indices, such as ENSO, PDO or AMO as predictor variables. If the predictive skill of these large scale modes is insufficient, specific predictor variables such as customized SST patterns are frequently included. Hence statistically based climate forecast models are either based on a fixed number of climate indices (and thus might not consider important predictor variables) or are highly site specific and barely transferable to other regions. With the aim of developing an operational seasonal forecast model, which is easily transferable to any region in the world, we present a generic data mining approach which automatically selects potential predictors from gridded SST observations and reanalysis derived large scale atmospheric circulation patterns and generates robust statistical relationships with posterior precipitation anomalies for user selected target regions. Potential predictor variables are derived by means of a cellwise correlation analysis of precipitation anomalies with gridded global climate variables under consideration of varying lead times. Significantly correlated grid cells are subsequently aggregated to predictor regions by means of a variability based cluster analysis. Finally for every month and lead time, an individual random forest based forecast model is automatically calibrated and evaluated by means of the preliminary generated predictor variables. The model is exemplarily applied and evaluated for selected headwater catchments in Central and South Asia. Particularly the for winter and spring precipitation (which is associated with westerly disturbances in the entire target domain) the model shows solid results with correlation coefficients up to 0.7, although the variability of precipitation rates is highly underestimated. Likewise for the monsoonal precipitation amounts in the South Asian target areas a certain skill of the model could be detected. The skill of the model for the dry summer season in Central Asia and the transition seasons over South Asia is found to be low. A sensitivity analysis by means on well known climate indices reveals the major large scale controlling mechanisms for the seasonal precipitation climate of each target area. For the Central Asian target areas, both, the El Nino Southern Oscillation and the North Atlantic Oscillation are identified as important controlling factors for precipitation totals during moist spring season. Drought conditions are found to be triggered by a warm ENSO phase in combination with a positive phase of the NAO. For the monsoonal summer precipitation amounts over Southern Asia, the model suggests a distinct negative response to El Nino events.

Seasonal cycle of precipitation over major river basins in South and Southeast Asia: A review of the CMIP5 climate models data for present climate and future climate projections

NASA Astrophysics Data System (ADS)

Hasson, Shabeh ul; Pascale, Salvatore; Lucarini, Valerio; Böhner, Jürgen

2016-11-01

We review the skill of thirty coupled climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in terms of reproducing properties of the seasonal cycle of precipitation over the major river basins of South and Southeast Asia (Indus, Ganges, Brahmaputra and Mekong) for the historical period (1961-2000). We also present how these models represent the impact of climate change by the end of century (2061-2100) under the extreme scenario RCP8.5. First, we assess the models' ability to reproduce the observed timings of the monsoon onset and the rate of rapid fractional accumulation (RFA) slope - a measure of seasonality within the active monsoon period. Secondly, we apply a threshold-independent seasonality index (SI) - a multiplicative measure of precipitation (P) and extent of its concentration relative to uniform distribution (relative entropy - RE). We apply SI distinctly over the monsoonal precipitation regime (MPR), westerly precipitation regime (WPR) and annual precipitation. For the present climate, neither any single model nor the multi-model mean performs best in all chosen metrics. Models show overall a modest skill in suggesting right timings of the monsoon onset while the RFA slope is generally underestimated. One third of the models fail to capture the monsoon signal over the Indus basin. Mostly, the estimates for SI during WPR are higher than observed for all basins. When looking at MPR, the models typically simulate an SI higher (lower) than observed for the Ganges and Brahmaputra (Indus and Mekong) basins, following the pattern of overestimation (underestimation) of precipitation. Most of the models are biased negative (positive) for RE estimates over the Brahmaputra and Mekong (Indus and Ganges) basins, implying the extent of precipitation concentration for MPR and number of dry days within WPR lower (higher) than observed for these basins. Such skill of the CMIP5 models in representing the present-day monsoonal hydroclimatology poses some caveats on their ability to represent correctly the climate change signal. Nevertheless, considering the majority-model agreement as a measure of robustness for the qualitative scale projected future changes, we find a slightly delayed onset, and a general increase in the RFA slope and in the extent of precipitation concentration (RE) for MPR. Overall, a modest inter-model agreement suggests an increase in the seasonality of MPR and a less intermittent WPR for all basins and for most of the study domain. The SI-based indicator of change in the monsoonal domain suggests its extension westward over northwest India and Pakistan and northward over China. These findings have serious implications for the food and water security of the region in the future.

Determining the relative importance of climatic drivers on spring phenology in grassland ecosystems of semi-arid areas.

PubMed

Zhu, Likai; Meng, Jijun

2015-02-01

Understanding climate controls on spring phenology in grassland ecosystems is critically important in predicting the impacts of future climate change on grassland productivity and carbon storage. The third-generation Global Inventory Monitoring and Modeling System (GIMMS3g) normalized difference vegetation index (NDVI) data were applied to derive the start of the growing season (SOS) from 1982-2010 in grassland ecosystems of Ordos, a typical semi-arid area in China. Then, the conditional Granger causality method was utilized to quantify the directed functional connectivity between key climatic drivers and the SOS. The results show that the asymmetric Gaussian (AG) function is better in reducing noise of NDVI time series than the double logistic (DL) function within our study area. The southeastern Ordos has earlier occurrence and lower variability of the SOS, whereas the northwestern Ordos has later occurrence and higher variability of the SOS. The research also reveals that spring precipitation has stronger causal connectivity with the SOS than other climatic factors over different grassland ecosystem types. There is no statistically significant trend across the study area, while the similar pattern is observed for spring precipitation. Our study highlights the link of spring phenology with different grassland types, and the use of coupling remote sensing and econometric tools. With the dramatic increase in global change research, Granger causality method augurs well for further development and application of time-series modeling of complex social-ecological systems at the intersection of remote sensing and landscape changes.

Climate Model Diagnostic Analyzer Web Service System

NASA Astrophysics Data System (ADS)

Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Jiang, J. H.

2013-12-01

The latest Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with newly available global observations. The traditional approach to climate model evaluation, which compares a single parameter at a time, identifies symptomatic model biases and errors but fails to diagnose the model problems. The model diagnosis process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. To address these challenges, we are developing a parallel, distributed web-service system that enables the physics-based multi-variable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. We have developed a methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks (i.e., Flask, Gunicorn, and Tornado). The web-service system, called Climate Model Diagnostic Analyzer (CMDA), currently supports (1) all the datasets from Obs4MIPs and a few ocean datasets from NOAA and Argo, which can serve as observation-based reference data for model evaluation and (2) many of CMIP5 model outputs covering a broad range of atmosphere, ocean, and land variables from the CMIP5 specific historical runs and AMIP runs. Analysis capabilities currently supported by CMDA are (1) the calculation of annual and seasonal means of physical variables, (2) the calculation of time evolution of the means in any specified geographical region, (3) the calculation of correlation between two variables, and (4) the calculation of difference between two variables. A web user interface is chosen for CMDA because it not only lowers the learning curve and removes the adoption barrier of the tool but also enables instantaneous use, avoiding the hassle of local software installation and environment incompatibility. CMDA is planned to be used as an educational tool for the summer school organized by JPL's Center for Climate Science in 2014. The requirements of the educational tool are defined with the interaction with the school organizers, and CMDA is customized to meet the requirements accordingly. The tool needs to be production quality for 30+ simultaneous users. The summer school will thus serve as a valuable testbed for the tool development, preparing CMDA to serve the Earth-science modeling and model-analysis community at the end of the project. This work was funded by the NASA Earth Science Program called Computational Modeling Algorithms and Cyberinfrastructure (CMAC).

Historical and projected climate in the Northern Rockies Region [Chapter 3

Treesearch

Linda A. Joyce; Marian Talbert; Darrin Sharp; Jeffrey Morisette; John Stevenson

2018-01-01

Climate influences the ecosystem services we obtain from forest and rangelands. Climate is described by the long-term characteristics of precipitation, temperature, wind, snowfall, and other measures of weather that occur over a long period in a particular place, and is typically expressed as long-term average conditions. Resource management practices are implemented...

Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil

NASA Astrophysics Data System (ADS)

Lowe, Rachel; Bailey, Trevor C.; Stephenson, David B.; Graham, Richard J.; Coelho, Caio A. S.; Sá Carvalho, Marilia; Barcellos, Christovam

2011-03-01

This paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2.5°×2.5° longitude-latitude grid with time lags relevant to dengue transmission, an El Niño Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM—generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil.

The energy performance of prototype holographic glazings

NASA Astrophysics Data System (ADS)

Papamichael, K.; Beltran, L.; Furler, R.; Lee, E. S.; Selkowitz, S.; Rubin, M.

1993-02-01

We report on the simulation of the energy performance of prototype holographic glazings in commercial office buildings in a California climate. These prototype glazings, installed above conventional side windows, are designed to diffract the transmitted solar radiation and reflect it off the ceiling, providing adequate daylight illumination for typical office tasks up to 10m from the window. In this study, we experimentally determined a comprehensive set of solar-optical properties and characterized the contribution of the prototype holographic glazings to workplane illuminance in a scale model of a typical office space. We then used the scale model measurements to simulate the energy performance of the holographic glazings over the course of an entire year for four window orientations (North, East, South and West) for the inland Los Angeles climate, using the DOE-2.lD building energy analysis computer program. The results of our experimental analyses indicate that these prototype holographic glazings diffract only a small fraction of the incident light. The results of this study indicate that these prototype holographic glazings will not save energy in commercial office buildings. Their performance is very similar to that of clear glass, which, through side windows, cannot efficiently illuminate more than a 4-6 m depth of a building's perimeter, because the cooling penalties due to solar heat gain are greater than the electric lighting savings due to daylighting.

The uncertain climate footprint of wetlands under human pressure

PubMed Central

Petrescu, Ana Maria Roxana; Lohila, Annalea; Tuovinen, Juha-Pekka; Baldocchi, Dennis D.; Roulet, Nigel T.; Vesala, Timo; Dolman, Albertus Johannes; Oechel, Walter C.; Marcolla, Barbara; Friborg, Thomas; Rinne, Janne; Matthes, Jaclyn Hatala; Merbold, Lutz; Meijide, Ana; Kiely, Gerard; Sottocornola, Matteo; Sachs, Torsten; Zona, Donatella; Varlagin, Andrej; Lai, Derrick Y. F.; Veenendaal, Elmar; Parmentier, Frans-Jan W.; Skiba, Ute; Lund, Magnus; Hensen, Arjan; van Huissteden, Jacobus; Flanagan, Lawrence B.; Shurpali, Narasinha J.; Grünwald, Thomas; Humphreys, Elyn R.; Jackowicz-Korczyński, Marcin; Aurela, Mika A.; Laurila, Tuomas; Grüning, Carsten; Corradi, Chiara A. R.; Schrier-Uijl, Arina P.; Christensen, Torben R.; Tamstorf, Mikkel P.; Mastepanov, Mikhail; Martikainen, Pertti J.; Verma, Shashi B.; Bernhofer, Christian; Cescatti, Alessandro

2015-01-01

Significant climate risks are associated with a positive carbon–temperature feedback in northern latitude carbon-rich ecosystems, making an accurate analysis of human impacts on the net greenhouse gas balance of wetlands a priority. Here, we provide a coherent assessment of the climate footprint of a network of wetland sites based on simultaneous and quasi-continuous ecosystem observations of CO2 and CH4 fluxes. Experimental areas are located both in natural and in managed wetlands and cover a wide range of climatic regions, ecosystem types, and management practices. Based on direct observations we predict that sustained CH4 emissions in natural ecosystems are in the long term (i.e., several centuries) typically offset by CO2 uptake, although with large spatiotemporal variability. Using a space-for-time analogy across ecological and climatic gradients, we represent the chronosequence from natural to managed conditions to quantify the “cost” of CH4 emissions for the benefit of net carbon sequestration. With a sustained pulse–response radiative forcing model, we found a significant increase in atmospheric forcing due to land management, in particular for wetland converted to cropland. Our results quantify the role of human activities on the climate footprint of northern wetlands and call for development of active mitigation strategies for managed wetlands and new guidelines of the Intergovernmental Panel on Climate Change (IPCC) accounting for both sustained CH4 emissions and cumulative CO2 exchange. PMID:25831506

The uncertain climate footprint of wetlands under human pressure.

PubMed

Petrescu, Ana Maria Roxana; Lohila, Annalea; Tuovinen, Juha-Pekka; Baldocchi, Dennis D; Desai, Ankur R; Roulet, Nigel T; Vesala, Timo; Dolman, Albertus Johannes; Oechel, Walter C; Marcolla, Barbara; Friborg, Thomas; Rinne, Janne; Matthes, Jaclyn Hatala; Merbold, Lutz; Meijide, Ana; Kiely, Gerard; Sottocornola, Matteo; Sachs, Torsten; Zona, Donatella; Varlagin, Andrej; Lai, Derrick Y F; Veenendaal, Elmar; Parmentier, Frans-Jan W; Skiba, Ute; Lund, Magnus; Hensen, Arjan; van Huissteden, Jacobus; Flanagan, Lawrence B; Shurpali, Narasinha J; Grünwald, Thomas; Humphreys, Elyn R; Jackowicz-Korczyński, Marcin; Aurela, Mika A; Laurila, Tuomas; Grüning, Carsten; Corradi, Chiara A R; Schrier-Uijl, Arina P; Christensen, Torben R; Tamstorf, Mikkel P; Mastepanov, Mikhail; Martikainen, Pertti J; Verma, Shashi B; Bernhofer, Christian; Cescatti, Alessandro

2015-04-14

Significant climate risks are associated with a positive carbon-temperature feedback in northern latitude carbon-rich ecosystems, making an accurate analysis of human impacts on the net greenhouse gas balance of wetlands a priority. Here, we provide a coherent assessment of the climate footprint of a network of wetland sites based on simultaneous and quasi-continuous ecosystem observations of CO2 and CH4 fluxes. Experimental areas are located both in natural and in managed wetlands and cover a wide range of climatic regions, ecosystem types, and management practices. Based on direct observations we predict that sustained CH4 emissions in natural ecosystems are in the long term (i.e., several centuries) typically offset by CO2 uptake, although with large spatiotemporal variability. Using a space-for-time analogy across ecological and climatic gradients, we represent the chronosequence from natural to managed conditions to quantify the "cost" of CH4 emissions for the benefit of net carbon sequestration. With a sustained pulse-response radiative forcing model, we found a significant increase in atmospheric forcing due to land management, in particular for wetland converted to cropland. Our results quantify the role of human activities on the climate footprint of northern wetlands and call for development of active mitigation strategies for managed wetlands and new guidelines of the Intergovernmental Panel on Climate Change (IPCC) accounting for both sustained CH4 emissions and cumulative CO2 exchange.

ClimateNet: A Machine Learning dataset for Climate Science Research

NASA Astrophysics Data System (ADS)

Prabhat, M.; Biard, J.; Ganguly, S.; Ames, S.; Kashinath, K.; Kim, S. K.; Kahou, S.; Maharaj, T.; Beckham, C.; O'Brien, T. A.; Wehner, M. F.; Williams, D. N.; Kunkel, K.; Collins, W. D.

2017-12-01

Deep Learning techniques have revolutionized commercial applications in Computer vision, speech recognition and control systems. The key for all of these developments was the creation of a curated, labeled dataset ImageNet, for enabling multiple research groups around the world to develop methods, benchmark performance and compete with each other. The success of Deep Learning can be largely attributed to the broad availability of this dataset. Our empirical investigations have revealed that Deep Learning is similarly poised to benefit the task of pattern detection in climate science. Unfortunately, labeled datasets, a key pre-requisite for training, are hard to find. Individual research groups are typically interested in specialized weather patterns, making it hard to unify, and share datasets across groups and institutions. In this work, we are proposing ClimateNet: a labeled dataset that provides labeled instances of extreme weather patterns, as well as associated raw fields in model and observational output. We develop a schema in NetCDF to enumerate weather pattern classes/types, store bounding boxes, and pixel-masks. We are also working on a TensorFlow implementation to natively import such NetCDF datasets, and are providing a reference convolutional architecture for binary classification tasks. Our hope is that researchers in Climate Science, as well as ML/DL, will be able to use (and extend) ClimateNet to make rapid progress in the application of Deep Learning for Climate Science research.

Irrigation as an Historical Climate Forcing

NASA Technical Reports Server (NTRS)

Cook, Benjamin I.; Shukla, Sonali P.; Puma, Michael J.; Nazarenko, Larissa S.

2014-01-01

Irrigation is the single largest anthropogenic water use, a modification of the land surface that significantly affects surface energy budgets, the water cycle, and climate. Irrigation, however, is typically not included in standard historical general circulation model (GCM) simulations along with other anthropogenic and natural forcings. To investigate the importance of irrigation as an anthropogenic climate forcing, we conduct two 5-member ensemble GCM experiments. Both are setup identical to the historical forced (anthropogenic plus natural) scenario used in version 5 of the Coupled Model Intercomparison Project, but in one experiment we also add water to the land surface using a dataset of historically estimated irrigation rates. Irrigation has a negligible effect on the global average radiative balance at the top of the atmosphere, but causes significant cooling of global average surface air temperatures over land and dampens regional warming trends. This cooling is regionally focused and is especially strong in Western North America, the Mediterranean, the Middle East, and Asia. Irrigation enhances cloud cover and precipitation in these same regions, except for summer in parts of Monsoon Asia, where irrigation causes a reduction in monsoon season precipitation. Irrigation cools the surface, reducing upward fluxes of longwave radiation (increasing net longwave), and increases cloud cover, enhancing shortwave reflection (reducing net shortwave). The relative magnitude of these two processes causes regional increases (northern India) or decreases (Central Asia, China) in energy availability at the surface and top of the atmosphere. Despite these changes in net radiation, however, climate responses are due primarily to larger magnitude shifts in the Bowen ratio from sensible to latent heating. Irrigation impacts on temperature, precipitation, and other climate variables are regionally significant, even while other anthropogenic forcings (anthropogenic aerosols, greenhouse gases, etc.) dominate the long term climate evolution in the simulations. To better constrain the magnitude and uncertainties of irrigation-forced climate anomalies, irrigation should therefore be considered as another important anthropogenic climate forcing in the next generation of historical climate simulations and multimodel assessments.

Conceptual model of a coastal hydrosystem in a semi-arid environment subjected to the climate change: the case of Lavrion, Greece

NASA Astrophysics Data System (ADS)

Pouliaris, Christos; Schumann, Philipp; Danneberg, Nils-Christian; Foglia, Laura; Kallioras, Andreas; Schüth, Christoph

2015-04-01

Groundwater management in arid areas has become a major issue worldwide, and it is expected to be exacerbated due to climate change. Low annual precipitation and high evaporation potential are the key features of these areas, with additional pressure added to the system due to abstractions for irrigation and water supply purposes. Typical example of such scenarios exist in the Mediterranean area, where drought and water scarcity, especially in the warm period of the hydrological year, give rise to major management issues in coastal areas. Among the different solutions, the implementation of Managed Aquifer Recharge (MAR) schemes have been suggested in the EU FP7 project MARSOL (Demonstrating Managed Aquifer Recharge as a Solution to Water Scarcity and Drought). In the project, different sites across the Mediterranean are tested for investigating the viability of various MAR techniques in different hydrological systems facing qualitative and quantitative deterioration of their groundwater resources. The coastal hydrosystem of Lavrion was selected due to its typical Mediterranean characteristics (climatic, hydrologic, hydrogeological, geological etc.); all within a rather small area of extent ( < 50km2), that render it as a reference site for hydrologic modeling applications. It consists of a set of aquifer layers (karstified limestone and alluvial) which are hydraulically connected to the sea and an ephemeral torrent (wadi) that flows through a typical small Mediterranean alluvial valley. The major water resources problems of the area are mainly qualitative issues of the groundwaters; in specific: (i) seawater intrusion, (ii) nitrate contamination and (iii) heavy metal pollution due to past and recent mining and metallurgical activities The modelling approach will include the development of three distinct models that will be integrated. The aim is to depict how systems with characteristics like the ones mentioned above perform and, which different scenarios can be applied, aiming at identifying the most viable (with respect to water budget) MAR strategy for the specific area. Meteorological data, field data and site investigations provide the input data for all the different models. The field activities already conducted included: an inventory of all existing pumping wells; the development of a monitoring network for qualitative and quantitative environmental data acquisition at different scales and hydrologic zones; installation of multi-level piezometers for tailored monitoring of the seawater wedge; and geophysical surveys of subsurface characterization. The combination of literature review and field investigations led to the development of the conceptual model of the area along with the realization of the spatial distribution of each model. The hydraulic connections of the two aquifers, the surface water system and the sea have been identified and the upcoming activities aim in quantifying them and include them in the models being under development. The groundwater chemical characteristics have been examined, with results showing the major influence from seawater intrusion. All the data mentioned above are used for the development of the integrated hydrological model of the Lavrion area.

Cloud cover determination in polar regions from satellite imagery

NASA Technical Reports Server (NTRS)

Barry, R. G.; Key, J. R.; Maslanik, J. A.

1988-01-01

The principal objectives of this project are: to develop suitable validation data sets to evaluate the effectiveness of the ISCCP operational algorithm for cloud retrieval in polar regions and to validate model simulations of polar cloud cover; to identify limitations of current procedures for varying atmospheric surface conditions, and to explore potential means to remedy them using textural classifiers: and to compare synoptic cloud data from a control run experiment of the Goddard Institute for Space Studies (GISS) climate model 2 with typical observed synoptic cloud patterns. Current investigations underway are listed and the progress made to date is summarized.

The representation of low-level clouds during the West African monsoon in weather and climate models

NASA Astrophysics Data System (ADS)

Kniffka, Anke; Hannak, Lisa; Knippertz, Peter; Fink, Andreas

2016-04-01

The West African monsoon is one of the most important large-scale circulation features in the tropics and the associated seasonal rainfalls are crucial to rain-fed agriculture and water resources for hundreds of millions of people. However, numerical weather and climate models still struggle to realistically represent salient features of the monsoon across a wide range of scales. Recently it has been shown that substantial errors in radiation and clouds exist in the southern parts of West Africa (8°W-8°E, 5-10°N) during summer. This area is characterised by strong low-level jets associated with the formation of extensive ultra-low stratus clouds. Often persisting long after sunrise, these clouds have a substantial impact on the radiation budget at the surface and thus the diurnal evolution of the planetary boundary layer (PBL). Here we present some first results from a detailed analysis of the representation of these clouds and the associated PBL features across a range of weather and climate models. Recent climate model simulations for the period 1991-2010 run in the framework of the Year of Tropical Convection (YOTC) offer a great opportunity for this analysis. The models are those used for the latest Assessment Report of the Intergovernmental Panel on Climate Change, but for YOTC the model output has a much better temporal resolution, allowing to resolve the diurnal cycle, and includes diabatic terms, allowing to much better assess physical reasons for errors in low-level temperature, moisture and thus cloudiness. These more statistical climate model analyses are complemented by experiments using ICON (Icosahedral non-hydrostatic general circulation model), the new numerical weather prediction model of the German Weather Service and the Max Planck Institute for Meteorology. ICON allows testing sensitivities to model resolution and numerical schemes. These model simulations are validated against (re-)analysis data, satellite observations (e.g. CM SAF cloud and radiation data) and ground-based eye observations of clouds and radiation measurements from weather stations. Our results show that many of the climate models have great difficulties representing the diurnal cycle of winds and clouds, leading to associated errors in radiation. Typical errors include a substantial underestimation of the lowest clouds accompanied by an overestimation of clouds at the top of the monsoon layer, indicating systematic problems in vertical exchange processes, which are also reflected in large errors in jet speed. Consequently, many models show a too flat diurnal cycle in cloudiness. This contribution is part of the EU-funded DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) project that aims to investigate the impact of the drastic increase in anthropogenic emissions in West Africa on the local weather and climate, for example through cloud-aerosol interactions. The analysis of the capability of state-of-the-art numerical models to represent low-level cloudiness presented here is an important requisite for the planned assessments of the influence of anthropogenic aerosol.

The Once and Future North Atlantic: How the Mid-Pliocene Warm Period Can Increase Stakeholder Preparedness in a Warming World

NASA Astrophysics Data System (ADS)

Jacobs, P.; de Mutsert, K.

2013-12-01

Paleoclimatic reconstructions, particularly from periods that may serve as an analog to the present and future greenhouse-driven warming, are increasingly being used to validate climate models as well as to provide constraints on broad impacts such as global temperature and sea level change. However, paleoclimatic data remains under-utilized in decision-making processes by stakeholders, who typically rely on scenarios produced by computer models or naive extrapolation of present trends. We hope to increase the information available to stakeholders by incorporating paleoclimatic data from the mid-Pliocene Warm Period (mPWP, ~3ma) into a fisheries model of the North Atlantic. North Atlantic fisheries are economically important and are expected to be sensitive to climatic change. State of the art climate models remain unable to realistically simulate the North Atlantic, both over the observational record as well as during times in the geologic past such as the mPWP. Given that the mPWP shares many of the same boundary conditions as those likely to be seen in the near future, we seek to answer the question 'What if the climate of the future looks more like the climate of the past?' relative to what state of the art computer models currently project. To that end we have created a suite of future North Atlantic Ocean scenarios using output from the CMIP3 and CMIP5 modeling experiments, as well as the PRISM group's Mid-Pliocene ocean reconstruction. We use these scenarios to drive an ecosystem-based fisheries model using the Ecopath with Ecosim (EwE) software to identify differences between the scenarios as the North Atlantic Ocean changes through time. Additionally, we examine the spatial component of these differences by using the Ecospace module of EwE. Whereas the Ecosim realizations are intended to capture the dynamic response to changing oceanographic parameters (SST, SSS, DO) over time, the Ecospace experiments are intended to explore the impact of different equilibrium conditions on fish community longer-term spatial redistribution. By making use not only of climate model output but also paleoclimatic data from a period that closely resembles our near future, stakeholders can make decisions informed by a more robust range of potential outcomes as greenhouse emissions warm the planet.

11. Detail view west from airlock chamber of typical refrigerator ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

11. Detail view west from airlock chamber of typical refrigerator door into Trophic Chamber. - Natick Research & Development Laboratories, Climatic Chambers Building, U.S. Army Natick Research, Development & Engineering Center (NRDEC), Natick, Middlesex County, MA

Integrated numerical modeling of a landslide early warning system in a context of adaptation to future climatic pressures

NASA Astrophysics Data System (ADS)

Khabarov, Nikolay; Huggel, Christian; Obersteiner, Michael; Ramírez, Juan Manuel

2010-05-01

Mountain regions are typically characterized by rugged terrain which is susceptible to different types of landslides during high-intensity precipitation. Landslides account for billions of dollars of damage and many casualties, and are expected to increase in frequency in the future due to a projected increase of precipitation intensity. Early warning systems (EWS) are thought to be a primary tool for related disaster risk reduction and climate change adaptation to extreme climatic events and hydro-meteorological hazards, including landslides. An EWS for hazards such as landslides consist of different components, including environmental monitoring instruments (e.g. rainfall or flow sensors), physical or empirical process models to support decision-making (warnings, evacuation), data and voice communication, organization and logistics-related procedures, and population response. Considering this broad range, EWS are highly complex systems, and it is therefore difficult to understand the effect of the different components and changing conditions on the overall performance, ultimately being expressed as human lives saved or structural damage reduced. In this contribution we present a further development of our approach to assess a landslide EWS in an integral way, both at the system and component level. We utilize a numerical model using 6 hour rainfall data as basic input. A threshold function based on a rainfall-intensity/duration relation was applied as a decision criterion for evacuation. Damage to infrastructure and human lives was defined as a linear function of landslide magnitude, with the magnitude modelled using a power function of landslide frequency. Correct evacuation was assessed with a ‘true' reference rainfall dataset versus a dataset of artificially reduced quality imitating the observation system component. Performance of the EWS using these rainfall datasets was expressed in monetary terms (i.e. damage related to false and correct evacuation). We applied this model to a landslide EWS in Colombia that is currently being implemented within a disaster prevention project. We evaluated the EWS against rainfall data with artificially introduced error and computed with multiple model runs the probabilistic damage functions depending on rainfall error. Then we modified the original precipitation pattern to reflect possible climatic changes e.g. change in annual precipitation as well as change in precipitation intensity with annual values remaining constant. We let the EWS model adapt for changed conditions to function optimally. Our results show that for the same errors in rainfall measurements the system's performance degrades with expected changing climatic conditions. The obtained results suggest that EWS cannot internally adapt to climate change and require exogenous adaptive measures to avoid increase in overall damage. The model represents a first attempt to integrally simulate and evaluate EWS under future possible climatic pressures. Future work will concentrate on refining model components and spatially explicit climate scenarios.

Implications of Climate Mitigation for Future Agricultural Production

NASA Technical Reports Server (NTRS)

Mueller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Deryng, Delphine; Folberth, Christian; Pugh, Thomas A. M.; Schmid, Erwin

2015-01-01

Climate change is projected to negatively impact biophysical agricultural productivity in much of the world. Actions taken to reduce greenhouse gas emissions and mitigate future climate changes, are thus of central importance for agricultural production. Climate impacts are, however, not unidirectional; some crops in some regions (primarily higher latitudes) are projected to benefit, particularly if increased atmospheric carbon dioxide is assumed to strongly increase crop productivity at large spatial and temporal scales. Climate mitigation measures that are implemented by reducing atmospheric carbon dioxide concentrations lead to reductions both in the strength of climate change and in the benefits of carbon dioxide fertilization. Consequently, analysis of the effects of climate mitigation on agricultural productivity must address not only regions for which mitigation is likely to reduce or even reverse climate damages. There are also regions that are likely to see increased crop yields due to climate change, which may lose these added potentials under mitigation action. Comparing data from the most comprehensive archive of crop yield projections publicly available, we find that climate mitigation leads to overall benefits from avoided damages at the global scale and especially in many regions that are already at risk of food insecurity today. Ignoring controversial carbon dioxide fertilization effects on crop productivity, we find that for the median projection aggressive mitigation could eliminate approximately 81% of the negative impacts of climate change on biophysical agricultural productivity globally by the end of the century. In this case, the benefits of mitigation typically extend well into temperate regions, but vary by crop and underlying climate model projections. Should large benefits to crop yields from carbon dioxide fertilization be realized, the effects of mitigation become much more mixed, though still positive globally and beneficial in many food insecure countries.

Spatial Autocorrelation Can Generate Stronger Correlations between Range Size and Climatic Niches Than the Biological Signal - A Demonstration Using Bird and Mammal Range Maps.

PubMed

Boucher-Lalonde, Véronique; Currie, David J

2016-01-01

Species' geographic ranges could primarily be physiological tolerances drawn in space. Alternatively, geographic ranges could be only broadly constrained by physiological climatic tolerances: there could generally be much more proximate constraints on species' ranges (dispersal limitation, biotic interactions, etc.) such that species often occupy a small and unpredictable subset of tolerable climates. In the literature, species' climatic tolerances are typically estimated from the set of conditions observed within their geographic range. Using this method, studies have concluded that broader climatic niches permit larger ranges. Similarly, other studies have investigated the biological causes of incomplete range filling. But, when climatic constraints are measured directly from species' ranges, are correlations between species' range size and climate necessarily consistent with a causal link? We evaluated the extent to which variation in range size among 3277 bird and 1659 mammal species occurring in the Americas is statistically related to characteristics of species' realized climatic niches. We then compared how these relationships differed from the ones expected in the absence of a causal link. We used a null model that randomizes the predictor variables (climate), while retaining their broad spatial autocorrelation structure, thereby removing any causal relationship between range size and climate. We found that, although range size is strongly positively related to climatic niche breadth, range filling and, to a lesser extent, niche position in nature, the observed relationships are not always stronger than expected from spatial autocorrelation alone. Thus, we conclude that equally strong relationships between range size and climate would result from any processes causing ranges to be highly spatially autocorrelated.

Spatial Autocorrelation Can Generate Stronger Correlations between Range Size and Climatic Niches Than the Biological Signal — A Demonstration Using Bird and Mammal Range Maps

PubMed Central

Boucher-Lalonde, Véronique; Currie, David J.

2016-01-01

Species’ geographic ranges could primarily be physiological tolerances drawn in space. Alternatively, geographic ranges could be only broadly constrained by physiological climatic tolerances: there could generally be much more proximate constraints on species’ ranges (dispersal limitation, biotic interactions, etc.) such that species often occupy a small and unpredictable subset of tolerable climates. In the literature, species’ climatic tolerances are typically estimated from the set of conditions observed within their geographic range. Using this method, studies have concluded that broader climatic niches permit larger ranges. Similarly, other studies have investigated the biological causes of incomplete range filling. But, when climatic constraints are measured directly from species’ ranges, are correlations between species’ range size and climate necessarily consistent with a causal link? We evaluated the extent to which variation in range size among 3277 bird and 1659 mammal species occurring in the Americas is statistically related to characteristics of species’ realized climatic niches. We then compared how these relationships differed from the ones expected in the absence of a causal link. We used a null model that randomizes the predictor variables (climate), while retaining their broad spatial autocorrelation structure, thereby removing any causal relationship between range size and climate. We found that, although range size is strongly positively related to climatic niche breadth, range filling and, to a lesser extent, niche position in nature, the observed relationships are not always stronger than expected from spatial autocorrelation alone. Thus, we conclude that equally strong relationships between range size and climate would result from any processes causing ranges to be highly spatially autocorrelated. PMID:27855201

In ecoregions across western USA streamflow increases during post-wildfire recovery

NASA Astrophysics Data System (ADS)

Wine, Michael L.; Cadol, Daniel; Makhnin, Oleg

2018-01-01

Continued growth of the human population on Earth will increase pressure on already stressed terrestrial water resources required for drinking water, agriculture, and industry. This stress demands improved understanding of critical controls on water resource availability, particularly in water-limited regions. Mechanistic predictions of future water resource availability are needed because non-stationary conditions exist in the form of changing climatic conditions, land management paradigms, and ecological disturbance regimes. While historically ecological disturbances have been small and could be neglected relative to climatic effects, evidence is accumulating that ecological disturbances, particularly wildfire, can increase regional water availability. However, wildfire hydrologic impacts are typically estimated locally and at small spatial scales, via disparate measurement methods and analysis techniques, and outside the context of climate change projections. Consequently, the relative importance of climate change driven versus wildfire driven impacts on streamflow remains unknown across the western USA. Here we show that considering wildfire in modeling streamflow significantly improves model predictions. Mixed effects modeling attributed 2%-14% of long-term annual streamflow to wildfire effects. The importance of this wildfire-linked streamflow relative to predicted climate change-induced streamflow reductions ranged from 20%-370% of the streamflow decrease predicted to occur by 2050. The rate of post-wildfire vegetation recovery and the proportion of watershed area burned controlled the wildfire effect. Our results demonstrate that in large areas of the western USA affected by wildfire, regional predictions of future water availability are subject to greater structural uncertainty than previously thought. These results suggest that future streamflows may be underestimated in areas affected by increased prevalence of hydrologically relevant ecological disturbances such as wildfire.

Dynamics of aggregate stability and soil organic C distribution as affected by climatic aggressiveness: a mesocosm approach

NASA Astrophysics Data System (ADS)

Pellegrini, Sergio; Elio Agnelli, Alessandro; Costanza Andrenelli, Maria; Barbetti, Roberto; Castelli, Fabio; Costantini, Edoardo A. C.; Lagomarsino, Alessandra; Pasqui, Massimiliano; Tomozeiu, Rodica; Razzaghi, Somayyeh; Vignozzi, Nadia

2014-05-01

In the framework of a research project aimed at evaluating the adaptation scenarios of the Italian agriculture to the current climate change, a mesocosm experiment under controlled conditions was set up for studying the dynamics of soil aggregate stability and organic C in different size fractions. Three alluvial loamy soils (BOV - Typic Haplustalfs coarse-loamy; CAS - Typic Haplustalfs fine-loamy; MED - Typic Hapludalfs fine-loamy) along a climatic gradient (from dryer to moister pedoclimatic conditions) in the river Po valley (northern Italy), under crop rotation for animal husbandry from more than 40 years, were selected. The Ap horizons (0-30cm) were taken and placed in 9 climatic chambers under controlled temperature and rainfall. Each soil was subjected to three different climate scenarios in terms of erosivity index obtained by combining Modified Fournier and Bagnouls-Gaussen indexes: i) typical (TYP), the median year of each site related to the 1961-1990 reference period; ii) maximum aggressive year (MAX) observed in the same period, and iii) the simulated climate (SIM), obtained by projections of climate change precipitation and temperature for the period 2021-2050 as provided by the IPCC-A1B emission scenario. In the climatic chambers the year climate was reduced to six months. The soils were analyzed for particle size distribution, aggregate stability by wet and dry sieving, and organic C content at the beginning and at the end of the trial. The soils showed different behaviour in terms of aggregate stability and dynamics of organic C in the diverse size fractions. The soils significantly differed in terms of initial mean weight diameter (MWD) (CAS>MED>BOV). A general reduction of MWD in all sites was observed at the end of the experiment, with the increase of the smallest aggregate fractions (0.250-0.05 mm). In particular, BOV showed the maximum decrease of the aggregate stability and MED the lowest. C distribution in aggregate fractions significantly changed at the end of the trial, depending of soil types. In CAS and MED a decrease of C content was observed in fractions larger than 0.250 mm, while an accumulation occurred only in CAS microaggregates. BOV showed a singular pattern, with an increase of organic C in all fractions. In this site an improvement of aggregation, involving the coarser fractions, seems to have been favoured during the experiment. Overall, the imposed climate did not affect significantly these trends, except in CAS, where TYP and SIM climates showed an increase of macroaggregates and their C concentration. Soil pedoclimatic characteristics showed to be the main factors affecting C and aggregates dynamics in this mesocosm experiment.

Primary production sensitivity to phytoplankton light attenuation parameter increases with transient forcing

NASA Astrophysics Data System (ADS)

Kvale, Karin F.; Meissner, Katrin J.

2017-10-01

Treatment of the underwater light field in ocean biogeochemical models has been attracting increasing interest, with some models moving towards more complex parameterisations. We conduct a simple sensitivity study of a typical, highly simplified parameterisation. In our study, we vary the phytoplankton light attenuation parameter over a range constrained by data during both pre-industrial equilibrated and future climate scenario RCP8.5. In equilibrium, lower light attenuation parameters (weaker self-shading) shift net primary production (NPP) towards the high latitudes, while higher values of light attenuation (stronger shelf-shading) shift NPP towards the low latitudes. Climate forcing magnifies this relationship through changes in the distribution of nutrients both within and between ocean regions. Where and how NPP responds to climate forcing can determine the magnitude and sign of global NPP trends in this high CO2 future scenario. Ocean oxygen is particularly sensitive to parameter choice. Under higher CO2 concentrations, two simulations establish a strong biogeochemical feedback between the Southern Ocean and low-latitude Pacific that highlights the potential for regional teleconnection. Our simulations serve as a reminder that shifts in fundamental properties (e.g. light attenuation by phytoplankton) over deep time have the potential to alter global biogeochemistry.

Tropical and Subtropical Cloud Transitions in Weather and Climate Prediction Models: The GCSS/WGNE Pacific Cross-Section Intercomparison (GPCI)

NASA Technical Reports Server (NTRS)

Teixeira, J.; Cardoso, S.; Bonazzola, M.; Cole, J.; DeGenio, A.; DeMott, C.; Franklin, C.; Hannay, C.; Jakob, C.; Jiao, Y.;

Comparing estimates of climate change impacts from process-based and statistical crop models

NASA Astrophysics Data System (ADS)

Lobell, David B.; Asseng, Senthold

2017-01-01

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

Coarse-grained component concurrency in Earth system modeling: parallelizing atmospheric radiative transfer in the GFDL AM3 model using the Flexible Modeling System coupling framework

NASA Astrophysics Data System (ADS)

Balaji, V.; Benson, Rusty; Wyman, Bruce; Held, Isaac

2016-10-01

Climate models represent a large variety of processes on a variety of timescales and space scales, a canonical example of multi-physics multi-scale modeling. Current hardware trends, such as Graphical Processing Units (GPUs) and Many Integrated Core (MIC) chips, are based on, at best, marginal increases in clock speed, coupled with vast increases in concurrency, particularly at the fine grain. Multi-physics codes face particular challenges in achieving fine-grained concurrency, as different physics and dynamics components have different computational profiles, and universal solutions are hard to come by. We propose here one approach for multi-physics codes. These codes are typically structured as components interacting via software frameworks. The component structure of a typical Earth system model consists of a hierarchical and recursive tree of components, each representing a different climate process or dynamical system. This recursive structure generally encompasses a modest level of concurrency at the highest level (e.g., atmosphere and ocean on different processor sets) with serial organization underneath. We propose to extend concurrency much further by running more and more lower- and higher-level components in parallel with each other. Each component can further be parallelized on the fine grain, potentially offering a major increase in the scalability of Earth system models. We present here first results from this approach, called coarse-grained component concurrency, or CCC. Within the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS), the atmospheric radiative transfer component has been configured to run in parallel with a composite component consisting of every other atmospheric component, including the atmospheric dynamics and all other atmospheric physics components. We will explore the algorithmic challenges involved in such an approach, and present results from such simulations. Plans to achieve even greater levels of coarse-grained concurrency by extending this approach within other components, such as the ocean, will be discussed.

Towards an integrated forecasting system for fisheries on habitat-bound stocks

NASA Astrophysics Data System (ADS)

Christensen, A.; Butenschön, M.; Gürkan, Z.; Allen, I. J.

2013-03-01

First results of a coupled modelling and forecasting system for fisheries on habitat-bound stocks are being presented. The system consists currently of three mathematically, fundamentally different model subsystems coupled offline: POLCOMS providing the physical environment implemented in the domain of the north-west European shelf, the SPAM model which describes sandeel stocks in the North Sea, and the third component, the SLAM model, which connects POLCOMS and SPAM by computing the physical-biological interaction. Our major experience by the coupling model subsystems is that well-defined and generic model interfaces are very important for a successful and extendable coupled model framework. The integrated approach, simulating ecosystem dynamics from physics to fish, allows for analysis of the pathways in the ecosystem to investigate the propagation of changes in the ocean climate and to quantify the impacts on the higher trophic level, in this case the sandeel population, demonstrated here on the basis of hindcast data. The coupled forecasting system is tested for some typical scientific questions appearing in spatial fish stock management and marine spatial planning, including determination of local and basin-scale maximum sustainable yield, stock connectivity and source/sink structure. Our presented simulations indicate that sandeel stocks are currently exploited close to the maximum sustainable yield, even though periodic overfishing seems to have occurred, but large uncertainty is associated with determining stock maximum sustainable yield due to stock inherent dynamics and climatic variability. Our statistical ensemble simulations indicates that the predictive horizon set by climate interannual variability is 2-6 yr, after which only an asymptotic probability distribution of stock properties, like biomass, are predictable.

Glacial-eustatic/climatic model for Upper Pennsylvanian marine and nonmarine cyclothems in the Appalachian basin

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

Heckel, P.H.

1992-01-01

Only glacial-eustatic sea-level fluctuations can account for all the characteristics of Upper Pennsylvanian marine cyclothems in the Midcontinent. Because this control is global, it must have affected deposition during this time everywhere. In the Appalachian basin widespread well developed paleosols represent long-term sea-level lowstand. During Conemaugh marine incursions, rising sea level ponded fresh-water influx to form peat swamps that migrated landward ahead of transgression and produced early transgressive coals. Marine highstand deposits commonly are conodont-rich limestones, typically skeletal packstone with glaucony and phosphorite. Regression resulted in progradation of detrital shorelines with local delta cycles, followed eventually by more paleosol formationmore » and local erosional incision that removed older sediments including the marine units in places. Fluvial sands filled many of these channels. During Monongahela deposition when marine incursions no longer entered the Appalachian basin, the climatic fluctuations recognized by Cecil can reasonably be related to sea-level fluctuations nearby, but with shifts in climatic significance of gross lithotopes. Coal swamps would more likely have formed at maximum marine highstand when the nearby sea would have provided both high base level and an abundant source of rainfall. Nonmarine limestones would more likely have formed at maximum lowstand when the sea was most distant and the climate driest. The intervening detrital deposits between the coals and limestones formed under intermediate seasonal rainfall regimes during both marine transgression and regression farther west in the Midcontinent. Conemaugh and Allegheny coals without overlying marine units probably also represent mainly marine highstand elsewhere, and nonmarine limestones of these ages typically are associated with lowstand paleosols.« less

Quantifying nonergodicity in nonautonomous dissipative dynamical systems: An application to climate change

NASA Astrophysics Data System (ADS)

Drótos, Gábor; Bódai, Tamás; Tél, Tamás

2016-08-01

In nonautonomous dynamical systems, like in climate dynamics, an ensemble of trajectories initiated in the remote past defines a unique probability distribution, the natural measure of a snapshot attractor, for any instant of time, but this distribution typically changes in time. In cases with an aperiodic driving, temporal averages taken along a single trajectory would differ from the corresponding ensemble averages even in the infinite-time limit: ergodicity does not hold. It is worth considering this difference, which we call the nonergodic mismatch, by taking time windows of finite length for temporal averaging. We point out that the probability distribution of the nonergodic mismatch is qualitatively different in ergodic and nonergodic cases: its average is zero and typically nonzero, respectively. A main conclusion is that the difference of the average from zero, which we call the bias, is a useful measure of nonergodicity, for any window length. In contrast, the standard deviation of the nonergodic mismatch, which characterizes the spread between different realizations, exhibits a power-law decrease with increasing window length in both ergodic and nonergodic cases, and this implies that temporal and ensemble averages differ in dynamical systems with finite window lengths. It is the average modulus of the nonergodic mismatch, which we call the ergodicity deficit, that represents the expected deviation from fulfilling the equality of temporal and ensemble averages. As an important finding, we demonstrate that the ergodicity deficit cannot be reduced arbitrarily in nonergodic systems. We illustrate via a conceptual climate model that the nonergodic framework may be useful in Earth system dynamics, within which we propose the measure of nonergodicity, i.e., the bias, as an order-parameter-like quantifier of climate change.

Spatial variation in the climatic predictors of species compositional turnover and endemism.

PubMed

Di Virgilio, Giovanni; Laffan, Shawn W; Ebach, Malte C; Chapple, David G

2014-08-01

Previous research focusing on broad-scale or geographically invariant species-environment dependencies suggest that temperature-related variables explain more of the variation in reptile distributions than precipitation. However, species-environment relationships may exhibit considerable spatial variation contingent upon the geographic nuances that vary between locations. Broad-scale, geographically invariant analyses may mask this local variation and their findings may not generalize to different locations at local scales. We assess how reptile-climatic relationships change with varying spatial scale, location, and direction. Since the spatial distributions of diversity and endemism hotspots differ for other species groups, we also assess whether reptile species turnover and endemism hotspots are influenced differently by climatic predictors. Using New Zealand reptiles as an example, the variation in species turnover, endemism and turnover in climatic variables was measured using directional moving window analyses, rotated through 360°. Correlations between the species turnover, endemism and climatic turnover results generated by each rotation of the moving window were analysed using multivariate generalized linear models applied at national, regional, and local scales. At national-scale, temperature turnover consistently exhibited the greatest influence on species turnover and endemism, but model predictive capacity was low (typically r (2) = 0.05, P < 0.001). At regional scales the relative influence of temperature and precipitation turnover varied between regions, although model predictive capacity was also generally low. Climatic turnover was considerably more predictive of species turnover and endemism at local scales (e.g., r (2) = 0.65, P < 0.001). While temperature turnover had the greatest effect in one locale (the northern North Island), there was substantial variation in the relative influence of temperature and precipitation predictors in the remaining four locales. Species turnover and endemism hotspots often occurred in different locations. Climatic predictors had a smaller influence on endemism. Our results caution against assuming that variability in temperature will always be most predictive of reptile biodiversity across different spatial scales, locations and directions. The influence of climatic turnover on the species turnover and endemism of other taxa may exhibit similar patterns of spatial variation. Such intricate variation might be discerned more readily if studies at broad scales are complemented by geographically variant, local-scale analyses.

Importance of Sea Ice for Validating Global Climate Models

NASA Technical Reports Server (NTRS)

Geiger, Cathleen A.

1997-01-01

Reproduction of current day large-scale physical features and processes is a critical test of global climate model performance. Without this benchmark, prognoses of future climate conditions are at best speculation. A fundamental question relevant to this issue is, which processes and observations are both robust and sensitive enough to be used for model validation and furthermore are they also indicators of the problem at hand? In the case of global climate, one of the problems at hand is to distinguish between anthropogenic and naturally occuring climate responses. The polar regions provide an excellent testing ground to examine this problem because few humans make their livelihood there, such that anthropogenic influences in the polar regions usually spawn from global redistribution of a source originating elsewhere. Concomitantly, polar regions are one of the few places where responses to climate are non-anthropogenic. Thus, if an anthropogenic effect has reached the polar regions (e.g. the case of upper atmospheric ozone sensitivity to CFCs), it has most likely had an impact globally but is more difficult to sort out from local effects in areas where anthropogenic activity is high. Within this context, sea ice has served as both a monitoring platform and sensitivity parameter of polar climate response since the time of Fridtjof Nansen. Sea ice resides in the polar regions at the air-sea interface such that changes in either the global atmospheric or oceanic circulation set up complex non-linear responses in sea ice which are uniquely determined. Sea ice currently covers a maximum of about 7% of the earth's surface but was completely absent during the Jurassic Period and far more extensive during the various ice ages. It is also geophysically very thin (typically <10 m in Arctic, <3 m in Antarctic) compared to the troposphere (roughly 10 km) and deep ocean (roughly 3 to 4 km). Because of these unique conditions, polar researchers regard sea ice as one of the more important features to monitor in terms of heat, mass, and momentum transfer between the air and sea and furthermore, the impact of such responses to global climate.

6. Detail view north of typical window and loading door ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

6. Detail view north of typical window and loading door at east end of south elevation. - Natick Research & Development Laboratories, Climatic Chambers Building, U.S. Army Natick Research, Development & Engineering Center (NRDEC), Natick, Middlesex County, MA

Assessment of the Breakup of the Antarctic Polar Vortex in Two New Chemistry-Climate Models

NASA Technical Reports Server (NTRS)

Hurwitz, M. M.; Newman, P. A.; Oman, L. D.; Li, F.; Morgenstern, O.; Braesicke, P.; Pyle, J. A.

2010-01-01

Successful simulation of the breakup of the Antarctic polar vortex depends on the representation of tropospheric stationary waves at Southern Hemisphere middle latitudes. This paper assesses the vortex breakup in two new chemistry-climate models (CCMs). The stratospheric version of the UK Chemistry and Aerosols model is able to reproduce the observed timing of the vortex breakup. Version 2 of the Goddard Earth Observing System (GEOS V2) model is typical of CCMs in that the Antarctic polar vortex breaks up too late; at 10 hPa, the mean transition to easterlies at 60 S is delayed by 12-13 days as compared with the ERA-40 and National Centers for Environmental Prediction reanalyses. The two models' skill in simulating planetary wave driving during the October-November period accounts for differences in their simulation of the vortex breakup, with GEOS V2 unable to simulate the magnitude and tilt of geopotential height anomalies in the troposphere and thus underestimating the wave driving. In the GEOS V2 CCM the delayed breakup of the Antarctic vortex biases polar temperatures and trace gas distributions in the upper stratosphere in November and December.

Simulations and Evaluation of Mesoscale Convective Systems in a Multi-scale Modeling Framework (MMF)

NASA Astrophysics Data System (ADS)

Chern, J. D.; Tao, W. K.

2017-12-01

It is well known that the mesoscale convective systems (MCS) produce more than 50% of rainfall in most tropical regions and play important roles in regional and global water cycles. Simulation of MCSs in global and climate models is a very challenging problem. Typical MCSs have horizontal scale of a few hundred kilometers. Models with a domain of several hundred kilometers and fine enough resolution to properly simulate individual clouds are required to realistically simulate MCSs. The multiscale modeling framework (MMF), which replaces traditional cloud parameterizations with cloud-resolving models (CRMs) within a host atmospheric general circulation model (GCM), has shown some capabilities of simulating organized MCS-like storm signals and propagations. However, its embedded CRMs typically have small domain (less than 128 km) and coarse resolution ( 4 km) that cannot realistically simulate MCSs and individual clouds. In this study, a series of simulations were performed using the Goddard MMF. The impacts of the domain size and model grid resolution of the embedded CRMs on simulating MCSs are examined. The changes of cloud structure, occurrence, and properties such as cloud types, updraft and downdraft, latent heating profile, and cold pool strength in the embedded CRMs are examined in details. The simulated MCS characteristics are evaluated against satellite measurements using the Goddard Satellite Data Simulator Unit. The results indicate that embedded CRMs with large domain and fine resolution tend to produce better simulations compared to those simulations with typical MMF configuration (128 km domain size and 4 km model grid spacing).

The economics (or lack thereof) of aerosol geoengineering

NASA Astrophysics Data System (ADS)

Goes, M.; Keller, K.; Tuana, N.

2009-04-01

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 the deployment of a different approach: to geoengineer climate by injecting aerosol precursors into the stratosphere. Published economic studies typically suggest that substituting aerosol geoengineering for abatement of carbon dioxide emissions results in large net monetary benefits. However, these studies neglect the risks of aerosol geoengineering due to (i) the potential for future geoengineering failures and (ii) the negative impacts associated with the aerosol forcing. 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 forcing. The simplicity of the model provides the advantages of parsimony and transparency, but it also imposes severe caveats on the interpretation of the results. For example, the analysis is based on a globally aggregated model and is hence silent on the question of intragenerational distribution of costs and benefits. In addition, the analysis neglects the effects of endogenous learning about the climate system. We show that the risks associated with a future geoengineering failure and negative impacts of aerosol forcings can cause geoenginering strategies to fail an economic cost-benefit test. One key to this finding is that a geoengineering failure would lead to dramatic and abrupt climatic changes. The monetary damages due to this failure can dominate the cost-benefit analysis because the monetary damages of climate change are expected to increase with the rate of change. Substituting aerosol geoengineering for greenhouse gas emission abatement might fail not only an economic cost-benefit test but also an ethical test of distributional justice. Substituting aerosol geoengineering for greenhouse gas emissions abatements constitutes a conscious risk transfer to future generations. Intergenerational justice demands distributional justice, namely that present generations may not create benefits for themselves in exchange for burdens on future generations. We use the economic model to quantify this risk transfer to better inform the judgment of whether substituting aerosol geoengineering for carbon dioxide emission abatement fails this ethical test.

Selection of meteorological conditions to apply in an Ecotron facility

NASA Astrophysics Data System (ADS)

Leemans, Vincent; De Cruz, Lesley; Dumont, Benjamin; Hamdi, Rafiq; Delaplace, Pierre; Heinesh, Bernard; Garré, Sarah; Verheggen, François; Theodorakopoulos, Nicolas; Longdoz, Bernard

2017-04-01

This presentation aims to propose a generic method to produce meteorological input data that is useful for climate research infrastructures such as an Ecotron, where researchers will face the need to generate representative actual or future climatic conditions. Depending on the experimental objectives and the research purposes, typical conditions or more extreme values such as dry or wet climatic scenarios might be requested. Four variables were considered here, the near-surface air temperature, the near-surface relative humidity, the cloud cover and precipitation. The meteorological datasets, among which a specific meteorological year can be picked up, are produced by the ALARO-0 model from the RMIB (Royal Meteorological Institute of Belgium). Two future climate scenarios (RCP 4.5 and 8.5) and two time periods (2041-2070 and 2071-2100) were used as well as a historical run of the model (1981-2010) which is used as a reference. When the data from a historical run were compared to the observed historical data, biases were noticed. A linear correction was proposed for all the variables except for precipitation, for which a non-linear correction (using a power function) was chosen to maintain a zero-precipitation occurrences. These transformations were able to remove most of the differences between the observed and historical run of the model for the means and for the standard deviations. For the relative humidity, because of non-linearities, only one half of the average bias was corrected and a different path might have to be chosen. For the selection of a meteorological year, a position and a dispersion parameter have been proposed to characterise each meteorological year for each variable. For precipitation, a third parameter quantifying the importance of dry and wet periods has been defined. In order to select a specific climate, for each of these nine parameters the experimenter should provide a percentile and a weight to prioritize the importance of each variable in the process of a global climate selection. The proposed algorithm computed the weighted distance for each year between the parameters and the point representing the position of the percentile in the nine-dimensional space. The five closest values were then selected and represented in different graphs. The proposed method is able to provide a decision aid in the selection of the meteorological conditions to be generated within an Ecotron. However, with a limited number of years available in each case (thirty years for each RCP and each time period), there is no perfect match and the ultimate trade-off will be the responsibility of the researcher. For typical years, close to the median, the relative frequency is higher and the trade-off is more easy than for more extreme years where the relative frequency is low.

Association between climate factors and diarrhoea in a Mekong Delta area

NASA Astrophysics Data System (ADS)

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

2015-09-01

The Mekong Delta is vulnerable to changes in climate and hydrological events which alter environmental conditions, resulting in increased risk of waterborne diseases. Research exploring the association between climate factors and diarrhoea, the most frequent waterborne disease in Mekong Delta region, is sparse. This study evaluated the climate-diarrhoea association in Can Tho city, a typical Mekong Delta area in Vietnam. Climate data (temperature, relative humidity, and rainfall) were obtained from the Southern Regional Hydro-Meteorological Centre, and weekly counts of diarrhoea visits were obtained from Can Tho Preventive Medicine Centre from 2004 to 2011. Analysis of climate and health variables was carried out using spline function to adjust for seasonal and long-term trends of variables. A distributed lag model was used to investigate possible delayed effects of climate variables on diarrhoea (considering 0-4 week lag periods), then the multivariate Poisson regression was used to examine any potential association between climate factors and diarrhoea. The results indicated that the diarrhoea incidence peaked within the period August-October annually. Significant positive associations were found between increased diarrhoea and high temperature at 4 weeks prior to the date of hospital visits (IRR = 1.07; 95 % CI = 1.04-1.08), high relative humidity (IRR = 1.13; 95 % CI = 1.12-1.15) and high (>90th percentile) cumulative rainfall (IRR = 1.05; 95 % CI = 1.05-1.08). The association between climate factors and diarrhoea was stronger in rural than urban areas. These findings in the context of the projected changes of climate conditions suggest that climate change will have important implications for residential health in Mekong Delta region.

Comparison of Stream Temperature Modeling Approaches: The Case of a High Alpine Watershed in the Context of Climate Change

NASA Astrophysics Data System (ADS)

Gallice, A.

2015-12-01

Stream temperature controls important aspects of the riverine habitat, such as the rate of spawning or death of many fish species, or the concentration of numerous dissolved substances. In the current context of accelerating climate change, the future evolution of stream temperature is regarded as uncertain, particularly in the Alps. This uncertainty fostered the development of many prediction models, which are usually classified in two categories: mechanistic models and statistical models. Based on the numerical resolution of physical conservation laws, mechanistic models are generally considered to provide more reliable long-term estimates than regression models. However, despite their physical basis, these models are observed to differ quite significantly in some aspects of their implementation, notably (1) the routing of water in the river channel and (2) the estimation of the temperature of groundwater discharging into the stream. For each one of these two aspects, we considered several of the standard modeling approaches reported in the literature and implemented them in a new modular framework. The latter is based on the spatially-distributed snow model Alpine3D, which is essentially used in the framework to compute the amount of water infiltrating in the upper soil layer. Starting from there, different methods can be selected for the computation of the water and energy fluxes in the hillslopes and in the river network. We relied on this framework to compare the various methodologies for river channel routing and groundwater temperature modeling. We notably assessed the impact of each these approaches on the long-term stream temperature predictions of the model under a typical climate change scenario. The case study was conducted over a high Alpine catchment in Switzerland, whose hydrological and thermal regimes are expected to be markedly affected by climate change. The results show that the various modeling approaches lead to significant differences in the model predictions, and that these differences may be larger than the uncertainties in future air temperature. It is also shown that the temperature of groundwater discharging into the stream has a marked impact on the modeled stream temperature at the catchment outlet.

Vegetation, climatic and floral changes at the Cretaceous-Tertiary boundary

USGS Publications Warehouse

Wolfe, J.A.; Upchurch, G.R.

1986-01-01

he western interior of North America has the only known non-marine sections that contain the iridium-rich clay interpreted as the Cretaceous-Tertiary (K-T) boundary1-7. Because vegetation and climate can be directly inferred from physiognomy of leaves8-15 and because leaf species typically represent low taxonomic categories, studies of leaf floras in these sections provide data on the effects of a terminal Cretaceous event on the land flora, vegetation and climate. A previous study based on detailed sampling of leaves and their dispersed cuticle16 in the Raton Basin provides a framework for interpretation of other leaf sequences over 20 degrees of latitude. We conclude that at the boundary there were: (1) High levels of extinction in the south and low levels in the north; (2) major ecological disruption followed by long-term vegetational changes that mimicked normal ecological succession; (3) a major increase in precipitation; and (4) a brief, low-temperature excursion, which supports models of an 'impact winter'. ?? 1986 Nature Publishing Group.

Retrieval of effective cloud field parameters from radiometric data

NASA Astrophysics Data System (ADS)

Paulescu, Marius; Badescu, Viorel; Brabec, Marek

2017-06-01

Clouds play a key role in establishing the Earth's climate. Real cloud fields are very different and very complex in both morphological and microphysical senses. Consequently, the numerical description of the cloud field is a critical task for accurate climate modeling. This study explores the feasibility of retrieving the effective cloud field parameters (namely the cloud aspect ratio and cloud factor) from systematic radiometric measurements at high frequency (measurement is taken every 15 s). Two different procedures are proposed, evaluated, and discussed with respect to both physical and numerical restrictions. None of the procedures is classified as best; therefore, the specific advantages and weaknesses are discussed. It is shown that the relationship between the cloud shade and point cloudiness computed using the estimated cloud field parameters recovers the typical relationship derived from measurements.

Solar absorption by elemental and brown carbon determined from spectral observations.

PubMed

Bahadur, Ranjit; Praveen, Puppala S; Xu, Yangyang; Ramanathan, V

2012-10-23

Black carbon (BC) is functionally defined as the absorbing component of atmospheric total carbonaceous aerosols (TC) and is typically dominated by soot-like elemental carbon (EC). However, organic carbon (OC) has also been shown to absorb strongly at visible to UV wavelengths and the absorbing organics are referred to as brown carbon (BrC), which is typically not represented in climate models. We propose an observationally based analytical method for rigorously partitioning measured absorption aerosol optical depths (AAOD) and single scattering albedo (SSA) among EC and BrC, using multiwavelength measurements of total (EC, OC, and dust) absorption. EC is found to be strongly absorbing (SSA of 0.38) whereas the BrC SSA varies globally between 0.77 and 0.85. The method is applied to the California region. We find TC (EC + BrC) contributes 81% of the total absorption at 675 nm and 84% at 440 nm. The BrC absorption at 440 nm is about 40% of the EC, whereas at 675 nm it is less than 10% of EC. We find an enhanced absorption due to OC in the summer months and in southern California (related to forest fires and secondary OC). The fractions and trends are broadly consistent with aerosol chemical-transport models as well as with regional emission inventories, implying that we have obtained a representative estimate for BrC absorption. The results demonstrate that current climate models that treat OC as nonabsorbing are underestimating the total warming effect of carbonaceous aerosols by neglecting part of the atmospheric heating, particularly over biomass-burning regions that emit BrC.

Potential impacts of climate change on flow regime and fish habitat in mountain rivers of the south-western Balkans.

PubMed

Papadaki, Christina; Soulis, Konstantinos; Muñoz-Mas, Rafael; Martinez-Capel, Francisco; Zogaris, Stamatis; Ntoanidis, Lazaros; Dimitriou, Elias

2016-01-01

The climate change in the Mediterranean area is expected to have significant impacts on the aquatic ecosystems and particular in the mountain rivers and streams that often host important species such as the Salmo farioides, Karaman 1938. These impacts will most possibly affect the habitat availability for various aquatic species resulting to an essential alteration of the water requirements, either for dams or other water abstractions, in order to maintain the essential levels of ecological flow for the rivers. The main scope of this study was to assess potential climate change impacts on the hydrological patterns and typical biota for a south-western Balkan mountain river, the Acheloos. The altered flow regimes under different emission scenarios of the Intergovernmental Panel on Climate Change (IPCC) were estimated using a hydrological model and based on regional climate simulations over the study area. The Indicators of Hydrologic Alteration (IHA) methodology was then used to assess the potential streamflow alterations in the studied river due to predicted climate change conditions. A fish habitat simulation method integrating univariate habitat suitability curves and hydraulic modeling techniques were used to assess the impacts on the relationships between the aquatic biota and hydrological status utilizing a sentinel species, the West Balkan trout. The most prominent effects of the climate change scenarios depict severe flow reductions that are likely to occur especially during the summer flows, changing the duration and depressing the magnitude of the natural low flow conditions. Weighted Usable Area-flow curves indicated the limitation of suitable habitat for the native trout. Finally, this preliminary application highlighted the potential of science-based hydrological and habitat simulation approaches that are relevant to both biological quality elements (fish) and current EU Water policy to serve as efficient tools for the estimation of possible climate change impacts on the south-western Balkan river ecosystems. Copyright © 2015 Elsevier B.V. All rights reserved.

Large uncertainty in carbon uptake potential of land-based climate-change mitigation efforts.

PubMed

Krause, Andreas; Pugh, Thomas A M; Bayer, Anita D; Li, Wei; Leung, Felix; Bondeau, Alberte; Doelman, Jonathan C; Humpenöder, Florian; Anthoni, Peter; Bodirsky, Benjamin L; Ciais, Philippe; Müller, Christoph; Murray-Tortarolo, Guillermo; Olin, Stefan; Popp, Alexander; Sitch, Stephen; Stehfest, Elke; Arneth, Almut

2018-07-01

Most climate mitigation scenarios involve negative emissions, especially those that aim to limit global temperature increase to 2°C or less. However, the carbon uptake potential in land-based climate change mitigation efforts is highly uncertain. Here, we address this uncertainty by using two land-based mitigation scenarios from two land-use models (IMAGE and MAgPIE) as input to four dynamic global vegetation models (DGVMs; LPJ-GUESS, ORCHIDEE, JULES, LPJmL). Each of the four combinations of land-use models and mitigation scenarios aimed for a cumulative carbon uptake of ~130 GtC by the end of the century, achieved either via the cultivation of bioenergy crops combined with carbon capture and storage (BECCS) or avoided deforestation and afforestation (ADAFF). Results suggest large uncertainty in simulated future land demand and carbon uptake rates, depending on the assumptions related to land use and land management in the models. Total cumulative carbon uptake in the DGVMs is highly variable across mitigation scenarios, ranging between 19 and 130 GtC by year 2099. Only one out of the 16 combinations of mitigation scenarios and DGVMs achieves an equivalent or higher carbon uptake than achieved in the land-use models. The large differences in carbon uptake between the DGVMs and their discrepancy against the carbon uptake in IMAGE and MAgPIE are mainly due to different model assumptions regarding bioenergy crop yields and due to the simulation of soil carbon response to land-use change. Differences between land-use models and DGVMs regarding forest biomass and the rate of forest regrowth also have an impact, albeit smaller, on the results. Given the low confidence in simulated carbon uptake for a given land-based mitigation scenario, and that negative emissions simulated by the DGVMs are typically lower than assumed in scenarios consistent with the 2°C target, relying on negative emissions to mitigate climate change is a highly uncertain strategy. © 2018 John Wiley & Sons Ltd.

An Object-Based Approach to Evaluation of Climate Variability Projections and Predictions

NASA Astrophysics Data System (ADS)

Ammann, C. M.; Brown, B.; Kalb, C. P.; Bullock, R.

2017-12-01

Evaluations of the performance of earth system model predictions and projections are of critical importance to enhance usefulness of these products. Such evaluations need to address specific concerns depending on the system and decisions of interest; hence, evaluation tools must be tailored to inform about specific issues. Traditional approaches that summarize grid-based comparisons of analyses and models, or between current and future climate, often do not reveal important information about the models' performance (e.g., spatial or temporal displacements; the reason behind a poor score) and are unable to accommodate these specific information needs. For example, summary statistics such as the correlation coefficient or the mean-squared error provide minimal information to developers, users, and decision makers regarding what is "right" and "wrong" with a model. New spatial and temporal-spatial object-based tools from the field of weather forecast verification (where comparisons typically focus on much finer temporal and spatial scales) have been adapted to more completely answer some of the important earth system model evaluation questions. In particular, the Method for Object-based Diagnostic Evaluation (MODE) tool and its temporal (three-dimensional) extension (MODE-TD) have been adapted for these evaluations. More specifically, these tools can be used to address spatial and temporal displacements in projections of El Nino-related precipitation and/or temperature anomalies, ITCZ-associated precipitation areas, atmospheric rivers, seasonal sea-ice extent, and other features of interest. Examples of several applications of these tools in a climate context will be presented, using output of the CESM large ensemble. In general, these tools provide diagnostic information about model performance - accounting for spatial, temporal, and intensity differences - that cannot be achieved using traditional (scalar) model comparison approaches. Thus, they can provide more meaningful information that can be used in decision-making and planning. Future extensions and applications of these tools in a climate context will be considered.

Seasonal and spatial variation in broadleaf forest model parameters

NASA Astrophysics Data System (ADS)

Groenendijk, M.; van der Molen, M. K.; Dolman, A. J.

2009-04-01

Process based, coupled ecosystem carbon, energy and water cycle models are used with the ultimate goal to project the effect of future climate change on the terrestrial carbon cycle. A typical dilemma in such exercises is how much detail the model must be given to describe the observations reasonably realistic while also be general. We use a simple vegetation model (5PM) with five model parameters to study the variability of the parameters. These parameters are derived from the observed carbon and water fluxes from the FLUXNET database. For 15 broadleaf forests the model parameters were derived for different time resolutions. It appears that in general for all forests, the correlation coefficient between observed and simulated carbon and water fluxes improves with a higher parameter time resolution. The quality of the simulations is thus always better when a higher time resolution is used. These results show that annual parameters are not capable of properly describing weather effects on ecosystem fluxes, and that two day time resolution yields the best results. A first indication of the climate constraints can be found by the seasonal variation of the covariance between Jm, which describes the maximum electron transport for photosynthesis, and climate variables. A general seasonality we found is that during winter the covariance with all climate variables is zero. Jm increases rapidly after initial spring warming, resulting in a large covariance with air temperature and global radiation. During summer Jm is less variable, but co-varies negatively with air temperature and vapour pressure deficit and positively with soil water content. A temperature response appears during spring and autumn for broadleaf forests. This shows that an annual model parameter cannot be representative for the entire year. And relations with mean annual temperature are not possible. During summer the photosynthesis parameters are constrained by water availability, soil water content and vapour pressure deficit.

Are We Telling Decision-makers the Wrong Things - and with Too Much Confidence?

NASA Astrophysics Data System (ADS)

Arnold, J.; Nowak, K. C.; Vano, J. A.; Newman, A. J.; Mizukami, N.; Mendoza, P. A.; Nijssen, B.; Wood, A.; Gutmann, E. D.; Clark, M. P.; Rasmussen, R.

2016-12-01

Water-resource management relies on decision-making over a wide range of space-time scales, nearly none of which maps cleanly onto the scales of current hydroclimatic scenarios of anthropogenic change. Myriad choices are made during vulnerability and impact assessments to quantify the changed-climate sensitivities of models used in that decision-making, including choices of hydrologic models, parameters, and parameterizations; their input forcings determined with various climate downscaling approaches; selected GCMs and output variables to be downscaled; and the forcing emissions scenarios, to name a few. Choosing alternative methods for producing gridded meteorological fields, for examples, can produce very different effects on the projected hydrologic outcomes they drive, with uncertainties across those methods revealed to be as large or larger than the climate change signal itself in some cases. Additionally, many popular climate downscaling methods simply rescale GCM precipitation, producing hydroclimatic projections with too much drizzle, incorrect representations of extreme events, and improper spatial scaling of variables crucial to water-resource vulnerability assessments and, importantly, the decisions they seek to inform. Real-world water-resource vulnerability and impacts assessments can be highly time-sensitive and resource limited, though, so they typically do not confront or even fully represent uncertainties associated with all choices. That deficiency results in assessments built on only partially revealed uncertainties which can misrepresent significant sensitivities and impacts in the final assessments of climate threats and hydrologic vulnerabilities. This talk will describe recent work by the U.S. Army Corps of Engineers, Bureau of Reclamation, University of Washington, and National Center for Atmospheric Research to develop and test methods to characterize more fully the uncertainties in the modeling chain for real-world uses. Examples will illustrate new implementations for communicating that fuller characterization in the ways most useful to inform water-resource management across multiple space-time scales under climate-changed futures.

A Variable-Instar Climate-Driven Individual Beetle-Based Phenology Model for the Invasive Asian Longhorned Beetle (Coleoptera: Cerambycidae).

PubMed

Trotter, R Talbot; Keena, Melody A

2016-12-01

Efforts to manage and eradicate invasive species can benefit from an improved understanding of the physiology, biology, and behavior of the target species, and ongoing efforts to eradicate the Asian longhorned beetle (Anoplophora glabripennis Motschulsky) highlight the roles this information may play. Here, we present a climate-driven phenology model for A. glabripennis that provides simulated life-tables for populations of individual beetles under variable climatic conditions that takes into account the variable number of instars beetles may undergo as larvae. Phenology parameters in the model are based on a synthesis of published data and studies of A. glabripennis, and the model output was evaluated using a laboratory-reared population maintained under varying temperatures mimicking those typical of Central Park in New York City. The model was stable under variations in population size, simulation length, and the Julian dates used to initiate individual beetles within the population. Comparison of model results with previously published field-based phenology studies in native and invasive populations indicates both this new phenology model, and the previously published heating-degree-day model show good agreement in the prediction of the beginning of the flight season for adults. However, the phenology model described here avoids underpredicting the cumulative emergence of adults through the season, in addition to providing tables of life stages and estimations of voltinism for local populations. This information can play a key role in evaluating risk by predicting the potential for population growth, and may facilitate the optimization of management and eradication efforts. Published by Oxford University Press on behalf of Entomological Society of America 2016. This work is written by US Government employees and is in the public domain in the US.

Weighted Iterative Bayesian Compressive Sensing (WIBCS) for High Dimensional Polynomial Surrogate Construction

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Debusschere, B.; Najm, H. N.; Thornton, P. E.

2016-12-01

Surrogate construction has become a routine procedure when facing computationally intensive studies requiring multiple evaluations of complex models. In particular, surrogate models, otherwise called emulators or response surfaces, replace complex models in uncertainty quantification (UQ) studies, including uncertainty propagation (forward UQ) and parameter estimation (inverse UQ). Further, surrogates based on Polynomial Chaos (PC) expansions are especially convenient for forward UQ and global sensitivity analysis, also known as variance-based decomposition. However, the PC surrogate construction strongly suffers from the curse of dimensionality. With a large number of input parameters, the number of model simulations required for accurate surrogate construction is prohibitively large. Relatedly, non-adaptive PC expansions typically include infeasibly large number of basis terms far exceeding the number of available model evaluations. We develop Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth and PC surrogate construction leading to a sparse, high-dimensional PC surrogate with a very few model evaluations. The surrogate is then readily employed for global sensitivity analysis leading to further dimensionality reduction. Besides numerical tests, we demonstrate the construction on the example of Accelerated Climate Model for Energy (ACME) Land Model for several output QoIs at nearly 100 FLUXNET sites covering multiple plant functional types and climates, varying 65 input parameters over broad ranges of possible values. This work is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Accelerated Climate Modeling for Energy (ACME) project. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

USGS Publications Warehouse

Zimmermann, N.E.; Edwards, T.C.; Moisen, Gretchen G.; Frescino, T.S.; Blackard, J.A.

2007-01-01

1. Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. 2. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. 3. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. 4. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. 5. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. ?? 2007 The Authors.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

PubMed Central

ZIMMERMANN, N E; EDWARDS, T C; MOISEN, G G; FRESCINO, T S; BLACKARD, J A

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. PMID:18642470

A New Formulation for Fresh Snow Density over Antarctica for the regional climate model Modèle Atmosphérique Régionale (MAR).

NASA Astrophysics Data System (ADS)

Tedesco, M.; Datta, R.; Fettweis, X.; Agosta, C.

2015-12-01

Surface-layer snow density is important to processes contributing to surface mass balance, but is highly variable over Antarctica due to a wide range of near-surface climate conditions over the continent. Formulations for fresh snow density have typically either used fixed values or been modeled empirically using field data that is limited to specific seasons or regions. There is also currently limited work exploring how the sensitivity to fresh snow density in regional climate models varies with resolution. Here, we present a new formulation compiled from (a) over 1600 distinct density profiles from multiple sources across Antarctica and (b) near-surface variables from the regional climate model Modèle Atmosphérique Régionale (MAR). Observed values represent coastal areas as well as the plateau, in both West and East Antarctica (although East Antarctica is dominant). However, no measurements are included from the Antarctic Peninsula, which is both highly topographically variable and extends to lower latitudes than the remainder of the continent. In order to assess the applicability of this fresh snow density formulation to the Antarctic Peninsula at high resolutions, a version of MAR is run for several years both at low-resolution at the continental scale and at a high resolution for the Antarctic Peninsula alone. This setup is run both with and without the new fresh density formulation to quantify the sensitivity of the energy balance and SMB components to fresh snow density. Outputs are compared with near-surface atmospheric variables available from AWS stations (provided by the University of Wisconsin Madison) as well as net accumulation values from the SAMBA database (provided from the Laboratoire de Glaciologie et Géophysique de l'Environnement).

Impact of wildfire-induced land cover modification on local meteorology: A sensitivity study of the 2003 wildfires in Portugal

NASA Astrophysics Data System (ADS)

Hernandez, Charles; Drobinski, Philippe; Turquety, Solène

2015-10-01

Wildfires alter land cover creating changes in dynamic, vegetative, radiative, thermal and hydrological properties of the surface. However, how so drastic changes induced by wildfires and how the age of the burnt scar affect the small and meso-scale atmospheric boundary layer dynamics are largely unknown. These questions are relevant for process analysis, meteorological and air quality forecast but also for regional climate analysis. Such questions are addressed numerically in this study on the case of the Portugal wildfires in 2003 as a testbed. In order to study the effects of burnt scars, an ensemble of numerical simulations using the Weather Research and Forecasting modeling system (WRF) have been performed with different surface properties mimicking the surface state immediately after the fire, few days after the fire and few months after the fire. In order to investigate such issue in a seamless approach, the same modelling framework has been used with various horizontal resolutions of the model grid and land use, ranging from 3.5 km, which can be considered as the typical resolution of state-of-the art regional numerical weather prediction models to 14 km which is now the typical target resolution of regional climate models. The study shows that the combination of high surface heat fluxes over the burnt area, large differential heating with respect to the preserved surroundings and lower surface roughness produces very intense frontogenesis with vertical velocity reaching few meters per second. This powerful meso-scale circulation can pump more humid air from the surroundings not impacted by the wildfire and produce more cloudiness over the burnt area. The influence of soil temperature immediately after the wildfire ceases is mainly seen at night as the boundary-layer remains unstably stratified and lasts only few days. So the intensity of the induced meso-scale circulation decreases with time, even though it remains until full recovery of the vegetation. Finally all these effects are simulated whatever the land cover and model resolution and there are thus robust processes in both regional climate simulations and process studies or short-time forecast. However, the impact of burnt scars on the precipitation signal remains very uncertain, especially because low precipitation is at stake.

Adaptive management of irrigation and crops' biodiversity: a case study on tomato

NASA Astrophysics Data System (ADS)

De Lorenzi, Francesca; Alfieri, Silvia Maria; Basile, Angelo; Bonfante, Antonello; Monaco, Eugenia; Riccardi, Maria; Menenti, Massimo

2013-04-01

We have assessed the impacts of climate change and evaluated options to adapt irrigation management in the face of predicted changes of agricultural water demand. We have evaluated irrigation scheduling and its effectiveness (versus crop transpiration), and cultivars' adaptability. The spatial and temporal variations of effectiveness and adaptability were studied in an irrigated district of Southern Italy. Two climate scenarios were considered: reference (1961-90) and future (2021-2050) climate, the former from climatic statistics, and the latter from statistical downscaling of general circulation models (AOGCM). Climatic data consist of daily time series of maximum and minimum temperature, and daily rainfall on a grid with a spatial resolution of 35 km. The work was carried out in the Destra Sele irrigation scheme (18.000 ha. Twenty-five soil units were identified and their hydrological properties were determined (measured or estimated from texture through pedo-transfer functions). A tomato crop, in a rotation typical of the area, was considered. A mechanistic model of water flow in the soil-plant-atmosphere system (SWAP) was used to study crop water requirements and water consumption. The model was calibrated and validated in the same area for many different crops. Tomato crop input data and model parameters were estimated on the basis of scientific literature and assumed to be generically representative of the species. Simulations were performed for reference and future climate, and for different irrigation scheduling options. In all soil units, six levels of irrigation volumes were applied: full irrigation (100%), deficit irrigation (80%, 60%, 40%, 20%), no irrigation. From simulation runs, indicators of soil water availability were calculated, moreover the marginal increases of transpiration per unit of irrigation volume, i.e. the effectiveness of irrigation (ΔT/I), were computed, in both climate scenarios. Indicators and marginal increases were used to evaluate the tomato crop adaptability to future climate. To this purpose, for several tomato cultivars, threshold values of their yield responses to soil water availability were determined (data from scientific literature). Cultivars' threshold values were evaluated, in all soil units, against the indicators' values, for irrigation levels with different ΔT/I. Less water intensive cultivars and irrigation volumes that optimize transpiration (and yield) could thus be identified in both climate scenarios, and irrigation management scenarios were determined taking into account soils' hydrological properties, crop biodiversity, and efficient use of water resource. The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008) Keywords: climate change, adaptation, simulation models, deficit irrigation, water resource efficiency, SWAP

A Dynamical Systems Explanation of the Hurst Effect and Atmospheric Low-Frequency Variability

PubMed Central

Franzke, Christian L. E.; Osprey, Scott M.; Davini, Paolo; Watkins, Nicholas W.

2015-01-01

The Hurst effect plays an important role in many areas such as physics, climate and finance. It describes the anomalous growth of range and constrains the behavior and predictability of these systems. The Hurst effect is frequently taken to be synonymous with Long-Range Dependence (LRD) and is typically assumed to be produced by a stationary stochastic process which has infinite memory. However, infinite memory appears to be at odds with the Markovian nature of most physical laws while the stationarity assumption lacks robustness. Here we use Lorenz's paradigmatic chaotic model to show that regime behavior can also cause the Hurst effect. By giving an alternative, parsimonious, explanation using nonstationary Markovian dynamics, our results question the common belief that the Hurst effect necessarily implies a stationary infinite memory process. We also demonstrate that our results can explain atmospheric variability without the infinite memory previously thought necessary and are consistent with climate model simulations. PMID:25765880

Pinatubo eruption winter climate effects: Model versus observations

NASA Technical Reports Server (NTRS)

Graf, HANS-F.; Kirchner, Ingo; Schult, Ingrid; Robock, Alan

1992-01-01

Large volcanic eruptions, in addition to the well-known effect of producing global cooling for a year or two, have been observed to produce shorter-term responses in the climate system involving non-linear dynamical processes. In this paper, we use the ECHAM2 general circulation model forced with stratospheric aerosols to test some of these ideas. Run in a perpetual-January mode, with tropical stratospheric heating from the volcanic aerosols typical of the 1982 El Chichon eruption or the 1991 Pinatubo eruption, we find a dynamical response with an increased polar night jet in the Northern Hemisphere (NH) and stronger zonal winds which extended down into the troposphere. The Azores High shifts northward with increased tropospheric westerlies at 60N and increased easterlies at 30N. Surface temperatures are higher both in northern Eurasia and North America, in agreement with observations for the NH winters or 1982-83 and 1991-92 as well as the winters following the other 10 largest volcanic eruptions since 1883.

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

NASA Astrophysics Data System (ADS)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

2018-01-01

Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

The Importance of Biologically Relevant Microclimates in Habitat Suitability Assessments

PubMed Central

Varner, Johanna; Dearing, M. Denise

2014-01-01

Predicting habitat suitability under climate change is vital to conserving biodiversity. However, current species distribution models rely on coarse scale climate data, whereas fine scale microclimate data may be necessary to assess habitat suitability and generate predictive models. Here, we evaluate disparities between temperature data at the coarse scale from weather stations versus fine-scale data measured in microhabitats required for a climate-sensitive mammal, the American pika (Ochotona princeps). We collected two years of temperature data in occupied talus habitats predicted to be suitable (high elevation) and unsuitable (low elevation) by the bioclimatic envelope approach. At low elevations, talus surface and interstitial microclimates drastically differed from ambient temperatures measured on-site and at a nearby weather station. Interstitial talus temperatures were frequently decoupled from high ambient temperatures, resulting in instantaneous disparities of over 30°C between these two measurements. Microhabitat temperatures were also highly heterogeneous, such that temperature measurements within the same patch of talus were not more correlated than measurements at distant patches. An experimental manipulation revealed that vegetation cover may cool the talus surface by up to 10°C during the summer, which may contribute to this spatial heterogeneity. Finally, low elevation microclimates were milder and less variable than typical alpine habitat, suggesting that, counter to species distribution model predictions, these seemingly unsuitable habitats may actually be better refugia for this species under climate change. These results highlight the importance of fine-scale microhabitat data in habitat assessments and underscore the notion that some critical refugia may be counterintuitive. PMID:25115894

The importance of biologically relevant microclimates in habitat suitability assessments.

PubMed

Varner, Johanna; Dearing, M Denise

2014-01-01

Predicting habitat suitability under climate change is vital to conserving biodiversity. However, current species distribution models rely on coarse scale climate data, whereas fine scale microclimate data may be necessary to assess habitat suitability and generate predictive models. Here, we evaluate disparities between temperature data at the coarse scale from weather stations versus fine-scale data measured in microhabitats required for a climate-sensitive mammal, the American pika (Ochotona princeps). We collected two years of temperature data in occupied talus habitats predicted to be suitable (high elevation) and unsuitable (low elevation) by the bioclimatic envelope approach. At low elevations, talus surface and interstitial microclimates drastically differed from ambient temperatures measured on-site and at a nearby weather station. Interstitial talus temperatures were frequently decoupled from high ambient temperatures, resulting in instantaneous disparities of over 30 °C between these two measurements. Microhabitat temperatures were also highly heterogeneous, such that temperature measurements within the same patch of talus were not more correlated than measurements at distant patches. An experimental manipulation revealed that vegetation cover may cool the talus surface by up to 10 °C during the summer, which may contribute to this spatial heterogeneity. Finally, low elevation microclimates were milder and less variable than typical alpine habitat, suggesting that, counter to species distribution model predictions, these seemingly unsuitable habitats may actually be better refugia for this species under climate change. These results highlight the importance of fine-scale microhabitat data in habitat assessments and underscore the notion that some critical refugia may be counterintuitive.

What Can Earth Paleoclimates Reveal About the Resiliency of Habitable States? An Example from the Neoproterozoic Snowball Earth

NASA Astrophysics Data System (ADS)

Sohl, L.

2014-04-01

The Neoproterozoic "Snowball Earth" glaciations ( 750-635 Ma) have been a special focus for outer habitable zone investigations, owing in large part to a captivating and controversial hypothesis suggesting that Earth may have only narrowly escaped a runaway icehouse state on multiple occasions (a.k.a. "the hard snowball"; Hoffman and Schrag 2001). A review of climate simulations exploring snowball inception (Godderis et al. 2011) reveals that a broad range of models (EBMs, EMICs and AGCMs) tend to yield hard snowball solutions, whereas models with greater 3-D dynamic response capabilities (AOGCMs) typically do not, unless some of their climate feedback responses (e.g., wind-driven ocean circulation, cloud forcings) are disabled (Poulsen and Jacobs 2004). This finding raises the likelihood that models incorporating dynamic climate feedbacks are essential to understanding how much flexibility there may be in the definition of a planet's habitable zone boundaries for a given point in its history. In the first of a series of new Snowball Earth simulations, we use the NASA/GISS ModelE2 Global Climate Model - a 3-D coupled atmosphere/ocean model with dynamic sea ice response - to explore the impacts of wind-driven ocean circulation, clouds and deep ocean circulation on the sea ice front when solar luminosity and atmospheric carbon dioxide are reduced to Neoproterozoic levels (solar = 94%, CO2 = 40 ppmv). The simulation includes a realistic Neoproterozoic land mass distribution, which is concentrated at mid- to tropical latitudes. After 300 years, the sea ice front is established near 30 degrees latitude, and after 600 years it remains stable. As with earlier coupled model simulations we conclude that runaway glacial states would have been difficult to achieve during the Neoproterozoic, and would be more likely to have occurred during earlier times in Earth history when solar luminosity was less. Inclusion of dynamic climate feedback capabilities in habitable zone modeling studies is likely to result in an expansion of our view of what a "Goldilocks" state can entail. Future simulations with a modified version of the NASA/GISS GCM, ROCKE-3D, will take advantage of newly-added model capabilities that evaluate the influence of rotation rate, solar spectral variability, CO2 surface condensation and CO2 clouds on the outer edge of Earth's habitable zone.

Land-use changes reinforce the impacts of climate change on annual runoff dynamics in a southeast China coastal watershed

NASA Astrophysics Data System (ADS)

Ervinia, A.; Huang, J.; Zhang, Z.

2015-06-01

Study on runoff dynamics across different physiographic regions is fundamentally important to formulate the sound strategies for water resource management especially in the coastal watershed where peoples heavily concentrated and relied on water resources. The L-R diagram, a conceptual model by which the land-changes evapotranspiration (ΔL) was estimated as the difference between actual and climate evapotranspiration to identify the specific impact of land-use changes on annual runoff changes (ΔR), was developed using the 53-year hydro-climatic data of Jiulong River Watershed, a typical medium-sized subtropical coastal watershed in China. This study found that land-use changes have reinforced the impact of climatic changes on runoff changes where nearly all points were scattered in II and IV quadrant. Deforestation and expansion of built up area has diminished the water retention capacity in a catchment as well as evapotranspiration thus produce extra runoff accounting for 12-183 % of total runoff increase. In contrast, reforestation makes the significant contribution to decreasing annual runoff for about 21-82 % of total runoff loss. This study revealed the river runoff has become more vulnerable to intensive anthropogenic disturbances under the context of climate changes in a coastal watershed.

Efficient occupancy model-fitting for extensive citizen-science data.

PubMed

Dennis, Emily B; Morgan, Byron J T; Freeman, Stephen N; Ridout, Martin S; Brereton, Tom M; Fox, Richard; Powney, Gary D; Roy, David B

2017-01-01

Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species' range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists.

Efficient occupancy model-fitting for extensive citizen-science data

PubMed Central

Morgan, Byron J. T.; Freeman, Stephen N.; Ridout, Martin S.; Brereton, Tom M.; Fox, Richard; Powney, Gary D.; Roy, David B.

2017-01-01

Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species’ range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists. PMID:28328937

Palaeoclimate records 60-8 ka in the Austrian and Swiss Alps and their forelands

NASA Astrophysics Data System (ADS)

Heiri, Oliver; Koinig, Karin A.; Spötl, Christoph; Barrett, Sam; Brauer, Achim; Drescher-Schneider, Ruth; Gaar, Dorian; Ivy-Ochs, Susan; Kerschner, Hanns; Luetscher, Marc; Moran, Andrew; Nicolussi, Kurt; Preusser, Frank; Schmidt, Roland; Schoeneich, Philippe; Schwörer, Christoph; Sprafke, Tobias; Terhorst, Birgit; Tinner, Willy

2014-12-01

The European Alps and their forelands provide a range of different archives and climate proxies for developing climate records in the time interval 60-8 thousand years (ka) ago. We review quantitative and semi-quantitative approaches for reconstructing climatic variables in the Austrian and Swiss sector of the Alpine region within this time interval. Available quantitative to semi-quantitative climate records in this region are mainly based on fossil assemblages of biota such as chironomids, cladocerans, coleopterans, diatoms and pollen preserved in lake sediments and peat, the analysis of oxygen isotopes in speleothems and lake sediment records, the reconstruction of past variations in treeline altitude, the reconstruction of past equilibrium line altitude and extent of glaciers based on geomorphological evidence, and the interpretation of past soil formation processes, dust deposition and permafrost as apparent in loess-palaeosol sequences. Palaeoclimate reconstructions in the Alpine region are affected by dating uncertainties increasing with age, the fragmentary nature of most of the available records, which typically only incorporate a fraction of the time interval of interest, and the limited replication of records within and between regions. Furthermore, there have been few attempts to cross-validate different approaches across this time interval to confirm reconstructed patterns of climatic change by several independent lines of evidence. Based on our review we identify a number of developments that would provide major advances for palaeoclimate reconstruction for the period 60-8 ka in the Alps and their forelands. These include (1) the compilation of individual, fragmentary records to longer and continuous reconstructions, (2) replication of climate records and the development of regional reconstructions for different parts of the Alps, (3) the cross-validation of different proxy-types and approaches, and (4) the reconstruction of past variations in climate gradients across the Alps and their forelands. Furthermore, the development of downscaled climate model runs for the Alpine region 60-8 ka, and of forward modelling approaches for climate proxies would expand the opportunities for quantitative assessments of climatic conditions in Europe within this time-interval.

Anthropogenic greenhouse gas contribution to UK autumn flood risk

NASA Astrophysics Data System (ADS)

Pall, Pardeep; Aina, Tolu; Stone, Dáithí; Stott, Peter; Nozawa, Toru; Hilberts, Arno; Lohmann, Dag; Allen, Myles

2010-05-01

Interest in attributing the risk of damaging weather-related events to anthropogenic climate change is increasing[1]. Yet climate models typically used for studying the attribution problem do not resolve weather at scales causing damage[2]. Here we present the first multi-step study that attributes increasing risk of a damaging regional weather-related event to global anthropogenic greenhouse gas emissions. The event was the UK flooding of October and November 2000, occurring during the wettest autumn in England & Wales since records began in 1766[3] and inundating several river catchments[4]. Nearly 10,000 properties were flooded and transport services and power supplies severely disrupted, with insured losses estimated at £1.3bn[5,6]. Though the floods were deemed a ‘wake up call' to the impacts of climate change[7], anthropogenic drivers cannot be blamed for this individual event: but they could be blamed for changing its risk[8,9]. Indeed, typically quoted thermodynamic arguments do suggest increased probability of precipitation extremes under anthropogenic warming[10]. But these arguments are too simple[11,12,13] to fully account for the complex weather[4,14] associated with the flooding. Instead we use a Probabilistic Event Attribution framework, to rigorously estimate the contribution of anthropogenic greenhouse gas emissions to England & Wales Autumn 2000 flood risk. This involves comparing an unprecedented number of daily river runoff realisations for the region, under Autumn 2000 scenarios both with and without the emissions. These realisations are produced using publicly volunteered distributed computing power to generate several thousand seasonal forecast resolution climate model simulations[15,16] that are then fed into a precipitation-runoff model[17,18]. Autumn 2000 flooding is characterised by realisations exceeding the highest daily river runoff for that period, derived from the observational-based ERA-40 re-anaylsis[19]. We find that our climate model adequately represents autumn synoptic conditions, and that our precipitation-runoff model adequately represents England & Wales runoff variability. Moreover, our model results indicate 20th century anthropogenic greenhouse gas emissions significantly (at the 10% level) increased England & Wales flood risk in Autumn 2000 and most probably about trebled it. This pilot demonstration of the Probabilistic Event Attribution framework forms the foundation for an ongoing long-term project to provide operational attribution statements for extreme weather-related events worldwide. References: -------------- 1. Hegerl, G.C. et al. Understanding and attributing climate change. 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 [eds Solomon, S. et al.] (Cambridge University Press, United Kingdom and New York, NY, USA) (2007). 2. Stott, P.A. et al. Detection and attribution of climate change: a regional perspective. Wiley Interdisciplinary Reviews: Climate Change, submitted. 3. Alexander, L.V. & Jones, P.D. Updated precipitation series for the U.K. and discussion of recent extremes. Atmos. Sci. Lett. 1, 142-150 (2001). 4. Marsh, T.J. & Dale, M. The UK floods of 2000-2001 : A hydrometeorological appraisal. J. Chartered Inst. Water Environ. Manage. 16, 180-188 (2002). 5. Association of British Insurers. Flooding: A partnership approach to protecting people. http://www.abi.org.uk/Display/File/301/Flooding_-_A_Partnership_Approach_to_Protecting_People.doc (2001). 6. Department for Environment, Food and Rural Affairs. To what degree can the October/November 2000 flood events be attributed to climate change? DEFRA FD2304 Final Report, London, 36 pp. (2001). 7. Environment Agency. Lessons learned: Autumn 2000 floods. Environment Agency, Bristol, 56 pp. (2001). 8. Allen, M.R. Liability for climate change. Nature 421, 891-892 (2003). 9. Stone, D.A. & Allen, M.R. The end-to-end attribution problem: From emissions to impacts. Climatic Change 71, 303-318 (2005). 10. Allen, M.R. & Ingram, W.I. Constraints on future changes in climate and the hydrologic cycle. Nature 419, 224-232 (2002). 11. Pall, P., Allen, M.R. & Stone, D.A. Testing the Clausius-Clapeyron constraint on changes in extreme precipitation under CO2 warming. Clim. Dyn. 28, 351-363 (2007). 12. Lenderink, G. & Van Meijgaard, E. Increase in hourly precipitation extremes beyond expectations from temperature changes. Nature Geosci. 1, 511-514 (2008). 13. O'Gorman, P.A. & Schneider, T. The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc. Natl. Acad. Sci. U.S.A. 106, 14773-14777 (2009). 14. Blackburn, M. & Hoskins, B.J. Atmospheric variability and extreme autumn rainfall in the UK. http://www.met.rdg.ac.uk/~mike/autumn2000.html (2001). 15. Allen, M.R. Do-it-yourself climate prediction. Nature 401, 642 (1999). 16. Massey, N. et al. Data access and analysis with distributed federated data servers in climateprediction.net. Adv. Geosci. 8, 49-56 (2006). 17. Lohmann, D., Raschke, E., Nijssen, B. & Lettenmaier, D.P. Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model. Hydrol. Sci. J. 43, 131-141 (1998). 18. Lohmann, D., Raschke, E., Nijssen, B. & Lettenmaier, D.P. Regional scale hydrology: II. Application of the VIC-2L model to the Weser river, Germany. Hydrol. Sci. J. 43, 143-158 (1998). 19. Uppala, S.M. et al. The ERA-40 re-analysis. Quart. J. Roy. Meteor. Soc. 131, 2961-3012 (2005).

SPAGETTA: a Multi-Purpose Gridded Stochastic Weather Generator

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Huth, R.; Rotach, M. W.; Dabhi, H.

2017-12-01

SPAGETTA is a new multisite/gridded multivariate parametric stochastic weather generator (WG). Site-specific precipitation occurrence and amount are modelled by Markov chain and Gamma distribution, the non-precipitation variables are modelled by an autoregressive (AR) model conditioned on precipitation occurrence, and the spatial coherence of all variables is modelled following the Wilks' (2009) approach. SPAGETTA may be run in two modes. Mode 1: it is run as a classical WG, which is calibrated using weather series from multiple sites, and only then it may produce arbitrarily long synthetic series mimicking the spatial and temporal structure of the calibration data. To generate the weather series representing the future climate, the WG parameters are modified according to the climate change scenario, typically derived from GCM or RCM simulations. Mode 2: the user provides only basic information (not necessarily to be realistic) on the temporal and spatial auto-correlation structure of the weather variables and their mean annual cycle; the generator itself derives the parameters of the underlying AR model, which produces the multi-site weather series. Optionally, the user may add the spatially varying trend, which is superimposed to the synthetic series. The contribution consists of following parts: (a) Model of the WG. (b) Validation of WG in terms of the spatial temperature and precipitation characteristics, including characteristics of spatial hot/cold/dry/wet spells. (c) Results of the climate change impact experiment, in which the WG parameters representing the spatial and temporal variability are modified using the climate change scenarios and the effect on the above spatial validation indices is analysed. In this experiment, the WG is calibrated using the E-OBS gridded daily weather data for several European regions, and the climate change scenarios are derived from the selected RCM simulations (CORDEX database). (d) The second mode of operation will be demonstrated by results obtained while developing the methodology for assessing collective significance of trends in multi-site weather series. The performance of the proposed test statistics is assessed based on large number of realisations of synthetic series produced by WG assuming a given statistical structure and trend of the weather series.

Age, origin and evolution of Antarctic debris-covered glaciers: Implications for landscape evolution and long-term climate change

NASA Astrophysics Data System (ADS)

Mackay, Sean Leland

Antarctic debris-covered glaciers are potential archives of long-term climate change. However, the geomorphic response of these systems to climate forcing is not well understood. To address this concern, I conducted a series of field-based and numerical modeling studies in the McMurdo Dry Valleys of Antarctica (MDV), with a focus on Mullins and Friedman glaciers. I used data and results from geophysical surveys, ice-core collection and analysis, geomorphic mapping, micro-meteorological stations, and numerical-process models to (1) determine the precise origin and distribution of englacial and supraglacial debris within these buried-ice systems, (2) quantify the fundamental processes and feedbacks that govern interactions among englacial and supraglacial debris, (3) establish a process-based model to quantify the inventory of cosmogenic nuclides within englacial and supraglacial debris, and (4) isolate the governing relationships between the evolution of englacial /supraglacial debris and regional climate forcing. Results from 93 field excavations, 21 ice cores, and 24 km of ground-penetrating radar data show that Mullins and Friedman glaciers contain vast areas of clean glacier ice interspersed with inclined layers of concentrated debris. The similarity in the pattern of englacial debris bands across both glaciers, along with model results that call for negligible basal entrainment, is best explained by episodic environmental change at valley headwalls. To constrain better the timing of debris-band formation, I developed a modeling framework that tracks the accumulation of cosmogenic 3He in englacial and supraglacial debris. Results imply that ice within Mullins Glacier increases in age non-linearly from 12 ka to ˜220 ka in areas of active flow (up to >> 1.6 Ma in areas of slow-moving-to-stagnant ice) and that englacial debris bands originate with a periodicity of ˜41 ka. Modeling studies suggest that debris bands originate in synchronicity with changes in obliquity-paced, total integrated summer insolation. The implication is that the englacial structure and surface morphology of some cold-based, debris-covered glaciers can preserve high-resolution climate archives that exceed the typical resolution of Antarctic terrestrial deposits and moraine records.

Climate uncertainty and implications for U.S. state-level risk assessment through 2050.

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

Loose, Verne W.; Lowry, Thomas Stephen; Malczynski, Leonard A.

2009-10-01

Decisions for climate policy will need to take place in advance of climate science resolving all relevant uncertainties. Further, if the concern of policy is to reduce risk, then the best-estimate of climate change impacts may not be so important as the currently understood uncertainty associated with realizable conditions having high consequence. This study focuses on one of the most uncertain aspects of future climate change - precipitation - to understand the implications of uncertainty on risk and the near-term justification for interventions to mitigate the course of climate change. We show that the mean risk of damage to themore » economy from climate change, at the national level, is on the order of one trillion dollars over the next 40 years, with employment impacts of nearly 7 million labor-years. At a 1% exceedance-probability, the impact is over twice the mean-risk value. Impacts at the level of individual U.S. states are then typically in the multiple tens of billions dollar range with employment losses exceeding hundreds of thousands of labor-years. We used results of the Intergovernmental Panel on Climate Change's (IPCC) Fourth Assessment Report 4 (AR4) climate-model ensemble as the referent for climate uncertainty over the next 40 years, mapped the simulated weather hydrologically to the county level for determining the physical consequence to economic activity at the state level, and then performed a detailed, seventy-industry, analysis of economic impact among the interacting lower-48 states. We determined industry GDP and employment impacts at the state level, as well as interstate population migration, effect on personal income, and the consequences for the U.S. trade balance.« less

The Role of the Persian Gulf in Shaping Southwest Asian Surface Climate

NASA Astrophysics Data System (ADS)

Pal, J. S.; Eltahir, E. A. B.

2015-12-01

Summer surface climate of the Persian Gulf region is characterized by hot and humid conditions. Despite such conditions - which in other regions tends to trigger moist convection - typically this region experiences clear sky conditions and very little rainfall in the summer. In this study, we customize the MIT Regional Climate Model specifically for the Southwest Asia region and apply it at a 25-km grid spacing using reanalysis boundary conditions for present-day climate (1975-2005). Specific customizations include accurate representations of surface albedo and emissivity as well as mineral dust processes, all of which improve model bias. To assess the role of the Persian Gulf in shaping the region's climate, a 30-year experiment is performed without the Persian Gulf characterized. Results suggest that observed conditions over the Persian Gulf are due to a combination of physical processes involving adiabatic and diabatic descent. First, virtually clear sky conditions, due to subsidence during summer associated with the rising air motion over the monsoon region to the east, suppress upward motion and deep convection and increase incoming solar radiation. Second, the low surface albedo of the Persian Gulf results in enhanced absorption of solar radiation and total heat flux. Third, high evaporation rates increase water vapor, and therefore trap heat at the surface via the greenhouse effect for water vapor. Fourth, the relatively shallow boundary layer over the Persian Gulf concentrates water vapor and heat close to the surface. These combined factors maximize the total flux of heat in the boundary layer and hence moist static energy over the Persian Gulf.

GEOSS AIP-2 Climate Change and Biodiversity Use Scenarios: Interoperability Infrastructures

NASA Astrophysics Data System (ADS)

Nativi, Stefano; Santoro, Mattia

2010-05-01

In the last years, scientific community is producing great efforts in order to study the effects of climate change on life on Earth. In this general framework, a key role is played by the impact of climate change on biodiversity. To assess this, several use scenarios require the modeling of climatological change impact on the regional distribution of biodiversity species. Designing and developing interoperability infrastructures which enable scientists to search, discover, access and use multi-disciplinary resources (i.e. datasets, services, models, etc.) is currently one of the main research fields for the Earth and Space Science Informatics. This presentation introduces and discusses an interoperability infrastructure which implements the discovery, access, and chaining of loosely-coupled resources in the climatology and biodiversity domains. This allows to set up and run forecast and processing models. The presented framework was successfully developed and experimented in the context of GEOSS AIP-2 (Global Earth Observation System of Systems, Architecture Implementation Pilot- Phase 2) Climate Change & Biodiversity thematic Working Group. This interoperability infrastructure is comprised of the following main components and services: a)GEO Portal: through this component end user is able to search, find and access the needed services for the scenario execution; b)Graphical User Interface (GUI): this component provides user interaction functionalities. It controls the workflow manager to perform the required operations for the scenario implementation; c)Use Scenario controller: this component acts as a workflow controller implementing the scenario business process -i.e. a typical climate change & biodiversity projection scenario; d)Service Broker implementing Mediation Services: this component realizes a distributed catalogue which federates several discovery and access components (exposing them through a unique CSW standard interface). Federated components publish climate, environmental and biodiversity datasets; e)Ecological Niche Model Server: this component is able to run one or more Ecological Niche Models (ENM) on selected biodiversity and climate datasets; f)Data Access Transaction server: this component publishes the model outputs. This framework was assessed in two use scenarios of GEOSS AIP-2 Climate Change and Biodiversity WG. Both scenarios concern the prediction of species distributions driven by climatological change forecasts. The first scenario dealt with the Pikas specie regional distribution in the Great Basin area (North America). While, the second one concerned the modeling of the Arctic Food Chain species in the North Pole area -the relationships between different environmental parameters and Polar Bears distribution was analyzed. The scientific patronage was provided by the University of Colorado and the University of Alaska, respectively. Results are published in the GEOSS AIP-2 web site: http://www.ogcnetwork.net/AIP2develop.

Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2016-02-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drivers of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by frequent wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are favored over statistical models.

Understanding Peripheral Bat Populations Using Maximum-Entropy Suitability Modeling

PubMed Central

Barnhart, Paul R.; Gillam, Erin H.

2016-01-01

Individuals along the periphery of a species distribution regularly encounter more challenging environmental and climatic conditions than conspecifics near the center of the distribution. Due to these potential constraints, individuals in peripheral margins are expected to change their habitat and behavioral characteristics. Managers typically rely on species distribution maps when developing adequate management practices. However, these range maps are often too simplistic and do not provide adequate information as to what fine-scale biotic and abiotic factors are driving a species occurrence. In the last decade, habitat suitability modelling has become widely used as a substitute for simplistic distribution mapping which allows regional managers the ability to fine-tune management resources. The objectives of this study were to use maximum-entropy modeling to produce habitat suitability models for seven species that have a peripheral margin intersecting the state of North Dakota, according to current IUCN distributions, and determine the vegetative and climatic characteristics driving these models. Mistnetting resulted in the documentation of five species outside the IUCN distribution in North Dakota, indicating that current range maps for North Dakota, and potentially the northern Great Plains, are in need of update. Maximum-entropy modeling showed that temperature and not precipitation were the variables most important for model production. This fine-scale result highlights the importance of habitat suitability modelling as this information cannot be extracted from distribution maps. Our results provide baseline information needed for future research about how and why individuals residing in the peripheral margins of a species’ distribution may show marked differences in habitat use as a result of urban expansion, habitat loss, and climate change compared to more centralized populations. PMID:27935936

Quantifying the risks of winter damage on overwintering crops under future climates: Will low-temperature damage be more likely in warmer climates?

NASA Astrophysics Data System (ADS)

Vico, G.; Weih, M.

2014-12-01

Autumn-sown crops act as winter cover crop, reducing soil erosion and nutrient leaching, while potentially providing higher yields than spring varieties in many environments. Nevertheless, overwintering crops are exposed for longer periods to the vagaries of weather conditions. Adverse winter conditions, in particular, may negatively affect the final yield, by reducing crop survival or its vigor. The net effect of the projected shifts in climate is unclear. On the one hand, warmer temperatures may reduce the frequency of low temperatures, thereby reducing damage risk. On the other hand, warmer temperatures, by reducing plant acclimation level and the amount and duration of snow cover, may increase the likelihood of damage. Thus, warmer climates may paradoxically result in more extensive low temperature damage and reduced viability for overwintering plants. The net effect of a shift in climate is explored by means of a parsimonious probabilistic model, based on a coupled description of air temperature, snow cover, and crop tolerable temperature. Exploiting an extensive dataset of winter wheat responses to low temperature exposure, the risk of winter damage occurrence is quantified under conditions typical of northern temperate latitudes. The full spectrum of variations expected with climate change is explored, quantifying the joint effects of alterations in temperature averages and their variability as well as shifts in precipitation. The key features affecting winter wheat vulnerability to low temperature damage under future climates are singled out.

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

NASA Astrophysics Data System (ADS)

Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

2017-04-01

Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger-)causes vegetation dynamics in most regions globally. More specifically, water availability is the most dominant vegetation driver, being the dominant vegetation driver in 54% of the vegetated surface. Furthermore, our results show that precipitation and soil moisture have prolonged impacts on vegetation in semiarid regions, with up to 10% of additional explained variance on the vegetation dynamics occurring three months later. Finally, hydro-climatic extremes seem to have a remarkable impact on vegetation, since they also explain up to 10% of additional variance of vegetation in certain regions despite their infrequent occurrence. References [1] Papagiannopoulou, C., Miralles, D. G., Verhoest, N. E. C., Dorigo, W. A., and Waegeman, W.: A non-linear Granger causality framework to investigate climate-vegetation dynamics, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-266, in review, 2016.

Soil Moisture-Atmosphere Feedbacks on Atmospheric Tracers: The Effects of Soil Moisture on Precipitation and Near-Surface Chemistry

NASA Astrophysics Data System (ADS)

Tawfik, Ahmed B.

The atmospheric component is described by rapid fluctuations in typical state variables, such as temperature and water vapor, on timescales of hours to days and the land component evolves on daily to yearly timescales. This dissertation examines the connection between soil moisture and atmospheric tracers under varying degrees of soil moisture-atmosphere coupling. Land-atmosphere coupling is defined over the United States using a regional climate model. A newly examined soil moisture-precipitation feedback is identified for winter months extending the previous summer feedback to colder temperature climates. This feedback is driven by the freezing and thawing of soil moisture, leading to coupled land-atmosphere conditions near the freezing line. Soil moisture can also affect the composition of the troposphere through modifying biogenic emissions of isoprene (C5H8). A novel first-order Taylor series decomposition indicates that isoprene emissions are jointly driven by temperature and soil moisture in models. These compounds are important precursors for ozone formation, an air pollutant and a short-lived forcing agent for climate. A mechanistic description of commonly observed relationships between ground-level ozone and meteorology is presented using the concept of soil moisture-temperature coupling regimes. The extent of surface drying was found to be a better predictor of ozone concentrations than temperature or humidity for the Eastern U.S. This relationship is evaluated in a coupled regional chemistry-climate model under several land-atmosphere coupling and isoprene emissions cases. The coupled chemistry-climate model can reproduce the observed soil moisture-temperature coupling pattern, yet modeled ozone is insensitive to changes in meteorology due to the balance between isoprene and the primary atmospheric oxidant, the hydroxyl radical (OH). Overall, this work highlights the importance of soil moisture-atmosphere coupling for previously neglected cold climate regimes, controlling isoprene emissions variability, and providing a processed-based description of observed ozone-meteorology relationships. From the perspective of ozone air quality, the lack of sensitivity of ozone to meteorology suggests a systematic deficiency in chemistry models in high isoprene emission regions. This shortcoming must be addressed to better estimate tropospheric ozone radiative forcing and to understanding how ozone air quality may respond to future warming.

Global warming, energy use, and economic growth

NASA Astrophysics Data System (ADS)

Khanna, Neha

The dissertation comprises four papers that explore the interactions between global warming, energy use, and economic growth. While the papers are separate entities, they share the underlying theme of highlighting national differences in the growth experience and their implications for long-term energy use and climate change. The first paper provides an overview of some key economic issues in the climate change literature. In doing so, the paper critically appraises the 1995 draft report of Working Group III of the Intergovernmental Panel on Climate Change. The focus is the choice of a pure rate of time preference in the economic modeling of climate change, abatement costs differentials between developed and developing countries, and contrasting implications of standard discount rates and value of life estimates for these two country groups. The second paper develops a global model that takes account of the depletion of oil resources in the context of a geo-economic model for climate change. It is found that in the presence of non-decreasing carbon and energy intensities and declining petroleum availability, the carbon emissions trajectory is much higher than that typically projected by other models of this genre. Furthermore, by introducing price and income sensitive demand functions for fossil fuels, the model provides a framework to assess the effectiveness of fuel specific carbon taxes in reducing the COsb2 emissions trajectory. Cross-price substitution effects necessitate unrealistically high tax rates in order to lower the projected emissions trajectory to the optimal level. The economic structure of five integrated assessment models for climate change is reviewed in the third paper, with a special focus on the macroeconomic and damage assessment modules. The final paper undertakes an econometric estimation of the changing shares of capital, labour, energy, and technical change in explaining the growth patterns of 38 countries. Production elasticities vary by country group and also in response to the levels of factor use. It is found that classifying countries according to the GDP growth rate yields statistically different slope coefficients. Using the estimated translog production function, the capital and labour requirements of reductions in energy use are approximated. Analytical expressions for the elasticity of energy intensity with respect to factor inputs and also autonomous energy efficiency improvements are provided.

Simulated big sagebrush regeneration supports predicted changes at the trailing and leading edges of distribution shifts

USGS Publications Warehouse

Schlaepfer, Daniel R.; Taylor, Kyle A.; Pennington, Victoria E.; Nelson, Kellen N.; Martin, Trace E.; Rottler, Caitlin M.; Lauenroth, William K.; Bradford, John B.

2015-01-01

Many semi-arid plant communities in western North America are dominated by big sagebrush. These ecosystems are being reduced in extent and quality due to economic development, invasive species, and climate change. These pervasive modifications have generated concern about the long-term viability of sagebrush habitat and sagebrush-obligate wildlife species (notably greater sage-grouse), highlighting the need for better understanding of the future big sagebrush distribution, particularly at the species' range margins. These leading and trailing edges of potential climate-driven sagebrush distribution shifts are likely to be areas most sensitive to climate change. We used a process-based regeneration model for big sagebrush, which simulates potential germination and seedling survival in response to climatic and edaphic conditions and tested expectations about current and future regeneration responses at trailing and leading edges that were previously identified using traditional species distribution models. Our results confirmed expectations of increased probability of regeneration at the leading edge and decreased probability of regeneration at the trailing edge below current levels. Our simulations indicated that soil water dynamics at the leading edge became more similar to the typical seasonal ecohydrological conditions observed within the current range of big sagebrush ecosystems. At the trailing edge, an increased winter and spring dryness represented a departure from conditions typically supportive of big sagebrush. Our results highlighted that minimum and maximum daily temperatures as well as soil water recharge and summer dry periods are important constraints for big sagebrush regeneration. Overall, our results confirmed previous predictions, i.e., we see consistent changes in areas identified as trailing and leading edges; however, we also identified potential local refugia within the trailing edge, mostly at sites at higher elevation. Decreasing regeneration probability at the trailing edge underscores the Schlaepfer et al. Future regeneration potential of big sagebrush potential futility of efforts to preserve and/or restore big sagebrush in these areas. Conversely, increasing regeneration probability at the leading edge suggest a growing potential for conflicts in management goals between maintaining existing grasslands by preventing sagebrush expansion versus accepting a shift in plant community composition to sagebrush dominance.

Multiscale combination of climate model simulations and proxy records over the last millennium

NASA Astrophysics Data System (ADS)

Chen, Xin; Xing, Pei; Luo, Yong; Nie, Suping; Zhao, Zongci; Huang, Jianbin; Tian, Qinhua

2018-05-01

To highlight the compatibility of climate model simulation and proxy reconstruction at different timescales, a timescale separation merging method combining proxy records and climate model simulations is presented. Annual mean surface temperature anomalies for the last millennium (851-2005 AD) at various scales over the land of the Northern Hemisphere were reconstructed with 2° × 2° spatial resolution, using an optimal interpolation (OI) algorithm. All target series were decomposed using an ensemble empirical mode decomposition method followed by power spectral analysis. Four typical components were obtained at inter-annual, decadal, multidecadal, and centennial timescales. A total of 323 temperature-sensitive proxy chronologies were incorporated after screening for each component. By scaling the proxy components using variance matching and applying a localized OI algorithm to all four components point by point, we obtained merged surface temperatures. Independent validation indicates that the most significant improvement was for components at the inter-annual scale, but this became less evident with increasing timescales. In mid-latitude land areas, 10-30% of grids were significantly corrected at the inter-annual scale. By assimilating the proxy records, the merged results reduced the gap in response to volcanic forcing between a pure reconstruction and simulation. Difficulty remained in verifying the centennial information and quantifying corresponding uncertainties, so additional effort should be devoted to this aspect in future research.

The Spatial Scaling of Global Rainfall Extremes

NASA Astrophysics Data System (ADS)

Devineni, N.; Xi, C.; Lall, U.; Rahill-Marier, B.

2013-12-01

Floods associated with severe storms are a significant source of risk for property, life and supply chains. These property losses tend to be determined as much by the duration of flooding as by the depth and velocity of inundation. High duration floods are typically induced by persistent rainfall (upto 30 day duration) as seen recently in Thailand, Pakistan, the Ohio and the Mississippi Rivers, France, and Germany. Events related to persistent and recurrent rainfall appear to correspond to the persistence of specific global climate patterns that may be identifiable from global, historical data fields, and also from climate models that project future conditions. A clear understanding of the space-time rainfall patterns for events or for a season will enable in assessing the spatial distribution of areas likely to have a high/low inundation potential for each type of rainfall forcing. In this paper, we investigate the statistical properties of the spatial manifestation of the rainfall exceedances. We also investigate the connection of persistent rainfall events at different latitudinal bands to large-scale climate phenomena such as ENSO. Finally, we present the scaling phenomena of contiguous flooded areas as a result of large scale organization of long duration rainfall events. This can be used for spatially distributed flood risk assessment conditional on a particular rainfall scenario. Statistical models for spatio-temporal loss simulation including model uncertainty to support regional and portfolio analysis can be developed.

Assessment of future climate change impacts on nonpoint source pollution in snowmelt period for a cold area using SWAT.

PubMed

Wang, Yu; Bian, Jianmin; Zhao, Yongsheng; Tang, Jie; Jia, Zhuo

2018-02-05

The source area of Liao River is a typical cold region in northeastern China, which experiences serious problems with agricultural nonpoint source pollution (NPS), it is important to understand future climate change impacts on NPS in the watershed. This issue has been investigated by coupling semi distributed hydrological model (SWAT), statistical downscaling model (SDSM) and global circulation model (GCMs). The results show that annual average temperature would rise by 2.1 °C (1.3 °C) in the 2080 s under scenario RCP8.5 (RCP4.5), and annual precipitation would increase by 67 mm (33 mm). The change in winter temperature and precipitation is most significant with an increase by 0.23 °C/10a (0.17 °C/10a) and 1.94 mm/10a (2.78 mm/10a). The future streamflow, TN and TP loads would decrease by 19.05% (10.59%), 12.27% (8.81%) and 10.63% (6.11%), respectively. Monthly average streamflow, TN and TP loads would decrease from March to November, and increase from December to February. This is because the increased precipitation and temperature in winter, which made the spring snowpack melting earlier. These study indicate the trends of nonpoint source pollution during the snowmelt period under climate change conditions, accordingly adaptation measures will be necessary.

Climate Change Through a Poverty Lens

NASA Astrophysics Data System (ADS)

Rozenberg, J.; Hallegatte, S.

2017-12-01

Analysis of the economic impact of climate change typically considers regional or national economies and assesses its impact on macroeconomic aggregates such as gross domestic product. These studies therefore do not investigate the distributional impacts of climate change within countries or the impacts on poverty. This Perspective aims to close this gap and provide an assessment of climate change impacts at the household level to investigate the consequences of climate change for poverty and for poor people. It does so by combining assessments of the physical impacts of climate change in various sectors with household surveys. In particular, it highlights how rapid and inclusive development can reduce the future impact of climate change on poverty.

Climate change through a poverty lens

NASA Astrophysics Data System (ADS)

Hallegatte, Stephane; Rozenberg, Julie

2017-04-01

Analysis of the economic impact of climate change typically considers regional or national economies and assesses its impact on macroeconomic aggregates such as gross domestic product. These studies therefore do not investigate the distributional impacts of climate change within countries or the impacts on poverty. This Perspective aims to close this gap and provide an assessment of climate change impacts at the household level to investigate the consequences of climate change for poverty and for poor people. It does so by combining assessments of the physical impacts of climate change in various sectors with household surveys. In particular, it highlights how rapid and inclusive development can reduce the future impact of climate change on poverty.

How well do we understand the Earth's radiation budget and the role of clouds? Selected results of the GEWEX radiation flux assessment

NASA Astrophysics Data System (ADS)

Raschke, E.; Kinne, S.

2013-05-01

Multi-year average radiative flux maps of three satellite data-sets (CERES, ISSCP and GEWEX-SRB) are compared to each other and to typical values by global modeling (median values of results of 20 climate models of the 4th IPCC Assessment). Diversity assessments address radiative flux products and at the top of the atmosphere (TOA) and the surface, with particular attention to impacts by clouds. Involving both data from surface and TOA special attention is given to the vertical radiation flux divergence and on the infrared Greenhouse effect, which are rarely shown in literature.

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

Jakob, Christian

This report summarises an investigation into the relationship of tropical thunderstorms to the atmospheric conditions they are embedded in. The study is based on the use of radar observations at the Atmospheric Radiation Measurement site in Darwin run under the auspices of the DOE Atmospheric Systems Research program. Linking the larger scales of the atmosphere with the smaller scales of thunderstorms is crucial for the development of the representation of thunderstorms in weather and climate models, which is carried out by a process termed parametrisation. Through the analysis of radar and wind profiler observations the project made several fundamental discoveriesmore » about tropical storms and quantified the relationship of the occurrence and intensity of these storms to the large-scale atmosphere. We were able to show that the rainfall averaged over an area the size of a typical climate model grid-box is largely controlled by the number of storms in the area, and less so by the storm intensity. This allows us to completely rethink the way we represent such storms in climate models. We also found that storms occur in three distinct categories based on their depth and that the transition between these categories is strongly related to the larger scale dynamical features of the atmosphere more so than its thermodynamic state. Finally, we used our observational findings to test and refine a new approach to cumulus parametrisation which relies on the stochastic modelling of the area covered by different convective cloud types.« less

Improving Baseline Model Assumptions: Evaluating the Impacts of Typical Methodological Approaches in Watershed Models

NASA Astrophysics Data System (ADS)

Muenich, R. L.; Kalcic, M. M.; Teshager, A. D.; Long, C. M.; Wang, Y. C.; Scavia, D.

2017-12-01

Thanks to the availability of open-source software, online tutorials, and advanced software capabilities, watershed modeling has expanded its user-base and applications significantly in the past thirty years. Even complicated models like the Soil and Water Assessment Tool (SWAT) are being used and documented in hundreds of peer-reviewed publications each year, and likely more applied in practice. These models can help improve our understanding of present, past, and future conditions, or analyze important "what-if" management scenarios. However, baseline data and methods are often adopted and applied without rigorous testing. In multiple collaborative projects, we have evaluated the influence of some of these common approaches on model results. Specifically, we examined impacts of baseline data and assumptions involved in manure application, combined sewer overflows, and climate data incorporation across multiple watersheds in the Western Lake Erie Basin. In these efforts, we seek to understand the impact of using typical modeling data and assumptions, versus using improved data and enhanced assumptions on model outcomes and thus ultimately, study conclusions. We provide guidance for modelers as they adopt and apply data and models for their specific study region. While it is difficult to quantitatively assess the full uncertainty surrounding model input data and assumptions, recognizing the impacts of model input choices is important when considering actions at the both the field and watershed scales.

Towards Building Reliable, High-Accuracy Solar Irradiance Database For Arid Climates

NASA Astrophysics Data System (ADS)

Munawwar, S.; Ghedira, H.

2012-12-01

Middle East's growing interest in renewable energy has led to increased activity in solar technology development with the recent commissioning of several utility-scale solar power projects and many other commercial installations across the Arabian Peninsula. The region, lying in a virtually rainless sunny belt with a typical daily average solar radiation exceeding 6 kWh/m2, is also one of the most promising candidates for solar energy deployment. However, it is not the availability of resource, but its characterization and reasonably accurate assessment that determines the application potential. Solar irradiance, magnitude and variability inclusive, is the key input in assessing the economic feasibility of a solar system. The accuracy of such data is of critical importance for realistic on-site performance estimates. This contribution aims to identify the key stages in developing a robust solar database for desert climate by focusing on the challenges that an arid environment presents to parameterization of solar irradiance attenuating factors. Adjustments are proposed based on the currently available resource assessment tools to produce high quality data for assessing bankability. Establishing and maintaining ground solar irradiance measurements is an expensive affair and fairly limited in time (recently operational) and space (fewer sites) in the Gulf region. Developers within solar technology industry, therefore, rely on solar radiation models and satellite-derived data for prompt resource assessment needs. It is imperative that such estimation tools are as accurate as possible. While purely empirical models have been widely researched and validated in the Arabian Peninsula's solar modeling history, they are known to be intrinsically site-specific. A primal step to modeling is an in-depth understanding of the region's climate, identifying the key players attenuating radiation and their appropriate characterization to determine solar irradiance. Physical approach based models and tools can subsequently be recalibrated or improvised to address the unique features of characteristically high aerosol load, frequent dust episodes and yet, typically clear skies, in the Gulf's primarily arid region. For example, though clouds are an important factor, radiative extinction in desert climates is primarily due to aerosols; a fact that needs to be taken into consideration since most of the existing solar radiation models are technically cloud-based. Satellite derived irradiance, although carrying a tag of uncertainties (5-10%), have special relevance in the Gulf due to lack of long-term data at the source of application. Satellite data can be merged or combined with short-term ground measurements, by various techniques available in the literature, to smooth out uncertainties in the data and build high-accuracy long-term solar irradiance profiles. Such concatenation is of particular interest to investors as it provides vital information on solar resource variability.

Evaluation of water conservation capacity of loess plateau typical mountain ecosystems based on InVEST model simulation

NASA Astrophysics Data System (ADS)

Lv, Xizhi; Zuo, Zhongguo; Xiao, Peiqing

2017-06-01

With increasing demand for water resources and frequently a general deterioration of local water resources, water conservation by forests has received considerable attention in recent years. To evaluate water conservation capacities of different forest ecosystems in mountainous areas of Loess Plateau, the landscape of forests was divided into 18 types in Loess Plateau. Under the consideration of the factors such as climate, topography, plant, soil and land use, the water conservation of the forest ecosystems was estimated by means of InVEST model. The result showed that 486417.7 hm2 forests in typical mountain areas were divided into 18 forest types, and the total water conservation quantity was 1.64×1012m3, equaling an average of water conversation quantity of 9.09×1010m3. There is a great difference in average water conversation capacity among various forest types. The water conservation function and its evaluation is crucial and complicated issues in the study of ecological service function in modern times.

Impacts of Near-Term Climate Change on Irrigation Demands and Crop Yields in the Columbia River Basin

NASA Astrophysics Data System (ADS)

Rajagopalan, K.; Chinnayakanahalli, K. J.; Stockle, C. O.; Nelson, R. L.; Kruger, C. E.; Brady, M. P.; Malek, K.; Dinesh, S. T.; Barber, M. E.; Hamlet, A. F.; Yorgey, G. G.; Adam, J. C.

2018-03-01

Adaptation to a changing climate is critical to address future global food and water security challenges. While these challenges are global, successful adaptation strategies are often generated at regional scales; therefore, regional-scale studies are critical to inform adaptation decision making. While climate change affects both water supply and demand, water demand is relatively understudied, especially at regional scales. The goal of this work is to address this gap, and characterize the direct impacts of near-term (for the 2030s) climate change and elevated CO2 levels on regional-scale crop yields and irrigation demands for the Columbia River basin (CRB). This question is addressed through a coupled crop-hydrology model that accounts for site-specific and crop-specific characteristics that control regional-scale response to climate change. The overall near-term outlook for agricultural production in the CRB is largely positive, with yield increases for most crops and small overall increases in irrigation demand. However, there are crop-specific and location-specific negative impacts as well, and the aggregate regional response of irrigation demands to climate change is highly sensitive to the spatial crop mix. Low-value pasture/hay varieties of crops—typically not considered in climate change assessments—play a significant role in determining the regional response of irrigation demands to climate change, and thus cannot be overlooked. While, the overall near-term outlook for agriculture in the region is largely positive, there may be potential for a negative outlook further into the future, and it is important to consider this in long-term planning.

Are whooping cranes destined for extinction? Climate change imperils recruitment and population growth.

PubMed

Butler, Matthew J; Metzger, Kristine L; Harris, Grant M

2017-04-01

Identifying climatic drivers of an animal population's vital rates and locating where they operate steers conservation efforts to optimize species recovery. The population growth of endangered whooping cranes ( Grus americana ) hinges on juvenile recruitment. Therefore, we identify climatic drivers (solar activity [sunspots] and weather) of whooping crane recruitment throughout the species' life cycle (breeding, migration, wintering). Our method uses a repeated cross-validated absolute shrinkage and selection operator approach to identify drivers of recruitment. We model effects of climate change on those drivers to predict whooping crane population growth given alternative scenarios of climate change and solar activity. Years with fewer sunspots indicated greater recruitment. Increased precipitation during autumn migration signified less recruitment. On the breeding grounds, fewer days below freezing during winter and more precipitation during breeding suggested less recruitment. We predicted whooping crane recruitment and population growth may fall below long-term averages during all solar cycles when atmospheric CO 2 concentration increases, as expected, to 500 ppm by 2050. Species recovery during a typical solar cycle with 500 ppm may require eight times longer than conditions without climate change and the chance of population decline increases to 31%. Although this whooping crane population is growing and may appear secure, long-term threats imposed by climate change and increased solar activity may jeopardize its persistence. Weather on the breeding grounds likely affects recruitment through hydrological processes and predation risk, whereas precipitation during autumn migration may influence juvenile mortality. Mitigating threats or abating climate change should occur within ≈30 years or this wild population of whooping cranes may begin declining.

Constructing the reduced dynamical models of interannual climate variability from spatial-distributed time series

NASA Astrophysics Data System (ADS)

Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander

2016-04-01

We suggest a method for empirical forecast of climate dynamics basing on the reconstruction of reduced dynamical models in a form of random dynamical systems [1,2] derived from observational time series. The construction of proper embedding - the set of variables determining the phase space the model works in - is no doubt the most important step in such a modeling, but this task is non-trivial due to huge dimension of time series of typical climatic fields. Actually, an appropriate expansion of observational time series is needed yielding the number of principal components considered as phase variables, which are to be efficient for the construction of low-dimensional evolution operator. We emphasize two main features the reduced models should have for capturing the main dynamical properties of the system: (i) taking into account time-lagged teleconnections in the atmosphere-ocean system and (ii) reflecting the nonlinear nature of these teleconnections. In accordance to these principles, in this report we present the methodology which includes the combination of a new way for the construction of an embedding by the spatio-temporal data expansion and nonlinear model construction on the basis of artificial neural networks. The methodology is aplied to NCEP/NCAR reanalysis data including fields of sea level pressure, geopotential height, and wind speed, covering Northern Hemisphere. Its efficiency for the interannual forecast of various climate phenomena including ENSO, PDO, NAO and strong blocking event condition over the mid latitudes, is demonstrated. Also, we investigate the ability of the models to reproduce and predict the evolution of qualitative features of the dynamics, such as spectral peaks, critical transitions and statistics of extremes. This research was supported by the Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with the Institute of Applied Physics RAS) [1] Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, "Random dynamical models from time series," Phys. Rev. E, vol. 85, no. 3, p. 036216, 2012. [2] D. Mukhin, D. Kondrashov, E. Loskutov, A. Gavrilov, A. Feigin, and M. Ghil, "Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models," J. Clim., vol. 28, no. 5, pp. 1962-1976, 2015.

Evaluating cloud processes in large-scale models: Of idealized case studies, parameterization testbeds and single-column modelling on climate time-scales

NASA Astrophysics Data System (ADS)

Neggers, Roel

2016-04-01

Boundary-layer schemes have always formed an integral part of General Circulation Models (GCMs) used for numerical weather and climate prediction. The spatial and temporal scales associated with boundary-layer processes and clouds are typically much smaller than those at which GCMs are discretized, which makes their representation through parameterization a necessity. The need for generally applicable boundary-layer parameterizations has motivated many scientific studies, which in effect has created its own active research field in the atmospheric sciences. Of particular interest has been the evaluation of boundary-layer schemes at "process-level". This means that parameterized physics are studied in isolated mode from the larger-scale circulation, using prescribed forcings and excluding any upscale interaction. Although feedbacks are thus prevented, the benefit is an enhanced model transparency, which might aid an investigator in identifying model errors and understanding model behavior. The popularity and success of the process-level approach is demonstrated by the many past and ongoing model inter-comparison studies that have been organized by initiatives such as GCSS/GASS. A red line in the results of these studies is that although most schemes somehow manage to capture first-order aspects of boundary layer cloud fields, there certainly remains room for improvement in many areas. Only too often are boundary layer parameterizations still found to be at the heart of problems in large-scale models, negatively affecting forecast skills of NWP models or causing uncertainty in numerical predictions of future climate. How to break this parameterization "deadlock" remains an open problem. This presentation attempts to give an overview of the various existing methods for the process-level evaluation of boundary-layer physics in large-scale models. This includes i) idealized case studies, ii) longer-term evaluation at permanent meteorological sites (the testbed approach), and iii) process-level evaluation at climate time-scales. The advantages and disadvantages of each approach will be identified and discussed, and some thoughts about possible future developments will be given.

The impact of climatological model biases in the North Atlantic jet on predicted future circulation change

NASA Astrophysics Data System (ADS)

Simpson, I.

2015-12-01

A long standing bias among global climate models (GCMs) is their incorrect representation of the wintertime circulation of the North Atlantic region. Specifically models tend to exhibit a North Atlantic jet (and associated storm track) that is too zonal, extending across central Europe, when it should tilt northward toward Scandinavia. GCM's consistently predict substantial changes in the large scale circulation in this region, consisting of a localized anti-cyclonic circulation, centered over the Mediterranean and accompanied by increased aridity there and increased storminess over Northern Europe.Here, we present preliminary results from experiments that are designed to address the question of what the impact of the climatological circulation biases might be on this predicted future response. Climate change experiments will be compared in two versions of the Community Earth System Model: the first is a free running version of the model, as typically used in climate prediction; the second is a bias corrected version of the model in which a seasonally varying cycle of bias correction tendencies are applied to the wind and temperature fields. These bias correction tendencies are designed to account for deficiencies in the fast parameterized processes, with an aim to push the model toward a more realistic climatology.While these experiments come with the caveat that they assume the bias correction tendencies will remain constant with time, they allow for an initial assessment, through controlled experiments, of the impact that biases in the climatological circulation can have on future predictions in this region. They will also motivate future work that can make use of the bias correction tendencies to understand the underlying physical processes responsible for the incorrect tilt of the jet.

Potential soil organic carbon stocks in semi arid areas under climate change scenarios: an application of CarboSOIL model in northern Egypt

NASA Astrophysics Data System (ADS)

Muñoz-Rojas, Miriam; Abd-Elmabod, Sameh K.; Jordán, Antonio; Zavala, Lorena M.; Anaya-Romero, Maria; De la Rosa, Diego

2014-05-01

1. INTRODUCTION Climate change is predicted to have a large impact on semi arid areas which are often degraded and vulnerable to environmental changes (Muñoz-Rojas et al., 2012a; 2012b; 2013). However, these areas might play a key role in mitigation of climate change effects through sequestration of carbon in soils (United Nations, 2011). At the same time, increasing organic carbon in these environments could be beneficial for soil erosion control, soil fertility and, ultimately, food production (Lal, 2004). Several approaches have been carried out to evaluate climate change impacts on soil organic carbon (SOC) stocks, but soil carbon models are amongst the most effective tools to assess C stocks, dynamics and distribution and to predict trends under climate change scenarios (Jones et al., 2005 ). CarboSOIL is an empirical model based on regression techniques and developed to predict SOC contents at standard soil depths of 0 to 25, 25 to 50 and 50-75 cm (Muñoz-Rojas et al., 2013). CarboSOIL model has been designed as a GIS-integrated tool and is a new component of the agroecological decision support system for land evaluation MicroLEIS DSS (De la Rosa et al., 2004). 2. GENERAL METHODS In this research, CarboSOIL was applied in El-Fayoum depression, a semi arid region located in northern Egypt with a large potential for agriculture (Abd-Elmabod et al, 2012). The model was applied in a total of six soil-units classified according the USDA Soil Taxonomy system within the orders Entisols and Aridisols under different climate climate change scenarios. Global climate models based on the Organisation for Economic Co-operation and Development (Agrawala at al., 2004) and the Intergovernmental Panel on Climate Change (IPCC, 2007) were applied to predict short-, medium- and long-term trends (2030, 2050 and 2100) of SOC dynamics and sequestration at different soil depths (0-25, 25-50 and 50-75) and land use types (irrigated areas, olive groves, wheat, cotton and other annual crops, and fruit trees and berries). 3. RESULTS AND CONCLUSIONS According to results, considerable decreases of SOC stocks are expected in the 25-50 cm soil section under all considered land use types and all projected scenarios, in particular in Vertic Torrifluvents and Typic Torrifluvents under wheat, cotton and other annual crops. Oppositely, SOC stocks tend to increase in the deeper soil section (50-75 cm), mostly in Typic Haplocalcids under permanently irrigated areas and olive groves in the 2100 scenario. In the upper layer (0-25 cm), slight increases have been predicted under all considered land use types. The methodology used in this research could be applied to other semi arid areas with available soil, land use and climate data. Moreover, the information developed in this study might support decision-making for land use planning, agricultural management and climate adaptation strategies in semi arid regions. REFERENCES Abd-Elmabod, S.K., Ali, R.R., Anaya-Romero, M., Jordán, A., Muñoz-Rojas, M., Abdelmageed, T.A., Zavala, L.M., De la Rosa, D. 2012. Evaluating soil degradation under different scenarios of agricultural land management in Mediterranean region. Nature and Science 10, 103-116. Agrawala, S., A. Moehner, M. El Raey, D. Conway, M. van Aalst, M. Hagenstad and J. Smith. 2004. Development and Climate Change In Egypt: Focus on Coastal Resources and the Nile. Organisation for Economic Co-operation and Development. De la Rosa, D., Mayol, F., Moreno, F., Cabrera, F., Díaz-Pereira, E., Antoine, J. 2002. A multilingual soil profile database (SDBm Plus) as an essential part of land resources information systems. Environmental Modelling & Software 17, 721-730. DOI: 10.1016/S1364-8152(02)00031-2. IPCC. 2007. Climate Change 2007: The Physical Science Basis. Cambridge/New York: Cambridge University Press Jones, C., McConnell, C., Coleman, K., Cox, P., Falloon, P., Jenkinson, D. and Powlson, D. 2005. Global climate change and soil carbon stocks; predictions from two contrasting models for the turnover of organic carbon in soil. Global Change Biology 11: 154-166 Lal, R. 2004. Soil carbon sequestration to mitigate climate change. Geoderma 123, 1-22. DOI: 10.1016/j.geoderma.2004.01.032. Muñoz-Rojas, M., Jordán, A., Zavala, L.M., De la Rosa, D., Abd-Elmabod, S.K., Anaya-Romero, M. 2012a. Impact of land use and land cover changes on organic carbon stocks in Mediterranean soils (1956-2007). Land Degradation & Development. In press. DOI: 10.1002/ldr.2194. Muñoz-Rojas, M., Jordán, A., Zavala, L.M., De la Rosa, D., Abd-Elmabod, S.K., Anaya-Romero, M. 2012b. Organic carbon stocks in Mediterranean soil types under different land uses (Southern Spain). Solid Earth 3, 375-386. DOI: 10.5194/se-3-375-2012. Muñoz-Rojas, M., Jordán, A., Zavala, L.M., González-Peñaloza, F.A., De la Rosa, D., Anaya-Romero, M. 2013. Modelling soil organic carbon stocks in global change scenarios: a CarboSOIL application. Biogeosciences Discussions 10, 10997-11035. DOI: 10.5194/bgd-10-10997-2013. United Nations. 2011. Global Drylands: A UN system-wide response. Full Report. United Nations Environment Management Group.

The evolution of scaling laws in the sea ice floe size distribution

NASA Astrophysics Data System (ADS)

Horvat, Christopher; Tziperman, Eli

2017-09-01

The sub-gridscale floe size and thickness distribution (FSTD) is an emerging climate variable, playing a leading-order role in the coupling between sea ice, the ocean, and the atmosphere. The FSTD, however, is difficult to measure given the vast range of horizontal scales of individual floes, leading to the common use of power-law scaling to describe it. The evolution of a coupled mixed-layer-FSTD model of a typical marginal ice zone is explicitly simulated here, to develop a deeper understanding of how processes active at the floe scale may or may not lead to scaling laws in the floe size distribution. The time evolution of mean quantities obtained from the FSTD (sea ice concentration, mean thickness, volume) is complex even in simple scenarios, suggesting that these quantities, which affect climate feedbacks, should be carefully calculated in climate models. The emergence of FSTDs with multiple separate power-law regimes, as seen in observations, is found to be due to the combination of multiple scale-selective processes. Limitations in assuming a power-law FSTD are carefully analyzed, applying methods used in observations to FSTD model output. Two important sources of error are identified that may lead to model biases: one when observing an insufficiently small range of floe sizes, and one from the fact that floe-scale processes often do not produce power-law behavior. These two sources of error may easily lead to biases in mean quantities derived from the FSTD of greater than 100%, and therefore biases in modeled sea ice evolution.

Model Analysis of the Factors Regulating Trends and Variability of Methane, Carbon Monoxide and OH: 1. Model Validation

NASA Technical Reports Server (NTRS)

Elshorbany, Y. F.; Strode, S.; Wang, J.; Duncan, B.

2014-01-01

Methane (CH4) is the second most important anthropogenic greenhouse gas (GHG). Its 100-year global warming potential (GWP) is 25 times larger than that for carbon dioxide. The 100-yr integrated GWP of CH4 is sensitive to changes in OH levels. Methane's atmospheric growth rate was estimated to be more than 10 ppb yr(exp -1) in 1998 but less than zero in 2001, 2004 and 2005 (Kirschke et al., 2013). Since 2006, the CH4 is increasing again. This phenomena is yet not well understood. Oxidation of CH4 by OH is the main loss process, thus affecting the oxidizing capacity of the atmosphere and contributing to the global ozone background. Current models typically use an annual cycle of offline OH fields to simulate CH4. The implemented OH fields in these models are typically tuned so that simulated CH4 growth rates match that measured. For future and climate simulations, the OH tuning technique may not be suitable. In addition, running full chemistry, multi-decadal CH4 simulations is a serious challenge and currently, due to computational intensity, almost impossible.

Improved wet weather wastewater influent modelling at Viikinmäki WWTP by on-line weather radar information.

PubMed

Heinonen, M; Jokelainen, M; Fred, T; Koistinen, J; Hohti, H

2013-01-01

Municipal wastewater treatment plant (WWTP) influent is typically dependent on diurnal variation of urban production of liquid waste, infiltration of stormwater runoff and groundwater infiltration. During wet weather conditions the infiltration phenomenon typically increases the risk of overflows in the sewer system as well as the risk of having to bypass the WWTP. Combined sewer infrastructure multiplies the role of rainwater runoff in the total influent. Due to climate change, rain intensity and magnitude is tending to rise as well, which can already be observed in the normal operation of WWTPs. Bypass control can be improved if the WWTP is prepared for the increase of influent, especially if there is some storage capacity prior to the treatment plant. One option for this bypass control is utilisation of on-line weather-radar-based forecast data of rainfall as an input for the on-line influent model. This paper reports the Viikinmäki WWTP wet weather influent modelling project results where gridded exceedance probabilities of hourly rainfall accumulations for the next 3 h from the Finnish Meteorological Institute are utilised as on-line input data for the influent model.

A Tropospheric Emission Spectrometer HDO/H2O Retrieval Simulator for Climate Models

NASA Technical Reports Server (NTRS)

Field, R. D.; Risi, C.; Schmidt, G. A.; Worden, J.; Voulgarakis, A.; LeGrande, A. N.; Sobel, A. H.; Healy, R. J.

2012-01-01

Retrievals of the isotopic composition of water vapor from the Aura Tropospheric Emission Spectrometer (TES) have unique value in constraining moist processes in climate models. Accurate comparison between simulated and retrieved values requires that model profiles that would be poorly retrieved are excluded, and that an instrument operator be applied to the remaining profiles. Typically, this is done by sampling model output at satellite measurement points and using the quality flags and averaging kernels from individual retrievals at specific places and times. This approach is not reliable when the model meteorological conditions influencing retrieval sensitivity are different from those observed by the instrument at short time scales, which will be the case for free-running climate simulations. In this study, we describe an alternative, categorical approach to applying the instrument operator, implemented within the NASA GISS ModelE general circulation model. Retrieval quality and averaging kernel structure are predicted empirically from model conditions, rather than obtained from collocated satellite observations. This approach can be used for arbitrary model configurations, and requires no agreement between satellite-retrieved and model meteorology at short time scales. To test this approach, nudged simUlations were conducted using both the retrieval-based and categorical operators. Cloud cover, surface temperature and free-tropospheric moisture content were the most important predictors of retrieval quality and averaging kernel structure. There was good agreement between the D fields after applying the retrieval-based and more detailed categorical operators, with increases of up to 30 over the ocean and decreases of up to 40 over land relative to the raw model fields. The categorical operator performed better over the ocean than over land, and requires further refinement for use outside of the tropics. After applying the TES operator, ModelE had D biases of 8 over ocean and 34 over land compared to TES D, which were less than the biases using raw model D fields.

Global Assessment of Land Surface Temperature From Geostationary Satellites and Model Estimates

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Liu, Q.; Minnis, P.; daSilva, A. M., Jr.; Palikonda, R.; Yost, C. R.

2012-01-01

Land surface (or 'skin') temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research we compare two global and independent data sets: (i) LST retrievals from five geostationary satellites generated at the NASA Langley Research Center (LaRC) and (ii) LST estimates from the quasi-operational NASA GEOS-5 global modeling and assimilation system. The objective is to thoroughly understand both data sets and their systematic differences in preparation for the assimilation of the LaRC LST retrievals into GEOS-5. As expected, mean differences (MD) and root-mean-square differences (RMSD) between modeled and retrieved LST vary tremendously by region and time of day. Typical (absolute) MD values range from 1-3 K in Northern Hemisphere mid-latitude regions to near 10 K in regions where modeled clouds are unrealistic, for example in north-eastern Argentina, Uruguay, Paraguay, and southern Brazil. Typically, model estimates of LST are higher than satellite retrievals during the night and lower during the day. RMSD values range from 1-3 K during the night to 2-5 K during the day, but are larger over the 50-120 W longitude band where the LST retrievals are derived from the FY2E platform

Empirical Relationships Between Optical Properties and Equivalent Diameters of Fractal Soot Aggregates at 550 Nm Wavelength.

NASA Technical Reports Server (NTRS)

Pandey, Apoorva; Chakrabarty, Rajan K.; Liu, Li; Mishchenko, Michael I.

2015-01-01

Soot aggregates (SAs)-fractal clusters of small, spherical carbonaceous monomers-modulate the incoming visible solar radiation and contribute significantly to climate forcing. Experimentalists and climate modelers typically assume a spherical morphology for SAs when computing their optical properties, causing significant errors. Here, we calculate the optical properties of freshly-generated (fractal dimension Df = 1.8) and aged (Df = 2.6) SAs at 550 nm wavelength using the numericallyexact superposition T-Matrix method. These properties were expressed as functions of equivalent aerosol diameters as measured by contemporary aerosol instruments. This work improves upon previous efforts wherein SA optical properties were computed as a function of monomer number, rendering them unusable in practical applications. Future research will address the sensitivity of variation in refractive index, fractal prefactor, and monomer overlap of SAs on the reported empirical relationships.

Food web structure shaped by habitat size and climate across a latitudinal gradient.

PubMed

Romero, Gustavo Q; Piccoli, Gustavo C O; de Omena, Paula M; Gonçalves-Souza, Thiago

2016-10-01

Habitat size and climate are known to affect the trophic structure and dynamics of communities, but their interactive effects are poorly understood. Organisms from different trophic levels vary in terms of metabolic requirements and heat dissipation. Indeed, larger species such as keystone predators require more stable climatic conditions than their prey. Likewise, habitat size disproportionally affects large-sized predators, which require larger home ranges and are thus restricted to larger habitats. Therefore, food web structure in patchy ecosystems is expected to be shaped by habitat size and climate variations. Here we investigate this prediction using natural aquatic microcosm (bromeliad phytotelmata) food webs composed of litter resources (mainly detritus), detritivores, mesopredators, and top predators (damselflies). We surveyed 240 bromeliads of varying sizes (water retention capacity) across 12 open restingas in SE Brazil spread across a wide range of tropical latitudes (-12.6° to -27.6°, ca. 2,000 km) and climates (Δ mean annual temperature = 5.3°C). We found a strong increase in predator-to-detritivore mass ratio with habitat size, which was representative of a typical inverted trophic pyramid in larger ecosystems. However, this relationship was contingent among the restingas; slopes of linear models were steeper in more stable and favorable climates, leading to inverted trophic pyramids (and top-down control) being more pronounced in environments with more favorable climatic conditions. By contrast, detritivore-resource and mesopredator-detritivore mass ratios were not affected by habitat size or climate variations across latitudes. Our results highlight that the combined effects of habitat size, climate and predator composition are pivotal to understanding the impacts of multiple environmental factors on food web structure and dynamics. © 2016 by the Ecological Society of America.

Learning Progressions & Climate Change

ERIC Educational Resources Information Center

Parker, Joyce M.; de los Santos, Elizabeth X.; Anderson, Charles W.

2015-01-01

Our society is currently having serious debates about sources of energy and global climate change. But do students (and the public) have the requisite knowledge to engage these issues as informed citizenry? The learning-progression research summarized here indicates that only 10% of high school students typically have a level of understanding…

Central Region Headquarters

Science.gov Websites

local climate science or impacts, please contact the National Weather Service office in your area. link impacts everyone. How warm is a typical June across the Midwest? What is the average snowfall in January local climate science or impacts, contact the National Weather Service Office in your area. State

Carbon Offsets and Renewable Energy Certificates | Climate Neutral Research

Science.gov Websites

Campuses | NREL Carbon Offsets and Renewable Energy Certificates Carbon Offsets and Renewable Energy Certificates Carbon offsets are typically less expensive than installing hardware or undertaking climate reduction targets. While research campuses should not rely on carbon offsets long term, they can

A framework for identifying tailored subsets of climate projections for impact and adaptation studies

NASA Astrophysics Data System (ADS)

Vidal, Jean-Philippe; Hingray, Benoît

2014-05-01

In order to better understand the uncertainties in the climate of the next decades, an increasingly large number of increasingly diverse climate projections is being produced by the climate research community through coordinated initiatives (e.g., CMIP5, CORDEX), but also through more specific experiments at both the global scale (perturbed parameter ensembles) and the regional-to-local scale (empirical statistical downscaling ensembles). When significant efforts are put into making such projections available online, very few works focus on how to make such an enormous amount of information actually usable by the impact and adaptation community. Climate services should therefore include guidelines and recommendations for identifying subsets of climate projections that would have (1) a size manageable by downstream modelling approaches and (2) the relevant properties for informing adaptation strategies. This works proposes a generic framework for identifying tailored subsets of climate projections that would meet both the objectives and the constraints of a specific impact / adaptation study in a typical top-down approach. This decision framework builds on two main preliminary tasks that lead to critical choices in the selection strategy: (1) understanding the requirements of the specific impact / adaptation study, and (2) characterizing the (downscaled) climate projections dataset available. An impact / adaptation study has two types of requirements. First, the study may aim at various outcomes for a given climate-related feature: the best estimate of the future, the range of possible futures, a set of representative futures, or a statistically interpretable ensemble of futures. Second, impact models may come with specific constraints on climate input variables, like spatio-temporal and between-variables coherence. Additionally, when concurrent impact models are used, the most restrictive constraints have to be considered in order to be able to assess the uncertainty associated from this modelling step. Besides, the climate projection dataset available for a given study has several characteristics that will heavily condition the type of conclusions that can be reached. Indeed, the dataset at hand may or not sample different types of uncertainty (socio-economic, structural, parametric, along with internal variability). Moreover, these types are present at different steps in the well-known cascade of uncertainty, from the emission / concentration scenarios and the global climate to the regional-to-local climate. Critical choices for the selection are therefore conditioned on all features above. The type of selection (picking out, culling, or statistical sampling) is closely related to the study objectives and the uncertainty types present in the dataset. Moreover, grounds for picking out or culling projections may stem from global, regional or feature-specific present-day performance, representativeness, or covered range. An example use of this framework is a hierarchical selection for 3 classes of impact models among 3000 transient climate projections from different runs of 4 GCMs, statistically downscaled by 3 probabilistic methods, and made available for an integrated water resource adaptation study in the Durance catchment (southern French Alps). This work is part of the GICC R2D2-20501 project (Risk, water Resources and sustainable Development of the Durance catchment in 2050) and the EU FP7 COMPLEX2 project (Knowledge Based Climate Mitigation Systems for a Low Carbon Economy).