Science.gov

Sample records for administration climate prediction

  1. Climate prediction and predictability

    NASA Astrophysics Data System (ADS)

    Allen, Myles

    2010-05-01

    Climate prediction is generally accepted to be one of the grand challenges of the Geophysical Sciences. What is less widely acknowledged is that fundamental issues have yet to be resolved concerning the nature of the challenge, even after decades of research in this area. How do we verify or falsify a probabilistic forecast of a singular event such as anthropogenic warming over the 21st century? How do we determine the information content of a climate forecast? What does it mean for a modelling system to be "good enough" to forecast a particular variable? How will we know when models and forecasting systems are "good enough" to provide detailed forecasts of weather at specific locations or, for example, the risks associated with global geo-engineering schemes. This talk will provide an overview of these questions in the light of recent developments in multi-decade climate forecasting, drawing on concepts from information theory, machine learning and statistics. I will draw extensively but not exclusively from the experience of the climateprediction.net project, running multiple versions of climate models on personal computers.

  2. Predicting climate change

    SciTech Connect

    Drake, J.B.

    1995-12-31

    Few scientific topics evoke such general interests and public discussion as climate change. It is a subject that has been highly politicized. New results enter the environmental debate as evidence supporting a position. Usually the qualifiers, the background, and perspective needed to understand the result have been stripped away to form an appropriate sound bite. The attention is understandable given the importance of climate to agriculture and energy use. Fear of global warming and the greenhouse effect has been justification for reducing the use of fossil fuels and increasing use of nuclear energy and alternative energy sources. It has been suggested to avoid climate change, a return to a preindustrial level of emissions is necessary. The subject of this article is not the policy implications of greenhouse warming, or even the validity of the premise that global warming caused by the greenhouse effect is occurring. The subject is the current array of concepts and tools available to understand and predict the earth`s climate based on mathematical models of physical processes. These tools for climate simulations include some of the world`s most powerful computers, including the Intel Paragon XP/S 150 at ORNL. With these tools, the authors are attempting to predict the climate changes that may occur 100 years from now for different temperatures of the earth`s surface that will likely result from rising levels of carbon dioxide in the atmosphere.

  3. Improving Climate Prediction By Climate Monitoring

    NASA Astrophysics Data System (ADS)

    Leroy, S. S.; Redaelli, G.; Grassi, B.

    2014-12-01

    Various climate agencies are pursuing concepts of space-based atmospheric monitoring based on ideas of empirically verifiable accuracy in observations. Anticipating that atmospheric monitoring systems based in observing the emitted longwave spectrum, the reflected shortwave spectrum, and radio occultation are implemented, we seek to discover how long-term records in these quantities might be used to improve our ability to predict climate change. This is a follow-up to a previous study that found that climate monitoring by remote sensing better informs climate prediction than does climate monitoring in situ. We have used the output of a CMIP5 historical scenario to hind-cast observation types being considered for space-based atmospheric monitoring to modify ensemble prediction of multi-decadal climate change produced by a CMIP5 future scenario. Specifically, we have considered spatial fingerprints of 1970­-2005 averages and trends in hind-cast observations to improve global average surface air temperature change from 2005 to 2100. Correlations between hind-cast observations at individual locations on the globe and multi-decadal change are generally consistent with a null-correlation distribution. We have found that the modes in inter-model differences in hind-casts are clearly identified with tropical clouds, but only Arctic warming as can be identified in radio occultation observations correlates with multi-decadal change, but only with 80% confidence. Understanding how long-term monitoring can be used to improve climate prediction remains an unsolved problem, but it is anticipated that improving climate prediction will depend strongly on an ability to distinguish between climate forcing and climate response in remotely sensed observables.

  4. Solar weather/climate predictions

    NASA Technical Reports Server (NTRS)

    Schatten, K. H.; Goldberg, R. A.; Mitchell, J. M.; Olson, R.; Schaefer, J.; Silverman, S.; Wilcox, J.; Williams, G.

    1979-01-01

    Solar variability influences upon terrestrial weather and climate are addressed. Both the positive and negative findings are included and specific predictions, areas of further study, and recommendations listed.

  5. Is Climate Change Predictable? Really?

    SciTech Connect

    Dannevik, W P; Rotman, D A

    2005-11-14

    This project is the first application of a completely different approach to climate modeling, in which new prognostic equations are used to directly compute the evolution of two-point correlations. This project addresses three questions that are critical for the credibility of the science base for climate prediction: (1) What is the variability spectrum at equilibrium? (2) What is the rate of relaxation when subjected to external perturbations? (3) Can variations due to natural processes be distinguished from those due to transient external forces? The technical approach starts with the evolution equation for the probability distribution function and arrives at a prognostic equation for ensemble-mean two-point correlations, bypassing the detailed weather calculation. This work will expand our basic understanding of the theoretical limits of climate prediction and stimulate new experiments to perform with conventional climate models. It will furnish statistical estimates that are inaccessible with conventional climate simulations and likely will raise important new questions about the very nature of climate change and about how (and whether) climate change can be predicted. Solid progress on such issues is vital to the credibility of the science base for climate change research and will provide policymakers evaluating tradeoffs among energy technology options and their attendant environmental and economic consequences.

  6. Detecting failure of climate predictions

    NASA Astrophysics Data System (ADS)

    Runge, Michael C.; Stroeve, Julienne C.; Barrett, Andrew P.; McDonald-Madden, Eve

    2016-09-01

    The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055.

  7. Detecting failure of climate predictions

    USGS Publications Warehouse

    Runge, Michael C.; Stroeve, Julienne C.; Barrett, Andrew P.; McDonald-Madden, Eve

    2016-01-01

    The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty1, 2. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies3. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055.

  8. Administrative, Faculty, and Staff Perceptions of Organizational Climate and Commitment in Christian Higher Education

    ERIC Educational Resources Information Center

    Thomas, John Charles

    2008-01-01

    Findings of 957 surveyed employees from four evangelical higher education institutions found a negative correlation for climate and commitment and staff members. Administrators were found to have a more favorable view of their institutional climate than staff. Employee age, tenure, and classification had predictive value for organizational…

  9. Receivers Gather Data for Climate, Weather Prediction

    NASA Technical Reports Server (NTRS)

    2012-01-01

    Signals from global positioning system (GPS) satellites are now being used for more than just location and navigation information. By looking at the radio waves from GPS satellites, a technology developed at NASA s Jet Propulsion Laboratory (JPL) not only precisely calculates its position, but can also use a technique known as radio occultation to help scientists study the Earth s atmosphere and gravity field to improve weather forecasts, monitor climate change, and enhance space weather research. The University Corporation for Atmospheric Research (UCAR), a nonprofit group of universities in Boulder, Colorado, compares radio occultation to the appearance of a pencil when viewed though a glass of water. The water molecules change the path of visible light waves so that the pencil appears bent, just like molecules in the air bend GPS radio signals as they pass through (or are occulted by) the atmosphere. Through measurements of the amount of bending in the signals, scientists can construct detailed images of the ionosphere (the energetic upper part of the atmosphere) and also gather information about atmospheric density, pressure, temperature, and moisture. Once collected, this data can be input into weather forecasting and climate models for weather prediction and climate studies. Traditionally, such information is obtained through the use of weather balloons. In 1998, JPL started developing a new class of GPS space science receivers, called Black Jack, that could take precise measurements of how GPS signals are distorted or delayed along their way to the receiver. By 2006, the first demonstration of a GPS radio occultation constellation was launched through a collaboration among Taiwan s National Science Council and National Space Organization, the U.S. National Science Foundation, NASA, the National Oceanic and Atmospheric Administration (NOAA), and other Federal entities. Called the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC

  10. TODAY: EPA Administrator Joins Senior Administration Officials at White House for Climate and Health Event

    EPA Pesticide Factsheets

    W ASHINGTON- Today, during National Public Health Week, EPA Administrator Gina McCarthy will join senior Obama Administration officials and representatives from the public and private sectors at the White House for a climate and public health

  11. Prediction and predictability of North American seasonal climate variability

    NASA Astrophysics Data System (ADS)

    Infanti, Johnna M.

    Climate prediction on short time-scales such as months to seasons is of broad and current interest in the scientific research community. Monthly and seasonal climate prediction of variables such as precipitation, temperature, and sea surface temperature (SST) has implications for users in the agricultural and water management domains, among others. It is thus important to further understand the complexities of prediction of these variables using the most recent practices in climate prediction. The overarching goal of this dissertation is to determine the important contributions to seasonal prediction skill, predictability, and variability over North America using current climate prediction models and approaches. This dissertation aims to study a variety of approaches to seasonal climate prediction of variables over North America, including both climate prediction systems and methods of analysis. We utilize the North American Multi-Model Ensemble (NMME) System for Intra-Seasonal to Inter-Annual Prediction (ISI) to study seasonal climate prediction skill of North American and in particular for southeast US precipitation. We find that NMME results are often equal to or better than individual model results in terms of skill, as expected, making it a reasonable choice for southeast US seasonal climate predictions. However, climate models, including those involved in NMME, typically overestimate eastern Pacific warming during central Pacific El Nino events, which can affect regions that are influenced by teleconnections, such as the southeast US. Community Climate System Model version 4.0 (CCSM4) hindacasts and forecasts are included in NMME, and we preform a series of experiments that examine contributions to skill from certain drivers of North American climate prediction. The drivers we focus on are sea surface temperatures (SSTs) and their accuracy, land and atmosphere initialization, and ocean-atmosphere coupling. We compare measures of prediction skill of

  12. Climate Model Predictions and Climate Observations: Where are we going?

    NASA Astrophysics Data System (ADS)

    Wielicki, B. A.

    2011-12-01

    Climate Model Predictions and Climate Observations: Where are we going? A climate model is the explicit expression of a scientific hypothesis on how the Earth's climate works. We test these models against a wide variety of climate observations: climatological mean global maps of many climate variables, seasonal cycles, inter-annual variability, decadal change, and even glacial/interglacial cycles. The most direct method of testing the accuracy of climate model decadal change predictions is to use decades of highly accurate data for radiative forcing and climate response. Relevant data for these tests include all of the key climate variables known to play a role in climate change. There are two primary advantages of this approach: a) it uses the most complete set of climate variables, and b) its directly tests decadal prediction against decadal observations. There are two disadvantages, however, of this direct approach: a) it takes decades to collect enough data to overcome natural variability, and b) the accuracy required for small decadal change signals is very high: much higher than typical weather or research observations. As a result, like paleo data, data accuracy becomes a critical issue. Despite the critical need for climate models to be tested against decadal change observations, we currently have no international designed and implemented climate observing system. There are no international commitments to create one (accuracy) or to maintain one (decades of observations). What is called the Global Climate Observing System is instead a set of documents about how weather and research observing systems might be improved to better provide a climate observing system. Given the importance of this challenge, this seems a strange condition. How did we get here? Is a rigorous climate observing system so expensive as to be unaffordable? Has the science community failed to clearly prioritize and define the requirements of such a system? Is the technology to create

  13. Climate Modeling and Prediction at NSIPP

    NASA Technical Reports Server (NTRS)

    Suarez, Max; Einaudi, Franco (Technical Monitor)

    2001-01-01

    The talk will review modeling and prediction efforts undertaken as part of NASA's Seasonal to Interannual Prediction Project (NSIPP). The focus will be on atmospheric model results, including its use for experimental seasonal prediction and the diagnostic analysis of climate anomalies. The model's performance in coupled experiments with land and atmosphere models will also be discussed.

  14. Predicting Pleistocene climate from vegetation

    NASA Astrophysics Data System (ADS)

    Loehle, C.

    2006-10-01

    Climates at the Last Glacial Maximum have been inferred from fossil pollen assemblages, but these inferred climates are colder than those produced by climate simulations. Biogeographic evidence also argues against these inferred cold climates. The recolonization of glaciated zones in eastern North America following the last ice age produced distinct biogeographic patterns. It has been assumed that a wide zone south of the ice was tundra or boreal parkland (Boreal-Parkland Zone or BPZ), which would have been recolonized from southern refugia as the ice melted, but the patterns in this zone differ from those in the glaciated zone, which creates a major biogeographic anomaly. In the glacial zone, there are few endemics but in the BPZ there are many across multiple taxa. In the glacial zone, there are the expected gradients of genetic diversity with distance from the ice-free zone, but no evidence of this is found in the BPZ. Many races and related species exist in the BPZ which would have merged or hybridized if confined to the same refugia. Evidence for distinct southern refugia for most temperate species is lacking. Extinctions of temperate flora were rare. The interpretation of spruce as a boreal climate indicator may be mistaken over much of the region if the spruce was actually an extinct temperate species. All of these anomalies call into question the concept that climates in the zone south of the ice were very cold or that temperate species had to migrate far to the south. Similar anomalies exist in Europe and on tropical mountains. An alternate hypothesis is that low CO2 levels gave an advantage to pine and spruce, which are the dominant trees in the BPZ, and to herbaceous species over trees, which also fits the observed pattern. Most temperate species could have survived across their current ranges at lower abundance by retreating to moist microsites. These would be microrefugia not easily detected by pollen records, especially if most species became rare

  15. How Agribusiness Uses Climate Predictions: Implications for Climate Research and Provision of Predictions.

    NASA Astrophysics Data System (ADS)

    Sonka, S. T.; Changnon, S. A., Jr.; Hofing, S. L.

    1992-12-01

    The paper presents an analysis of climate prediction needs and uses within six important subsegments of the agribusiness sector. Results are based on a mail survey of 114 managers. Although nearly 70% of the respondents indicated some use of climate predictions in the last year, only 1 in 8 of the respondents used that information in a specific decision. Lack of sufficient accuracy and prediction lead time were identified as two important impediments to current use of climate predictions. Estimates of necessary accuracy levels and lead time are reported both for the group average and by segments of need. Recommendations are offered regarding research needs to enhance climate prediction and activities of the government and the private sector to improve use of climate predictions.

  16. MONDAY: EPA Administrator Joins Senior Administration Officials at White House for Climate and Health Event

    EPA Pesticide Factsheets

    W ASHINGTON- On Monday, EPA Administrator Gina McCarthy will join senior Obama Administration officials and representatives from the public and private sectors at the White House for a climate and health announcement from the U.S. Global Chang

  17. Developing Models for Predictive Climate Science

    SciTech Connect

    Drake, John B; Jones, Philip W

    2007-01-01

    The Community Climate System Model results from a multi-agency collaboration designed to construct cutting-edge climate science simulation models for a broad research community. Predictive climate simulations are currently being prepared for the petascale computers of the near future. Modeling capabilities are continuously being improved in order to provide better answers to critical questions about Earth's climate. Climate change and its implications are front page news in today's world. Could global warming be responsible for the July 2006 heat waves in Europe and the United States? Should more resources be devoted to preparing for an increase in the frequency of strong tropical storms and hurricanes like Katrina? Will coastal cities be flooded due to a rise in sea level? The National Climatic Data Center (NCDC), which archives all weather data for the nation, reports that global surface temperatures have increased over the last century, and that the rate of increase is three times greater since 1976. Will temperatures continue to climb at this rate, will they decline again, or will the rate of increase become even steeper? To address such a flurry of questions, scientists must adopt a systematic approach and develop a predictive framework. With responsibility for advising on energy and technology strategies, the DOE is dedicated to advancing climate research in order to elucidate the causes of climate change, including the role of carbon loading from fossil fuel use. Thus, climate science--which by nature involves advanced computing technology and methods--has been the focus of a number of DOE's SciDAC research projects. Dr. John Drake (ORNL) and Dr. Philip Jones (LANL) served as principal investigators on the SciDAC project, 'Collaborative Design and Development of the Community Climate System Model for Terascale Computers.' The Community Climate System Model (CCSM) is a fully-coupled global system that provides state-of-the-art computer simulations of the

  18. Climate Predictability and Long Term Memory

    NASA Astrophysics Data System (ADS)

    Zhu, X.; Blender, R.; Fraedrich, K.; Liu, Z.

    2010-09-01

    The benefit of climate Long Term Memory (LTM) for long term prediction is assessed using data from a millennium control simulation with the atmosphere ocean general circulation model ECHAM5/MPIOM. The forecast skills are evaluated for surface temperature time series at individual grid points. LTM is characterised by the Hurst exponent in the power-law scaling of the fluctuation function which is determined by detrended fluctuation analysis (DFA). LTM with a Hurst exponent close to 0.9 occurs mainly in high latitude oceans, which are also characterized by high potential predictability. Climate predictability is diagnosed in terms of potentially predictable variance fractions. Explicit prediction experiments for various time steps are conducted on a grid point basis using an auto-correlation (AR1) predictor: in regions with LTM, prediction skills are beyond that expected from red noise persistence; exceptions occur in some areas in the southern oceans and over the northern hemisphere continents. Extending the predictability analysis to the fully forced simulation shows large improvement in prediction skills.

  19. Administrative Climate and Novices' Intent to Remain Teaching

    ERIC Educational Resources Information Center

    Pogodzinski, Ben; Youngs, Peter; Frank, Kenneth A.; Belman, Dale

    2012-01-01

    Using survey data from novice teachers at the elementary and middle school level across 11 districts, multilevel logistic regressions were estimated to examine the association between novices' perceptions of the administrative climate and their desire to remain teaching within their schools. We find that the probability that a novice teacher…

  20. Probabilistic Predictions of Regional Climate Change

    NASA Astrophysics Data System (ADS)

    Harris, G. R.; Sexton, D. M.; Booth, B. B.; Brown, K.; Collins, M.; Murphy, J. M.

    2009-12-01

    We present a methodology for quantifying the leading sources of uncertainty in climate change projections that allows more robust prediction of probability distribution functions (PDFs) for transient regional climate change than is possible, for example, with the multimodel ensemble in the the CMIP3 archive used for the IPCC Fourth Assessment. Uncertainty in equilibrium climate response has been systematically explored by varying uncertain parameters in the atmosphere, sea-ice and surface components in a ensemble of simulations with the third version of the Hadley Centre model coupled to a slab ocean. The ensemble is used to emulate the response for one million parameter combinations, ensuring robust prediction of the prior distributions of equilibrium response for this model. Posterior PDFs are estimated using a weighting scheme that calculates the likelihood for each model version, based upon its ability to reproduce a large set of observed seasonal-mean climate variables. Information from the CMIP3 simulations is used to assess the effect of structural uncertainty, and this is included as an additional variance in the weighting. The posterior distributions of equilibrium response are shown to be relatively robust to variation in key assumptions of the method. A time-scaling technique that maps equilibrium to transient change is then used to predict PDFs for transient regional climate change for specified emissions scenarios. The scaling uses a simple climate model (SCM), with global climate feedbacks and local response sampled from the equilibrium response, and other SCM parameters tuned to the response of other AOGCM ensembles. Use of the SCM allows efficient sampling of uncertainties not fully sampled by expensive GCM simulation, including uncertainty in aerosol radiative forcing, the rate of ocean heat uptake, and the strength of carbon-cycle feedbacks. Uncertainties arising from statistical components of the method, such as emulation or scaling, are

  1. Drought Predictability and Prediction in a Changing Climate: Assessing Current Predictive Knowledge and Capabilities, User Requirements and Research Priorities

    NASA Technical Reports Server (NTRS)

    Schubert, Siegfried

    2011-01-01

    Drought is fundamentally the result of an extended period of reduced precipitation lasting anywhere from a few weeks to decades and even longer. As such, addressing drought predictability and prediction in a changing climate requires foremost that we make progress on the ability to predict precipitation anomalies on subseasonal and longer time scales. From the perspective of the users of drought forecasts and information, drought is however most directly viewed through its impacts (e.g., on soil moisture, streamflow, crop yields). As such, the question of the predictability of drought must extend to those quantities as well. In order to make progress on these issues, the WCRP drought information group (DIG), with the support of WCRP, the Catalan Institute of Climate Sciences, the La Caixa Foundation, the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and the National Science Foundation, has organized a workshop to focus on: 1. User requirements for drought prediction information on sub-seasonal to centennial time scales 2. Current understanding of the mechanisms and predictability of drought on sub-seasonal to centennial time scales 3. Current drought prediction/projection capabilities on sub-seasonal to centennial time scales 4. Advancing regional drought prediction capabilities for variables and scales most relevant to user needs on sub-seasonal to centennial time scales. This introductory talk provides an overview of these goals, and outlines the occurrence and mechanisms of drought world-wide.

  2. On Prediction and Predictability of the Arctic Climate System

    NASA Astrophysics Data System (ADS)

    Maslowski, W.; Clement Kinney, J.; Roberts, A.; Higgins, M.; Osinski, R.; Cassano, J. J.; Craig, A.; Gutowski, W. J.; Lettenmaier, D. P.; Lipscomb, W. H.; Tulaczyk, S. M.; Zeng, X.

    2012-12-01

    Arctic sea ice is a key indicator of the state of Earth's climate because of both its sensitivity to warming and its role in amplifying climate change. However, the current system-level understanding and representation of critical arctic processes and feedbacks in state-of-the-art Earth System Models (EaSMs) is still inadequate. This becomes increasingly critical as the perennial and total summer sea ice cover continues its accelerated decline that started in the late 1990s. Growing evidence suggests that the shrinking Arctic ice pack affects pan-Arctic atmospheric and oceanic circulation, snow cover, the Greenland ice sheet, permafrost and vegetation. Such changes could have significant ramifications for global sea level, the global surface energy and moisture budget, atmospheric and oceanic circulations, geosphere-biosphere feedbacks, as well as affecting native coastal communities, and international commerce. We evaluate available results from CMIP5 models against limited observations for their skill in representing recent decadal variability of Arctic sea ice area, thickness, drift and export. We also intercompare results from CMIP5 models with selected CMIP3 models and a hierarchy of regional ice-ocean and fully coupled climate models to demonstrate possible gains or outstanding limitations in representing past and present climate variability in the Arctic. Some of the limitations we have diagnosed in the CMIP3 family of models include: northward oceanic heat fluxes and their interface with the atmosphere, distribution of sea ice area and thickness, variability of sea ice volume in the Arctic Ocean, and freshwater (both solid and liquid) export into the North Atlantic. We argue that the ability of global models to realistically reproduce the above processes affecting recent warming and sea ice melt in the Arctic Ocean distorts predictability of EaSMs and limits the accuracy of their future arctic and global climate predictions. To better understand the past

  3. Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models

    USGS Publications Warehouse

    Romanach, Stephanie; Watling, James I.; Fletcher, Robert J.; Speroterra, Carolina; Bucklin, David N.; Brandt, Laura A.; Pearlstine, Leonard G.; Escribano, Yesenia; Mazzotti, Frank J.

    2014-01-01

    Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

  4. An prediction and explanation of 'climatic swing

    NASA Astrophysics Data System (ADS)

    Barkin, Yury

    2010-05-01

    Introduction. In works of the author [1, 2] the mechanism has been offered and the scenario of formation of congelations and warming of the Earth and their inversion and asymmetric displays in opposite hemispheres has been described. These planetary thermal processes are connected with gravitational forced oscillations of the core-mantle system of the Earth, controlling and directing submission of heat in the top layers of the mantle and on a surface of the Earth. It is shown, that action of this mechanism should observed in various time scales. In particular significant changes of a climate should occur to the thousand-year periods, with the periods in tens and hundred thousand years. Thus excitation of system the core-mantle is caused by planetary secular orbital perturbations and by perturbations of the Earth rotation which as is known are characterized by significant amplitudes. But also in a short time scale the climate variations with the interannual and decade periods also should be observed, how dynamic consequences of the swing of the core-mantle system of the Earth with the same periods [3]. The fundamental phenomenon of secular polar drift of the core relatively to the viscous-elastic and changeable mantle [4] in last years has obtained convincing confirmations various geosciences. Reliable an attribute of influence of oscillations of the core on a variation of natural processes is their property of inversion when, for example, activity of process accrues in northern hemisphere and decreases in a southern hemisphere. Such contrast secular changes in northern and southern (N/S) hemispheres have been predicted on the base of geodynamic model [1] and revealed according to observations: from gravimetry measurements of a gravity [5]; in determination of a secular trend of a sea level, as global, and in northern and southern hemispheres [6, 7]; in redistribution of air masses [6, 8]; in geodetic measurements of changes of average radiuses of northern and

  5. Initialized near-term regional climate change prediction

    PubMed Central

    Doblas-Reyes, F. J.; Andreu-Burillo, I.; Chikamoto, Y.; García-Serrano, J.; Guemas, V.; Kimoto, M.; Mochizuki, T.; Rodrigues, L. R. L.; van Oldenborgh, G. J.

    2013-01-01

    Climate models are seen by many to be unverifiable. However, near-term climate predictions up to 10 years into the future carried out recently with these models can be rigorously verified against observations. Near-term climate prediction is a new information tool for the climate adaptation and service communities, which often make decisions on near-term time scales, and for which the most basic information is unfortunately very scarce. The Fifth Coupled Model Intercomparison Project set of co-ordinated climate-model experiments includes a set of near-term predictions in which several modelling groups participated and whose forecast quality we illustrate here. We show that climate forecast systems have skill in predicting the Earth's temperature at regional scales over the past 50 years and illustrate the trustworthiness of their predictions. Most of the skill can be attributed to changes in atmospheric composition, but also partly to the initialization of the predictions. PMID:23591882

  6. Initialized near-term regional climate change prediction.

    PubMed

    Doblas-Reyes, F J; Andreu-Burillo, I; Chikamoto, Y; García-Serrano, J; Guemas, V; Kimoto, M; Mochizuki, T; Rodrigues, L R L; van Oldenborgh, G J

    2013-01-01

    Climate models are seen by many to be unverifiable. However, near-term climate predictions up to 10 years into the future carried out recently with these models can be rigorously verified against observations. Near-term climate prediction is a new information tool for the climate adaptation and service communities, which often make decisions on near-term time scales, and for which the most basic information is unfortunately very scarce. The Fifth Coupled Model Intercomparison Project set of co-ordinated climate-model experiments includes a set of near-term predictions in which several modelling groups participated and whose forecast quality we illustrate here. We show that climate forecast systems have skill in predicting the Earth's temperature at regional scales over the past 50 years and illustrate the trustworthiness of their predictions. Most of the skill can be attributed to changes in atmospheric composition, but also partly to the initialization of the predictions.

  7. Predicting climate effects on Pacific sardine.

    PubMed

    Deyle, Ethan R; Fogarty, Michael; Hsieh, Chih-hao; Kaufman, Les; MacCall, Alec D; Munch, Stephan B; Perretti, Charles T; Ye, Hao; Sugihara, George

    2013-04-16

    For many marine species and habitats, climate change and overfishing present a double threat. To manage marine resources effectively, it is necessary to adapt management to changes in the physical environment. Simple relationships between environmental conditions and fish abundance have long been used in both fisheries and fishery management. In many cases, however, physical, biological, and human variables feed back on each other. For these systems, associations between variables can change as the system evolves in time. This can obscure relationships between population dynamics and environmental variability, undermining our ability to forecast changes in populations tied to physical processes. Here we present a methodology for identifying physical forcing variables based on nonlinear forecasting and show how the method provides a predictive understanding of the influence of physical forcing on Pacific sardine.

  8. Predicting climate effects on Pacific sardine

    PubMed Central

    Deyle, Ethan R.; Fogarty, Michael; Hsieh, Chih-hao; Kaufman, Les; MacCall, Alec D.; Munch, Stephan B.; Perretti, Charles T.; Ye, Hao; Sugihara, George

    2013-01-01

    For many marine species and habitats, climate change and overfishing present a double threat. To manage marine resources effectively, it is necessary to adapt management to changes in the physical environment. Simple relationships between environmental conditions and fish abundance have long been used in both fisheries and fishery management. In many cases, however, physical, biological, and human variables feed back on each other. For these systems, associations between variables can change as the system evolves in time. This can obscure relationships between population dynamics and environmental variability, undermining our ability to forecast changes in populations tied to physical processes. Here we present a methodology for identifying physical forcing variables based on nonlinear forecasting and show how the method provides a predictive understanding of the influence of physical forcing on Pacific sardine. PMID:23536299

  9. How does spatial variability of climate affect catchment streamflow predictions?

    EPA Science Inventory

    Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distribute...

  10. Operationalizing climate-based epidemic prediction models: Rift Valley fever prediction system experience

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Background There is considerable optimism that climate data and predictions will facilitate early warning of infectious disease epidemics. Interest in climate-based epidemic forecasting stems from climate-disease associations and global climate change (rising temperatures may extend arthropod vecto...

  11. Enhancing seasonal climate prediction capacity for the Pacific countries

    NASA Astrophysics Data System (ADS)

    Kuleshov, Y.; Jones, D.; Hendon, H.; Charles, A.; Cottrill, A.; Lim, E.-P.; Langford, S.; de Wit, R.; Shelton, K.

    2012-04-01

    Seasonal and inter-annual climate variability is a major factor in determining the vulnerability of many Pacific Island Countries to climate change and there is need to improve weekly to seasonal range climate prediction capabilities beyond what is currently available from statistical models. In the seasonal climate prediction project under the Australian Government's Pacific Adaptation Strategy Assistance Program (PASAP), we describe a comprehensive project to strengthen the climate prediction capacities in National Meteorological Services in 14 Pacific Island Countries and East Timor. The intent is particularly to reduce the vulnerability of current services to a changing climate, and improve the overall level of information available assist with managing climate variability. Statistical models cannot account for aspects of climate variability and change that are not represented in the historical record. In contrast, dynamical physics-based models implicitly include the effects of a changing climate whatever its character or cause and can predict outcomes not seen previously. The transition from a statistical to a dynamical prediction system provides more valuable and applicable climate information to a wide range of climate sensitive sectors throughout the countries of the Pacific region. In this project, we have developed seasonal climate outlooks which are based upon the current dynamical model POAMA (Predictive Ocean-Atmosphere Model for Australia) seasonal forecast system. At present, meteorological services of the Pacific Island Countries largely employ statistical models for seasonal outlooks. Outcomes of the PASAP project enhanced capabilities of the Pacific Island Countries in seasonal prediction providing National Meteorological Services with an additional tool to analyse meteorological variables such as sea surface temperatures, air temperature, pressure and rainfall using POAMA outputs and prepare more accurate seasonal climate outlooks.

  12. Processes in Decadal Climate Variability and their Incorporation into a Decadal Climate Prediction System

    NASA Astrophysics Data System (ADS)

    Proemmel, K.; Cubasch, U.; Vamborg, F.

    2012-12-01

    The quality of decadal climate predictions rests fundamentally on the ability of the forecast models realistically to simulate climate and its variability, in particular at decadal timescales. The new German research project "MiKlip - Decadal Predictions" (http://www.fona-miklip.de/en/) aims to develop a system for climate predictions for up to a decade ahead that can then be applied by an operational agency such as the German Meteorological Service DWD. This climate prediction system is based on the MPI-M Earth System Model (MPI-ESM) from the Max Planck Institute for Meteorology in Germany. Different aspects of decadal climate predictions are considered in MiKlip like initialisation strategies, the predictive skill on the regional scale with focus on Europe and Africa and the systematic evaluation of the prediction system. Another part of MiKlip deals with the incorporation of those processes in climate models that are important for the realistic representation of decadal climate variability, and the understanding of the important processes in the numerical prediction system. Processes that have the potential to improve decadal climate predictions are related to e.g. Arctic sea ice, atmospheric chemistry, large volcanic eruptions, atmosphere-ocean coupling, stratosphere and land-atmosphere interaction. The work dealing with the processes can be categorized into assessing the effects of enhanced resolution and of advanced parameterizations and numerics, investigating mechanisms of decadal variability, improvement of existing system components and coupling of additional climate subsystems.

  13. Predicting phenology by integrating ecology, evolution and climate science

    USGS Publications Warehouse

    Pau, Stephanie; Wolkovich, Elizabeth M.; Cook, Benjamin I.; Davies, T. Jonathan; Kraft, Nathan J.B.; Bolmgren, Kjell; Betancourt, Julio L.; Cleland, Elsa E.

    2011-01-01

    Forecasting how species and ecosystems will respond to climate change has been a major aim of ecology in recent years. Much of this research has focused on phenology — the timing of life-history events. Phenology has well-demonstrated links to climate, from genetic to landscape scales; yet our ability to explain and predict variation in phenology across species, habitats and time remains poor. Here, we outline how merging approaches from ecology, climate science and evolutionary biology can advance research on phenological responses to climate variability. Using insight into seasonal and interannual climate variability combined with niche theory and community phylogenetics, we develop a predictive approach for species' reponses to changing climate. Our approach predicts that species occupying higher latitudes or the early growing season should be most sensitive to climate and have the most phylogenetically conserved phenologies. We further predict that temperate species will respond to climate change by shifting in time, while tropical species will respond by shifting space, or by evolving. Although we focus here on plant phenology, our approach is broadly applicable to ecological research of plant responses to climate variability.

  14. Validating predictions from climate envelope models

    USGS Publications Warehouse

    Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.

    2013-01-01

    Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.

  15. Contents of Climate Predictions Desired by Agricultural Decision Makers.

    NASA Astrophysics Data System (ADS)

    Changnon, Stanley A.

    1992-12-01

    In-depth interviews with 27 executives in various agribusiness defined usage and needs for climate predictions. Predictions are acquired from various public and private sources but are seldom used in making major decision. Users exhibited little trust of climate predictions, relying heavily on recent weather conditions as the basis of prediction. Additions to predictions involving climatic information would better serve the needs of most of agribusiness. Improved predictive accuracies alone will not materially increase usage. A need exists to familiarize agribusiness leaders with the information currently available, and to realize benefits from this information; many agribusinesses will need to develop models and procedures that allow integration of future weather conditions (actual and predicted) with their corporate activities and economic conditions.

  16. The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

    NASA Astrophysics Data System (ADS)

    Boer, George J.; Smith, Douglas M.; Cassou, Christophe; Doblas-Reyes, Francisco; Danabasoglu, Gokhan; Kirtman, Ben; Kushnir, Yochanan; Kimoto, Masahide; Meehl, Gerald A.; Msadek, Rym; Mueller, Wolfgang A.; Taylor, Karl E.; Zwiers, Francis; Rixen, Michel; Ruprich-Robert, Yohan; Eade, Rosie

    2016-10-01

    The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Prediction (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the "hiatus", volcanoes), including the study of the mechanisms that determine these behaviours. Groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to

  17. Ensemble-based Regional Climate Prediction: Political Impacts

    NASA Astrophysics Data System (ADS)

    Miguel, E.; Dykema, J.; Satyanath, S.; Anderson, J. G.

    2008-12-01

    Accurate forecasts of regional climate, including temperature and precipitation, have significant implications for human activities, not just economically but socially. Sub Saharan Africa is a region that has displayed an exceptional propensity for devastating civil wars. Recent research in political economy has revealed a strong statistical relationship between year to year fluctuations in precipitation and civil conflict in this region in the 1980s and 1990s. To investigate how climate change may modify the regional risk of civil conflict in the future requires a probabilistic regional forecast that explicitly accounts for the community's uncertainty in the evolution of rainfall under anthropogenic forcing. We approach the regional climate prediction aspect of this question through the application of a recently demonstrated method called generalized scalar prediction (Leroy et al. 2009), which predicts arbitrary scalar quantities of the climate system. This prediction method can predict change in any variable or linear combination of variables of the climate system averaged over a wide range spatial scales, from regional to hemispheric to global. Generalized scalar prediction utilizes an ensemble of model predictions to represent the community's uncertainty range in climate modeling in combination with a timeseries of any type of observational data that exhibits sensitivity to the scalar of interest. It is not necessary to prioritize models in deriving with the final prediction. We present the results of the application of generalized scalar prediction for regional forecasts of temperature and precipitation and Sub Saharan Africa. We utilize the climate predictions along with the established statistical relationship between year-to-year rainfall variability in Sub Saharan Africa to investigate the potential impact of climate change on civil conflict within that region.

  18. Towards predictive understanding of regional climate change

    NASA Astrophysics Data System (ADS)

    Xie, Shang-Ping; Deser, Clara; Vecchi, Gabriel A.; Collins, Matthew; Delworth, Thomas L.; Hall, Alex; Hawkins, Ed; Johnson, Nathaniel C.; Cassou, Christophe; Giannini, Alessandra; Watanabe, Masahiro

    2015-10-01

    Regional information on climate change is urgently needed but often deemed unreliable. To achieve credible regional climate projections, it is essential to understand underlying physical processes, reduce model biases and evaluate their impact on projections, and adequately account for internal variability. In the tropics, where atmospheric internal variability is small compared with the forced change, advancing our understanding of the coupling between long-term changes in upper-ocean temperature and the atmospheric circulation will help most to narrow the uncertainty. In the extratropics, relatively large internal variability introduces substantial uncertainty, while exacerbating risks associated with extreme events. Large ensemble simulations are essential to estimate the probabilistic distribution of climate change on regional scales. Regional models inherit atmospheric circulation uncertainty from global models and do not automatically solve the problem of regional climate change. We conclude that the current priority is to understand and reduce uncertainties on scales greater than 100 km to aid assessments at finer scales.

  19. Safety climate and prediction of ergonomic behavior.

    PubMed

    Khandan, Mohammad; Maghsoudipour, Maryam; Vosoughi, Shahram; Kavousi, Amir

    2013-01-01

    One of the most important ways to prevent accidents is to consider safety climate or culture. Moreover, some studies suggest that behavior contributes to 86%-96% of all injuries. This cross-sectional study took place in an Iranian petrochemical company in 2010. Vinodkumar and Bhasi's safety climate questionnaire and an ergonomic behavior sampling checklist were the data collection tools. Cronbach's α for questionnaire reliability was .928. With reference to the results of a pilot study, a sample of 1755 was determined for behavior sampling. We used principal component analysis (PCA) to derive the coefficient of paths in the path model and the Anderson-Rabin method to calculate factor scores. The results showed that safety climate was an effective predictor of ergonomic behavior (p < .01). They also showed the importance of decreasing the number of workers with negative safety climate. Moreover, it is necessary to promote workers' ergonomic behaviors in the workplace.

  20. Prediction of primary climate variability modes at the Beijing Climate Center

    NASA Astrophysics Data System (ADS)

    Ren, Hong-Li; Jin, Fei-Fei; Song, Lianchun; Lu, Bo; Tian, Ben; Zuo, Jinqing; Liu, Ying; Wu, Jie; Zhao, Chongbo; Nie, Yu; Zhang, Peiqun; Ba, Jin; Wu, Yujie; Wan, Jianghua; Yan, Yuping; Zhou, Fang

    2017-02-01

    Climate variability modes, usually known as primary climate phenomena, are well recognized as the most important predictability sources in subseasonal-interannual climate prediction. This paper begins by reviewing the research and development carried out, and the recent progress made, at the Beijing Climate Center (BCC) in predicting some primary climate variability modes. These include the El Niño-Southern Oscillation (ENSO), Madden-Julian Oscillation (MJO), and Arctic Oscillation (AO), on global scales, as well as the sea surface temperature (SST) modes in the Indian Ocean and North Atlantic, western Pacific subtropical high (WPSH), and the East Asian winter and summer monsoons (EAWM and EASM, respectively), on regional scales. Based on its latest climate and statistical models, the BCC has established a climate phenomenon prediction system (CPPS) and completed a hindcast experiment for the period 1991-2014. The performance of the CPPS in predicting such climate variability modes is systematically evaluated. The results show that skillful predictions have been made for ENSO, MJO, the Indian Ocean basin mode, the WPSH, and partly for the EASM, whereas less skillful predictions were made for the Indian Ocean Dipole (IOD) and North Atlantic SST Tripole, and no clear skill at all for the AO, subtropical IOD, and EAWM. Improvements in the prediction of these climate variability modes with low skill need to be achieved by improving the BCC's climate models, developing physically based statistical models as well as correction methods for model predictions. Some of the monitoring/prediction products of the BCC-CPPS are also introduced in this paper.

  1. 77 FR 74174 - National Oceanic and Atmospheric Administration (NOAA) National Climate Assessment and...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-12-13

    ... National Oceanic and Atmospheric Administration (NOAA) National Climate Assessment and Development Advisory... Atmospheric Administration (NOAA), Department of Commerce (DOC). ACTION: Notice of Open Meeting. SUMMARY: This notice sets forth the schedule of a forthcoming meeting of the DoC NOAA National Climate Assessment...

  2. Prediction technologies for assessment of climate change impacts

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Temperatures, precipitation, and weather patterns are changing, in response to increasing carbon dioxide in the atmosphere. With these relatively rapid changes, existing soil erosion prediction technologies that rely upon climate stationarity are potentially becoming less reliable. This is especiall...

  3. The interactive roles of mastery climate and performance climate in predicting intrinsic motivation.

    PubMed

    Buch, R; Nerstad, C G L; Säfvenbom, R

    2017-02-01

    This study examined the interplay between perceived mastery and performance climates in predicting increased intrinsic motivation. The results of a two-wave longitudinal study comprising of 141 individuals from three military academies revealed a positive relationship between a perceived mastery climate and increased intrinsic motivation only for individuals who perceived a low performance climate. This finding suggests a positive relationship between a perceived mastery climate and increased intrinsic motivation only when combined with low perceptions of a performance climate. Hence, introducing a performance climate in addition to a mastery climate can be an undermining motivational strategy, as it attenuates the positive relationship between a mastery climate and increased intrinsic motivation. Implications for future research and practice are discussed.

  4. Climatic extremes improve predictions of spatial patterns of tree species

    USGS Publications Warehouse

    Zimmermann, N.E.; Yoccoz, N.G.; Edwards, T.C.; Meier, E.S.; Thuiller, W.; Guisan, A.; Schmatz, D.R.; Pearman, P.B.

    2009-01-01

    Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D2, +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

  5. Predicting the Impacts of Climate Change on Central American Agriculture

    NASA Astrophysics Data System (ADS)

    Winter, J. M.; Ruane, A. C.; Rosenzweig, C.

    2011-12-01

    Agriculture is a vital component of Central America's economy. Poor crop yields and harvest reliability can produce food insecurity, malnutrition, and conflict. Regional climate models (RCMs) and agricultural models have the potential to greatly enhance the efficiency of Central American agriculture and water resources management under both current and future climates. A series of numerical experiments was conducted using Regional Climate Model Version 3 (RegCM3) and the Weather Research and Forecasting Model (WRF) to evaluate the ability of RCMs to reproduce the current climate of Central America and assess changes in temperature and precipitation under multiple future climate scenarios. Control simulations were thoroughly compared to a variety of observational datasets, including local weather station data, gridded meteorological data, and high-resolution satellite-based precipitation products. Future climate simulations were analyzed for both mean shifts in climate and changes in climate variability, including extreme events (droughts, heat waves, floods). To explore the impacts of changing climate on maize, bean, and rice yields in Central America, RCM output was used to force the Decision Support System for Agrotechnology Transfer Model (DSSAT). These results were synthesized to create climate change impacts predictions for Central American agriculture that explicitly account for evolving distributions of precipitation and temperature extremes.

  6. Advancements in decadal climate predictability: The role of nonoceanic drivers

    NASA Astrophysics Data System (ADS)

    Bellucci, A.; Haarsma, R.; Bellouin, N.; Booth, B.; Cagnazzo, C.; Hurk, B.; Keenlyside, N.; Koenigk, T.; Massonnet, F.; Materia, S.; Weiss, M.

    2015-06-01

    We review recent progress in understanding the role of sea ice, land surface, stratosphere, and aerosols in decadal-scale predictability and discuss the perspectives for improving the predictive capabilities of current Earth system models (ESMs). These constituents have received relatively little attention because their contribution to the slow climatic manifold is controversial in comparison to that of the large heat capacity of the oceans. Furthermore, their initialization as well as their representation in state-of-the-art climate models remains a challenge. Numerous extraoceanic processes that could be active over the decadal range are proposed. Potential predictability associated with the aforementioned, poorly represented, and scarcely observed constituents of the climate system has been primarily inspected through numerical simulations performed under idealized experimental settings. The impact, however, on practical decadal predictions, conducted with realistically initialized full-fledged climate models, is still largely unexploited. Enhancing initial-value predictability through an improved model initialization appears to be a viable option for land surface, sea ice, and, marginally, the stratosphere. Similarly, capturing future aerosol emission storylines might lead to an improved representation of both global and regional short-term climatic changes. In addition to these factors, a key role on the overall predictive ability of ESMs is expected to be played by an accurate representation of processes associated with specific components of the climate system. These act as "signal carriers," transferring across the climatic phase space the information associated with the initial state and boundary forcings, and dynamically bridging different (otherwise unconnected) subsystems. Through this mechanism, Earth system components trigger low-frequency variability modes, thus extending the predictability beyond the seasonal scale.

  7. The Urgent Need for Improved Climate Models and Predictions

    NASA Astrophysics Data System (ADS)

    Goddard, Lisa; Baethgen, Walter; Kirtman, Ben; Meehl, Gerald

    2009-09-01

    An investment over the next 10 years of the order of US$2 billion for developing improved climate models was recommended in a report (http://wcrp.wmo.int/documents/WCRP_WorldModellingSummit_Jan2009.pdf) from the May 2008 World Modelling Summit for Climate Prediction, held in Reading, United Kingdom, and presented by the World Climate Research Programme. The report indicated that “climate models will, as in the past, play an important, and perhaps central, role in guiding the trillion dollar decisions that the peoples, governments and industries of the world will be making to cope with the consequences of changing climate.” If trillions of dollars are going to be invested in making decisions related to climate impacts, an investment of $2 billion, which is less than 0.1% of that amount, to provide better climate information seems prudent. One example of investment in adaptation is the World Bank's Climate Investment Fund, which has drawn contributions of more than $6 billion for work on clean technologies and adaptation efforts in nine pilot countries and two pilot regions. This is just the beginning of expenditures on adaptation efforts by the World Bank and other mechanisms, focusing on only a small fraction of the nations of the world and primarily aimed at anticipated anthropogenic climate change. Moreover, decisions are being made now, all around the world—by individuals, companies, and governments—that affect people and their livelihoods today, not just 50 or more years in the future. Climate risk management, whether related to projects of the scope of the World Bank's or to the planning and decisions of municipalities, will be best guided by meaningful climate information derived from observations of the past and model predictions of the future.

  8. Climate predictions: the influence of nonlinearity and randomness.

    PubMed

    Thompson, J Michael T; Sieber, Jan

    2012-03-13

    The current threat of global warming and the public demand for confident projections of climate change pose the ultimate challenge to science: predicting the future behaviour of a system of such overwhelming complexity as the Earth's climate. This Theme Issue addresses two practical problems that make even prediction of the statistical properties of the climate, when treated as the attractor of a chaotic system (the weather), so challenging. The first is that even for the most detailed models, these statistical properties of the attractor show systematic biases. The second is that the attractor may undergo sudden large-scale changes on a time scale that is fast compared with the gradual change of the forcing (the so-called climate tipping).

  9. Concordance Between Administrator and Clinician Ratings of Organizational Culture and Climate.

    PubMed

    Beidas, Rinad S; Williams, Nathaniel J; Green, Philip D; Aarons, Gregory A; Becker-Haimes, Emily M; Evans, Arthur C; Rubin, Ronnie; Adams, Danielle R; Marcus, Steven C

    2016-11-05

    Organizational culture and climate are important determinants of behavioral health service delivery for youth. The Organizational Social Context measure is a well validated assessment of organizational culture and climate that has been developed and extensively used in public sector behavioral health service settings. The degree of concordance between administrators and clinicians in their reports of organizational culture and climate may have implications for research design, inferences, and organizational intervention. However, the extent to which administrators' and clinicians' reports demonstrate concordance is just beginning to garner attention in public behavioral health settings in the United States. We investigated the concordance between 73 administrators (i.e., supervisors, clinical directors, and executive directors) and 247 clinicians in 28 child-serving programs in a public behavioral health system. Findings suggest that administrators, compared to clinicians, reported more positive cultures and climates. Organizational size moderated this relationship such that administrators in small programs (<466 youth clients served annually) provided more congruent reports of culture and climate in contrast to administrators in large programs (≥466 youth clients served annually) who reported more positive cultures and climates than clinicians. We propose a research agenda that examines the effect of concordance between administrators and clinicians on organizational outcomes in public behavioral health service settings.

  10. Understanding climate: A strategy for climate modeling and predictability research, 1985-1995

    NASA Technical Reports Server (NTRS)

    Thiele, O. (Editor); Schiffer, R. A. (Editor)

    1985-01-01

    The emphasis of the NASA strategy for climate modeling and predictability research is on the utilization of space technology to understand the processes which control the Earth's climate system and it's sensitivity to natural and man-induced changes and to assess the possibilities for climate prediction on time scales of from about two weeks to several decades. Because the climate is a complex multi-phenomena system, which interacts on a wide range of space and time scales, the diversity of scientific problems addressed requires a hierarchy of models along with the application of modern empirical and statistical techniques which exploit the extensive current and potential future global data sets afforded by space observations. Observing system simulation experiments, exploiting these models and data, will also provide the foundation for the future climate space observing system, e.g., Earth observing system (EOS), 1985; Tropical Rainfall Measuring Mission (TRMM) North, et al. NASA, 1984.

  11. Relationship of hospital organizational culture to patient safety climate in the Veterans Health Administration.

    PubMed

    Hartmann, Christine W; Meterko, Mark; Rosen, Amy K; Shibei Zhao; Shokeen, Priti; Singer, Sara; Gaba, David M

    2009-06-01

    Improving safety climate could enhance patient safety, yet little evidence exists regarding the relationship between hospital characteristics and safety climate. This study assessed the relationship between hospitals' organizational culture and safety climate in Veterans Health Administration (VA) hospitals nationally. Data were collected from a sample of employees in a stratified random sample of 30 VA hospitals over a 6-month period (response rate = 50%; n = 4,625). The Patient Safety Climate in Healthcare Organizations (PSCHO) and the Zammuto and Krakower surveys were used to measure safety climate and organizational culture, respectively. Higher levels of safety climate were significantly associated with higher levels of group and entrepreneurial cultures, while lower levels of safety climate were associated with higher levels of hierarchical culture. Hospitals could use these results to design specific interventions aimed at improving safety climate.

  12. Anthropocene changes in desert area: Sensitivity to climate model predictions

    NASA Astrophysics Data System (ADS)

    Mahowald, Natalie M.

    2007-09-01

    Changes in desert area due to humans have important implications from a local, regional to global level. Here I focus on the latter in order to better understand estimated changes in desert dust aerosols and the associated iron deposition into oceans. Using 17 model simulations from the World Climate Research Programme's Coupled Model Intercomparison Project phase 3 multi-model dataset and the BIOME4 equilibrium vegetation model, I estimate changes in desert dust source areas due to climate change and carbon dioxide fertilization. If I assume no carbon dioxide fertilization, the mean of the model predictions is that desert areas expand from the 1880s to the 2080s, due to increased aridity. If I allow for carbon dioxide fertilization, the desert areas become smaller. Thus better understanding carbon dioxide fertilization is important for predicting desert response to climate. There is substantial spread in the model simulation predictions for regional and global averages.

  13. FRIDAY: EPA Administrator to Attend the Virginia Coastal Policy Centers Climate Change Conference

    EPA Pesticide Factsheets

    WASHINGTON - Tomorrow, U.S. Environmental Protection Agency (EPA) Administrator Gina McCarthy will be the luncheon speaker at the William ' Mary Law School's Virginia Coastal Policy Center third annual climate change conference, Show Me the Mon

  14. TODAY: EPA Administrator to Attend the Virginia Coastal Policy Centers Climate Change Conference

    EPA Pesticide Factsheets

    WASHINGTON - Today, U.S. Environmental Protection Agency (EPA) Administrator Gina McCarthy will be the luncheon speaker at the William ' Mary Law School's Virginia Coastal Policy Center third annual climate change conference, Show Me the Money:

  15. FRIDAY: EPA Administrator on Bill Maher Show to Discuss Climate Action Plan

    EPA Pesticide Factsheets

    Administrator McCarthy will discuss EPA's new report that shows global climate action has huge economic, environmental and public health benefits for the United States. With global action, we could prevent 57,000 premature American deaths every year

  16. WEDNESDAY: EPA Administrator McCarthy to Give Keynote Address at 2016 Climate Leadership Conference

    EPA Pesticide Factsheets

    SEATTLE - On Wednesday, March 9, 2016, U.S. Environmental Protection Agency Administrator Gina McCarthy will give the keynote address at the 2016 Climate Leadership Conference in Seattle. The conference calls national attention to exemplary leadersh

  17. Using climate data to predict grizzly bear litter size

    USGS Publications Warehouse

    Picton, Harold D.; Knight, Richard R.

    1986-01-01

    A 5-year double-bind test was conducted to test the predictive capability of a previously published (Picton 1978) regression (Y= 2.01 + 0.042x), which described the relationship between the littler size of grizzly bears (Ursus arctos horribilis) and an index of climate plus carrion availability (climate-carrion index). This regression showed an efficient in excess of 99% in predicting the observed grizzly bear littler size. The predictions made using the climate-carrion index had a mean absolute error of less than 25% of forecasts using other methods. The updated climate-carrion index regression, which includes all of the 16 years for which data are available, is Y= 2.009 + 0.042x (r = 0.078; P N = 16). We concluded that the climate-carrion index can be a helpful tool in predicting grizzly bear littler size. The relation of this information to the effects of the closure of Yellowstone Park garbage dumps is discussed.

  18. Challenges in predicting climate change impacts on pome fruit phenology

    NASA Astrophysics Data System (ADS)

    Darbyshire, Rebecca; Webb, Leanne; Goodwin, Ian; Barlow, E. W. R.

    2014-08-01

    Climate projection data were applied to two commonly used pome fruit flowering models to investigate potential differences in predicted full bloom timing. The two methods, fixed thermal time and sequential chill-growth, produced different results for seven apple and pear varieties at two Australian locations. The fixed thermal time model predicted incremental advancement of full bloom, while results were mixed from the sequential chill-growth model. To further investigate how the sequential chill-growth model reacts under climate perturbed conditions, four simulations were created to represent a wider range of species physiological requirements. These were applied to five Australian locations covering varied climates. Lengthening of the chill period and contraction of the growth period was common to most results. The relative dominance of the chill or growth component tended to predict whether full bloom advanced, remained similar or was delayed with climate warming. The simplistic structure of the fixed thermal time model and the exclusion of winter chill conditions in this method indicate it is unlikely to be suitable for projection analyses. The sequential chill-growth model includes greater complexity; however, reservations in using this model for impact analyses remain. The results demonstrate that appropriate representation of physiological processes is essential to adequately predict changes to full bloom under climate perturbed conditions with greater model development needed.

  19. Development of a wind energy climate service based on seasonal climate prediction

    NASA Astrophysics Data System (ADS)

    Torralba, Veronica; Doblas-Reyes, Francisco J.; Cortesi, Nicola; Christel, Isadora; González-Reviriego, Nube; Turco, Marco; Soret, Albert

    2016-04-01

    Climate predictions tailored to the wind energy sector represent an innovation to better understand the future variability of wind energy resources. At seasonal time scales current energy practices employ a simple approach based on a retrospective climatology. Instead, probabilistic climate forecasting can better address specific decisions that affect energy demand and supply, as well as decisions relative to the planning of maintenance work. Here we illustrate the advantages that seasonal climate predictions might offer to a wide range of users and discuss the best way to provide them with this information. We use the predictions of 10-meter wind speed from the ECMWF seasonal forecast System 4 (S4). S4, as every operational seasonal forecast system, is affected by a range of biases. Hence, to produce usable climate information from the predictions, different bias-adjustment techniques and downscaling methods should be applied, their choice depending on the user requirements. An ensemble of post-processing methods is described, and their relative merit evaluated as a function of their impact of the characteristics of the forecast error and the usability of the resulting forecasts. Both reanalyses (ERA-Interim, JRA-55, MERRA) and in-situ observations are used as observational references. As an illustration of the downstream impact of the forecasts as a source of climate information, the post-processed seasonal predictions of wind speed will be used as input in a transfer model that translates climate information into generated power at different spatial scales.

  20. Genetically informed ecological niche models improve climate change predictions.

    PubMed

    Ikeda, Dana H; Max, Tamara L; Allan, Gerard J; Lau, Matthew K; Shuster, Stephen M; Whitham, Thomas G

    2017-01-01

    We examined the hypothesis that ecological niche models (ENMs) more accurately predict species distributions when they incorporate information on population genetic structure, and concomitantly, local adaptation. Local adaptation is common in species that span a range of environmental gradients (e.g., soils and climate). Moreover, common garden studies have demonstrated a covariance between neutral markers and functional traits associated with a species' ability to adapt to environmental change. We therefore predicted that genetically distinct populations would respond differently to climate change, resulting in predicted distributions with little overlap. To test whether genetic information improves our ability to predict a species' niche space, we created genetically informed ecological niche models (gENMs) using Populus fremontii (Salicaceae), a widespread tree species in which prior common garden experiments demonstrate strong evidence for local adaptation. Four major findings emerged: (i) gENMs predicted population occurrences with up to 12-fold greater accuracy than models without genetic information; (ii) tests of niche similarity revealed that three ecotypes, identified on the basis of neutral genetic markers and locally adapted populations, are associated with differences in climate; (iii) our forecasts indicate that ongoing climate change will likely shift these ecotypes further apart in geographic space, resulting in greater niche divergence; (iv) ecotypes that currently exhibit the largest geographic distribution and niche breadth appear to be buffered the most from climate change. As diverse agents of selection shape genetic variability and structure within species, we argue that gENMs will lead to more accurate predictions of species distributions under climate change.

  1. New Congressional Climate Change Task Force Calls on President to Use Administrative Authority

    NASA Astrophysics Data System (ADS)

    Showstack, Randy

    2013-02-01

    Spurred by U.S. congressional inaction on climate change and by President Barack Obama's comments on the topic in his 21 January inaugural address, several Democratic members of Congress announced at a Capitol Hill briefing the formation of a bicameral task force on climate change. In addition, they have called on the president to use his administrative authority to deal with the issue.

  2. The Impact of Ocean Observations in Seasonal Climate Prediction

    NASA Technical Reports Server (NTRS)

    Rienecker, Michele; Keppenne, Christian; Kovach, Robin; Marshak, Jelena

    2010-01-01

    The ocean provides the most significant memory for the climate system. Hence, a critical element in climate forecasting with coupled models is the initialization of the ocean with states from an ocean data assimilation system. Remotely-sensed ocean surface fields (e.g., sea surface topography, SST, winds) are now available for extensive periods and have been used to constrain ocean models to provide a record of climate variations. Since the ocean is virtually opaque to electromagnetic radiation, the assimilation of these satellite data is essential to extracting the maximum information content. More recently, the Argo drifters have provided unprecedented sampling of the subsurface temperature and salinity. Although the duration of this observation set has been too short to provide solid statistical evidence of its impact, there are indications that Argo improves the forecast skill of coupled systems. This presentation will address the impact these different observations have had on seasonal climate predictions with the GMAO's coupled model.

  3. The Relationship between Organizational Climate and the Organizational Silence of Administrative Staff in Education Department

    ERIC Educational Resources Information Center

    Pozveh, Asghar Zamani; Karimi, Fariba

    2016-01-01

    The aim of the present study was to determine the relationship between organizational climate and the organizational silence of administrative staff in Education Department in Isfahan. The research method was descriptive and correlational-type method. The study population was administrative staff of Education Department in Isfahan during the…

  4. Climate fails to predict wood decomposition at regional scales

    NASA Astrophysics Data System (ADS)

    Bradford, Mark A.; Warren, Robert J., II; Baldrian, Petr; Crowther, Thomas W.; Maynard, Daniel S.; Oldfield, Emily E.; Wieder, William R.; Wood, Stephen A.; King, Joshua R.

    2014-07-01

    Decomposition of organic matter strongly influences ecosystem carbon storage. In Earth-system models, climate is a predominant control on the decomposition rates of organic matter. This assumption is based on the mean response of decomposition to climate, yet there is a growing appreciation in other areas of global change science that projections based on mean responses can be irrelevant and misleading. We test whether climate controls on the decomposition rate of dead wood--a carbon stock estimated to represent 73 +/- 6 Pg carbon globally--are sensitive to the spatial scale from which they are inferred. We show that the common assumption that climate is a predominant control on decomposition is supported only when local-scale variation is aggregated into mean values. Disaggregated data instead reveal that local-scale factors explain 73% of the variation in wood decomposition, and climate only 28%. Further, the temperature sensitivity of decomposition estimated from local versus mean analyses is 1.3-times greater. Fundamental issues with mean correlations were highlighted decades ago, yet mean climate-decomposition relationships are used to generate simulations that inform management and adaptation under environmental change. Our results suggest that to predict accurately how decomposition will respond to climate change, models must account for local-scale factors that control regional dynamics.

  5. Predicting evolutionary responses to climate change in the sea.

    PubMed

    Munday, Philip L; Warner, Robert R; Monro, Keyne; Pandolfi, John M; Marshall, Dustin J

    2013-12-01

    An increasing number of short-term experimental studies show significant effects of projected ocean warming and ocean acidification on the performance on marine organisms. Yet, it remains unclear if we can reliably predict the impact of climate change on marine populations and ecosystems, because we lack sufficient understanding of the capacity for marine organisms to adapt to rapid climate change. In this review, we emphasise why an evolutionary perspective is crucial to understanding climate change impacts in the sea and examine the approaches that may be useful for addressing this challenge. We first consider what the geological record and present-day analogues of future climate conditions can tell us about the potential for adaptation to climate change. We also examine evidence that phenotypic plasticity may assist marine species to persist in a rapidly changing climate. We then outline the various experimental approaches that can be used to estimate evolutionary potential, focusing on molecular tools, quantitative genetics, and experimental evolution, and we describe the benefits of combining different approaches to gain a deeper understanding of evolutionary potential. Our goal is to provide a platform for future research addressing the evolutionary potential for marine organisms to cope with climate change.

  6. Predicting vulnerabilities of North American shorebirds to climate change.

    PubMed

    Galbraith, Hector; DesRochers, David W; Brown, Stephen; Reed, J Michael

    2014-01-01

    Despite an increase in conservation efforts for shorebirds, there are widespread declines of many species of North American shorebirds. We wanted to know whether these declines would be exacerbated by climate change, and whether relatively secure species might become at-risk species. Virtually all of the shorebird species breeding in the USA and Canada are migratory, which means climate change could affect extinction risk via changes on the breeding, wintering, and/or migratory refueling grounds, and that ecological synchronicities could be disrupted at multiple sites. To predict the effects of climate change on shorebird extinction risks, we created a categorical risk model complementary to that used by Partners-in-Flight and the U.S. Shorebird Conservation Plan. The model is based on anticipated changes in breeding, migration, and wintering habitat, degree of dependence on ecological synchronicities, migration distance, and degree of specialization on breeding, migration, or wintering habitat. We evaluated 49 species, and for 3 species we evaluated 2 distinct populations each, and found that 47 (90%) taxa are predicted to experience an increase in risk of extinction. No species was reclassified into a lower-risk category, although 6 species had at least one risk factor decrease in association with climate change. The number of species that changed risk categories in our assessment is sensitive to how much of an effect of climate change is required to cause the shift, but even at its least sensitive, 20 species were at the highest risk category for extinction. Based on our results it appears that shorebirds are likely to be highly vulnerable to climate change. Finally, we discuss both how our approach can be integrated with existing risk assessments and potential future directions for predicting change in extinction risk due to climate change.

  7. Predicting Vulnerabilities of North American Shorebirds to Climate Change

    PubMed Central

    Galbraith, Hector; DesRochers, David W.; Brown, Stephen; Reed, J. Michael

    2014-01-01

    Despite an increase in conservation efforts for shorebirds, there are widespread declines of many species of North American shorebirds. We wanted to know whether these declines would be exacerbated by climate change, and whether relatively secure species might become at–risk species. Virtually all of the shorebird species breeding in the USA and Canada are migratory, which means climate change could affect extinction risk via changes on the breeding, wintering, and/or migratory refueling grounds, and that ecological synchronicities could be disrupted at multiple sites. To predict the effects of climate change on shorebird extinction risks, we created a categorical risk model complementary to that used by Partners–in–Flight and the U.S. Shorebird Conservation Plan. The model is based on anticipated changes in breeding, migration, and wintering habitat, degree of dependence on ecological synchronicities, migration distance, and degree of specialization on breeding, migration, or wintering habitat. We evaluated 49 species, and for 3 species we evaluated 2 distinct populations each, and found that 47 (90%) taxa are predicted to experience an increase in risk of extinction. No species was reclassified into a lower–risk category, although 6 species had at least one risk factor decrease in association with climate change. The number of species that changed risk categories in our assessment is sensitive to how much of an effect of climate change is required to cause the shift, but even at its least sensitive, 20 species were at the highest risk category for extinction. Based on our results it appears that shorebirds are likely to be highly vulnerable to climate change. Finally, we discuss both how our approach can be integrated with existing risk assessments and potential future directions for predicting change in extinction risk due to climate change. PMID:25268907

  8. State-of-the-Art Climate Predictions for Energy Climate Services

    NASA Astrophysics Data System (ADS)

    Torralba-Fernandez, Veronica; Davis, Melanie; Doblas-Reyes, Francisco J.; Gonzalez-Reviriego, Nube

    2015-04-01

    Climate predictions tailored to the energy sector represent the cutting edge in climate sciences to forecast wind power generation. At seasonal time scales, current energy practices use a deterministic approach based on retrospective climatology, but climate predictions have recently been shown to provide additional value. For this reason, probabilistic climate predictions of near surface winds can allow end users to take calculated, precautionary action with a potential cost savings to their operations. As every variable predicted in a coupled model forecast system, the prediction of wind speed is affected by biases. To overcome this, two different techniques for the post-processing of ensemble forecasts are considered: a simple bias correction and a calibration method. The former is based on the assumption that the reference and predicted distributions are well approximated by a normal distribution. The latter is a calibration technique which inflates the model variance, and the inflation of the ensemble is required in order to obtain a reliable outcome. Both methods use the "one-year out" cross-validated mode, and they provide corrected forecasts with improved statistical properties. The impact of these bias corrections on the quality of the ECMWF S4 predictions of near surface wind speed during winter is explored. To offer a comprehensive picture of the post-processing effect on the forecast quality of the system, it is necessary to use several scoring measures: rank histograms, reliability diagrams and skill maps. These tools are essential to assess different aspects of the forecasts, and to observe changes in their properties when the two methods are applied. This study reveals that the different techniques to correct the predictions produce a statistically consistent ensemble. However, the operations performed on the forecasts decrease their skill which correspond to an increase in the uncertainty. Therefore, even though the bias correction is fundamental

  9. Regional predictability and the linearity of climate feedbacks

    NASA Astrophysics Data System (ADS)

    Feldl, N.; Roe, G.

    2011-12-01

    At the global scale, feedback analysis is a powerful tool for constraining climate sensitivity through understanding uncertainty in the component model physics. Our focus here is to evaluate the extent to which this framework can be applied to the question of regional climate predictability. We have developed a clean and clear approach to address these challenges. We employ the GFDL AM2 model in aquaplanet mode, coupled to simple ocean mixed-layer and sea-ice schemes, and run under perpetual equinox conditions. This simplified, aquaplanet simulation enables us to investigate the atmospheric response to carbon dioxide without the effects of a seasonal cycle or land-sea distribution, which can obscure the response. Further, we explicitly calculate radiative kernels (necessary to diagnose the feedbacks) for this precise model set-up, thus removing much of the ambiguity in the feedback approximation. We find that linking regional predictability and individual climate feedbacks depends on the balance between local radiative feedbacks and meridional energy transport in response to changes in climate forcing. An important aspect of this energy budget is the linearity of the kernel-calculated feedbacks, which we evaluate. Spatial patterns of these factors can be related to the basic structure of atmospheric circulation, and our results highlight regional differences in the effect of feedbacks on the regional climate response.

  10. Extracting climate memory using Fractional Integrated Statistical Model: A new perspective on climate prediction

    PubMed Central

    Yuan, Naiming; Fu, Zuntao; Liu, Shida

    2014-01-01

    Long term memory (LTM) in climate variability is studied by means of fractional integral techniques. By using a recently developed model, Fractional Integral Statistical Model (FISM), we in this report proposed a new method, with which one can estimate the long-lasting influences of historical climate states on the present time quantitatively, and further extract the influence as climate memory signals. To show the usability of this method, two examples, the Northern Hemisphere monthly Temperature Anomalies (NHTA) and the Pacific Decadal Oscillation index (PDO), are analyzed in this study. We find the climate memory signals indeed can be extracted and the whole variations can be further decomposed into two parts: the cumulative climate memory (CCM) and the weather-scale excitation (WSE). The stronger LTM is, the larger proportion the climate memory signals will account for in the whole variations. With the climate memory signals extracted, one can at least determine on what basis the considered time series will continue to change. Therefore, this report provides a new perspective on climate prediction. PMID:25300777

  11. Predicting potential responses to future climate in an alpine ungulate: interspecific interactions exceed climate effects.

    PubMed

    Mason, Tom H E; Stephens, Philip A; Apollonio, Marco; Willis, Stephen G

    2014-12-01

    The altitudinal shifts of many montane populations are lagging behind climate change. Understanding habitual, daily behavioural rhythms, and their climatic and environmental influences, could shed light on the constraints on long-term upslope range-shifts. In addition, behavioural rhythms can be affected by interspecific interactions, which can ameliorate or exacerbate climate-driven effects on ecology. Here, we investigate the relative influences of ambient temperature and an interaction with domestic sheep (Ovis aries) on the altitude use and activity budgets of a mountain ungulate, the Alpine chamois (Rupicapra rupicapra). Chamois moved upslope when it was hotter but this effect was modest compared to that of the presence of sheep, to which they reacted by moving 89-103 m upslope, into an entirely novel altitudinal range. Across the European Alps, a range-shift of this magnitude corresponds to a 46% decrease in the availability of suitable foraging habitat. This highlights the importance of understanding how factors such as competition and disturbance shape a given species' realised niche when predicting potential future responses to change. Furthermore, it exposes the potential for manipulations of species interactions to ameliorate the impacts of climate change, in this case by the careful management of livestock. Such manipulations could be particularly appropriate for species where competition or disturbance already strongly restricts their available niche. Our results also reveal the potential role of behavioural flexibility in responses to climate change. Chamois reduced their activity when it was warmer, which could explain their modest altitudinal migrations. Considering this behavioural flexibility, our model predicts a small 15-30 m upslope shift by 2100 in response to climate change, less than 4% of the altitudinal shift that would be predicted using a traditional species distribution model-type approach (SDM), which assumes that species' behaviour

  12. New Methods for Estimating Seasonal Potential Climate Predictability

    NASA Astrophysics Data System (ADS)

    Feng, Xia

    This study develops two new statistical approaches to assess the seasonal potential predictability of the observed climate variables. One is the univariate analysis of covariance (ANOCOVA) model, a combination of autoregressive (AR) model and analysis of variance (ANOVA). It has the advantage of taking into account the uncertainty of the estimated parameter due to sampling errors in statistical test, which is often neglected in AR based methods, and accounting for daily autocorrelation that is not considered in traditional ANOVA. In the ANOCOVA model, the seasonal signals arising from external forcing are determined to be identical or not to assess any interannual variability that may exist is potentially predictable. The bootstrap is an attractive alternative method that requires no hypothesis model and is available no matter how mathematically complicated the parameter estimator. This method builds up the empirical distribution of the interannual variance from the resamplings drawn with replacement from the given sample, in which the only predictability in seasonal means arises from the weather noise. These two methods are applied to temperature and water cycle components including precipitation and evaporation, to measure the extent to which the interannual variance of seasonal means exceeds the unpredictable weather noise compared with the previous methods, including Leith-Shukla-Gutzler (LSG), Madden, and Katz. The potential predictability of temperature from ANOCOVA model, bootstrap, LSG and Madden exhibits a pronounced tropical-extratropical contrast with much larger predictability in the tropics dominated by El Nino/Southern Oscillation (ENSO) than in higher latitudes where strong internal variability lowers predictability. Bootstrap tends to display highest predictability of the four methods, ANOCOVA lies in the middle, while LSG and Madden appear to generate lower predictability. Seasonal precipitation from ANOCOVA, bootstrap, and Katz, resembling that

  13. Decadal climate prediction with a refined anomaly initialisation approach

    NASA Astrophysics Data System (ADS)

    Volpi, Danila; Guemas, Virginie; Doblas-Reyes, Francisco J.; Hawkins, Ed; Nichols, Nancy K.

    2017-03-01

    In decadal prediction, the objective is to exploit both the sources of predictability from the external radiative forcings and from the internal variability to provide the best possible climate information for the next decade. Predicting the climate system internal variability relies on initialising the climate model from observational estimates. We present a refined method of anomaly initialisation (AI) applied to the ocean and sea ice components of the global climate forecast model EC-Earth, with the following key innovations: (1) the use of a weight applied to the observed anomalies, in order to avoid the risk of introducing anomalies recorded in the observed climate, whose amplitude does not fit in the range of the internal variability generated by the model; (2) the AI of the ocean density, instead of calculating it from the anomaly initialised state of temperature and salinity. An experiment initialised with this refined AI method has been compared with a full field and standard AI experiment. Results show that the use of such refinements enhances the surface temperature skill over part of the North and South Atlantic, part of the South Pacific and the Mediterranean Sea for the first forecast year. However, part of such improvement is lost in the following forecast years. For the tropical Pacific surface temperature, the full field initialised experiment performs the best. The prediction of the Arctic sea-ice volume is improved by the refined AI method for the first three forecast years and the skill of the Atlantic multidecadal oscillation is significantly increased compared to a non-initialised forecast, along the whole forecast time.

  14. Decadal climate prediction with a refined anomaly initialisation approach

    NASA Astrophysics Data System (ADS)

    Volpi, Danila; Guemas, Virginie; Doblas-Reyes, Francisco J.; Hawkins, Ed; Nichols, Nancy K.

    2016-06-01

    In decadal prediction, the objective is to exploit both the sources of predictability from the external radiative forcings and from the internal variability to provide the best possible climate information for the next decade. Predicting the climate system internal variability relies on initialising the climate model from observational estimates. We present a refined method of anomaly initialisation (AI) applied to the ocean and sea ice components of the global climate forecast model EC-Earth, with the following key innovations: (1) the use of a weight applied to the observed anomalies, in order to avoid the risk of introducing anomalies recorded in the observed climate, whose amplitude does not fit in the range of the internal variability generated by the model; (2) the AI of the ocean density, instead of calculating it from the anomaly initialised state of temperature and salinity. An experiment initialised with this refined AI method has been compared with a full field and standard AI experiment. Results show that the use of such refinements enhances the surface temperature skill over part of the North and South Atlantic, part of the South Pacific and the Mediterranean Sea for the first forecast year. However, part of such improvement is lost in the following forecast years. For the tropical Pacific surface temperature, the full field initialised experiment performs the best. The prediction of the Arctic sea-ice volume is improved by the refined AI method for the first three forecast years and the skill of the Atlantic multidecadal oscillation is significantly increased compared to a non-initialised forecast, along the whole forecast time.

  15. Predicting climate change impacts on polar bear litter size.

    PubMed

    Molnár, Péter K; Derocher, Andrew E; Klanjscek, Tin; Lewis, Mark A

    2011-02-08

    Predicting the ecological impacts of climate warming is critical for species conservation. Incorporating future warming into population models, however, is challenging because reproduction and survival cannot be measured for yet unobserved environmental conditions. In this study, we use mechanistic energy budget models and data obtainable under current conditions to predict polar bear litter size under future conditions. In western Hudson Bay, we predict climate warming-induced litter size declines that jeopardize population viability: ∼28% of pregnant females failed to reproduce for energetic reasons during the early 1990s, but 40-73% could fail if spring sea ice break-up occurs 1 month earlier than during the 1990s, and 55-100% if break-up occurs 2 months earlier. Simultaneously, mean litter size would decrease by 22-67% and 44-100%, respectively. The expected timeline for these declines varies with climate-model-specific sea ice predictions. Similar litter size declines may occur in over one-third of the global polar bear population.

  16. Phylogeny Predicts Future Habitat Shifts Due to Climate Change

    PubMed Central

    Kuntner, Matjaž; Năpăruş, Magdalena; Li, Daiqin; Coddington, Jonathan A.

    2014-01-01

    Background Taxa may respond differently to climatic changes, depending on phylogenetic or ecological effects, but studies that discern among these alternatives are scarce. Here, we use two species pairs from globally distributed spider clades, each pair representing two lifestyles (generalist, specialist) to test the relative importance of phylogeny versus ecology in predicted responses to climate change. Methodology We used a recent phylogenetic hypothesis for nephilid spiders to select four species from two genera (Nephilingis and Nephilengys) that match the above criteria, are fully allopatric but combined occupy all subtropical-tropical regions. Based on their records, we modeled each species niche spaces and predicted their ecological shifts 20, 40, 60, and 80 years into the future using customized GIS tools and projected climatic changes. Conclusions Phylogeny better predicts the species current ecological preferences than do lifestyles. By 2080 all species face dramatic reductions in suitable habitat (54.8–77.1%) and adapt by moving towards higher altitudes and latitudes, although at different tempos. Phylogeny and life style explain simulated habitat shifts in altitude, but phylogeny is the sole best predictor of latitudinal shifts. Models incorporating phylogenetic relatedness are an important additional tool to predict accurately biotic responses to global change. PMID:24892737

  17. Climate-Induced Boreal Forest Change: Predictions versus Current Observations

    NASA Technical Reports Server (NTRS)

    Soja, Amber J.; Tchebakova, Nadezda M.; French, Nancy H. F.; Flannigan, Michael D.; Shugart, Herman H.; Stocks, Brian J.; Sukhinin, Anatoly I.; Parfenova, E. I.; Chapin, F. Stuart, III; Stackhouse, Paul W., Jr.

    2007-01-01

    For about three decades, there have been many predictions of the potential ecological response in boreal regions to the currently warmer conditions. In essence, a widespread, naturally occurring experiment has been conducted over time. In this paper, we describe previously modeled predictions of ecological change in boreal Alaska, Canada and Russia, and then we investigate potential evidence of current climate-induced change. For instance, ecological models have suggested that warming will induce the northern and upslope migration of the treeline and an alteration in the current mosaic structure of boreal forests. We present evidence of the migration of keystone ecosystems in the upland and lowland treeline of mountainous regions across southern Siberia. Ecological models have also predicted a moisture-stress-related dieback in white spruce trees in Alaska, and current investigations show that as temperatures increase, white spruce tree growth is declining. Additionally, it was suggested that increases in infestation and wildfire disturbance would be catalysts that precipitate the alteration of the current mosaic forest composition. In Siberia, five of the last seven years have resulted in extreme fire seasons, and extreme fire years have also been more frequent in both Alaska and Canada. In addition, Alaska has experienced extreme and geographically expansive multi-year outbreaks of the spruce beetle, which had been previously limited by the cold, moist environment. We suggest that there is substantial evidence throughout the circumboreal region to conclude that the biosphere within the boreal terrestrial environment has already responded to the transient effects of climate change. Additionally, temperature increases and warming-induced change are progressing faster than had been predicted in some regions, suggesting a potential non-linear rapid response to changes in climate, as opposed to the predicted slow linear response to climate change.

  18. Timing and Prediction of Climate Change and Hydrological Impacts: Periodicity in Natural Variations

    EPA Science Inventory

    Hydrological impacts from climate change are of principal interest to water resource policy-makers and practicing engineers, and predictive climatic models have been extensively investigated to quantify the impacts. In palaeoclmatic investigations, climate proxy evidence has une...

  19. Improved management of small pelagic fisheries through seasonal climate prediction.

    PubMed

    Tommasi, Désirée; Stock, Charles A; Pegion, Kathleen; Vecchi, Gabriel A; Methot, Richard D; Alexander, Michael A; Checkley, David M

    2017-03-01

    Populations of small pelagic fish are strongly influenced by climate. The inability of managers to anticipate environment-driven fluctuations in stock productivity or distribution can lead to overfishing and stock collapses, inflexible management regulations inducing shifts in the functional response to human predators, lost opportunities to harvest populations, bankruptcies in the fishing industry, and loss of resilience in the human food supply. Recent advances in dynamical global climate prediction systems allow for sea surface temperature (SST) anomaly predictions at a seasonal scale over many shelf ecosystems. Here we assess the utility of SST predictions at this "fishery relevant" scale to inform management, using Pacific sardine as a case study. The value of SST anomaly predictions to management was quantified under four harvest guidelines (HGs) differing in their level of integration of SST data and predictions. The HG that incorporated stock biomass forecasts informed by skillful SST predictions led to increases in stock biomass and yield, and reductions in the probability of yield and biomass falling below socioeconomic or ecologically acceptable levels. However, to mitigate the risk of collapse in the event of an erroneous forecast, it was important to combine such forecast-informed harvest controls with additional harvest restrictions at low biomass.

  20. Predicting impacts of climate change on Fasciola hepatica risk.

    PubMed

    Fox, Naomi J; White, Piran C L; McClean, Colin J; Marion, Glenn; Evans, Andy; Hutchings, Michael R

    2011-01-10

    Fasciola hepatica (liver fluke) is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

  1. Predicting when climate-driven phenotypic change affects population dynamics.

    PubMed

    McLean, Nina; Lawson, Callum R; Leech, Dave I; van de Pol, Martijn

    2016-06-01

    Species' responses to climate change are variable and diverse, yet our understanding of how different responses (e.g. physiological, behavioural, demographic) relate and how they affect the parameters most relevant for conservation (e.g. population persistence) is lacking. Despite this, studies that observe changes in one type of response typically assume that effects on population dynamics will occur, perhaps fallaciously. We use a hierarchical framework to explain and test when impacts of climate on traits (e.g. phenology) affect demographic rates (e.g. reproduction) and in turn population dynamics. Using this conceptual framework, we distinguish four mechanisms that can prevent lower-level responses from impacting population dynamics. Testable hypotheses were identified from the literature that suggest life-history and ecological characteristics which could predict when these mechanisms are likely to be important. A quantitative example on birds illustrates how, even with limited data and without fully-parameterized population models, new insights can be gained; differences among species in the impacts of climate-driven phenological changes on population growth were not explained by the number of broods or density dependence. Our approach helps to predict the types of species in which climate sensitivities of phenotypic traits have strong demographic and population consequences, which is crucial for conservation prioritization of data-deficient species.

  2. Predictions of avian Plasmodium expansion under climate change

    PubMed Central

    Loiseau, Claire; Harrigan, Ryan J.; Bichet, Coraline; Julliard, Romain; Garnier, Stéphane; Lendvai, Ádám Z.; Chastel, Olivier; Sorci, Gabriele

    2013-01-01

    Vector-borne diseases are particularly responsive to changing environmental conditions. Diurnal temperature variation has been identified as a particularly important factor for the development of malaria parasites within vectors. Here, we conducted a survey across France, screening populations of the house sparrow (Passer domesticus) for malaria (Plasmodium relictum). We investigated whether variation in remotely-sensed environmental variables accounted for the spatial variation observed in prevalence and parasitemia. While prevalence was highly correlated to diurnal temperature range and other measures of temperature variation, environmental conditions could not predict spatial variation in parasitemia. Based on our empirical data, we mapped malaria distribution under climate change scenarios and predicted that Plasmodium occurrence will spread to regions in northern France, and that prevalence levels are likely to increase in locations where transmission already occurs. Our findings, based on remote sensing tools coupled with empirical data suggest that climatic change will significantly alter transmission of malaria parasites. PMID:23350033

  3. Selenium deficiency risk predicted to increase under future climate change.

    PubMed

    Jones, Gerrad D; Droz, Boris; Greve, Peter; Gottschalk, Pia; Poffet, Deyan; McGrath, Steve P; Seneviratne, Sonia I; Smith, Pete; Winkel, Lenny H E

    2017-03-14

    Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980-1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate-soil interactions. Using moderate climate-change scenarios for 2080-2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate-soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change.

  4. Accelerating development of a predictive science of climate.

    SciTech Connect

    Drake, John B; Jones, Phil

    2007-01-01

    Climate change and studies of its implications are front page news. Could the heat waves of July 2006 in Europe and the US be caused by global warming? Are increased incidences of strong tropical storms and hurricanes like Katrina to be expected? Will coastal cities be flooded due to sea level rise? The National Climatic Data Center (NCDC) which archives all weather data for the nation reports that global surface temperatures have increased at a rate near 0.6 C over the last century but that the trend is three times larger since 1976 [Easterling, 2006]. Will this rate continue or will climate change be even more abrupt? Stepping back from the flurry of questions, scientists must take a systematic approach and develop a predictive framework. With responsibility for advising on energy and technology strategies, the Department of Energy Office of Biological and Environmental Research has chosen to bolster the science of climate in order to get the story straight on the factors that cause climate change and the role of carbon loading from fossil fuel use.

  5. Toward seamless weather-climate and environmental prediction

    NASA Astrophysics Data System (ADS)

    Brunet, Gilbert

    2016-04-01

    Over the last decade or so, predicting the weather, climate and atmospheric composition has emerged as one of the most important areas of scientific endeavor. This is partly because the remarkable increase in skill of current weather forecasts has made society more and more dependent on them day to day for a whole range of decision making. And it is partly because climate change is now widely accepted and the realization is growing rapidly that it will affect every person in the world profoundly, either directly or indirectly. One of the important endeavors of our societies is to remain at the cutting-edge of modelling and predicting the evolution of the fully coupled environmental system: atmosphere (weather and composition), oceans, land surface (physical and biological), and cryosphere. This effort will provide an increasingly accurate and reliable service across all the socio-economic sectors that are vulnerable to the effects of adverse weather and climatic conditions, whether now or in the future. This emerging challenge was at the center of the World Weather Open Science Conference (Montreal, 2014).The outcomes of the conference are described in the World Meteorological Organization (WMO) book: Seamless Prediction of the Earth System: from Minutes to Months, (G. Brunet, S. Jones, P. Ruti Eds., WMO-No. 1156, 2015). It is freely available on line at the WMO website. We will discuss some of the outcomes of the conference for the WMO World Weather Research Programme (WWRP) and Global Atmospheric Watch (GAW) long term goals and provide examples of seamless modelling and prediction across a range of timescales at convective and sub-kilometer scales for regional coupled forecasting applications at Environment and Climate Change Canada (ECCC).

  6. A Standardized Evaluation System for Decadal Climate Prediction

    NASA Astrophysics Data System (ADS)

    Kadow, C.; Cubasch, U.

    2012-12-01

    The evaluation of decadal prediction systems is a scientific challenge as well as a technical challenge in the climate research. The major project MiKlip (www.fona-miklip.de) for medium-term climate prediction funded by the Federal Ministry of Education and Research in Germany (BMBF) has the aim to create a model system that can provide reliable decadal forecasts on climate and weather. The model system to be developed will be novel in several aspects, with great challenges for the methodology development. This concerns especially the determination of the initial conditions, the inclusion into the model of processes relevant to decadal predictions, the increase of the spatial resolution through regionalisation, the improvement or adjustment of statistical post-processing, and finally the synthesis and validation of the entire model system. Therefore, a standardized evaluation system will be part of the MiKlip system to validate it - developed by the project 'Integrated data and evaluation system for decadal scale prediction' (INTEGRATION). The presentation gives an overview of the different linkages of such a project, shows the different development stages and gives an outlook for users and possible end users in climate service. The technical interface combines all projects inside of MiKlip and invites them to participate in a common evaluation system. The system design and the validation strategy from a standalone tool in the beginning to a user friendly web based system using GRID technologies to an integrated part of the operational MiKlip system for industry and society will give the opportunity to enhance the MiKlip strategy. First results of different possibilities of such a system will be shown to present the scientific background through Taylor diagrams, ensemble skill scores and e.g. climatological means to show the usability and possibilities of MiKlip and the INTEGRATION project.

  7. TODAY: EPA Administrator in Chicago to Headline The New Republic Conference: The Next Frontier of Climate Change

    EPA Pesticide Factsheets

    WASHINGTON - Today, EPA Administrator Gina McCarthy will give the headline interview on the economic need for acting on climate change at The New Republic's conference, The Next Frontier of Climate Change: State and Local Action in Chicago. Admini

  8. TOMORROW: EPA Administrator in Chicago to Headline The New Republic Conference: The Next Frontier of Climate Change

    EPA Pesticide Factsheets

    WASHINGTON - On Friday, April 10 , EPA Administrator Gina McCarthy will give the headline interview on the economic need for acting on climate change at The New Republic's conference, The Next Frontier of Climate Change: State and Local Actio

  9. Improving perceptions of teamwork climate with the Veterans Health Administration medical team training program.

    PubMed

    Carney, Brian T; West, Priscilla; Neily, Julia B; Mills, Peter D; Bagian, James P

    2011-01-01

    There are differences between nurse and physician perceptions of teamwork. The purpose of this study was to determine whether these differences would be reduced with medical team training (MTT). The Safety Attitudes Questionnaire was administered to nurses and physicians working in the operating rooms of 101 consecutive hospitals before and at the completion of an MTT program. Responses to the 6 teamwork climate items on the Safety Attitudes Questionnaire were analyzed using nonparametric testing. At baseline, physicians had more favorable perceptions on teamwork climate items than nurses. Physicians demonstrated improvement on all 6 teamwork climate items. Nurses demonstrated improvement in perceptions on all teamwork climate items except "Nurse input is well received." Physicians still had a more favorable perception than nurses on all 6 teamwork climate items at follow-up. Despite an improvement in perceptions by physicians and nurses, baseline nurse-physician differences persisted at completion of the Veterans Health Administration MTT Program.

  10. Third National Aeronautics and Space Administration Weather and climate program science review

    NASA Technical Reports Server (NTRS)

    Kreins, E. R. (Editor)

    1977-01-01

    Research results of developing experimental and prototype operational systems, sensors, and space facilities for monitoring, and understanding the atmosphere are reported. Major aspects include: (1) detection, monitoring, and prediction of severe storms; (2) improvement of global forecasting; and (3) monitoring and prediction of climate change.

  11. Seasonal Climate Extremes : Mechanism, Predictability and Responses to Global Warming

    NASA Astrophysics Data System (ADS)

    Shongwe, M. E.

    2010-01-01

    Climate extremes are rarely occurring natural phenomena in the climate system. They often pose one of the greatest environmental threats to human and natural systems. Statistical methods are commonly used to investigate characteristics of climate extremes. The fitted statistical properties are often interpolated or extrapolated to give an indication of the likelihood of a certain event within a given period or interval. Under changing climatic conditions, the statistical properties of climate extremes are also changing. It is an important scientific goal to predict how the properties of extreme events change. To achieve this goal, observational and model studies aimed at revealing important features are a necessary prerequisite. Notable progress has been made in understanding mechanisms that influence climate variability and extremes in many parts of the globe including Europe. However, some of the recently observed unprecedented extremes cannot be fully explained from the already identified forcing factors. A better understanding of why these extreme events occur and their sensitivity to certain reinforcing and/or competing factors is useful. Understanding their basic form as well as their temporal variability is also vital and can contribute to global scientific efforts directed at advancing climate prediction capabilities, particularly making skilful forecasts and realistic projections of extremes. In this thesis temperature and precipitation extremes in Europe and Africa, respectively, are investigated. Emphasis is placed on the mechanisms underlying the occurrence of the extremes, their predictability and their likely response to global warming. The focus is on some selected seasons when extremes typically occur. An atmospheric energy budget analysis for the record-breaking European Autumn 2006 event has been carried out with the goal to identify the sources of energy for the extreme event. Net radiational heating is compared to surface turbulent fluxes of

  12. On the Potential Predictability of Seasonal Land-Surface Climate

    SciTech Connect

    Phillips, T J

    2001-10-01

    The chaotic behavior of the continental climate of an atmospheric general circulation model is investigated from an ensemble of decadal simulations with common specifications of radiative forcings and monthly ocean boundary conditions, but different initial states of atmosphere and land. The variability structures of key model land-surface processes appear to agree sufficiently with observational estimates to warrant detailed examination of their predictability on seasonal time scales. This predictability is inferred from several novel measures of spatio-temporal reproducibility applied to eleven model variables. The reproducibility statistics are computed for variables in which the seasonal cycle is included or excluded, the former case being most pertinent to climate model simulations, and the latter to predictions of the seasonal anomalies. Because the reproducibility metrics in the latter case are determined in the context of a ''perfectly'' known ocean state, they are properly viewed as estimates of the potential predictability of seasonal climate. Inferences based on these reproducibility metrics are shown to be in general agreement with those derived from more conventional measures of potential predictability. It is found that the land-surface variables which include the seasonal cycle are impacted only marginally by changes in initial conditions; moreover, their seasonal climatologies exhibit high spatial reproducibility. In contrast, the reproducibility of a seasonal land-surface anomaly is generally low, although it is considerably higher in the Tropics; its spatial reproducibility also fluctuates in tandem with warm and cold phases of the El Nino/Southern Oscillation phenomenon. However, the detailed sensitivities to initial conditions depend somewhat on the land-surface process: pressure and temperature anomalies exhibit the highest temporal reproducibilities, while hydrological and turbulent flux anomalies show the highest spatial reproducibilities

  13. A federal partnership to pursue operational prediction at the weather-climate interface

    NASA Astrophysics Data System (ADS)

    Sandgathe, Scott A.; Eleuterio, Daniel; Warren, Steven

    2012-10-01

    Earth System Prediction Capability Workshop Washington, D. C., 21-23 March 2012 A meeting to advance a federal partnership toward operational prediction of the physical environment at subseasonal to decadal time scales was held in Washington, D. C. Scientists, headquarters representatives, and program managers from the Department of Energy, NASA, the National Oceanic and Atmospheric Administration (NOAA), the National Science Foundation, the U.S. Air Force, and the U.S. Navy met to discuss pressing agency requirements for extended-range environmental prediction to inform economic, energy, agricultural, national security, and infrastructure decisions. After significant review and discussion, participants agreed that the highest potential for progress was at the interseasonal to interannual (ISI) time scales (Advancing the Science of Climate Change (2010), Board on Atmospheric Sciences and Climate (BASC), http://www.nap.edu/openbook.php?record_id=12782). They agreed to pursue a joint effort, identifying five areas for near-term demonstrations of predictability and establishing volunteer coordinators to organize the demonstration efforts. The demonstrations will establish operational extended-range predictive skill, inform further research, enhance interagency collaboration, and push forward environmental prediction technical and computational capabilities.

  14. Darcy's law predicts widespread forest mortalityunder climate warming

    NASA Astrophysics Data System (ADS)

    Allen, C. D.; McDowell, N. G.

    2015-12-01

    Drought and heat-induced tree mortality is accelerating in many forest biomes as a consequence of a warming climate, resulting in a threat to global forests unlike any in recorded history. Forests store the majority of terrestrial carbon, thus their loss may have significant and sustained impacts on the global carbon cycle. We used a hydraulic corollary to Darcy's law, a core principle of vascular plant physiology, to predict characteristics of plants that will survive and die during drought under warmer future climates. Plants that are tall with isohydric stomatal regulation, low hydraulic conductance, and high leaf area are most likely to die from future drought stress. Thus, tall trees of old-growth forests are at the greatest risk of loss, which has ominous implications for terrestrial carbon storage. This application of Darcy's law indicates today's forests generally should be replaced by shorter and more xeric plants, owing to future warmer droughts and associated wildfires and pest attacks. The Darcy's corollary also provides a simple, robust framework for informing forest management interventions needed to promote the survival of current forests. There are assumptions and omissions in this theoretical prediction, as well as new evidence supporting its predictions, both of which I will review. Given the robustness of Darcy's law for predictions of vascular plant function, we conclude with high certainty that today's forests are going to be subject to continued increases in mortality rates that will result in substantial reorganization of their structure and carbon storage.

  15. FACT SHEET: Administration Takes Steps Forward on Climate Action Plan by Announcing Actions to Cut Methane Emissions

    EPA Pesticide Factsheets

    The Obama Administration is committed to taking responsible steps to address climate change and help ensure a cleaner, more stable environment for future generations. As part of that effort, today, the Administration is announcing a new goal to cut methane

  16. Values and uncertainties in the predictions of global climate models.

    PubMed

    Winsberg, Eric

    2012-06-01

    Over the last several years, there has been an explosion of interest and attention devoted to the problem of Uncertainty Quantification (UQ) in climate science-that is, to giving quantitative estimates of the degree of uncertainty associated with the predictions of global and regional climate models. The technical challenges associated with this project are formidable, and so the statistical community has understandably devoted itself primarily to overcoming them. But even as these technical challenges are being met, a number of persistent conceptual difficulties remain. So why is UQ so important in climate science? UQ, I would like to argue, is first and foremost a tool for communicating knowledge from experts to policy makers in a way that is meant to be free from the influence of social and ethical values. But the standard ways of using probabilities to separate ethical and social values from scientific practice cannot be applied in a great deal of climate modeling, because the roles of values in creating the models cannot be discerned after the fact-the models are too complex and the result of too much distributed epistemic labor. I argue, therefore, that typical approaches for handling ethical/social values in science do not work well here.

  17. Prediction of precipitation in Golestan dam watershed using climate signals

    NASA Astrophysics Data System (ADS)

    Ruigar, Hossein; Golian, Saeed

    2016-02-01

    Global and regional scale climate teleconnection signals, including sea level pressure (SLP) and sea surface temperature (SST), are the main factors influencing the earth's climate oscillations and are among the most important indices used to predict climatic variables. In this research, the effect of teleconnection signals on monthly maximum 1-day precipitation is examined using artificial neural network (ANN) and 40 years of rainfall data for the Madarsoo watershed located at the upstream of Golestan dam in Northern Iran. The Pearson correlation coefficient was used to determine the correlation between monthly maximum 1-day precipitation and climate signals with different lags. Different ANN models with various combinations of inputs, i.e., correlated SLP and SST with different lags, were then used for forecasting precipitation. Results revealed acceptable performance of ANN in forecasting monthly maximum 1-day precipitation using SST/SLP datasets. For instance, the performance indices including root mean square error (RMSE), correlation ( R), and Nash-Sutcliffe (CNS) coefficients for monthly maximum 1-day precipitation of Tangrah rain gauge in August were found to be 6.12, 0.95, and 0.945 mm, respectively, for the test period.

  18. Assessment of Predictability of Philippine Rice Production with Climate Information

    NASA Astrophysics Data System (ADS)

    Koide, N.; Robertson, A. W.; Qian, J.; Ines, A. M.

    2010-12-01

    El Niño Southern Oscillation is the most influential factor on the Philippine climate and has measurable impacts on rice production. The previous studies suggested potential of climate information for prediction of the rice production. For example, Roberts et al. (2009) showed the statistically significant relationship of dry-season rice production in Luzon with Niño sea surface temperature anomalies (SSTA) averaged over the Niño 3.4 region (5°N-5°S, 120°-170°W) for July to September of the year before the harvest. However, the predictive skills of climate information for rice production have not been previously analyzed yet. Thus, we have conducted the assessment of predictive skills of one uncoupled general circulation models (GCMs) (ECHAM-CA) and two coupled GCMs (ECHAM-MOM, and ECHAM-CFS), as well as those of Niño 3.4 SSTAs and the volume of water warmer than 20°C (WWV) in the equatorial Pacific Ocean (5°N-5°S, 120°E to 80°W), based on cross validation with MLR, PCR, CCA. The result clearly shows high potential of these climate information as a tool for prediction of rice production with sufficient lead time for decision makers. Detailed results are as below. Dry Season Dry season rice production of the Philippines of both irrigation and rainfed systems significantly depend on rainfall in OND of the year before the harvest (same results were found by Roberts et al. (2009)). Two coupled GCMs have high predictive skills for dry-season rice production of the Philippines with six months lead time (six months before the beginning of the harvest). In addition, we found that WWV plus zonal wind anomalies over an equatorial west Pacific also has similar predictive skills to those of these coupled GCMs. On the other hand, the uncoupled GCM has high predictive skills only with a few months lead time similar to predictive skills of Niño 3.4 SSTAs. Predictive skills at regional levels are generally lower than that for the Philippines. Many regions in Mindanao

  19. FRIDAY: EPA Administrator Visiting Notre Dame to Discuss Moral Obligation for Climate Action

    EPA Pesticide Factsheets

    WASHINGTON - On Friday, EPA Administrator Gina McCarthy will visit Notre Dame to speak about the need for action on behalf of those who bear the brunt of the effects of climate change and the steps the U.S. is taking to meet that challenge. McCarthy

  20. The Effects of Teacher Perceptions of Administrative Support, School Climate, and Academic Success in Urban Schools

    ERIC Educational Resources Information Center

    Robinson, Lakishia N.

    2015-01-01

    Teacher turnover refers to major changes in teachers' assignments from one school year to the next. Past research has given an overview of several factors of teacher turnover. These factors include the school environment, teacher collaborative efforts, administrative support, school climate, location, salary, classroom management, academic…

  1. An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC

    NASA Astrophysics Data System (ADS)

    Chikamoto, Yoshimitsu; Kimoto, Masahide; Ishii, Masayoshi; Mochizuki, Takashi; Sakamoto, Takashi T.; Tatebe, Hiroaki; Komuro, Yoshiki; Watanabe, Masahiro; Nozawa, Toru; Shiogama, Hideo; Mori, Masato; Yasunaka, Sayaka; Imada, Yukiko

    2013-03-01

    Decadal climate predictability is examined in hindcast experiments by a multi-model ensemble using three versions of the coupled atmosphere-ocean model MIROC. In these hindcast experiments, initial conditions are obtained from an anomaly assimilation procedure using the observed oceanic temperature and salinity with prescribed natural and anthropogenic forcings on the basis of the historical data and future emission scenarios in the Intergovernmental Panel of Climate Change. Results of the multi-model ensemble in our hindcast experiments show that predictability of surface air temperature (SAT) anomalies on decadal timescales mostly originates from externally forced variability. Although the predictable component of internally generated variability has considerably smaller SAT variance than that of externally forced variability, ocean subsurface temperature variability has predictive skills over almost a decade, particularly in the North Pacific and the North Atlantic where dominant signals associated with Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) are observed. Initialization enhances the predictive skills of AMO and PDO indices and slightly improves those of global mean temperature anomalies. Improvement of these predictive skills in the multi-model ensemble is higher than that in a single-model ensemble.

  2. Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

    NASA Astrophysics Data System (ADS)

    Osman, Marisol; Vera, C. S.

    2016-11-01

    This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to

  3. Should we believe model predictions of future climate change? (Invited)

    NASA Astrophysics Data System (ADS)

    Knutti, R.

    2009-12-01

    As computers get faster and our understanding of the climate system improves, climate models to predict the future are getting more complex by including more and more processes, and they are run at higher and higher resolution to resolve more of the small scale processes. As a result, some of the simulated features and structures, e.g. ocean eddies or tropical cyclones look surprisingly real. But are these deceptive? A pattern can look perfectly real but be in the wrong place. So can the current global models really provide the kind of information on local scales and on the quantities (e.g. extreme events) that the decision maker would need to know to invest for example in adaptation? A closer look indicates that evaluating skill of climate models and quantifying uncertainties in predictions is very difficult. This presentation shows that while models are improving in simulating the climate features we observe (e.g. the present day mean state, or the El Nino Southern Oscillation), the spread from multiple models in predicting future changes is often not decreasing. The main problem is that (unlike with weather forecasts for example) we cannot evaluate the model on a prediction (for example for the year 2100) and we have to use the present, or past changes as metrics of skills. But there are infinite ways of testing a model, and many metrics used to test models do not clearly relate to the prediction. Therefore there is little agreement in the community on metrics to separate ‘good’ and ‘bad’ models, and there is a concern that model development, evaluation and posterior weighting or ranking of models are all using the same datasets. While models are continuously improving in representing what we believe to be the key processes, many models also share ideas, parameterizations or even pieces of model code. The current models can therefore not be considered independent. Robustness of a model simulated result is often interpreted as increasing the confidence

  4. Real-time multi-model decadal climate predictions

    NASA Astrophysics Data System (ADS)

    Smith, Doug M.; Scaife, Adam A.; Boer, George J.; Caian, Mihaela; Doblas-Reyes, Francisco J.; Guemas, Virginie; Hawkins, Ed; Hazeleger, Wilco; Hermanson, Leon; Ho, Chun Kit; Ishii, Masayoshi; Kharin, Viatcheslav; Kimoto, Masahide; Kirtman, Ben; Lean, Judith; Matei, Daniela; Merryfield, William J.; Müller, Wolfgang A.; Pohlmann, Holger; Rosati, Anthony; Wouters, Bert; Wyser, Klaus

    2013-12-01

    We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Niña in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Niña. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Niño3 region is predicted to warm slightly by about 0.5 °C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the

  5. Predictability and Diagnosis of Low-Frequency Climate Processes in the Pacific

    SciTech Connect

    Dr. Arthur J. Miller

    2008-10-15

    Predicting the climate for the coming decades requires understanding both natural and anthropogenically forced climate variability. This variability is important because it has major societal impacts, for example by causing floods or droughts on land or altering fishery stocks in the ocean. Our results fall broadly into three topics: evaluating global climate model predictions; regional impacts of climate changes over western North America; and regional impacts of climate changes over the eastern North Pacific Ocean.

  6. Workshop on Satellite and In situ Observations for Climate Prediction

    NASA Technical Reports Server (NTRS)

    Acker, James G.; Busalacchi, Antonio

    1995-01-01

    Participants in this workshop, which convened in Venice, Italy, 6-8 May 1993, met to consider the current state of climate monitoring programs and instrumentation for the purpose of climatological prediction on short-term (seasonal to interannual) timescales. Data quality and coverage requirements for definition of oceanographic heat and momentum fluxes, scales of inter- and intra-annual variability, and land-ocean-atmosphere exchange processes were examined. Advantages and disadvantages of earth-based and spaceborne monitoring systems were considered, as were the structures for future monitoring networks, research programs, and modeling studies.

  7. Selenium deficiency risk predicted to increase under future climate change

    PubMed Central

    Jones, Gerrad D.; Droz, Boris; Greve, Peter; Gottschalk, Pia; Poffet, Deyan; McGrath, Steve P.; Seneviratne, Sonia I.; Smith, Pete; Winkel, Lenny H. E.

    2017-01-01

    Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980–1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate–soil interactions. Using moderate climate-change scenarios for 2080–2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate–soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change. PMID:28223487

  8. Herbivore teeth predict climatic limits in Kenyan ecosystems

    PubMed Central

    Rinne, Janne; Tóth, Anikó B.; Mechenich, Michael; Liu, Liping; Behrensmeyer, Anna K.; Fortelius, Mikael

    2016-01-01

    A major focus in evolutionary biology is to understand how the evolution of organisms relates to changes in their physical environment. In the terrestrial realm, the interrelationships among climate, vegetation, and herbivores lie at the heart of this question. Here we introduce and test a scoring scheme for functional traits present on the worn surfaces of large mammalian herbivore teeth to capture their relationship to environmental conditions. We modeled local precipitation, temperature, primary productivity, and vegetation index as functions of dental traits of large mammal species in 13 national parks in Kenya over the past 60 y. We found that these dental traits can accurately estimate local climate and environment, even at small spatial scales within areas of relatively uniform climate (within two ecoregions), and that they predict limiting conditions better than average conditions. These findings demonstrate that the evolution of key functional properties of organisms may be more reflective of demands during recurring adverse episodes than under average conditions or during isolated severe events. PMID:27791116

  9. MJO prediction skill, predictability, and teleconnection impacts in the Beijing Climate Center Atmospheric General Circulation Model

    NASA Astrophysics Data System (ADS)

    Wu, Jie; Ren, Hong-Li; Zuo, Jinqing; Zhao, Chongbo; Chen, Lijuan; Li, Qiaoping

    2016-09-01

    This study evaluates performance of Madden-Julian oscillation (MJO) prediction in the Beijing Climate Center Atmospheric General Circulation Model (BCC_AGCM2.2). By using the real-time multivariate MJO (RMM) indices, it is shown that the MJO prediction skill of BCC_AGCM2.2 extends to about 16-17 days before the bivariate anomaly correlation coefficient drops to 0.5 and the root-mean-square error increases to the level of the climatological prediction. The prediction skill showed a seasonal dependence, with the highest skill occurring in boreal autumn, and a phase dependence with higher skill for predictions initiated from phases 2-4. The results of the MJO predictability analysis showed that the upper bounds of the prediction skill can be extended to 26 days by using a single-member estimate, and to 42 days by using the ensemble-mean estimate, which also exhibited an initial amplitude and phase dependence. The observed relationship between the MJO and the North Atlantic Oscillation was accurately reproduced by BCC_AGCM2.2 for most initial phases of the MJO, accompanied with the Rossby wave trains in the Northern Hemisphere extratropics driven by MJO convection forcing. Overall, BCC_AGCM2.2 displayed a significant ability to predict the MJO and its teleconnections without interacting with the ocean, which provided a useful tool for fully extracting the predictability source of subseasonal prediction.

  10. Regional Design Approach in Designing Climatic Responsive Administrative Building in the 21st Century

    NASA Astrophysics Data System (ADS)

    Haja Bava Mohidin, Hazrina Binti; Ismail, Alice Sabrina

    2015-01-01

    The objective of this paper is to explicate on the study of modern administrative building in Malaysia which portrays regional design approach that conforms to the local context and climate by reviewing two case studies; Perdana Putra (1999) and former Prime Minister's Office (1967). This paper is significant because the country's stature and political statement was symbolized by administrative building as a national icon. In other words, it is also viewed as a cultural object that is closely tied to a particular social context and nation historical moment. Administrative building, therefore, may exhibit various meanings. This paper uses structuralism paradigm and semiotic principles as a methodological approach. This paper is of importance for practicing architects and society in the future as it offers new knowledge and understanding in identifying the suitable climatic consideration that may reflect regionalist design approach in modern administrative building. These elements then may be adopted in designing public buildings in the future with regional values that are important for expressing national culture to symbolize the identity of place and society as well as responsive to climate change.

  11. Investigation into regional climate variability using tree-ring reconstruction, climate diagnostics and prediction

    NASA Astrophysics Data System (ADS)

    Barandiaran, Daniel A.

    This document is a summary of research conducted to develop and apply climate analysis tools toward a better understanding of the past and future of hydroclimate variability in the state of Utah. Two pilot studies developed data management and climate analysis tools subsequently applied to our region of interest. The first investigated the role of natural atmospheric forcing in the inter-annual variability of precipitation of the Sahel region in Africa, and found a previously undocumented link with the East Atlantic mode, which explains 29% of variance in regional precipitation. An analysis of output from an operational seasonal climate forecast model revealed a failure in the model to reproduce this linkage, thus highlighting a shortcoming in model performance. The second pilot study studied long-term trends in the strength of the Great Plains low-level jet, an driver of storm development in the region's wet spring season. Our analysis showed that since 1979 the low-level jet has strengthened as shifted the timing of peak activity, resulting in shifts both in time and location for peak precipitation, possibly the result of anthropogenic forcing. Our third study used a unique tree-ring dataset to create a reconstruction of April 1 snow water equivalent, an important measure of water supply in the Intermountain West, for the state of Utah to 1850. Analysis of the reconstruction shows the majority of snowpack variability occurs monotonically over the whole state at decadal to multidecadal frequencies. The final study evaluated decadal prediction performance of climate models participating in the Coupled Model Intercomparison Project 5. We found that the analyzed models exhibit modest skill in prediction of the Pacific Decadal Oscillation and better skill in prediction of global temperature trends post 1960.

  12. Reducing Uncertainty in Chemistry Climate Model Predictions of Stratospheric Ozone

    NASA Technical Reports Server (NTRS)

    Douglass, A. R.; Strahan, S. E.; Oman, L. D.; Stolarski, R. S.

    2014-01-01

    Chemistry climate models (CCMs) are used to predict the future evolution of stratospheric ozone as ozone-depleting substances decrease and greenhouse gases increase, cooling the stratosphere. CCM predictions exhibit many common features, but also a broad range of values for quantities such as year of ozone-return-to-1980 and global ozone level at the end of the 21st century. Multiple linear regression is applied to each of 14 CCMs to separate ozone response to chlorine change from that due to climate change. We show that the sensitivity of lower atmosphere ozone to chlorine change deltaO3/deltaCly is a near linear function of partitioning of total inorganic chlorine (Cly) into its reservoirs; both Cly and its partitioning are controlled by lower atmospheric transport. CCMs with realistic transport agree with observations for chlorine reservoirs and produce similar ozone responses to chlorine change. After 2035 differences in response to chlorine contribute little to the spread in CCM results as the anthropogenic contribution to Cly becomes unimportant. Differences among upper stratospheric ozone increases due to temperature decreases are explained by differences in ozone sensitivity to temperature change deltaO3/deltaT due to different contributions from various ozone loss processes, each with their own temperature dependence. In the lower atmosphere, tropical ozone decreases caused by a predicted speed-up in the Brewer-Dobson circulation may or may not be balanced by middle and high latitude increases, contributing most to the spread in late 21st century predictions.

  13. Operational climate prediction in the era of big data in China: Reviews and prospects

    NASA Astrophysics Data System (ADS)

    Wang, Xin; Song, Lianchun; Wang, Guofu; Ren, Hongli; Wu, Tongwen; Jia, Xiaolong; Wu, Huanping; Wu, Jie

    2016-06-01

    Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.

  14. Do GCM's predict the climate.... Or the low frequency weather?

    NASA Astrophysics Data System (ADS)

    Lovejoy, S.; Schertzer, D.; Varon, D.

    2012-04-01

    control runs (i.e. without climate forcing) of GCM based climate forecasting systems including those of the Institut Pierre Simon Laplace (Paris) and the Earth Forecasting System (Hamburg). In order for these systems to go beyond simply predicting low frequency weather i.e. in order for them to predict the climate, they need appropriate climate forcings and/ or new internal mechanisms of variability. Using statistical scaling techniques we examine the scale dependence of fluctuations from forced and unforced GCM outputs, including from the ECHO-G and EFS simulations in the Millenium climate reconstruction project and compare this with data, multiproxies and paleo data. Our general conclusion is that the models systematically underestimate the multidecadal, multicentennial scale variability.

  15. Abrupt Climate Change: A Magnetic Coupling Model (MCM) Prediction.

    NASA Astrophysics Data System (ADS)

    Ely, John T. A.

    2002-04-01

    Recent findings [p.8 ISBN 0-309-07434-7] show major climate changes often occur in a decade. This is another of many MCM predictions (see refs). All of them tested from 1968 to date have been proven, including: Global warming is real and driven by fossil fuel (1970's); This CO2 forcing has ended Major Ice Ages; All Major and Minor Ice Ages are caused by decreases in existing (primarily subvisible and other thin, especially newly forming) cirrus at mid to high geomagnetic latitudes; Ionization of the atmosphere near 250 grams per square cm depth by GCR (galactic cosmic ray protons circa 1 gev) cause cirrus depression; Ice cores and other proxy records show ice ages exhibit increased beryllium-10, carbon-14, etc, due to GCR. As noted in the Mar and Apr abstracts, the MCM predictable climate ended in 2000, following over 30 yrs of our ignoring its easily testable warnings re fossil fuel. Hence, we now face the somber question of whether human intervention is still possible in a CO2 Runaway and sea level rise that may be on a decade time scale. [Ely, Session A8, APS Mtg, Seattle, Mar 01; Ely, Session H14.013, APS Mtg, Apr 01; MCM pub list http://faculty.washington.edu/ely/MCM.html

  16. Indoor climate problems in day institutions for children. Practical, Administrative and policy perspectives.

    PubMed

    Steensberg, J

    1985-01-01

    Based on case material from the late 1970s and early 1980s from the Institution of Medical Officers of Health covering a Danish county some examples of practical indoor climate problems in day institutions for children are given. Insufficient ventilation of premises is probably the single most important factor in the development of indoor climate problems. An effective cleaning generally improves the indoor air. The study particularly illustrates the administrative and policy perspectives of the decision making process. Those that make decisions on indoor climate problems unfortunately seem to favour a narrow definition of health, i.e. the absence of overt disease; and they are not always aware that the relationship between indoor climate factors and health effects cannot be proven in an absolute sense. Experts on the scientific aspects are needed but their statements are influenced by personal values and their perception of the reasonable balance between health protection and social costs. One of the main factors influencing the indoor climate situation in Danish day institutions for children has been the lack of an adequate regulatory framework; and the central administration and responsible ministers have failed to use the already existing legislative powers to prevent problems. Decision making in cases on the indoor climate of institutions should be accelerated; we cannot wait for proof before taking preventive measures. The indoor air of institutions is a "public good" to the same extent as the ambient air and the responsible authorities have an obligation to regulate accordingly. When building regulations prove insufficient other central authorities must support local decision makers with more specific directions. Testing of building materials, hazard rating and an approval system is needed. Guidelines on indoor climate requirements for public institutions should be developed. In countries with a built-up system of child institutions and a decreasing birth

  17. A linear regression model for predicting PNW estuarine temperatures in a changing climate

    EPA Science Inventory

    Pacific Northwest coastal regions, estuaries, and associated ecosystems are vulnerable to the potential effects of climate change, especially to changes in nearshore water temperature. While predictive climate models simulate future air temperatures, no such projections exist for...

  18. The status and prospect of seasonal climate prediction of climate over Korea and East Asia: A review

    NASA Astrophysics Data System (ADS)

    Jeong, Jee-Hoon; Lee, Hyunsoo; Yoo, Jin Ho; Kwon, MinHo; Yeh, Sang-Wook; Kug, Jong-Seong; Lee, Jun-Yi; Kim, Baek-Min; Son, Seok-Woo; Min, Seung-Ki; Lee, Hansu; Lee, Woo-Seop; Yoon, Jin-Ho; Kim, Hyun-kyung

    2017-02-01

    Over the last few decades, there have been startling advances in our understanding of climate system and in modelling techniques. However, the skill of seasonal climate prediction is still not enough to meet the various needs from industrial and public sectors. Therefore, there are tremendous on-going efforts to improve the skill of climate prediction in the seasonal to interannual time scales. Since seasonal to interannual climate variabilities in Korea and East Asia are influenced by many internal and external factors including East Asian monsoon, tropical ocean variability, and other atmospheric low-frequency variabilities, comprehensive understanding of these factors are essential for skillful seasonal climate prediction for Korea and East Asia. Also, there are newly suggested external factors providing additional prediction skill like soil moisture, snow, Arctic sea ice, and stratospheric variability, and techniques to realize skills from underlying potential predictability. In this review paper, we describe current status of seasonal climate prediction and future prospect for improving climate prediction over Korea and East Asia.

  19. Using unknown knowns to predict coastal response to future climate

    NASA Astrophysics Data System (ADS)

    Plant, N. G.; Lentz, E. E.; Gutierrez, B.; Thieler, E. R.; Passeri, D. L.

    2015-12-01

    The coastal zone, including its bathymetry, topography, ecosystem, and communities, depends on and responds to a wide array of natural and engineered processes associated with climate variability. Climate affects the frequency of coastal storms, which are only resolved probabilistically for future conditions, as well as setting the pace for persistent processes (e.g., waves driving daily alongshore transport; beach nourishment). It is not clear whether persistent processes or extreme events contribute most to the integrated evolution of the coast. Yet, observations of coastal change record the integration of persistent and extreme processes. When these observations span a large spatial domain and/or temporal range they may reflect a wide range of forcing and boundary conditions that include different levels of sea-level rise, storminess, sediment input, engineering activities, and elevation distributions. We have been using a statistical approach to characterize the interrelationships between oceanographic, ecological, and geomorphic processes—including the role played by human activities via coastal protection, beach nourishment, and other forms of coastal management. The statistical approach, Bayesian networks, incorporates existing information to establish underlying prior expectations for the distributions and inter-correlations of variables most relevant to coastal geomorphic evolution. This underlying information can then be used to make predictions. We demonstrate several examples of the utility of this approach using data as constraints and then propagating the constraints and uncertainty to make predictions of unobserved variables that include changes in shorelines, dunes, and overwash deposits. We draw on data from the Gulf and Atlantic Coasts of the United States, resolving time scales of years to a century. The examples include both short-term storm impacts and long-term evolution associated with sea-level rise. We show that the Bayesian network can

  20. Measured Climate Induced Volume Changes of Three Glaciers and Current Glacier-Climate Response Prediction

    NASA Astrophysics Data System (ADS)

    Trabant, D. C.; March, R. S.; Cox, L. H.; Josberger, E. G.

    2003-12-01

    analyzing the response of glaciers to climate. Volume response times are relatively simple to determine and can be used to evaluate the temporal, areal, and volumetric affects of a climate change. However, the quasi-decadal period between the recent climate-regime shifts is several times less than the theoretical volume readjustment response times for the benchmark glaciers. If hydrologically significant climate shifts recur at quasi-decadal intervals and if most glaciers' volume-response times are several times longer \\(true for all but a few small, steep glaciers\\), most medium and large glaciers are responding to the current climate and a fading series of regime shifts which, themselves, vary in magnitude. This confused history of driver trends prevent conventional balances from being simply correlated with climate. Reference-surface balances remove the dynamic response of glaciers from the balance trend by holding the surface area distribution constant. This effectively makes the reference surface balances directly correlated with the current climatic forcing. The challenging problem of predicting how a glacier will respond to real changes in climate may require a combination of the volume response time and reference surface mass balances applied to a long time-series of measured values that contain hydrologically significant variations.

  1. Impact of spatial climate variability on catchment streamflow predictions

    NASA Astrophysics Data System (ADS)

    Patil, Sopan; Wigington, Jim; Leibowitz, Scott; Sproles, Eric; Comeleo, Randy

    2014-05-01

    The ability of hydrological models to predict a catchment's streamflow response serves several important needs of our society, such as flood protection, irrigation demand, domestic water supply, and preservation of fish habitat. However, spatial variability of climate within a catchment can negatively affect streamflow predictions if it is not explicitly accounted for in hydrological models. In this study, we examined the changes in streamflow predictability when a hydrological model is run with spatially variable (distributed) meteorological inputs instead of spatially uniform (lumped) meteorological inputs. Both lumped and distributed versions of the EXP-HYDRO model were implemented at 41 meso-scale (500 - 5000 km2) catchments in the Pacific Northwest region of USA (Oregon, Washington, and Idaho). We used two complementary metrics of long-term spatial climate variability, moisture homogeneity index (IM) and temperature variability index (ITV), to analyse the performance improvement with distributed model. Results showed that the distributed model performed better than the lumped model in 38 catchments, and noticeably better (>10% improvement) in 13 catchments. Furthermore, spatial variability of moisture distribution alone was insufficient to explain the observed patterns of model performance improvement. For catchments with low moisture homogeneity (IM < 80%), IM was a better predictor of model performance improvement than ITV; whereas for catchments with high moisture homogeneity (IM > 80%), ITV was a better predictor of performance improvement than IM. Based on the results, we conclude that: (1) catchments that have low homogeneity of moisture distribution are the obvious candidates for using spatially distributed meteorological inputs, and (2) catchments with homogeneous moisture distribution benefit from spatially distributed meteorological inputs if those catchments have high spatial variability of precipitation phase (rain vs. snow). Our use of spatially

  2. Predicting chaotic climates: from Earth to super-Earths?

    NASA Astrophysics Data System (ADS)

    Read, Peter L.

    2010-10-01

    The prediction of atmospheric behaviour for the Earth has been a major arena for the application of complex mathematical ideas and techniques to geophysics and astronomy. Objective forecasting of the weather provided the first stimulus for the development of numerical methods to integrate the equations of fluid motion by L. F. Richardson in the early 20th century, leading on to their implementation in electronic computers in the 1940s and 1950s. Such an approach has now reached a highly sophisticated state with weather and climate models attempting to forecast weather and climate changes in immense detail. Such techniques have been applied to model the atmospheres of other planets in the Solar System since the 1960s, and are catching up rapidly in their sophistication with models used for the Earth. But with the expanding discoveries of planets around other stars, it is likely that alternative approaches may be needed that are more general but seek to quantify trends in the gross features of atmospheric circulation systems as a function of a small number of global parameters. By the study of simple analogues, either in the form of simplified numerical models or laboratory experiments, considerable insights may be gained as to the likely roles of planetary size, rotation, thermal stratification and other factors in determining the principal length scales, styles of global circulation and dominant waves and instability processes active in all planetary atmospheres. In this review, we explore aspects of these analogues and demonstrate the importance of a number of key dimensionless parameters, most notably thermal Rossby and Burger numbers and a measure of the dominant frictional or radiative timescale, in defining the type of circulation regime to be expected in a prototype planetary atmosphere subject to axisymmetric driving. These considerations help to place Mars, Venus, Titan and Earth into an appropriate context, and may also lay the foundations for predicting

  3. Development of an integrated method for long-term water quality prediction using seasonal climate forecast

    NASA Astrophysics Data System (ADS)

    Cho, Jaepil; Shin, Chang-Min; Choi, Hwan-Kyu; Kim, Kyong-Hyeon; Choi, Ji-Yong

    2016-10-01

    The APEC Climate Center (APCC) produces climate prediction information utilizing a multi-climate model ensemble (MME) technique. In this study, four different downscaling methods, in accordance with the degree of utilizing the seasonal climate prediction information, were developed in order to improve predictability and to refine the spatial scale. These methods include: (1) the Simple Bias Correction (SBC) method, which directly uses APCC's dynamic prediction data with a 3 to 6 month lead time; (2) the Moving Window Regression (MWR) method, which indirectly utilizes dynamic prediction data; (3) the Climate Index Regression (CIR) method, which predominantly uses observation-based climate indices; and (4) the Integrated Time Regression (ITR) method, which uses predictors selected from both CIR and MWR. Then, a sampling-based temporal downscaling was conducted using the Mahalanobis distance method in order to create daily weather inputs to the Soil and Water Assessment Tool (SWAT) model. Long-term predictability of water quality within the Wecheon watershed of the Nakdong River Basin was evaluated. According to the Korean Ministry of Environment's Provisions of Water Quality Prediction and Response Measures, modeling-based predictability was evaluated by using 3-month lead prediction data issued in February, May, August, and November as model input of SWAT. Finally, an integrated approach, which takes into account various climate information and downscaling methods for water quality prediction, was presented. This integrated approach can be used to prevent potential problems caused by extreme climate in advance.

  4. Development of Crop Yield Estimation Method by Applying Seasonal Climate Prediction in Asia-Pacific Region

    NASA Astrophysics Data System (ADS)

    Shin, Y.; Lee, E.

    2015-12-01

    Under the influence of recent climate change, abnormal weather condition such as floods and droughts has issued frequently all over the world. The occurrence of abnormal weather in major crop production areas leads to soaring world grain prices because it influence the reduction of crop yield. Development of crop yield estimation method is important means to accommodate the global food crisis caused by abnormal weather. However, due to problems with the reliability of the seasonal climate prediction, application research on agricultural productivity has not been much progress yet. In this study, it is an object to develop long-term crop yield estimation method in major crop production countries worldwide using multi seasonal climate prediction data collected by APEC Climate Center. There are 6-month lead seasonal predictions produced by six state-of-the-art global coupled ocean-atmosphere models(MSC_CANCM3, MSC_CANCM4, NASA, NCEP, PNU, POAMA). First of all, we produce a customized climate data through temporal and spatial downscaling methods for use as a climatic input data to the global scale crop model. Next, we evaluate the uncertainty of climate prediction by applying multi seasonal climate prediction in the crop model. Because rice is the most important staple food crop in the Asia-Pacific region, we assess the reliability of the rice yields using seasonal climate prediction for main rice production countries. RMSE(Root Mean Squire Error) and TCC(Temporal Correlation Coefficient) analysis is performed in Asia-Pacific countries, major 14 rice production countries, to evaluate the reliability of the rice yield according to the climate prediction models. We compare the rice yield data obtained from FAOSTAT and estimated using the seasonal climate prediction data in Asia-Pacific countries. In addition, we show that the reliability of seasonal climate prediction according to the climate models in Asia-Pacific countries where rice cultivation is being carried out.

  5. A global empirical system for probabilistic seasonal climate prediction

    NASA Astrophysics Data System (ADS)

    Eden, J. M.; van Oldenborgh, G. J.; Hawkins, E.; Suckling, E. B.

    2015-12-01

    Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño-Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

  6. An empirical system for probabilistic seasonal climate prediction

    NASA Astrophysics Data System (ADS)

    Eden, Jonathan; van Oldenborgh, Geert Jan; Hawkins, Ed; Suckling, Emma

    2016-04-01

    Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño-Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

  7. NASA's Earth Observing System: The Transition from Climate Monitoring to Climate Change Prediction

    NASA Technical Reports Server (NTRS)

    King, Michael D.; Herring, David D.

    1998-01-01

    Earth's 4.5 billion year history is a study in change. Natural geological forces have been rearranging the surface features and climatic conditions of our planet since its beginning. There is scientific evidence that some of these natural changes have not only led to mass extinctions of species (e.g., dinosaurs), but have also severely impacted human civilizations. For instance, there is evidence that a relatively sudden climate change caused a 300-year drought that contributed to the downfall of Akkadia, one of the most powerful empires in the Middle-East region around 2200 BC. More recently, the "little ice age" from 1200-1400 AD forced the Vikings to abandon Greenland when temperatures there dropped by about 1.5 C, rendering it too difficult to grow enough crops to sustain the population. Today, there is compelling scientific evidence that human activities have attained the magnitude of a geological force and are speeding up the rate of global change. For example, carbon dioxide levels have risen 30 percent since the industrial revolution and about 40 percent of the world's land surface has been transformed by humans. We don't understand the cause-and-effect relationships among Earth's land, ocean, and atmosphere well enough to predict what, if any, impacts these rapid changes will have on future climate conditions. We need to make many measurements all over the world, over a long period of time, in order to assemble the information needed to construct accurate computer models that will enable us to forecast climate change. In 1988, the Earth System Sciences Committee, sponsored by NASA, issued a report calling for an integrated, long-term strategy for measuring the vital signs of Earth's climate system. The report urged that the measurements must all be intimately coupled with focused process studies, they must facilitate development of Earth system models, and they must be stored in an information system that ensures open access to consistent, long-term data

  8. Projection and prediction: Climate sensitivity on the rise

    NASA Astrophysics Data System (ADS)

    Armour, Kyle C.

    2016-10-01

    Recent observations of Earth's energy budget indicate low climate sensitivity. Research now shows that these estimates should be revised upward, resolving an apparent mismatch with climate models and implying a warmer future.

  9. Towards the Prediction of Decadal to Centennial Climate Processes in the Coupled Earth System Model

    SciTech Connect

    Liu, Zhengyu; Kutzbach, J.; Jacob, R.; Prentice, C.

    2011-12-05

    In this proposal, we have made major advances in the understanding of decadal and long term climate variability. (a) We performed a systematic study of multidecadal climate variability in FOAM-LPJ and CCSM-T31, and are starting exploring decadal variability in the IPCC AR4 models. (b) We develop several novel methods for the assessment of climate feedbacks in the observation. (c) We also developed a new initialization scheme DAI (Dynamical Analogue Initialization) for ensemble decadal prediction. (d) We also studied climate-vegetation feedback in the observation and models. (e) Finally, we started a pilot program using Ensemble Kalman Filter in CGCM for decadal climate prediction.

  10. Predictability of the Seasonal Climate Associated with ENSO in NCEP Climate Forecast System

    NASA Astrophysics Data System (ADS)

    Zhang, Q.

    2005-05-01

    The predictability of seasonal climate associated with ENSO is studied for NCEP Climate Forecast System (CFS) 23-year retrospective forecasts. Warm-minus-cold composites of the lead 1-6 month sea surface temperature (SST) anomalies show an ENSO-like horse-shoes pattern in the tropical Pacific, comparable with observation. There is a corresponding increased precipitation band along the equator near the dateline extending eastward to the South American coast, as well as the less precipitation over the Maritime Continents and off-equatorial western Pacific. Extended empirical orthogonal function (EEOF) analysis of the SST anomaly recovers ENSO -like dominant mode in the tropics for all seasons. Identification of patterns that optimize the signal-to-noise ratio is obtained by linear regression of the ensemble means on the principal component (PC) time series of SST. The optimized height patterns for boreal winter and spring are similar, although the winter response over the northern extratropics is somewhat weaker. Some subtle changes in amplitude are found in difference of leading initial conditions. The signal-to-noise ratio is significantly greater than unity in the Tropics (all seasons), the northern Pacific and continental North America subtropics (boreal winter and spring), and the southern Pacific subtropics (boreal fall).

  11. Pacific Walrus and climate change: observations and predictions.

    PubMed

    Maccracken, James G

    2012-08-01

    The extent and duration of sea-ice habitats used by Pacific walrus (Odobenus rosmarus divergens) are diminishing resulting in altered walrus behavior, mortality, and distribution. I document changes that have occurred over the past several decades and make predictions to the end of the 21st century. Climate models project that sea ice will monotonically decline resulting in more ice-free summers of longer duration. Several stressors that may impact walruses are directly influenced by sea ice. How these stressors materialize were modeled as most likely-case, worst-case, and best-case scenarios for the mid- and late-21st century, resulting in four comprehensive working hypotheses that can help identify and prioritize management and research projects, identify comprehensive mitigation actions, and guide monitoring programs to track future developments and adjust programs as needed. In the short term, the most plausible hypotheses predict a continuing northward shift in walrus distribution, increasing use of coastal haulouts in summer and fall, and a population reduction set by the carrying capacity of the near shore environment and subsistence hunting. Alternatively, under worst-case conditions, the population will decline to a level where the probability of extinction is high. In the long term, walrus may seasonally abandon the Bering and Chukchi Seas for sea-ice refugia to the northwest and northeast, ocean warming and pH decline alter walrus food resources, and subsistence hunting exacerbates a large population decline. However, conditions that reverse current trends in sea ice loss cannot be ruled out. Which hypothesis comes to fruition depends on how the stressors develop and the success of mitigation measures. Best-case scenarios indicate that successful mitigation of unsustainable harvests and terrestrial haulout-related mortalities can be effective. Management and research should focus on monitoring, elucidating effects, and mitigation, while ultimately

  12. Pacific Walrus and climate change: observations and predictions

    PubMed Central

    MacCracken, James G

    2012-01-01

    The extent and duration of sea-ice habitats used by Pacific walrus (Odobenus rosmarus divergens) are diminishing resulting in altered walrus behavior, mortality, and distribution. I document changes that have occurred over the past several decades and make predictions to the end of the 21st century. Climate models project that sea ice will monotonically decline resulting in more ice-free summers of longer duration. Several stressors that may impact walruses are directly influenced by sea ice. How these stressors materialize were modeled as most likely-case, worst-case, and best-case scenarios for the mid- and late-21st century, resulting in four comprehensive working hypotheses that can help identify and prioritize management and research projects, identify comprehensive mitigation actions, and guide monitoring programs to track future developments and adjust programs as needed. In the short term, the most plausible hypotheses predict a continuing northward shift in walrus distribution, increasing use of coastal haulouts in summer and fall, and a population reduction set by the carrying capacity of the near shore environment and subsistence hunting. Alternatively, under worst-case conditions, the population will decline to a level where the probability of extinction is high. In the long term, walrus may seasonally abandon the Bering and Chukchi Seas for sea-ice refugia to the northwest and northeast, ocean warming and pH decline alter walrus food resources, and subsistence hunting exacerbates a large population decline. However, conditions that reverse current trends in sea ice loss cannot be ruled out. Which hypothesis comes to fruition depends on how the stressors develop and the success of mitigation measures. Best-case scenarios indicate that successful mitigation of unsustainable harvests and terrestrial haulout-related mortalities can be effective. Management and research should focus on monitoring, elucidating effects, and mitigation, while ultimately

  13. Weather and Climate Prediction for the North American Monsoon

    NASA Astrophysics Data System (ADS)

    Krishnamurti, T. N.; Chakraborty, A.

    2005-05-01

    Some of the major elements of the North American monsoon include the onset and seasonal behavior of precipitation, the moisture sources, orographic responses, effects of sea surface temperature (SST) anomalies over the Gulf of Mexico, Pacific and Atlantic Oceans, and the teleconnection with the intertropical convergence zone (ITCZ). This study addresses these issues on the medium range (a week) to seasonal (3 month) time scales. Our approach is one of constructing ensemble forecasts that include 11 weather models for the medium range and 13 coupled atmosphere-ocean models for seasonal time scales. The metrics for forecasts evaluation include deterministic measures such as RMS error and anomaly correlation, and probabilistic measures such as the equitable threat scores and Briar skill scores. The ensemble forecast approach includes a conventional FSU superensemble for weather and a variant called the synthetic superensemble for the seasonal climate. These superensemble strings covering a 13-year period show that it is possible to predict some of the important features of the North American monsoon at a higher skill with the superensemble compared to the participating member models.

  14. Integrating environmental and genetic effects to predict responses of tree populations to climate.

    PubMed

    Wang, Tongli; O'Neill, Gregory A; Aitken, Sally N

    2010-01-01

    Climate is a major environmental factor affecting the phenotype of trees and is also a critical agent of natural selection that has molded among-population genetic variation. Population response functions describe the environmental effect of planting site climates on the performance of a single population, whereas transfer functions describe among-population genetic variation molded by natural selection for climate. Although these approaches are widely used to predict the responses of trees to climate change, both have limitations. We present a novel approach that integrates both genetic and environmental effects into a single "universal response function" (URF) to better predict the influence of climate on phenotypes. Using a large lodgepole pine (Pinus contorta Dougl. ex Loud.) field transplant experiment composed of 140 populations planted on 62 sites to demonstrate the methodology, we show that the URF makes full use of data from provenance trials to: (1) improve predictions of climate change impacts on phenotypes; (2) reduce the size and cost of future provenance trials without compromising predictive power; (3) more fully exploit existing, less comprehensive provenance tests; (4) quantify and compare environmental and genetic effects of climate on population performance; and (5) predict the performance of any population growing in any climate. Finally, we discuss how the last attribute allows the URF to be used as a mechanistic model to predict population and species ranges for the future and to guide assisted migration of seed for reforestation, restoration, or afforestation and genetic conservation in a changing climate.

  15. Predicting the effects of climate change on marine communities and the consequences for fisheries

    NASA Astrophysics Data System (ADS)

    Jennings, Simon; Brander, Keith

    2010-02-01

    Climate effects on the structure and function of marine communities have received scant attention. The few existing approaches for predicting climate effects suggest that community responses might be predicted from the responses of component populations. These approaches require a very complex understanding of ecological interactions among populations. An alternate and informative parallel process is to ask whether it is possible to make predictions about community level responses to climate that are independent of knowledge about the identity and dynamics of component populations. We propose that it is possible to make such predictions, based on knowledge of the processes that determine the size-structure of communities. We suggest that theory that relates metabolic scaling, predator-prey interactions and energy transfer in size-based food webs, allows the size-structure and productivity of communities across a range of trophic levels to be predicted, provided that predictions of the effects of climate on primary production are available. One simple application of the community-focused predictions is to ask whether predictions of the size composition and abundance of populations for alternate climate scenarios are compatible with predictions for the size composition and relative abundance of communities. More sophisticated treatments could predict the effects of climate scenarios on multiple interacting populations and compare their combined size-abundance structure and production with that predicted for the community under the same climate scenario. The main weakness of the community approach is that the methods predict abundance and production by size-class rather than taxonomic group, and society would be particularly concerned if climate driven changes had a strong effect on the relative production of fishable and non-fishable species in the community. The main strength of the community approach is that it provides widely applicable 'null' models for assessing

  16. Predicting the evolutionary dynamics of seasonal adaptation to novel climates in Arabidopsis thaliana.

    PubMed

    Fournier-Level, Alexandre; Perry, Emily O; Wang, Jonathan A; Braun, Peter T; Migneault, Andrew; Cooper, Martha D; Metcalf, C Jessica E; Schmitt, Johanna

    2016-05-17

    Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico "resurrection experiments" showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation.

  17. Predicting the evolutionary dynamics of seasonal adaptation to novel climates in Arabidopsis thaliana

    PubMed Central

    Fournier-Level, Alexandre; Perry, Emily O.; Wang, Jonathan A.; Braun, Peter T.; Migneault, Andrew; Cooper, Martha D.; Metcalf, C. Jessica E.; Schmitt, Johanna

    2016-01-01

    Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico “resurrection experiments” showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation. PMID:27140640

  18. Megacities, air quality and climate: Seamless prediction approach

    NASA Astrophysics Data System (ADS)

    Baklanov, Alexander; Molina, Luisa T.; Gauss, Michael

    2016-04-01

    The rapid urbanization and growing number of megacities and urban complexes requires new types of research and services that make best use of science and available technology. With an increasing number of humans now living in urban sprawls, there are urgent needs of examining what the rising number of megacities means for air pollution, local climate and the effects these changes have on global climate. Such integrated studies and services should assist cities in facing hazards such as storm surge, flooding, heat waves, and air pollution episodes, especially in changing climates. While important advances have been made, new interdisciplinary research studies are needed to increase our understanding of the interactions between emissions, air quality, and regional and global climates. Studies need to address both basic and applied research and bridge the spatial and temporal scales connecting local emissions and air pollution and local weather, global atmospheric chemistry and climate. This paper reviews the current status of studies of the complex interactions between climate, air quality and megacities, and identifies the main gaps in our current knowledge as well as further research needs in this important field of research. Highlights • Climate, air quality and megacities interactions: gaps in knowledge, research needs. • Urban hazards: pollution episodes, storm surge, flooding, heat waves, public health. • Global climate change affects megacities' climate, environment and comfort. • Growing urbanization requires integrated weather, environment and climate monitoring systems. • New generation of multi-scale models and seamless integrated urban services are needed. Reference Baklanov, A., L.T. Molina, M. Gauss (2016) Megacities, air quality and climate. Atmospheric Environment, 126: 235-249. doi:10.1016/j.atmosenv.2015.11.059

  19. Climate and species richness predict the phylogenetic structure of African mammal communities.

    PubMed

    Kamilar, Jason M; Beaudrot, Lydia; Reed, Kaye E

    2015-01-01

    We have little knowledge of how climatic variation (and by proxy, habitat variation) influences the phylogenetic structure of tropical communities. Here, we quantified the phylogenetic structure of mammal communities in Africa to investigate how community structure varies with respect to climate and species richness variation across the continent. In addition, we investigated how phylogenetic patterns vary across carnivores, primates, and ungulates. We predicted that climate would differentially affect the structure of communities from different clades due to between-clade biological variation. We examined 203 communities using two metrics, the net relatedness (NRI) and nearest taxon (NTI) indices. We used simultaneous autoregressive models to predict community phylogenetic structure from climate variables and species richness. We found that most individual communities exhibited a phylogenetic structure consistent with a null model, but both climate and species richness significantly predicted variation in community phylogenetic metrics. Using NTI, species rich communities were composed of more distantly related taxa for all mammal communities, as well as for communities of carnivorans or ungulates. Temperature seasonality predicted the phylogenetic structure of mammal, carnivoran, and ungulate communities, and annual rainfall predicted primate community structure. Additional climate variables related to temperature and rainfall also predicted the phylogenetic structure of ungulate communities. We suggest that both past interspecific competition and habitat filtering have shaped variation in tropical mammal communities. The significant effect of climatic factors on community structure has important implications for the diversity of mammal communities given current models of future climate change.

  20. 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

  1. Predicting the Response of Electricity Load to Climate Change

    SciTech Connect

    Sullivan, Patrick; Colman, Jesse; Kalendra, Eric

    2015-07-28

    Our purpose is to develop a methodology to quantify the impact of climate change on electric loads in the United States. We perform simple linear regression, assisted by geospatial smoothing, on paired temperature and load time-series to estimate the heating- and coolinginduced sensitivity to temperature across 300 transmission zones and 16 seasonal and diurnal time periods. The estimated load sensitivities can be coupled with climate scenarios to quantify the potential impact of climate change on load, with a primary application being long-term electricity scenarios. The method allows regional and seasonal differences in climate and load response to be reflected in the electricity scenarios. While the immediate product of this analysis was designed to mesh with the spatial and temporal resolution of a specific electricity model to enable climate change scenarios and analysis with that model, we also propose that the process could be applied for other models and purposes.

  2. Improving Predictions and Management of Hydrological Extremes through Climate Services

    NASA Astrophysics Data System (ADS)

    van den Hurk, Bart; Wijngaard, Janet; Pappenberger, Florian; Bouwer, Laurens; Weerts, Albrecht; Buontempo, Carlo; Doescher, Ralf; Manez, Maria; Ramos, Maria-Helena; Hananel, Cedric; Ercin, Ertug; Hunink, Johannes; Klein, Bastian; Pouget, Laurent; Ward, Philip

    2016-04-01

    The EU Roadmap on Climate Services can be seen as a result of convergence between the society's call for "actionable research", and the climate research community providing tailored data, information and knowledge. However, although weather and climate have clearly distinct definitions, a strong link between weather and climate services exists that is not explored extensively. Stakeholders being interviewed in the context of the Roadmap consider climate as a far distant long term feature that is difficult to consider in present-day decision taking, which is dominated by daily experience with handling extreme events. It is argued that this experience is a rich source of inspiration to increase society's resilience to an unknown future. A newly started European research project, IMPREX, is built on the notion that "experience in managing current day weather extremes is the best learning school to anticipate consequences of future climate". This paper illustrates possible ways to increase the link between information and services addressing weather and climate time scales by discussing the underlying concepts of IMPREX and its expected outcome.

  3. Climatic patterns predict the elaboration of song displays in mockingbirds.

    PubMed

    Botero, Carlos A; Boogert, Neeltje J; Vehrencamp, Sandra L; Lovette, Irby J

    2009-07-14

    Climatic variability and unpredictability affect the distribution and abundance of resources and the timing and duration of breeding opportunities. In vertebrates, climatic variability selects for enhanced cognition when organisms compensate for environmental changes through learning and innovation. This hypothesis is supported by larger brain sizes, higher foraging innovation rates, higher reproductive flexibility, and higher sociality in species living in more variable climates. Male songbirds sing to attract females and repel rivals. Given the reliance of these displays on learning and innovation, we hypothesized that they could also be affected by climatic patterns. Here we show that in the mockingbird family (Aves: Mimidae), species subject to more variable and unpredictable climates have more elaborate song displays. We discuss two potential mechanisms for this result, both of which acknowledge that the complexity of song displays is largely driven by sexual selection. First, stronger selection in more variable and unpredictable climates could lead to the elaboration of signals of quality. Alternatively, selection for enhanced learning and innovation in more variable and unpredictable climates might lead to the evolution of signals of intelligence in the context of mate attraction.

  4. Subtask 2.4 - Integration and Synthesis in Climate Change Predictive Modeling

    SciTech Connect

    Jaroslav Solc

    2009-06-01

    The Energy & Environmental Research Center (EERC) completed a brief evaluation of the existing status of predictive modeling to assess options for integration of our previous paleohydrologic reconstructions and their synthesis with current global climate scenarios. Results of our research indicate that short-term data series available from modern instrumental records are not sufficient to reconstruct past hydrologic events or predict future ones. On the contrary, reconstruction of paleoclimate phenomena provided credible information on past climate cycles and confirmed their integration in the context of regional climate history is possible. Similarly to ice cores and other paleo proxies, acquired data represent an objective, credible tool for model calibration and validation of currently observed trends. It remains a subject of future research whether further refinement of our results and synthesis with regional and global climate observations could contribute to improvement and credibility of climate predictions on a regional and global scale.

  5. Knowledge discovery and nonlinear modeling can complement climate model simulations for predictive insights about climate extremes and their impacts

    NASA Astrophysics Data System (ADS)

    Ganguly, A. R.; Steinbach, M.; Kumar, V.

    2009-12-01

    The IPCC AR4 not only provided conclusive evidence about anticipated global warming at century scales, but also indicated with a high level of certainty that the warming is caused by anthropogenic emissions. However, an outstanding knowledge-gap is to develop credible projections of climate extremes and their impacts. Climate extremes are defined in this context as extreme weather and hydrological events, as well as changes in regional hydro-meteorological patterns, especially at decadal scales. While temperature extremes from climate models have relatively better skills, hydrological variables and their extremes have significant shortcomings. Credible projections about tropical storms, sea level rise, coastal storm surge, land glacier melts, and landslides remain elusive. The next generation of climate models is expected to have higher precision. However, their ability to provide more accurate projections of climate extremes remains to be tested. Projections of observed trends into the future may not be reliable in non-stationary environments like climate change, even though functional relationships derived from physics may hold. On the other hand, assessments of climate change impacts which are useful for stakeholders and policy makers depend critically on regional and decadal scale projections of climate extremes. Thus, climate impacts scientists often need to develop qualitative inferences about the not so-well predicted climate extremes based on insights from observations (e.g., increased hurricane intensity) or conceptual understanding (e.g., relation of wildfires to regional warming or drying and hurricanes to SST). However, neither conceptual understanding nor observed trends may be reliable when extrapolating in a non-stationary environment. These urgent societal priorities offer fertile grounds for nonlinear modeling and knowledge discovery approaches. Thus, qualitative inferences on climate extremes and impacts may be transformed into quantitative

  6. Prediction of agricultural drought for the Canadian prairies using climatic and satellite data

    NASA Astrophysics Data System (ADS)

    Kumar, Vijendra

    1999-11-01

    Wheat export is a significant component of the Canadian economy. In normal (nondrought) years, the export is as high as 30 million tonnes, but it is reduced to about 20 million tomes in drought years. This significant reduction in exports not only reduces direct profits but may also upset export targets and prices that are set in advance, if droughts are not accurately predicted. In this thesis, prediction of agricultural drought is attempted from both long-term and short-term perspectives. The long-term prediction refers to predicting wheat yield (production per unit area) prior to wheat planting; and, under the short-term prediction, wheat yield is estimated around harvesttime. Predictive analysis was performed on five crop districts of Saskatchewan (1b, 3bn, 4b, 6a, and 9a) using climate data (monthly and daily temperature and precipitation) from rune weather stations. In addition, Normalized Difference Vegetation Index values generated from NOAA (National Oceanic and Atmospheric Administration)/AVERR (Advanced Very High Radiometric Resolution) satellite data were used. The long-term prediction was made by fitting various time series techniques (trend, moving average, exponential smoothing, and autoregressive integrated moving average) to the yield series in a district. The technique providing minimum prediction-error was selected. The short-term prediction was made in both qualitative and quantitative forms. The qualitative prediction was attempted using the error correction procedure of pattern recognition. The. quantitative prediction involved modification of the computer program currently being used by the Canadian Wheat Board (CWB) to estimate wheat yield. The CWB program employs only monthly and precipitation and determines a drought index for a weather station. A hybrid model that employs daily climate data and a NDVI-based variable was developed. Among Various NDVI-based variables, the average NDVI during the entire growing period was found to be the

  7. Role of dynamic vegetation in regional climate predictions over western Africa

    NASA Astrophysics Data System (ADS)

    Alo, Clement Aga; Wang, Guiling

    2010-10-01

    This study examines the role of vegetation dynamics in regional predictions of future climate change in western Africa using a dynamic vegetation model asynchronously coupled to a regional climate model. Two experiments, one for present day and one for future, are conducted with the linked regional climate-vegetation model, and the third with the regional climate model standing alone that predicts future climate based on present-day vegetation. These simulations are so designed in order to tease out the impact of structural vegetation feedback on simulated climate and hydrological processes. According to future predictions by the regional climate-vegetation model, increase in LAI is widespread, with significant shift in vegetation type. Over the Guinean Coast in 2084-2093, evergreen tree coverage decreases by 49% compared to 1984-1993, while drought deciduous tree coverage increases by 56%. Over the Sahel region in the same period, grass cover increases by 31%. Such vegetation changes are accompanied by a decrease of JJA rainfall by 2% over the Guinean Coast and an increase by 23% over the Sahel. This rather small decrease or large increase of precipitation is largely attributable to the role of vegetation feedback. Without the feedback effect from vegetation, the regional climate model would have predicted a 5% decrease of JJA rainfall in both the Guinean Coast and the Sahel as a result of the radiative and physiological effects of higher atmospheric CO2 concentration. These results demonstrate that climate- and CO2-induced changes in vegetation structure modify hydrological processes and climate at magnitudes comparable to or even higher than the radiative and physiological effects, thus evincing the importance of including vegetation feedback in future climate predictions.

  8. Role of Climate Change in Predictions of Future Tropospheric Ozone and Aerosols

    NASA Astrophysics Data System (ADS)

    Liao, H.; Chen, W.; Seinfeld, J.

    2006-12-01

    A unified tropospheric chemistry-aerosol model within the Goddard Institute for Space Studies general circulation model II is applied to simulate equilibrium climate change driven by changes in greenhouse gases (GHGs) and/or aerosols over 2000-2100 to examine the effects of climate change on global distributions of tropospheric ozone and sulfate, nitrate, ammonium, black carbon, primary organic carbon, secondary organic carbon, sea salt, and mineral dust aerosols. We consider only direct radiative effect of aerosols on future climate in this study. Since aerosol levels will both affect and be affected by future climate, we identify the role of aerosol-driven climate in predicting future air pollutants by performing a number of sensitivity studies. The year 2100 GHG concentrations as well as the anthropogenic emissions of ozone precursors and aerosols/aerosol precursors are based on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) A2. Although greenhouse gases are the most important drivers of global climate change, aerosols are very influential on regional climate through absorption and scattering of solar radiation. As aerosol concentrations increase over 2000-2100, aerosol-induced cooling at the surface, increase in atmospheric stability, and reduction in precipitation are predicted to increase surface-layer concentrations of pollutants over populated areas; Aerosol-induced climate change is therefore predicted to have a positive feedback to tropospheric aerosol concentrations. We also compare the effect of GHG-driven climate on atmospheric composition with that of aerosol-driven climate. Results suggest that it is important to account for climate responses to aerosol forcing in predicting future ozone and aerosols.

  9. The Prediction Power of Servant and Ethical Leadership Behaviours of Administrators on Teachers' Job Satisfaction

    ERIC Educational Resources Information Center

    Güngör, Semra Kiranli

    2016-01-01

    The purpose of this study is to identify servant leadership and ethical leadership behaviors of administrators and the prediction power of these behaviors on teachers' job satisfaction according to the views of schoolteachers. This research, figured in accordance with the quantitative research processes. The target population of the research has…

  10. PREDICTING CLIMATE-INDUCED RANGE SHIFTS FOR MAMMALS: HOW GOOD ARE THE MODELS?

    EPA Science Inventory

    In order to manage wildlife and conserve biodiversity, it is critical that we understand the potential impacts of climate change on species distributions. Several different approaches to predicting climate-induced geographic range shifts have been proposed to address this proble...

  11. Predicting Climate Change using Response Theory: Global Averages and Spatial Patterns

    NASA Astrophysics Data System (ADS)

    Lucarini, Valerio; Lunkeit, Frank; Ragone, Francesco

    2016-04-01

    The provision of accurate methods for predicting the climate response to anthropogenic and natural forcings is a key contemporary scientific challenge. Using a simplified and efficient open-source climate model featuring O(105) degrees of freedom, we show how it is possible to approach such a problem using nonequilibrium statistical mechanics. Using the theoretical framework of the pullback attractor and the tools of response theory we propose a simple yet efficient method for predicting - at any lead time and in an ensemble sense - the change in climate properties resulting from increase in the concentration of CO2 using test perturbation model runs. We assess strengths and limitations of the response theory in predicting the changes in the globally averaged values of surface temperature and of the yearly total precipitation, as well as their spatial patterns. We also show how it is possible to define accurately concepts like the the inertia of the climate system or to predict when climate change is detectable given a scenario of forcing. Our analysis can be extended for dealing with more complex portfolios of forcings and can be adapted to treat, in principle, any climate observable. Our conclusion is that climate change is indeed a problem that can be effectively seen through a statistical mechanical lens, and that there is great potential for optimizing the current coordinated modelling exercises run for the preparation of the subsequent reports of the Intergovernmental Panel for Climate Change.

  12. Woody plants and the prediction of climate-change impacts on bird diversity.

    PubMed

    Kissling, W D; Field, R; Korntheuer, H; Heyder, U; Böhning-Gaese, K

    2010-07-12

    Current methods of assessing climate-induced shifts of species distributions rarely account for species interactions and usually ignore potential differences in response times of interacting taxa to climate change. Here, we used species-richness data from 1005 breeding bird and 1417 woody plant species in Kenya and employed model-averaged coefficients from regression models and median climatic forecasts assembled across 15 climate-change scenarios to predict bird species richness under climate change. Forecasts assuming an instantaneous response of woody plants and birds to climate change suggested increases in future bird species richness across most of Kenya whereas forecasts assuming strongly lagged woody plant responses to climate change indicated a reversed trend, i.e. reduced bird species richness. Uncertainties in predictions of future bird species richness were geographically structured, mainly owing to uncertainties in projected precipitation changes. We conclude that assessments of future species responses to climate change are very sensitive to current uncertainties in regional climate-change projections, and to the inclusion or not of time-lagged interacting taxa. We expect even stronger effects for more specialized plant-animal associations. Given the slow response time of woody plant distributions to climate change, current estimates of future biodiversity of many animal taxa may be both biased and too optimistic.

  13. Woody plants and the prediction of climate-change impacts on bird diversity

    PubMed Central

    Kissling, W. D.; Field, R.; Korntheuer, H.; Heyder, U.; Böhning-Gaese, K.

    2010-01-01

    Current methods of assessing climate-induced shifts of species distributions rarely account for species interactions and usually ignore potential differences in response times of interacting taxa to climate change. Here, we used species-richness data from 1005 breeding bird and 1417 woody plant species in Kenya and employed model-averaged coefficients from regression models and median climatic forecasts assembled across 15 climate-change scenarios to predict bird species richness under climate change. Forecasts assuming an instantaneous response of woody plants and birds to climate change suggested increases in future bird species richness across most of Kenya whereas forecasts assuming strongly lagged woody plant responses to climate change indicated a reversed trend, i.e. reduced bird species richness. Uncertainties in predictions of future bird species richness were geographically structured, mainly owing to uncertainties in projected precipitation changes. We conclude that assessments of future species responses to climate change are very sensitive to current uncertainties in regional climate-change projections, and to the inclusion or not of time-lagged interacting taxa. We expect even stronger effects for more specialized plant–animal associations. Given the slow response time of woody plant distributions to climate change, current estimates of future biodiversity of many animal taxa may be both biased and too optimistic. PMID:20513712

  14. Abrupt climate change and thermohaline circulation: mechanisms and predictability.

    PubMed

    Marotzke, J

    2000-02-15

    The ocean's thermohaline circulation has long been recognized as potentially unstable and has consequently been invoked as a potential cause of abrupt climate change on all timescales of decades and longer. However, fundamental aspects of thermohaline circulation changes remain poorly understood.

  15. Predicting Pleistocene climate from vegetation in North America

    NASA Astrophysics Data System (ADS)

    Loehle, C.

    2007-02-01

    Climates at the Last Glacial Maximum have been inferred from fossil pollen assemblages, but these inferred climates are colder for eastern North America than those produced by climate simulations. It has been suggested that low CO2 levels could account for this discrepancy. In this study biogeographic evidence is used to test the CO2 effect model. The recolonization of glaciated zones in eastern North America following the last ice age produced distinct biogeographic patterns. It has been assumed that a wide zone south of the ice was tundra or boreal parkland (Boreal-Parkland Zone or BPZ), which would have been recolonized from southern refugia as the ice melted, but the patterns in this zone differ from those in the glaciated zone, which creates a major biogeographic anomaly. In the glacial zone, there are few endemics but in the BPZ there are many across multiple taxa. In the glacial zone, there are the expected gradients of genetic diversity with distance from the ice-free zone, but no evidence of this is found in the BPZ. Many races and related species exist in the BPZ which would have merged or hybridized if confined to the same refugia. Evidence for distinct southern refugia for most temperate species is lacking. Extinctions of temperate flora were rare. The interpretation of spruce as a boreal climate indicator may be mistaken over much of the region if the spruce was actually an extinct temperate species. All of these anomalies call into question the concept that climates in the zone south of the ice were extremely cold or that temperate species had to migrate far to the south. An alternate hypothesis is that low CO2 levels gave an advantage to pine and spruce, which are the dominant trees in the BPZ, and to herbaceous species over trees, which also fits the observed pattern. Thus climate reconstruction from pollen data is probably biased and needs to incorporate CO2 effects. Most temperate species could have survived across their current ranges at lower

  16. The CCPP-ARM Parameterization Testbed (CAPT): Where Climate Simulation Meets Weather Prediction

    SciTech Connect

    Phillips, T J; Potter, G L; Williamson, D L; Cederwall, R T; Boyle, J S; Fiorino, M; Hnilo, J J; Olson, J G; Xie, S; Yio, J J

    2003-11-21

    To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands, in particular, that the GCM parameterizations of unresolved processes should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provied that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by realistically initialized climate GCM, and the application of six-hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be similarly tested. In order to further this method for evaluating and analyzing parameterizations in climate GCMs, the USDOE is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM. Numerical weather prediction methods show promise for improving parameterizations in climate GCMs.

  17. Ice core and climate reanalysis analogs to predict Antarctic and Southern Hemisphere climate changes

    NASA Astrophysics Data System (ADS)

    Mayewski, P. A.; Carleton, A. M.; Birkel, S. D.; Dixon, D.; Kurbatov, A. V.; Korotkikh, E.; McConnell, J.; Curran, M.; Cole-Dai, J.; Jiang, S.; Plummer, C.; Vance, T.; Maasch, K. A.; Sneed, S. B.; Handley, M.

    2017-01-01

    A primary goal of the SCAR (Scientific Committee for Antarctic Research) initiated AntClim21 (Antarctic Climate in the 21st Century) Scientific Research Programme is to develop analogs for understanding past, present and future climates for the Antarctic and Southern Hemisphere. In this contribution to AntClim21 we provide a framework for achieving this goal that includes: a description of basic climate parameters; comparison of existing climate reanalyses; and ice core sodium records as proxies for the frequencies of marine air mass intrusion spanning the past ∼2000 years. The resulting analog examples include: natural variability, a continuation of the current trend in Antarctic and Southern Ocean climate characterized by some regions of warming and some cooling at the surface of the Southern Ocean, Antarctic ozone healing, a generally warming climate and separate increases in the meridional and zonal winds. We emphasize changes in atmospheric circulation because the atmosphere rapidly transports heat, moisture, momentum, and pollutants, throughout the middle to high latitudes. In addition, atmospheric circulation interacts with temporal variations (synoptic to monthly scales, inter-annual, decadal, etc.) of sea ice extent and concentration. We also investigate associations between Antarctic atmospheric circulation features, notably the Amundsen Sea Low (ASL), and primary climate teleconnections including the SAM (Southern Annular Mode), ENSO (El Nîno Southern Oscillation), the Pacific Decadal Oscillation (PDO), the AMO (Atlantic Multidecadal Oscillation), and solar irradiance variations.

  18. Predicted impacts of climate change on malaria transmission in West Africa

    NASA Astrophysics Data System (ADS)

    Yamana, T. K.; Eltahir, E. A. B.

    2014-12-01

    Increases in temperature and changes in precipitation due to climate change are expected to alter the spatial distribution of malaria transmission. This is especially true in West Africa, where malaria prevalence follows the current north-south gradients in temperature and precipitation. We assess the skill of GCMs at simulating past and present climate in West Africa in order to select the most credible climate predictions for the periods 2030-2060 and 2070-2100. We then use the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS), a mechanistic model of malaria transmission, to translate the predicted changes in climate into predicted changes availability of mosquito breeding sites, mosquito populations, and malaria prevalence. We investigate the role of acquired immunity in determining a population's response to changes in exposure to the malaria parasite.

  19. Improved Predictions of the Geographic Distribution of Invasive Plants Using Climatic Niche Models.

    PubMed

    Ramírez-Albores, Jorge E; Bustamante, Ramiro O; Badano, Ernesto I

    2016-01-01

    Climatic niche models for invasive plants are usually constructed with occurrence records taken from literature and collections. Because these data neither discriminate among life-cycle stages of plants (adult or juvenile) nor the origin of individuals (naturally established or man-planted), the resulting models may mispredict the distribution ranges of these species. We propose that more accurate predictions could be obtained by modelling climatic niches with data of naturally established individuals, particularly with occurrence records of juvenile plants because this would restrict the predictions of models to those sites where climatic conditions allow the recruitment of the species. To test this proposal, we focused on the Peruvian peppertree (Schinus molle), a South American species that has largely invaded Mexico. Three climatic niche models were constructed for this species using high-resolution dataset gathered in the field. The first model included all occurrence records, irrespective of the life-cycle stage or origin of peppertrees (generalized niche model). The second model only included occurrence records of naturally established mature individuals (adult niche model), while the third model was constructed with occurrence records of naturally established juvenile plants (regeneration niche model). When models were compared, the generalized climatic niche model predicted the presence of peppertrees in sites located farther beyond the climatic thresholds that naturally established individuals can tolerate, suggesting that human activities influence the distribution of this invasive species. The adult and regeneration climatic niche models concurred in their predictions about the distribution of peppertrees, suggesting that naturally established adult trees only occur in sites where climatic conditions allow the recruitment of juvenile stages. These results support the proposal that climatic niches of invasive plants should be modelled with data of

  20. The Climate Variability & Predictability (CVP) Program at NOAA - Recent Program Advancements

    NASA Astrophysics Data System (ADS)

    Lucas, S. E.; Todd, J. F.

    2015-12-01

    The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International and U.S. Climate Variability and Predictability (CLIVAR/US CLIVAR) Program, and the U.S. Global Change Research Program (USGCRP). The CVP program sits within NOAA's Climate Program Office (http://cpo.noaa.gov/CVP). The CVP Program currently supports multiple projects in areas that are aimed at improved representation of physical processes in global models. Some of the topics that are currently funded include: i) Improved Understanding of Intraseasonal Tropical Variability - DYNAMO field campaign and post -field projects, and the new climate model improvement teams focused on MJO processes; ii) Climate Process Teams (CPTs, co-funded with NSF) with projects focused on Cloud macrophysical parameterization and its application to aerosol indirect effects, and Internal-Wave Driven Mixing in Global Ocean Models; iii) Improved Understanding of Tropical Pacific Processes, Biases, and Climatology; iv) Understanding Arctic Sea Ice Mechanism and Predictability;v) AMOC Mechanisms and Decadal Predictability Recent results from CVP-funded projects will be summarized. Additional information can be found at http://cpo.noaa.gov/CVP.

  1. Improved Predictions of the Geographic Distribution of Invasive Plants Using Climatic Niche Models

    PubMed Central

    Ramírez-Albores, Jorge E.; Bustamante, Ramiro O.

    2016-01-01

    Climatic niche models for invasive plants are usually constructed with occurrence records taken from literature and collections. Because these data neither discriminate among life-cycle stages of plants (adult or juvenile) nor the origin of individuals (naturally established or man-planted), the resulting models may mispredict the distribution ranges of these species. We propose that more accurate predictions could be obtained by modelling climatic niches with data of naturally established individuals, particularly with occurrence records of juvenile plants because this would restrict the predictions of models to those sites where climatic conditions allow the recruitment of the species. To test this proposal, we focused on the Peruvian peppertree (Schinus molle), a South American species that has largely invaded Mexico. Three climatic niche models were constructed for this species using high-resolution dataset gathered in the field. The first model included all occurrence records, irrespective of the life-cycle stage or origin of peppertrees (generalized niche model). The second model only included occurrence records of naturally established mature individuals (adult niche model), while the third model was constructed with occurrence records of naturally established juvenile plants (regeneration niche model). When models were compared, the generalized climatic niche model predicted the presence of peppertrees in sites located farther beyond the climatic thresholds that naturally established individuals can tolerate, suggesting that human activities influence the distribution of this invasive species. The adult and regeneration climatic niche models concurred in their predictions about the distribution of peppertrees, suggesting that naturally established adult trees only occur in sites where climatic conditions allow the recruitment of juvenile stages. These results support the proposal that climatic niches of invasive plants should be modelled with data of

  2. Potential Distribution Predicted for Rhynchophorus ferrugineus in China under Different Climate Warming Scenarios.

    PubMed

    Ge, Xuezhen; He, Shanyong; Wang, Tao; Yan, Wei; Zong, Shixiang

    2015-01-01

    As the primary pest of palm trees, Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) has caused serious harm to palms since it first invaded China. The present study used CLIMEX 1.1 to predict the potential distribution of R. ferrugineus in China according to both current climate data (1981-2010) and future climate warming estimates based on simulated climate data for the 2020s (2011-2040) provided by the Tyndall Center for Climate Change Research (TYN SC 2.0). Additionally, the Ecoclimatic Index (EI) values calculated for different climatic conditions (current and future, as simulated by the B2 scenario) were compared. Areas with a suitable climate for R. ferrugineus distribution were located primarily in central China according to the current climate data, with the northern boundary of the distribution reaching to 40.1°N and including Tibet, north Sichuan, central Shaanxi, south Shanxi, and east Hebei. There was little difference in the potential distribution predicted by the four emission scenarios according to future climate warming estimates. The primary prediction under future climate warming models was that, compared with the current climate model, the number of highly favorable habitats would increase significantly and expand into northern China, whereas the number of both favorable and marginally favorable habitats would decrease. Contrast analysis of EI values suggested that climate change and the density of site distribution were the main effectors of the changes in EI values. These results will help to improve control measures, prevent the spread of this pest, and revise the targeted quarantine areas.

  3. Potential Distribution Predicted for Rhynchophorus ferrugineus in China under Different Climate Warming Scenarios

    PubMed Central

    Ge, Xuezhen; He, Shanyong; Wang, Tao; Yan, Wei; Zong, Shixiang

    2015-01-01

    As the primary pest of palm trees, Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) has caused serious harm to palms since it first invaded China. The present study used CLIMEX 1.1 to predict the potential distribution of R. ferrugineus in China according to both current climate data (1981–2010) and future climate warming estimates based on simulated climate data for the 2020s (2011–2040) provided by the Tyndall Center for Climate Change Research (TYN SC 2.0). Additionally, the Ecoclimatic Index (EI) values calculated for different climatic conditions (current and future, as simulated by the B2 scenario) were compared. Areas with a suitable climate for R. ferrugineus distribution were located primarily in central China according to the current climate data, with the northern boundary of the distribution reaching to 40.1°N and including Tibet, north Sichuan, central Shaanxi, south Shanxi, and east Hebei. There was little difference in the potential distribution predicted by the four emission scenarios according to future climate warming estimates. The primary prediction under future climate warming models was that, compared with the current climate model, the number of highly favorable habitats would increase significantly and expand into northern China, whereas the number of both favorable and marginally favorable habitats would decrease. Contrast analysis of EI values suggested that climate change and the density of site distribution were the main effectors of the changes in EI values. These results will help to improve control measures, prevent the spread of this pest, and revise the targeted quarantine areas. PMID:26496438

  4. A Study of Faculty, Administrative, and Staff Perceptions of the Climate for Shared Governance at Appalachian College Association Member Institutions

    ERIC Educational Resources Information Center

    Easton, Tanya L.

    2014-01-01

    The purpose of this study was to investigate how faculty, administrators, and staff perceived the climate for shared governance at 36 member institutions of the Appalachian College Association (ACA), based on standards for sound shared governance in higher education as outlined by the American Association of University Professors (AAUP). Numerous…

  5. Predicting potential effects of climate change on Ozark Highlands streams

    SciTech Connect

    Willson, G.D.; Rabeni, C.F.; Galat, D.L. )

    1993-06-01

    The Ozark Highlands biogeographic area centers on two National Park Service units: Ozark National Scenic Riverways in Missouri and Buffalo National River in Arkansas. The Ozark Highlands is part of a national network of 20 research sites established to determine the potential influence of global change on ecosystems and their adaptability. The Ozark Highlands program will integrate historic and proxy stream flows, fluvial geomorphology, and trophic-level responses in streams to model aquatic systems under mid-continent, climate change scenarios. Climate change in Ozarks streams will likely alter hydrologic/geomorphic patterns and disrupt community structure and ecological processes. Initially, the program has focused on defining variation inherent in stream systems and how ecological processes and biota respond to that variability.

  6. The predictive state: Science, territory and the future of the Indian climate.

    PubMed

    Mahony, Martin

    2014-02-01

    Acts of scientific calculation have long been considered central to the formation of the modern nation state, yet the transnational spaces of knowledge generation and political action associated with climate change seem to challenge territorial modes of political order. This article explores the changing geographies of climate prediction through a study of the ways in which climate change is rendered knowable at the national scale in India. The recent controversy surrounding an erroneous prediction of melting Himalayan glaciers by the Intergovernmental Panel on Climate Change provides a window onto the complex and, at times, antagonistic relationship between the Panel and Indian political and scientific communities. The Indian reaction to the error, made public in 2009, drew upon a national history of contestation around climate change science and corresponded with the establishment of a scientific assessment network, the Indian Network for Climate Change Assessment, which has given the state a new platform on which to bring together knowledge about the future climate. I argue that the Indian Network for Climate Change Assessment is indicative of the growing use of regional climate models within longer traditions of national territorial knowledge-making, allowing a rescaling of climate change according to local norms and practices of linking scientific knowledge to political action. I illustrate the complex co-production of the epistemic and the normative in climate politics, but also seek to show how co-productionist understandings of science and politics can function as strategic resources in the ongoing negotiation of social order. In this case, scientific rationalities and modes of environmental governance contribute to the contested epistemic construction of territory and the evolving spatiality of the modern nation state under a changing climate.

  7. Monitoring and Predicting the African Climate for Food Security

    NASA Astrophysics Data System (ADS)

    Thiaw, W. M.

    2015-12-01

    Drought is one of the greatest challenges in Africa due to its impact on access to sanitary water and food. In response to this challenge, the international community has mobilized to develop famine early warning systems (FEWS) to bring safe food and water to populations in need. Over the past several decades, much attention has focused on advance risk planning in agriculture and water. This requires frequent updates of weather and climate outlooks. This paper describes the active role of NOAA's African Desk in FEWS. Emphasis is on the operational products from short and medium range weather forecasts to subseasonal and seasonal outlooks in support of humanitarian relief programs. Tools to provide access to real time weather and climate information to the public are described. These include the downscaling of the U.S. National Multi-model Ensemble (NMME) to improve seasonal forecasts in support of Regional Climate Outlook Forums (RCOFs). The subseasonal time scale has emerged as extremely important to many socio-economic sectors. Drawing from advances in numerical models that can now provide a better representation of the MJO, operational subseasonal forecasts are included in the African Desk product suite. These along with forecasts skill assessment and verifications are discussed. The presentation will also highlight regional hazards outlooks basis for FEWSNET food security outlooks.

  8. PREDICTING CLIMATE-INDUCED RANGE SHIFTS: MODEL DIFFERENCES AND MODEL RELIABILITY

    EPA Science Inventory

    Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common ...

  9. Predicting and understanding ecosystem responses to climate change at continental scales

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Climate is changing around the world across a range of scales from local to global, but ecological consequences remain difficult to understand and predict. Such predictions are complicated by changes in connectivity of resources, in particular water, nutrients, and propagules, that influence the way...

  10. Seasonal Forecasts of Climate Indices: Impact of Definition and Spatial Aggregation on Predictive Skill

    NASA Astrophysics Data System (ADS)

    Bhend, Jonas; Mahlstein, Irina; Liniger, Mark

    2016-04-01

    Seasonal forecasting models are increasingly being used to forecast application-relevant aspects. A simple way to make such user-oriented predictions are application-specific climate indices. Little is known, however, on how the predictive skill of forecasts of such climate indices relates to the predictive skill in forecasting seasonal mean conditions. Here we analyse forecasts of two types of indices derived from daily precipitation and temperature: counts of events such as the number of dry days and accumulated threshold exceedances such as degree days. We find that the predictive skill of forecasts of heating and cooling degree days and of consecutive dry days is generally lower than the skill of seasonal mean temperature and rainfall forecasts respectively. By use of a toy model we demonstrate that this reduction in skill is more pronounced for skilful forecasts and climate indices with a threshold in the tail of the statistical distribution. We further analyse the impact of spatial aggregation and find that aggregation generally improves the predictive skill. Using appropriate covariates for weighting - for example population density to derive a proxy for the national energy demand for heating - the usefulness of forecasts of climate indices can be further enhanced while retaining predictive skill. We conclude that processing of direct model output to derive climate indices in combination with spatial aggregation can be used to render still skilful and even more useful seasonal forecasts of user-relevant quantities.

  11. Joint Applications Pilot of the National Climate Predictions and Projections Platform and the North Central Climate Science Center: Delivering climate projections on regional scales to support adaptation planning

    NASA Astrophysics Data System (ADS)

    Ray, A. J.; Ojima, D. S.; Morisette, J. T.

    2012-12-01

    The DOI North Central Climate Science Center (NC CSC) and the NOAA/NCAR National Climate Predictions and Projections (NCPP) Platform and have initiated a joint pilot study to collaboratively explore the "best available climate information" to support key land management questions and how to provide this information. NCPP's mission is to support state of the art approaches to develop and deliver comprehensive regional climate information and facilitate its use in decision making and adaptation planning. This presentation will describe the evolving joint pilot as a tangible, real-world demonstration of linkages between climate science, ecosystem science and resource management. Our joint pilot is developing a deliberate, ongoing interaction to prototype how NCPP will work with CSCs to develop and deliver needed climate information products, including translational information to support climate data understanding and use. This pilot also will build capacity in the North Central CSC by working with NCPP to use climate information used as input to ecological modeling. We will discuss lessons to date on developing and delivering needed climate information products based on this strategic partnership. Four projects have been funded to collaborate to incorporate climate information as part of an ecological modeling project, which in turn will address key DOI stakeholder priorities in the region: Riparian Corridors: Projecting climate change effects on cottonwood and willow seed dispersal phenology, flood timing, and seedling recruitment in western riparian forests. Sage Grouse & Habitats: Integrating climate and biological data into land management decision models to assess species and habitat vulnerability Grasslands & Forests: Projecting future effects of land management, natural disturbance, and CO2 on woody encroachment in the Northern Great Plains The value of climate information: Supporting management decisions in the Plains and Prairie Potholes LCC. NCCSC's role in

  12. Grapevine bud break prediction for cool winter climates

    NASA Astrophysics Data System (ADS)

    Nendel, Claas

    2010-05-01

    Statistical analysis of bud break data for grapevine ( Vitis vinifera L. cvs. Riesling and Müller-Thurgau) at 13 sites along the northern boundary of commercial grapevine production in Europe revealed that, for all investigated sites, the heat summation method for bud break prediction can be improved if the starting date for the accumulation of heat units is specifically determined. Using the coefficient of variance as a criterion, a global minimum for each site can be identified, marking the optimum starting date. Furthermore, it was shown that the application of a threshold temperature for the heat summation method does not lead to an improved prediction of bud break. Using site-specific parameters, bud break of grapevine can be predicted with an accuracy of ± 2.5 days. Using average parameters, the prediction accuracy is reduced to ± 4.5 days, highlighting the sensitivity of the heat summation method to the quality and the representativeness of the driving temperature data.

  13. Predicting the future by explaining the past: constraining carbon-climate feedback using contemporary observations

    NASA Astrophysics Data System (ADS)

    Denning, S.

    2014-12-01

    The carbon-climate community has an historic opportunity to make a step-function improvement in climate prediction by using regional constraints to improve mechanistic model representation of carbon cycle processes. Interactions among atmospheric CO2, global biogeochemistry, and physical climate constitute leading sources of uncertainty in future climate. First-order differences among leading models of these processes produce differences in climate as large as differences in aerosol-cloud-radiation interactions and fossil fuel combustion. Emergent constraints based on global observations of interannual variations provide powerful constraints on model parameterizations. Additional constraints can be defined at regional scales. Organized intercomparison experiments have shown that uncertainties in future carbon-climate feedback arise primarily from model representations of the dependence of photosynthesis on CO2 and drought stress and the dependence of decomposition on temperature. Just as representations of net carbon fluxes have benefited from eddy flux, ecosystem manipulations, and atmospheric CO2, component carbon fluxes (photosynthesis, respiration, decomposition, disturbance) can be constrained at regional scales using new observations. Examples include biogeochemical tracers such as isotopes and carbonyl sulfide as well as remotely-sensed parameters such as chlorophyll fluorescence and biomass. Innovative model evaluation experiments will be needed to leverage the information content of new observations to improve process representations as well as to provide accurate initial conditions for coupled climate model simulations. Successful implementation of a comprehensive benchmarking program could have a huge impact on understanding and predicting future climate change.

  14. Activities of the Climate Forecast Unit (CFU) on regional decadal prediction

    NASA Astrophysics Data System (ADS)

    Guemas, V.; Prodhomme, C.; Doblas-Reyes, F.; Volpi, D.; Caron, L. P.; Davis, M.; Menegoz, M.; Saurral, R. I.; Bellprat, O.

    2014-12-01

    The Climate Forecasting Unit (CFU) is a research unit devoted to develop climate forecast systems to contribute to the creation of climate services that aims to 1) develop climate forecast systems and prediction methodologies, 2) investigate the potential sources of skill and understand the limitation of state-of-the-art forecast systems, 3) formulate reliable climate forecasts that meet specific user needs and 4) contribute to the development of climate services. This presentation will provide an overview of the latest results of this research unit in the field of regional decadal prediction focusing on 1) an assessment of the relative merits of the full-field and the anomaly initialisation techniques, 2) a description of the forecast quality of North Atlantic tropical cyclone activity and South Pacific climate, 3) an evaluation of the impact of volcanic aerosol prescription during decadal forecasts, and 4) the strategy for the development of a climate service to ensure that forecasts are both useful and action-oriented. Results from several European projects, SPECS, PREFACE and EUPORIAS, will be used to illustrate these findings.

  15. Data mining to predict climate hotspots: an experiment in aligning federal climate enterprises in the Northwest

    NASA Astrophysics Data System (ADS)

    Mote, P.; Foster, J. G.; Daley-Laursen, S. B.

    2014-12-01

    The Northwest has the nation's strongest geographic, institutional, and scientific alignment between NOAA RISA, DOI Climate Science Center, USDA Climate Hub, and participating universities. Considering each of those institutions' distinct mission, funding structures, governance, stakeholder engagement, methods of priority-setting, and deliverables, it is a challenge to find areas of common interest and ways for these institutions to work together. In view of the rich history of stakeholder engagement and the deep base of previous research on climate change in the region, these institutions are cooperating in developing a regional capacity to mine the vast available data in ways that are mutually beneficial, synergistic, and regionally relevant. Fundamentally, data mining means exploring connections across and within multiple datasets using advanced statistical techniques, development of multidimensional indices, machine learning, and more. The challenge is not just what we do with big datasets, but how we integrate the wide variety and types of data coming out of scenario analyses to create knowledge and inform decision-making. Federal agencies and their partners need to learn integrate big data on climate change and develop useful tools for important stake-holders to assist them in anticipating the main stresses of climate change to their own resources and preparing to abate those stresses.

  16. COLLABORATIVE RESEARCH: TOWARDS ADVANCED UNDERSTANDING AND PREDICTIVE CAPABILITY OF CLIMATE CHANGE IN THE ARCTIC USING A HIGH-RESOLUTION REGIONAL ARCTIC CLIMATE SYSTEM MODEL

    SciTech Connect

    Gutowski, William J.

    2013-02-07

    The motivation for this project was to advance the science of climate change and prediction in the Arctic region. Its primary goals were to (i) develop a state-of-the-art Regional Arctic Climate system Model (RACM) including high-resolution atmosphere, land, ocean, sea ice and land hydrology components and (ii) to perform extended numerical experiments using high performance computers to minimize uncertainties and fundamentally improve current predictions of climate change in the northern polar regions. These goals were realized first through evaluation studies of climate system components via one-way coupling experiments. Simulations were then used to examine the effects of advancements in climate component systems on their representation of main physics, time-mean fields and to understand variability signals at scales over many years. As such this research directly addressed some of the major science objectives of the BER Climate Change Research Division (CCRD) regarding the advancement of long-term climate prediction.

  17. A Safer Place? LGBT Educators, School Climate, and Implications for Administrators

    ERIC Educational Resources Information Center

    Wright, Tiffany E.; Smith, Nancy J.

    2015-01-01

    Over an 8-year span, two survey studies were conducted to analyze LGBT -teachers' perceptions of their school climate and the impact of school leaders on that climate. This article presents nonparametric, descriptive, and qualitative results of the National Survey of Educators' Perceptions of School Climate 2011 compared with survey results from…

  18. Advances and Challenges in Numerical Weather and Climate Prediction

    NASA Astrophysics Data System (ADS)

    Yu, Tsann-Wang

    2010-10-01

    In this review article, the dispersive nature of various waves that exist in the atmosphere is first reviewed. These waves include Rossby waves, Kelvin wave, acoustic wave, internal and external gravity waves and many others, whose intrinsic nature and great relevancy to weather and climate forecasts are described. This paper then describes the latest development in global observations and data analysis and assimilation methodologies. These include three-dimensional and four dimensional variational data assimilation systems that are being used in the world's major operational weather and climate forecasting centers. Some of the recent results in using novel atmospheric satellite and chemical observation data applied to these data assimilation systems and those from the latest development in high resolution modeling and the ensemble forecasting approach in the operational numerical weather forecasting centers are also presented. Finally, problems of inherent errors associated with initial conditions, and those associated with the coupling of dynamics and physics and their related numerical issues in variational data assimilation systems are discussed.

  19. Comparison of tropospheric scintillation prediction models of the Indonesian climate

    NASA Astrophysics Data System (ADS)

    Chen, Cheng Yee; Singh, Mandeep Jit

    2014-12-01

    Tropospheric scintillation is a phenomenon that will cause signal degradation in satellite communication with low fade margin. Few studies of scintillation have been conducted in tropical regions. To analyze tropospheric scintillation, we obtain data from a satellite link installed at Bandung, Indonesia, at an elevation angle of 64.7° and a frequency of 12.247 GHz from 1999 to 2000. The data are processed and compared with the predictions of several well-known scintillation prediction models. From the analysis, we found that the ITU-R model gives the lowest error rate when predicting the scintillation intensity for fade at 4.68%. However, the model should be further tested using data from higher-frequency bands, such as the K and Ka bands, to verify the accuracy of the model.

  20. Predicting responses to climate change requires all life-history stages.

    PubMed

    Zeigler, Sara

    2013-01-01

    In Focus: Radchuk, V., Turlure, C. & Schtickzelle, N. (2013) Each life stage matters: the importance of assessing response to climate change over the complete life cycle in butterflies. Journal of Animal Ecology, 82, 275-285. Population-level responses to climate change depend on many factors, including unexpected interactions among life history attributes; however, few studies examine climate change impacts over complete life cycles of focal species. Radchuk, Turlure & Schtickzelle () used experimental and modelling approaches to predict population dynamics for the bog fritillary butterfly under warming scenarios. Although they found that warming improved fertility and survival of all stages with one exception, populations were predicted to decline because overwintering larvae, whose survival declined with warming, were disproportionately important contributors to population growth. This underscores the importance of considering all life history stages in analyses of climate change's effects on population dynamics.

  1. Cetacean range and climate in the eastern North Atlantic: future predictions and implications for conservation.

    PubMed

    Lambert, Emily; Pierce, Graham J; Hall, Karen; Brereton, Tom; Dunn, Timothy E; Wall, Dave; Jepson, Paul D; Deaville, Rob; MacLeod, Colin D

    2014-06-01

    There is increasing evidence that the distributions of a large number of species are shifting with global climate change as they track changing surface temperatures that define their thermal niche. Modelling efforts to predict species distributions under future climates have increased with concern about the overall impact of these distribution shifts on species ecology, and especially where barriers to dispersal exist. Here we apply a bio-climatic envelope modelling technique to investigate the impacts of climate change on the geographic range of ten cetacean species in the eastern North Atlantic and to assess how such modelling can be used to inform conservation and management. The modelling process integrates elements of a species' habitat and thermal niche, and employs "hindcasting" of historical distribution changes in order to verify the accuracy of the modelled relationship between temperature and species range. If this ability is not verified, there is a risk that inappropriate or inaccurate models will be used to make future predictions of species distributions. Of the ten species investigated, we found that while the models for nine could successfully explain current spatial distribution, only four had a good ability to predict distribution changes over time in response to changes in water temperature. Applied to future climate scenarios, the four species-specific models with good predictive abilities indicated range expansion in one species and range contraction in three others, including the potential loss of up to 80% of suitable white-beaked dolphin habitat. Model predictions allow identification of affected areas and the likely time-scales over which impacts will occur. Thus, this work provides important information on both our ability to predict how individual species will respond to future climate change and the applicability of predictive distribution models as a tool to help construct viable conservation and management strategies.

  2. Predicting Climate Change Using Response Theory: Global Averages and Spatial Patterns

    NASA Astrophysics Data System (ADS)

    Lucarini, Valerio; Ragone, Francesco; Lunkeit, Frank

    2016-04-01

    The provision of accurate methods for predicting the climate response to anthropogenic and natural forcings is a key contemporary scientific challenge. Using a simplified and efficient open-source general circulation model of the atmosphere featuring O(10^5 ) degrees of freedom, we show how it is possible to approach such a problem using nonequilibrium statistical mechanics. Response theory allows one to practically compute the time-dependent measure supported on the pullback attractor of the climate system, whose dynamics is non-autonomous as a result of time-dependent forcings. We propose a simple yet efficient method for predicting—at any lead time and in an ensemble sense—the change in climate properties resulting from increase in the concentration of CO_2 using test perturbation model runs. We assess strengths and limitations of the response theory in predicting the changes in the globally averaged values of surface temperature and of the yearly total precipitation, as well as in their spatial patterns. The quality of the predictions obtained for the surface temperature fields is rather good, while in the case of precipitation a good skill is observed only for the global average. We also show how it is possible to define accurately concepts like the inertia of the climate system or to predict when climate change is detectable given a scenario of forcing. Our analysis can be extended for dealing with more complex portfolios of forcings and can be adapted to treat, in principle, any climate observable. Our conclusion is that climate change is indeed a problem that can be effectively seen through a statistical mechanical lens, and that there is great potential for optimizing the current coordinated modelling exercises run for the preparation of the subsequent reports of the Intergovernmental Panel for Climate Change.

  3. Predicting Climate Change Using Response Theory: Global Averages and Spatial Patterns

    NASA Astrophysics Data System (ADS)

    Lucarini, Valerio; Ragone, Francesco; Lunkeit, Frank

    2017-02-01

    The provision of accurate methods for predicting the climate response to anthropogenic and natural forcings is a key contemporary scientific challenge. Using a simplified and efficient open-source general circulation model of the atmosphere featuring O(10^5) degrees of freedom, we show how it is possible to approach such a problem using nonequilibrium statistical mechanics. Response theory allows one to practically compute the time-dependent measure supported on the pullback attractor of the climate system, whose dynamics is non-autonomous as a result of time-dependent forcings. We propose a simple yet efficient method for predicting—at any lead time and in an ensemble sense—the change in climate properties resulting from increase in the concentration of CO_2 using test perturbation model runs. We assess strengths and limitations of the response theory in predicting the changes in the globally averaged values of surface temperature and of the yearly total precipitation, as well as in their spatial patterns. The quality of the predictions obtained for the surface temperature fields is rather good, while in the case of precipitation a good skill is observed only for the global average. We also show how it is possible to define accurately concepts like the inertia of the climate system or to predict when climate change is detectable given a scenario of forcing. Our analysis can be extended for dealing with more complex portfolios of forcings and can be adapted to treat, in principle, any climate observable. Our conclusion is that climate change is indeed a problem that can be effectively seen through a statistical mechanical lens, and that there is great potential for optimizing the current coordinated modelling exercises run for the preparation of the subsequent reports of the Intergovernmental Panel for Climate Change.

  4. A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change.

    PubMed

    Ashraf, M Irfan; Meng, Fan-Rui; Bourque, Charles P-A; MacLean, David A

    2015-01-01

    Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm(2) 5-year(-1) and volume: 0.0008 m(3) 5-year(-1)). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm(2) 5-year(-1) and 0.0393 m(3) 5-year(-1) in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology

  5. A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change

    PubMed Central

    Ashraf, M. Irfan; Meng, Fan-Rui; Bourque, Charles P.-A.; MacLean, David A.

    2015-01-01

    Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm2 5-year-1 and volume: 0.0008 m3 5-year-1). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm2 5-year-1 and 0.0393 m3 5-year-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial

  6. Validating Empirical Bioclimatic Model Predictions of Climate Impacts: Spruce Decline in Northern Arizona

    NASA Astrophysics Data System (ADS)

    Truettner, C. M.; Ironside, K.; Vankat, J. L.; Cole, K. L.; Cobb, N. S.

    2011-12-01

    The importance of climate in determining the distribution of vegetation is well-established, although depiction of these relationships for forecasting potential impacts of climate change varies among studies. Over the last 20 years, various empirical models of species bioclimatic envelops have been developed, primarily for forecasting, yet little research has been conducted to evaluate their predictive ability. These correlative techniques have also been criticized for not providing insight into relationships between the occurrence of species and measures of climate. We compared the prediction of a bioclimatic model developed to describe suitability of climate and changes in spruce (Picea engelmannii + P. pungens) on the North Rim of Grand Canyon National Park on the Kaibab Plateau. Permanent plots show spruce density and basal area decreased in this region between 1984 and 2010. During this time, there were significant trends in increased temperature and decreased precipitation that suggest recent climatic trends have reduced suitability for spruce species on the Kaibab Plateau. This is consistent with model projections for the near future, both with changes in climate predicted by General Circulation Models (GCM) and the predicted response of spruce in this portion of its range. These changes indicate that spruce-fir forests on the North Rim of the Grand Canyon have surpassed their inflection point and are now displaying signs of recession due to the endogenous factor of density-dependent mortality and exogenous factors such as climate change (Vankat 2011). The consistency between the changes in the permanent plots and the model projections suggests the bioclimatic models are able to predict changes in suitability that translates into changes in species occurrence.

  7. Evaluating Parameterizations in General Circulation Models: Climate Simulation Meets Weather Prediction

    SciTech Connect

    Phillips, T J; Potter, G L; Williamson, D L; Cederwall, R T; Boyle, J S; Fiorino, M; Hnilo, J J; Olson, J G; Xie, S; Yio, J J

    2004-05-06

    To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six-hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be tested in the same framework. In order to further this method for evaluating and analyzing parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM.

  8. Environmental controls on the phenology of moths: predicting plasticity and constraint under climate change.

    PubMed

    Valtonen, Anu; Ayres, Matthew P; Roininen, Heikki; Pöyry, Juha; Leinonen, Reima

    2011-01-01

    Ecological systems have naturally high interannual variance in phenology. Component species have presumably evolved to maintain appropriate phenologies under historical climates, but cases of inappropriate phenology can be expected with climate change. Understanding controls on phenology permits predictions of ecological responses to climate change. We studied phenological control systems in Lepidoptera by analyzing flight times recorded at a network of sites in Finland. We evaluated the strength and form of controls from temperature and photoperiod, and tested for geographic variation within species. Temperature controls on phenology were evident in 51% of 112 study species and for a third of those thermal controls appear to be modified by photoperiodic cues. For 24% of the total, photoperiod by itself emerged as the most likely control system. Species with thermal control alone should be most immediately responsive in phenology to climate warming, but variably so depending upon the minimum temperature at which appreciable development occurs and the thermal responsiveness of development rate. Photoperiodic modification of thermal controls constrains phenotypic responses in phenologies to climate change, but can evolve to permit local adaptation. Our results suggest that climate change will alter the phenological structure of the Finnish Lepidoptera community in ways that are predictable with knowledge of the proximate physiological controls. Understanding how phenological controls in Lepidoptera compare to that of their host plants and enemies could permit general inferences regarding climatic effects on mid- to high-latitude ecosystems.

  9. Decadal prediction skill using a high-resolution climate model

    NASA Astrophysics Data System (ADS)

    Monerie, Paul-Arthur; Coquart, Laure; Maisonnave, Éric; Moine, Marie-Pierre; Terray, Laurent; Valcke, Sophie

    2017-02-01

    The ability of a high-resolution coupled atmosphere-ocean general circulation model (with a horizontal resolution of a quarter of a degree in the ocean and of about 0.5° in the atmosphere) to predict the annual means of temperature, precipitation, sea-ice volume and extent is assessed based on initialized hindcasts over the 1993-2009 period. Significant skill in predicting sea surface temperatures is obtained, especially over the North Atlantic, the tropical Atlantic and the Indian Ocean. The Sea Ice Extent and volume are also reasonably predicted in winter (March) and summer (September). The model skill is mainly due to the external forcing associated with well-mixed greenhouse gases. A decrease in the global warming rate associated with a negative phase of the Pacific Decadal Oscillation is simulated by the model over a suite of 10-year periods when initialized from starting dates between 1999 and 2003. The model ability to predict regional change is investigated by focusing on the mid-90's Atlantic Ocean subpolar gyre warming. The model simulates the North Atlantic warming associated with a meridional heat transport increase, a strengthening of the North Atlantic current and a deepening of the mixed layer over the Labrador Sea. The atmosphere plays a role in the warming through a modulation of the North Atlantic Oscillation: a negative sea level pressure anomaly, located south of the subpolar gyre is associated with a wind speed decrease over the subpolar gyre. This leads to a reduced oceanic heat-loss and favors a northward displacement of anomalously warm and salty subtropical water that both concur to the subpolar gyre warming. We finally conclude that the subpolar gyre warming is mainly triggered by ocean dynamics with a possible contribution of atmospheric circulation favoring its persistence.

  10. Simulating infectious disease risk based on climatic drivers: from numerical weather prediction to long term climate change scenario

    NASA Astrophysics Data System (ADS)

    Caminade, C.; Ndione, J. A.; Diallo, M.; MacLeod, D.; Faye, O.; Ba, Y.; Dia, I.; Medlock, J. M.; Leach, S.; McIntyre, K. M.; Baylis, M.; Morse, A. P.

    2012-04-01

    Climate variability is an important component in determining the incidence of a number of diseases with significant health and socioeconomic impacts. In particular, vector born diseases are the most likely to be affected by climate; directly via the development rates and survival of both the pathogen and the vector, and indirectly through changes in the surrounding environmental conditions. Disease risk models of various complexities using different streams of climate forecasts as inputs have been developed within the QWeCI EU and ENHanCE ERA-NET project frameworks. This work will present two application examples, one for Africa and one for Europe. First, we focus on Rift Valley fever over sub-Saharan Africa, a zoonosis that affects domestic animals and humans by causing an acute fever. We show that the Rift Valley fever outbreak that occurred in late 2010 in the northern Sahelian region of Mauritania might have been anticipated ten days in advance using the GFS numerical weather prediction system. Then, an ensemble of regional climate projections is employed to model the climatic suitability of the Asian tiger mosquito for the future over Europe. The Asian tiger mosquito is an invasive species originally from Asia which is able to transmit West Nile and Chikungunya Fever among others. This species has spread worldwide during the last decades, mainly through the shipments of goods from Asia. Different disease models are employed and inter-compared to achieve such a task. Results show that the climatic conditions over southern England, central Western Europe and the Balkans might become more suitable for the mosquito (including the proviso that the mosquito has already been introduced) to establish itself in the future.

  11. Predicting athletes' functional and dysfunctional emotions: The role of the motivational climate and motivation regulations.

    PubMed

    Ruiz, Montse C; Haapanen, Saara; Tolvanen, Asko; Robazza, Claudio; Duda, Joan L

    2016-08-26

    This study examined the relationships between perceptions of the motivational climate, motivation regulations, and the intensity and functionality levels of athletes' pleasant and unpleasant emotional states. Specifically, we examined the hypothesised mediational role of motivation regulations in the climate-emotion relationship. We also tested a sequence in which emotions were assumed to be predicted by the motivational climate dimensions and then served as antecedents to variability in motivation regulations. Participants (N = 494) completed a multi-section questionnaire assessing targeted variables. Structural equation modelling (SEM) revealed that a perceived task-involving climate was a positive predictor of autonomous motivation and of the impact of functional anger, and a negative predictor of the intensity of anxiety and dysfunctional anger. Autonomous motivation was a partial mediator of perceptions of a task-involving climate and the impact of functional anger. An ego-involving climate was a positive predictor of controlled motivation, and of the intensity and impact of functional anger and the intensity of dysfunctional anger. Controlled motivation partially mediated the relationship between an ego-involving climate and the intensity of dysfunctional anger. Good fit to the data also emerged for the motivational climate, emotional states, and motivation regulations sequence. Findings provide support for the consideration of hedonic tone and functionality distinctions in the assessment of athletes' emotional states.

  12. Role of Climate Change in Global Predictions of Future Tropospheric Ozone and Aerosols

    NASA Technical Reports Server (NTRS)

    Liao, Hong; Chen, Wei-Ting; Seinfeld, John H.

    2006-01-01

    A unified tropospheric chemistry-aerosol model within the Goddard Institute for Space Studies general circulation model II is applied to simulate an equilibrium CO2-forced climate in the year 2100 to examine the effects of climate change on global distributions of tropospheric ozone and sulfate, nitrate, ammonium, black carbon, primary organic carbon, secondary organic carbon, sea salt, and mineral dust aerosols. The year 2100 CO2 concentration as well as the anthropogenic emissions of ozone precursors and aerosols/aerosol precursors are based on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) A2. Year 2100 global O3 and aerosol burdens predicted with changes in both climate and emissions are generally 5-20% lower than those simulated with changes in emissions alone; as exceptions, the nitrate burden is 38% lower, and the secondary organic aerosol burden is 17% higher. Although the CO2-driven climate change alone is predicted to reduce the global O3 concentrations over or near populated and biomass burning areas because of slower transport, enhanced biogenic hydrocarbon emissions, decomposition of peroxyacetyl nitrate at higher temperatures, and the increase of O3 production by increased water vapor at high NOx levels. The warmer climate influences aerosol burdens by increasing aerosol wet deposition, altering climate-sensitive emissions, and shifting aerosol thermodynamic equilibrium. Climate change affects the estimates of the year 2100 direct radiative forcing as a result of the climate-induced changes in burdens and different climatological conditions; with full gas-aerosol coupling and accounting for ozone and direct radiative forcings by the O2, sulfate, nitrate, black carbon, and organic carbon are predicted to be +0.93, -0.72, -1.0, +1.26, and -0.56 W m(exp -2), respectively, using present-day climate and year 2100 emissions, while they are predicted to be +0.76, -0.72, 0.74, +0.97, and -0.58 W m(exp -2

  13. Using observed climate-landscape-vegetation patterns across a regional gradient to predict potential response to climate change

    NASA Astrophysics Data System (ADS)

    Smith, V. B.; Cardenas, B.; David, C. H.

    2010-12-01

    We quantify relationships between climate and drainage density (D) and vegetation cover (V) across a regionally pronounced climate gradient where there is more or less uniform relief and geology. The D is calculated using the National Hydrography Dataset Plus for the watersheds within the Texas Gulf Basin, the study region. 30-yr average climate data (precipitation P, evaporation E and runoff R=P-E) and V was obtained from the North American Regional Reanalysis. The D, V and R for each watershed were analyzed and we found that the D(R) relationship closely follows the comprehensive physically-based model by Moglen, Eltahir and Bras (MEB). Moreover, the D(R) relationship is fit well by a more parsimonious saturation-growth model which is a special case of the MEB model. V(P) is adequately described by a linear model which is consistent whether it is derived via a pixel-by-pixel basis or on a per watershed basis. Using the determined D(R) and V(P) models, we predict what the equilibrium V and D should be for each watershed under an A2 emissions scenario. This is done by considering R and P calculated from results of the Hadley Model and the Canadian Regional Climate Model (CRCM3). We quantify how much browner or greener each watershed will become and the level of disequilibrium in the drainage network.

  14. Predicting effects of climate change on the composition and function of soil microbial communities

    NASA Astrophysics Data System (ADS)

    Dubinsky, E.; Brodie, E.; Myint, C.; Ackerly, D.; van Nostrand, J.; Bird, J.; Zhou, J.; Andersen, G.; Firestone, M.

    2008-12-01

    Complex soil microbial communities regulate critical ecosystem processes that will be altered by climate change. A critical step towards predicting the impacts of climate change on terrestrial ecosystems is to determine the primary controllers of soil microbial community composition and function, and subsequently evaluate climate change scenarios that alter these controllers. We surveyed complex soil bacterial and archaeal communities across a range of climatic and edaphic conditions to identify critical controllers of soil microbial community composition in the field and then tested the resulting predictions using a 2-year manipulation of precipitation and temperature using mesocosms of California annual grasslands. Community DNA extracted from field soils sampled from six different ecosystems was assayed for bacterial and archaeal communities using high-density phylogenetic microarrays as well as functional gene arrays. Correlations among the relative abundances of thousands of microbial taxa and edaphic factors such as soil moisture and nutrient content provided a basis for predicting community responses to changing soil conditions. Communities of soil bacteria and archaea were strongly structured by single environmental predictors, particularly variables related to soil water. Bacteria in the Actinomycetales and Bacilli consistently demonstrated a strong negative response to increasing soil moisture, while taxa in a greater variety of lineages responded positively to increasing soil moisture. In the climate change experiment, overall bacterial community structure was impacted significantly by total precipitation but not by plant species. Changes in soil moisture due to decreased rainfall resulted in significant and predictable alterations in community structure. Over 70% of the bacterial taxa in common with the cross-ecosystem study responded as predicted to altered precipitation, with the most conserved response from Actinobacteria. The functional consequences

  15. Predicting climate-driven regime shifts versus rebound potential in coral reefs.

    PubMed

    Graham, Nicholas A J; Jennings, Simon; MacNeil, M Aaron; Mouillot, David; Wilson, Shaun K

    2015-02-05

    Climate-induced coral bleaching is among the greatest current threats to coral reefs, causing widespread loss of live coral cover. Conditions under which reefs bounce back from bleaching events or shift from coral to algal dominance are unknown, making it difficult to predict and plan for differing reef responses under climate change. Here we document and predict long-term reef responses to a major climate-induced coral bleaching event that caused unprecedented region-wide mortality of Indo-Pacific corals. Following loss of >90% live coral cover, 12 of 21 reefs recovered towards pre-disturbance live coral states, while nine reefs underwent regime shifts to fleshy macroalgae. Functional diversity of associated reef fish communities shifted substantially following bleaching, returning towards pre-disturbance structure on recovering reefs, while becoming progressively altered on regime shifting reefs. We identified threshold values for a range of factors that accurately predicted ecosystem response to the bleaching event. Recovery was favoured when reefs were structurally complex and in deeper water, when density of juvenile corals and herbivorous fishes was relatively high and when nutrient loads were low. Whether reefs were inside no-take marine reserves had no bearing on ecosystem trajectory. Although conditions governing regime shift or recovery dynamics were diverse, pre-disturbance quantification of simple factors such as structural complexity and water depth accurately predicted ecosystem trajectories. These findings foreshadow the likely divergent but predictable outcomes for reef ecosystems in response to climate change, thus guiding improved management and adaptation.

  16. Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions

    NASA Technical Reports Server (NTRS)

    Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon; Gaydon, Donald; Marcaida, Manuel, III; Nakagawa, Hiroshi; Oriol, Philippe; Ruane, Alex C.; Ruget, Francoise; Singh, Balwinder; Singh, Upendra; Tang, Liang; Tao, Fulu; Wilkens, Paul; Yoshida, Hiroe; Zhang, Zhao; Bouman, Bas

    2014-01-01

    Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10 percent of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.

  17. Evaluating high resolution climate model predictability and skill in response to the Mount Pinatubo eruption

    NASA Astrophysics Data System (ADS)

    Gaddis, A. L.; Evans, K. J.

    2013-12-01

    A central goal of climate research is to determine the perceptible effect of climate change on humans; in other words, the regional and decadal scale effects of carbon dioxide forcing. Identifying the most pronounced and long-lasting responses of climate variables to forcing is important for decadal prediction since forcing terms are one of the sources of predictability on those time scales. Volcanic eruptions provide a powerful, transient forcing on the global climate system by injecting tons of sulfur compounds into the stratosphere that react to form sulfate aerosols. The Community Earth System Model is used to explore predictability in response to the Mount Pinatubo eruption. In this study, the Mount Pinatubo eruption is simulated at very high resolution (T341) using the Community Earth System Model. The predictability of responses to the eruption are calculated and compared with previous studies of the same model at lower resolution for two configurations. All three configurations are compared with observations to evaluate model skill. The Northern Hemisphere winter warming response to the eruption improves in spatial distribution and strength at higher resolution. Stratospheric humidity increases predictably in all model configurations, with spatial analysis showing the that response is centered over the tropical tropopause. The polar night jet response to the eruption is not well replicated for any configuration.

  18. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions.

    PubMed

    Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon; Gaydon, Donald; Marcaida, Manuel; Nakagawa, Hiroshi; Oriol, Philippe; Ruane, Alex C; Ruget, Françoise; Singh, Balwinder-; Singh, Upendra; Tang, Liang; Tao, Fulu; Wilkens, Paul; Yoshida, Hiroe; Zhang, Zhao; Bouman, Bas

    2015-03-01

    Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2 ]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2 ] and temperature.

  19. Evaluation of precipitation predictions in a regional climate simulation

    SciTech Connect

    Costigan, K.R.; Bossert, J.E.; Langely, D.L.

    1998-12-01

    The research reported here is part of a larger project that is coupling a suite of environmental models to simulate the hydrologic cycle within river basins (Bossert et al., 1999). These models include the Regional Atmospheric Modeling System (RAMS), which provides meteorological variables and precipitation to the Simulator for Processes of Landscapes, Surface/Subsurface Hydrology (SPLASH). SPLASH partitions precipitation into evaporation, transpiration, soil water storage, surface runoff, and subsurface recharge. The runoff is collected within a simple river channel model and the Finite element Heat and Mass (FEHM) subsurface model is linked to the land surface and river flow model components to simulate saturated and unsaturated flow and changes in aquifer levels. The goal is to produce a fully interactive system of atmospheric, surface hydrology, river and groundwater models to allow water and energy feedbacks throughout the system. This paper focuses on the evaluation of the precipitation fields predicted by the RAMS model at different times during the 1992--1993 water year in the Rio Grande basin. The evaluation includes comparing the model predictions to the observed precipitation as reported by Cooperative Summary of the Day and SNOTEL reporting stations.

  20. Pragmatic hydraulic theory predicts stomatal responses to climatic water deficits.

    PubMed

    Sperry, John S; Wang, Yujie; Wolfe, Brett T; Mackay, D Scott; Anderegg, William R L; McDowell, Nate G; Pockman, William T

    2016-11-01

    Ecosystem models have difficulty predicting plant drought responses, partially from uncertainty in the stomatal response to water deficits in soil and atmosphere. We evaluate a 'supply-demand' theory for water-limited stomatal behavior that avoids the typical scaffold of empirical response functions. The premise is that canopy water demand is regulated in proportion to threat to supply posed by xylem cavitation and soil drying. The theory was implemented in a trait-based soil-plant-atmosphere model. The model predicted canopy transpiration (E), canopy diffusive conductance (G), and canopy xylem pressure (Pcanopy ) from soil water potential (Psoil ) and vapor pressure deficit (D). Modeled responses to D and Psoil were consistent with empirical response functions, but controlling parameters were hydraulic traits rather than coefficients. Maximum hydraulic and diffusive conductances and vulnerability to loss in hydraulic conductance dictated stomatal sensitivity and hence the iso- to anisohydric spectrum of regulation. The model matched wide fluctuations in G and Pcanopy across nine data sets from seasonally dry tropical forest and piñon-juniper woodland with < 26% mean error. Promising initial performance suggests the theory could be useful in improving ecosystem models. Better understanding of the variation in hydraulic properties along the root-stem-leaf continuum will simplify parameterization.

  1. Variability and predictability of the North Atlantic wave climate

    NASA Astrophysics Data System (ADS)

    Woolf, D. K.; Challenor, P. G.; Cotton, P. D.

    2002-10-01

    Wave climate across the ocean basins can be described using satellite altimetry; here, we concentrate on the North Atlantic region. Waves in the North Atlantic are strongly seasonal and peak in the winter season. The northeastern sector of the Atlantic and adjoining shelf seas also exhibit exceptionally high interannual variability in the winter, with monthly average significant wave height varying by up to a factor of 2 from one year to the next. The strength and geographical distribution of variability is broadly consistent throughout the winter months (December-March). A large fraction of these wave height anomalies is associated with a single pattern of pressure anomalies that resembles the North Atlantic Oscillation (NAO). A predictor based on NAO dependence is "trained" from relatively recent satellite data and then tested against earlier satellite and in situ data. The predictor is successful in large areas of the North Atlantic, confirming a robust relationship between wave height anomalies and the NAO over the last few decades. A substantial rise (up to 0.6 m) in monthly mean wave heights on the northeastern Atlantic during the latter part of the twentieth century is attributable to changes in the NAO. Substantial residual anomalies in wave heights exist after the influence of the NAO has been subtracted; these are partly explained by a second pair of North Atlantic patterns in wave height anomalies and sea level pressure anomalies. This "East Atlantic" pattern is particularly influential in midwinter and affects the southern part of the northeastern sector (including the region of Seven Stones Light Vessel).

  2. Climatic factors influencing dengue cases in Dhaka city: A model for dengue prediction

    PubMed Central

    Karim, Md. Nazmul; Munshi, Saif Ullah; Anwar, Nazneen; Alam, Md. Shah

    2012-01-01

    Background & objectives: Transmission of dengue virus depends on the presence of Aedes mosquito. Mosquito generation and development is known to be influenced by the climate. This study was carried out to examine whether the climatic factors data can be used to predict yearly dengue cases of Dhaka city, Bangladesh. Methods: Monthly reported dengue cases and climate data for the years 2000–2008 were obtained from the Directorate General of Health Services (DGHS) and Meteorological Department of Dhaka, Bangladesh, respectively. Data for the period 2000 to 2007 were used for development of a model through multiple linear regressions. Retrospective validation of the model was done with 2001, 2003, 2005 and 2008 data. Log transformation of the dependent variable was done to normalize data for linear regression. Average monthly humidity, rainfall, minimum and maximum temperature were used as independent variables and number of dengue cases reported monthly was used as dependent variable. Accuracy of the model for predicting outbreak was assessed through receiver operative characteristics (ROC) curve. Results: Climatic factors, i.e. rainfall, maximum temperature and relative humidity were significantly correlated with monthly reported dengue cases. The model incorporating climatic data of two-lag month explained 61 per cent of variation in number of reported dengue cases and this model was found to predict dengue outbreak (≥ 200 cases) with considerable accuracy [area under ROC curve = 0.89, 95%CI = (0.89-0.98)]. Interpretation & conclusions: Our results showed that the climate had a major effect on the occurrence of dengue infection in Dhaka city. Though the prediction model had some limitations in predicting the monthly number of dengue cases, it could forecast possible outbreak two months in advance with considerable accuracy. PMID:22885261

  3. A Quantum Annealing Computer Team Addresses Climate Change Predictability

    NASA Technical Reports Server (NTRS)

    Halem, M. (Principal Investigator); LeMoigne, J.; Dorband, J.; Lomonaco, S.; Yesha, Ya.; Simpson, D.; Clune, T.; Pelissier, C.; Nearing, G.; Gentine, P.; Fang, B.; Shehab, A.; Radov, Asen; Tikak, N.; Prouty, Roy; Harrison, Kenneth

    2016-01-01

    The near confluence of the successful launch of the Orbiting Carbon Observatory2 on July 2, 2014 and the acceptance on August 20, 2015 by Google, NASA Ames Research Center and USRA of a 1152 qubit D-Wave 2X Quantum Annealing Computer (QAC), offered an exceptional opportunity to explore the potential of this technology to address the scientific prediction of global annual carbon uptake by land surface processes. At UMBC,we have collected and processed 20 months of global Level 2 light CO2 data as well as fluorescence data. In addition we have collected ARM data at 2sites in the US and Ameriflux data at more than 20 stations. J. Dorband has developed and implemented a multi-hidden layer Boltzmann Machine (BM) algorithm on the QAC. Employing the BM, we are calculating CO2 fluxes by training collocated OCO-2 level 2 CO2 data with ARM ground station tower data to infer to infer measured CO2 flux data. We generate CO2 fluxes with a regression analysis using these BM derived weights on the level 2 CO2 data for three Ameriflux sites distinct from the ARM stations. P. Gentine has negotiated for the access of K34 Ameriflux data in the Amazon and is applying a neural net to infer the CO2 fluxes. N. Talik validated the accuracy of the BM performance on the QAC against a restricted BM implementation on the IBM Softlayer Cloud with the Nvidia co-processors utilizing the same data sets. G. Nearing and K. Harrison have extended the GSFC LIS model with the NCAR Noah photosynthetic parameterization and have run a 10 year global prediction of the net ecosystem exchange. C. Pellisier is preparing a BM implementation of the Kalman filter data assimilation of CO2 fluxes. At UMBC, R. Prouty is conducting OSSE experiments with the LISNoah model on the IBM iDataPlex to simulate the impact of CO2 fluxes to improve the prediction of global annual carbon uptake. J. LeMoigne and D. Simpson have developed a neural net image registration system that will be used for MODIS ENVI and will be

  4. Modeling bulk canopy resistance from climatic variables for predicting hourly evapotranspiration of maize and buckwheat

    NASA Astrophysics Data System (ADS)

    Yan, Haofang; Shi, Haibin; Hiroki, Oue; Zhang, Chuan; Xue, Zhu; Cai, Bin; Wang, Guoqing

    2015-06-01

    This study presents models for predicting hourly canopy resistance ( r c) and evapotranspiration (ETc) based on Penman-Monteith approach. The micrometeorological data and ET c were observed during maize and buckwheat growing seasons in 2006 and 2009 in China and Japan, respectively. The proposed models of r c were developed by a climatic resistance ( r *) that depends on climatic variables. Non-linear relationships between r c and r * were applied. The measured ETc using Bowen ratio energy balance method was applied for model validation. The statistical analysis showed that there were no significant differences between predicted ETc by proposed models and measured ETc for both maize and buckwheat crops. The model for predicting ETc at maize field showed better performance than predicting ETc at buckwheat field, the coefficients of determination were 0.92 and 0.84, respectively. The study provided an easy way for the application of Penman-Monteith equation with only general available meteorological database.

  5. Global warming and climate change - predictive models for temperate and tropical regions

    SciTech Connect

    Malini, B.H.

    1997-12-31

    Based on the assumption of 4{degree}C increase of global temperature by the turn of 21st century due to the accumulation of greenhouse gases an attempt is made to study the possible variations in different climatic regimes. The predictive climatic water balance model for Hokkaido island of Japan (a temperate zone) indicates the possible occurrence of water deficit for two to three months, which is a unknown phenomenon in this region at present. Similarly, India which represents tropical region also will experience much drier climates with increased water deficit conditions. As a consequence, the thermal region of Hokkaido which at present is mostly Tundra and Micro thermal will change into a Meso thermal category. Similarly, the moisture regime which at present supports per humid (A2, A3 and A4) and Humid (B4) climates can support A1, B4, B3, B2 and B1 climates indicating a shift towards drier side of the climatic spectrum. Further, the predictive modes of both the regions have indicated increased evapotranspiration rates. Although there is not much of change in the overall thermal characteristics of the Indian region the moisture regime indicates a clear shift towards the aridity in the country.

  6. Predicting demographically sustainable rates of adaptation: can great tit breeding time keep pace with climate change?

    PubMed Central

    Gienapp, Phillip; Lof, Marjolein; Reed, Thomas E.; McNamara, John; Verhulst, Simon; Visser, Marcel E.

    2013-01-01

    Populations need to adapt to sustained climate change, which requires micro-evolutionary change in the long term. A key question is how the rate of this micro-evolutionary change compares with the rate of environmental change, given that theoretically there is a ‘critical rate of environmental change’ beyond which increased maladaptation leads to population extinction. Here, we parametrize two closely related models to predict this critical rate using data from a long-term study of great tits (Parus major). We used stochastic dynamic programming to predict changes in optimal breeding time under three different climate scenarios. Using these results we parametrized two theoretical models to predict critical rates. Results from both models agreed qualitatively in that even ‘mild’ rates of climate change would be close to these critical rates with respect to great tit breeding time, while for scenarios close to the upper limit of IPCC climate projections the calculated critical rates would be clearly exceeded with possible consequences for population persistence. We therefore tentatively conclude that micro-evolution, together with plasticity, would rescue only the population from mild rates of climate change, although the models make many simplifying assumptions that remain to be tested. PMID:23209174

  7. Land-atmosphere coupling and climate prediction over the U.S. Southern Great Plains

    NASA Astrophysics Data System (ADS)

    Williams, Ian N.; Lu, Yaqiong; Kueppers, Lara M.; Riley, William J.; Biraud, Sebastien C.; Bagley, Justin E.; Torn, Margaret S.

    2016-10-01

    Biases in land-atmosphere coupling in climate models can contribute to climate prediction biases, but land models are rarely evaluated in the context of this coupling. We tested land-atmosphere coupling and explored effects of land surface parameterizations on climate prediction in a single-column version of the National Center for Atmospheric Research Community Earth System Model (CESM1.2.2) and an off-line Community Land Model (CLM4.5). The correlation between leaf area index (LAI) and surface evaporative fraction (ratio of latent to total turbulent heat flux) was substantially underpredicted compared to observations in the U.S. Southern Great Plains, while the correlation between soil moisture and evaporative fraction was overpredicted by CLM4.5. To estimate the impacts of these errors on climate prediction, we modified CLM4.5 by prescribing observed LAI, increasing soil resistance to evaporation, increasing minimum stomatal conductance, and increasing leaf reflectance. The modifications improved the predicted soil moisture-evaporative fraction (EF) and LAI-EF correlations in off-line CLM4.5 and reduced the root-mean-square error in summer 2 m air temperature and precipitation in the coupled model. The modifications had the largest effect on prediction during a drought in summer 2006, when a warm bias in daytime 2 m air temperature was reduced from +6°C to a smaller cold bias of -1.3°C, and a corresponding dry bias in precipitation was reduced from -111 mm to -23 mm. The role of vegetation in droughts and heat waves is underpredicted in CESM1.2.2, and improvements in land surface models can improve prediction of climate extremes.

  8. Climate Variability and Predictability in North West Africa

    NASA Astrophysics Data System (ADS)

    Baddour, O.; Djellouli, Y.

    2003-04-01

    North West Africa defined here as the area including Morocco, Algeria and Tunisia, it occupies a large territory in North Africa with more than 3.5 Millions KM2. The geographical contrast is very important: while most of the southern part is desert, the northern and north western part exhibits a contrasting geography including large flat areas in the western part of Morocco, northern Algeria and eastern part of Tunisia, And also the formidable Atlas mountains barrier that extends from south west of Morocco toward north west of Tunisia crossing central Morocco and north Algeria.Agriculture is one of major socio-economic activity in the region with an extensive cash-crop for exporting to Europe especially from Morocco and Tunisia. The influence of the recurring droughts during 80s and 90s was very crucial for the economic and societal aspects of the region. In Morocco, severe droughts has caused GDP fluctuation within past 20 years from 10% increase down to negative values in some particular years. Most of weather systems occurs during frontal excursion through the Atlantic and Europe bringing cold air and cloud and precipitation systems. The active precipitation period extends from October to May with almost 80% of the total rainfall. The dry season extends from June to September. Nevertheless some convective clouds develop occasionally during the dry season due to subtropical humid air mass that converge into the region and trigger the convection especially in the high area and Sahara. These less frequent precipitation systems could lead to weather hazards such as flash floods with damage to population and infrastructure. (The example of OURIKA in August 1995 in Morocco). The far south of the region experiences some tropical influence during August period especially in the south of Algeria when the ITCZ can migrate from the SAHEL area to its northernmost position in the region. Recent studies have investigated seasonal rainfall variability and prediction over

  9. Decision-maker expectations and the value of climate prediction information: conceptual considerations and preliminary evidence

    NASA Astrophysics Data System (ADS)

    Sherrick, Bruce J.; Sonka, Steven T.; Lamb, Peter J.; Mazzocco, Michael A.

    2000-12-01

    This paper examines the commonly used assumption that decision-makers possess accurate prior probability information about climate events that affect their well-being, and illustrates the impact of that assumption on the valuation of prediction information. A survey of large producers in the Mid-western United States is used to recover their prior beliefs about climate variables. It is found that producers systematically misrepresent the probabilities of climate events that materially affect their well-being. In particular, the most common form of the miscalibration between actual and subjective probabilities is to overstate the likelihood of adverse events and understate the likelihood of favourable events. As a result, common methods for valuing prediction information are likely to understate the true value when recipients begin with less accurate prior beliefs.

  10. Climate matching as a tool for predicting potential North American spread of Brown Treesnakes

    USGS Publications Warehouse

    Rodda, Gordon H.; Reed, Robert N.; Jarnevich, Catherine S.; Witmer, G.W.; Pitt, W. C.; Fagerstone, K.A.

    2007-01-01

    Climate matching identifies extralimital destinations that could be colonized by a potential invasive species on the basis of similarity to climates found in the species’ native range. Climate is a proxy for the factors that determine whether a population will reproduce enough to offset mortality. Previous climate matching models (e.g., Genetic Algorithm for Rule-set Prediction [GARP]) for brown treesnakes (Boiga irregularis) were unsatisfactory, perhaps because the models failed to allow different combinations of climate attributes to influence a species’ range limits in different parts of the range. Therefore, we explored the climate space described by bivariate parameters of native range temperature and rainfall, allowing up to two months of aestivation in the warmer portions of the range, or four months of hibernation in temperate climes. We found colonization area to be minimally sensitive to assumptions regarding hibernation temperature thresholds. Although brown treesnakes appear to be limited by dry weather in the interior of Australia, aridity rarely limits potential distribution in most of the world. Potential colonization area in North America is limited primarily by cold. Climatically suitable portions of the United States (US) mainland include the Central Valley of California, mesic patches in the Southwest, and the southeastern coastal plain from Texas to Virginia.

  11. PREDICTING CLIMATE-INDUCED GEOGRAPHIC RANGE SHIFTS FOR MAMMALS IN THE WESTERN HEMISPHERE

    EPA Science Inventory

    In order to manage wildlife and conserve biodiversity, it is critical that we understand the potential impacts of climate change on species distributions. I used six different modeling approaches to predict the future distributions of 100 mammal species in the western hemisphere...

  12. Snow-atmosphere coupling strength and its contribution to climate predictability

    NASA Astrophysics Data System (ADS)

    Xu, L.

    2010-12-01

    This study investigated the snow-atmosphere coupling strength (the degree to which atmosphere responds to anomalies in the land surface snow cover and their subsequently interaction) and this coupling strength contribution to short range climate predictability, based on the realistic snow information from the MODIS snow retrieval from NASA satellites and GLDAS land “reanalysis” data. A complex land surface model (CLM 3.5) with an advanced snow scheme coupled to the Community Atmospheric Model (CAM) were employed to quantify continental snow-atmosphere coupling strength. A series of ensemble experiment will be designed to investigate the snow albedo effect and hydrological effect separately. A recently derived index Ω was used to quantify the coupling strength and predictability estimated separately by the phase and shape characteristics of a forecast ensemble. In addition, the climate predictability represented by Signal-to-Total Ratio (STR) due to realistic snow information, including Snow Water Equivalent (SWE) and Snow Cover Fraction (SCF), will also be investigated. This study improved our understanding of the interaction between snow cover and atmosphere. Determining the seasonal forecast skill attributed by snow information increased our knowledge of climate predictability. These designed experiments also offer a prototype of testing snow-atmosphere coupling strength that could be implemented in other weather and climate models in the future.

  13. Livestock Helminths in a Changing Climate: Approaches and Restrictions to Meaningful Predictions.

    PubMed

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

    2012-03-06

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

  14. Predictability and Diagnosis of Low Frequency Climate Processes in the Pacific, Final Technical Report

    SciTech Connect

    Niklas Schneider

    2009-06-17

    The report summarized recent findings with respect to Predictability and Diagnosis of Low Frequency Climate Processes in the Pacific, with focus on the dynamics of the Pacific Decadal Oscillation, oceanic adjustments and the coupled feedback in the western boundary current of the North and South Pacific, decadal dynamics of oceanic salinity, and tropical processes with emphasis on the Indonesian Throughflow.

  15. Predicting Satisfaction in Physical Education from Motivational Climate and Self-Determined Motivation

    ERIC Educational Resources Information Center

    Baena-Extremera, Antonio; Gómez-López, Manuel; Granero-Gallegos, Antonio; Ortiz-Camacho, Maria del Mar

    2015-01-01

    The purpose of this research study was to determine to what extent the motivational climate perceived by students in Physical Education (PE) classes predicts self-determined motivation, and satisfaction with physical education classes. Questionnaires were administered to 758 high school students aged 13-18 years. We used the Spanish versions of…

  16. The Icarus challenge - Predicting vulnerability to climate change using an algorithm-based species' trait approach

    EPA Science Inventory

    The Icarus challenge - Predicting vulnerability to climate change using an algorithm-based species’ trait approachHenry Lee II, Christina Folger, Deborah A. Reusser, Patrick Clinton, and Rene Graham1 U.S. EPA, Western Ecology Division, Newport, OR USA E-mail: lee.henry@ep...

  17. Predicting Plausible Impacts of Sets of Climate and Land Use Change Scenarios on Water Resources

    EPA Science Inventory

    Global changes in climate and land use can alTect the quantity and quality of water resources. Hence, we need a methodology to predict these ramifications. Using the Little Miami River (LMR) watershed as a case study, this paper describes a spatial analytical approach integrating...

  18. Using Clustered Climate Regimes to Analyze and Compare Predictions from Fully Coupled General Circulation Models

    SciTech Connect

    Hoffman, Forrest M; Hargrove, William Walter; Erickson III, David J; Oglesby, Robert J

    2005-01-01

    Changes in Earth's climate in response to atmospheric greenhouse gas buildup impact the health of terrestrial ecosystems and the hydrologic cycle. The environmental conditions influential to plant and animal life are often mapped as ecoregions, which are land areas having similar combinations of environmental characteristics. This idea is extended to establish regions of similarity with respect to climatic characteristics that evolve through time using a quantitative statistical clustering technique called Multivariate Spatio-Temporal Clustering (MSTC). MSTC was applied to the monthly time series output from a fully coupled general circulation model (GCM) called the Parallel Climate Model (PCM). Results from an ensemble of five 99-yr Business-As-Usual (BAU) transient simulations from 2000 to 2098 were analyzed. MSTC establishes an exhaustive set of recurring climate regimes that form a 'skeleton' through the 'observations' (model output) throughout the occupied portion of the climate phase space formed by the characteristics being considered. MSTC facilitates direct comparison of ensemble members and ensemble and temporal averages since the derived climate regimes provide a basis for comparison. Moreover, by mapping all land cells to discrete climate states, the dynamic behavior of any part of the system can be studied by its time-varying sequence of climate state occupancy. MSTC is a powerful tool for model developers and environmental decision makers who wish to understand long, complex time series predictions of models. Strong predicted interannual trends were revealed in this analysis, including an increase in global desertification; a decrease in the cold, dry high-latitude conditions typical of North American and Asian winters; and significant warming in Antarctica and western Greenland.

  19. Predicting Ice Sheet and Climate Evolution at Extreme Scales

    SciTech Connect

    Heimbach, Patrick

    2016-02-06

    A main research objectives of PISCEES is the development of formal methods for quantifying uncertainties in ice sheet modeling. Uncertainties in simulating and projecting mass loss from the polar ice sheets arise primarily from initial conditions, surface and basal boundary conditions, and model parameters. In general terms, two main chains of uncertainty propagation may be identified: 1. inverse propagation of observation and/or prior onto posterior control variable uncertainties; 2. forward propagation of prior or posterior control variable uncertainties onto those of target output quantities of interest (e.g., climate indices or ice sheet mass loss). A related goal is the development of computationally efficient methods for producing initial conditions for an ice sheet that are close to available present-day observations and essentially free of artificial model drift, which is required in order to be useful for model projections (“initialization problem”). To be of maximum value, such optimal initial states should be accompanied by “useful” uncertainty estimates that account for the different sources of uncerainties, as well as the degree to which the optimum state is constrained by available observations. The PISCEES proposal outlined two approaches for quantifying uncertainties. The first targets the full exploration of the uncertainty in model projections with sampling-based methods and a workflow managed by DAKOTA (the main delivery vehicle for software developed under QUEST). This is feasible for low-dimensional problems, e.g., those with a handful of global parameters to be inferred. This approach can benefit from derivative/adjoint information, but it is not necessary, which is why it often referred to as “non-intrusive”. The second approach makes heavy use of derivative information from model adjoints to address quantifying uncertainty in high-dimensions (e.g., basal boundary conditions in ice sheet models). The use of local gradient, or

  20. Fourth National Aeronautics and Space Administration Weather and Climate Program Science Review

    NASA Technical Reports Server (NTRS)

    Kreins, E. R. (Editor)

    1979-01-01

    The NASA Weather and Climate Program has two major thrusts. The first involves the development of experimental and prototype operational satellite systems, sensors, and space facilities for monitoring and understanding the atmosphere. The second thrust involves basic scientific investigation aimed at studying the physical and chemical processes which control weather and climate. This fourth science review concentrated on the scientific research rather than the hardware development aspect of the program. These proceedings contain 65 papers covering the three general areas: severe storms and local weather research, global weather, and climate.

  1. Microbial models with data-driven parameters predict stronger soil carbon responses to climate change.

    PubMed

    Hararuk, Oleksandra; Smith, Matthew J; Luo, Yiqi

    2015-06-01

    Long-term carbon (C) cycle feedbacks to climate depend on the future dynamics of soil organic carbon (SOC). Current models show low predictive accuracy at simulating contemporary SOC pools, which can be improved through parameter estimation. However, major uncertainty remains in global soil responses to climate change, particularly uncertainty in how the activity of soil microbial communities will respond. To date, the role of microbes in SOC dynamics has been implicitly described by decay rate constants in most conventional global carbon cycle models. Explicitly including microbial biomass dynamics into C cycle model formulations has shown potential to improve model predictive performance when assessed against global SOC databases. This study aimed to data-constrained parameters of two soil microbial models, evaluate the improvements in performance of those calibrated models in predicting contemporary carbon stocks, and compare the SOC responses to climate change and their uncertainties between microbial and conventional models. Microbial models with calibrated parameters explained 51% of variability in the observed total SOC, whereas a calibrated conventional model explained 41%. The microbial models, when forced with climate and soil carbon input predictions from the 5th Coupled Model Intercomparison Project (CMIP5), produced stronger soil C responses to 95 years of climate change than any of the 11 CMIP5 models. The calibrated microbial models predicted between 8% (2-pool model) and 11% (4-pool model) soil C losses compared with CMIP5 model projections which ranged from a 7% loss to a 22.6% gain. Lastly, we observed unrealistic oscillatory SOC dynamics in the 2-pool microbial model. The 4-pool model also produced oscillations, but they were less prominent and could be avoided, depending on the parameter values.

  2. Pacific decadal oscillation hindcasts relevant to near-term climate prediction

    PubMed Central

    Mochizuki, Takashi; Ishii, Masayoshi; Kimoto, Masahide; Chikamoto, Yoshimitsu; Watanabe, Masahiro; Nozawa, Toru; Sakamoto, Takashi T.; Shiogama, Hideo; Awaji, Toshiyuki; Sugiura, Nozomi; Toyoda, Takahiro; Yasunaka, Sayaka; Tatebe, Hiroaki; Mori, Masato

    2010-01-01

    Decadal-scale climate variations over the Pacific Ocean and its surroundings are strongly related to the so-called Pacific decadal oscillation (PDO) which is coherent with wintertime climate over North America and Asian monsoon, and have important impacts on marine ecosystems and fisheries. In a near-term climate prediction covering the period up to 2030, we require knowledge of the future state of internal variations in the climate system such as the PDO as well as the global warming signal. We perform sets of ensemble hindcast and forecast experiments using a coupled atmosphere-ocean climate model to examine the predictability of internal variations on decadal timescales, in addition to the response to external forcing due to changes in concentrations of greenhouse gases and aerosols, volcanic activity, and solar cycle variations. Our results highlight that an initialization of the upper-ocean state using historical observations is effective for successful hindcasts of the PDO and has a great impact on future predictions. Ensemble hindcasts for the 20th century demonstrate a predictive skill in the upper-ocean temperature over almost a decade, particularly around the Kuroshio-Oyashio extension (KOE) and subtropical oceanic frontal regions where the PDO signals are observed strongest. A negative tendency of the predicted PDO phase in the coming decade will enhance the rising trend in surface air-temperature (SAT) over east Asia and over the KOE region, and suppress it along the west coasts of North and South America and over the equatorial Pacific. This suppression will contribute to a slowing down of the global-mean SAT rise. PMID:20080684

  3. Prediction of Seasonal Climate-induced Variations in Global Food Production

    NASA Technical Reports Server (NTRS)

    Iizumi, Toshichika; Sakuma, Hirofumi; Yokozawa, Masayuki; Luo, Jing-Jia; Challinor, Andrew J.; Brown, Molly E.; Sakurai, Gen; Yamagata, Toshio

    2013-01-01

    Consumers, including the poor in many countries, are increasingly dependent on food imports and are therefore exposed to variations in yields, production, and export prices in the major food-producing regions of the world. National governments and commercial entities are paying increased attention to the cropping forecasts of major food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We assessed the reliability of hindcasts (i.e., retrospective forecasts for the past) of crop yield loss relative to the previous year for two lead times. Pre-season yield predictions employ climatic forecasts and have lead times of approximately 3 to 5 months for providing information regarding variations in yields for the coming cropping season. Within-season yield predictions use climatic forecasts with lead times of 1 to 3 months. Pre-season predictions can be of value to national governments and commercial concerns, complemented by subsequent updates from within-season predictions. The latter incorporate information on the most recent climatic data for the upcoming period of reproductive growth. In addition to such predictions, hindcasts using observations from satellites were performed to demonstrate the upper limit of the reliability of crop forecasting.

  4. Prediction Markets and Beliefs about Climate: Results from Agent-Based Simulations

    NASA Astrophysics Data System (ADS)

    Gilligan, J. M.; John, N. J.; van der Linden, M.

    2015-12-01

    Climate scientists have long been frustrated by persistent doubts a large portion of the public expresses toward the scientific consensus about anthropogenic global warming. The political and ideological polarization of this doubt led Vandenbergh, Raimi, and Gilligan [1] to propose that prediction markets for climate change might influence the opinions of those who mistrust the scientific community but do trust the power of markets.We have developed an agent-based simulation of a climate prediction market in which traders buy and sell future contracts that will pay off at some future year with a value that depends on the global average temperature at that time. The traders form a heterogeneous population with different ideological positions, different beliefs about anthropogenic global warming, and different degrees of risk aversion. We also vary characteristics of the market, including the topology of social networks among the traders, the number of traders, and the completeness of the market. Traders adjust their beliefs about climate according to the gains and losses they and other traders in their social network experience. This model predicts that if global temperature is predominantly driven by greenhouse gas concentrations, prediction markets will cause traders' beliefs to converge toward correctly accepting anthropogenic warming as real. This convergence is largely independent of the structure of the market and the characteristics of the population of traders. However, it may take considerable time for beliefs to converge. Conversely, if temperature does not depend on greenhouse gases, the model predicts that traders' beliefs will not converge. We will discuss the policy-relevance of these results and more generally, the use of agent-based market simulations for policy analysis regarding climate change, seasonal agricultural weather forecasts, and other applications.[1] MP Vandenbergh, KT Raimi, & JM Gilligan. UCLA Law Rev. 61, 1962 (2014).

  5. Predicting the Impact of Climate Change on Threatened Species in UK Waters

    PubMed Central

    Jones, Miranda C.; Dye, Stephen R.; Fernandes, Jose A.; Frölicher, Thomas L.; Pinnegar, John K.; Warren, Rachel; Cheung, William W. L.

    2013-01-01

    Global climate change is affecting the distribution of marine species and is thought to represent a threat to biodiversity. Previous studies project expansion of species range for some species and local extinction elsewhere under climate change. Such range shifts raise concern for species whose long-term persistence is already threatened by other human disturbances such as fishing. However, few studies have attempted to assess the effects of future climate change on threatened vertebrate marine species using a multi-model approach. There has also been a recent surge of interest in climate change impacts on protected areas. This study applies three species distribution models and two sets of climate model projections to explore the potential impacts of climate change on marine species by 2050. A set of species in the North Sea, including seven threatened and ten major commercial species were used as a case study. Changes in habitat suitability in selected candidate protected areas around the UK under future climatic scenarios were assessed for these species. Moreover, change in the degree of overlap between commercial and threatened species ranges was calculated as a proxy of the potential threat posed by overfishing through bycatch. The ensemble projections suggest northward shifts in species at an average rate of 27 km per decade, resulting in small average changes in range overlap between threatened and commercially exploited species. Furthermore, the adverse consequences of climate change on the habitat suitability of protected areas were projected to be small. Although the models show large variation in the predicted consequences of climate change, the multi-model approach helps identify the potential risk of increased exposure to human stressors of critically endangered species such as common skate (Dipturus batis) and angelshark (Squatina squatina). PMID:23349829

  6. Long-range variability and predictability of the Ozark Highlands climate elements

    NASA Astrophysics Data System (ADS)

    Lee, Jae-Won

    Interannual variations and intraannual variation of regional-scale and global-scale climate variables are characterized by principal component analysis (PCA). Climate consistency is detected among the entire United States, the North Central states, and the Ozark Highlands (OZHI). The regional-scale modes of the OZHI climate are classified as the predictands of the statistical climate model. Characteristic patterns and time coefficients are examined in global-scale climate variables as the predictor of the models. Relationships between regional-scale and global-scale climate variables are identified by the month lead cross- correlation analysis. The OZHI temperatures in January and July are highly correlated to lead time global-scale climate variables in the tropical and subtropical Pacific and Atlantic and those of lead time in the eastern subtropical and midlatitude Pacific, respectively. The OZHI precipitation levels in January and May are highly correlated to lead time global-scale climate variables in the western tropical Pacific and in the western tropical Indian, and South Pacific Convergence Zone (SPCZ), respectively. From multiple linear regression (MLR) and principal components regression (PCR) analysis, the predictability of OZHI regional temperature and precipitation are discussed with model diagnostics and measurements of forecasting performance. This study suggests that PCR can clearly eliminate the multicollinearity among predictors. For the purpose of building the statistical climate model, the sensitivities of the main predictors (i.e., temperature and precipitation) are investigated, and relatively long-memory and short-memory predictors are uncovered. The sea surface temperatures have a relatively long-memory effect.

  7. Predicting the impact of climate change on threatened species in UK waters.

    PubMed

    Jones, Miranda C; Dye, Stephen R; Fernandes, Jose A; Frölicher, Thomas L; Pinnegar, John K; Warren, Rachel; Cheung, William W L

    2013-01-01

    Global climate change is affecting the distribution of marine species and is thought to represent a threat to biodiversity. Previous studies project expansion of species range for some species and local extinction elsewhere under climate change. Such range shifts raise concern for species whose long-term persistence is already threatened by other human disturbances such as fishing. However, few studies have attempted to assess the effects of future climate change on threatened vertebrate marine species using a multi-model approach. There has also been a recent surge of interest in climate change impacts on protected areas. This study applies three species distribution models and two sets of climate model projections to explore the potential impacts of climate change on marine species by 2050. A set of species in the North Sea, including seven threatened and ten major commercial species were used as a case study. Changes in habitat suitability in selected candidate protected areas around the UK under future climatic scenarios were assessed for these species. Moreover, change in the degree of overlap between commercial and threatened species ranges was calculated as a proxy of the potential threat posed by overfishing through bycatch. The ensemble projections suggest northward shifts in species at an average rate of 27 km per decade, resulting in small average changes in range overlap between threatened and commercially exploited species. Furthermore, the adverse consequences of climate change on the habitat suitability of protected areas were projected to be small. Although the models show large variation in the predicted consequences of climate change, the multi-model approach helps identify the potential risk of increased exposure to human stressors of critically endangered species such as common skate (Dipturus batis) and angelshark (Squatina squatina).

  8. Aspen Global Change Institute (AGCI) Interdisciplinary Science Workshop: Decadal Climate Prediction; Aspen, CO; June 22-28, 2008

    SciTech Connect

    Katzenberger, John

    2010-03-12

    Decadal prediction lies between seasonal/interannual forecasting and longer-term climate change projections, and focuses on time-evolving regional climate conditions over the next 10?30 yr. Numerous assessments of climate information user needs have identified this time scale as being important to infrastructure planners, water resource managers, and many others. It is central to the information portfolio required to adapt effectively to and through climatic changes.

  9. Space can substitute for time in predicting climate-change effects on biodiversity

    USGS Publications Warehouse

    Blois, Jessica L.; Williams, John W.; Fitzpatrick, Matthew C.; Jackson, Stephen T.; Ferrier, Simon

    2013-01-01

    “Space-for-time” substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption—that drivers of spatial gradients of species composition also drive temporal changes in diversity—rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as “time-for-time” predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  10. Space can substitute for time in predicting climate-change effects on biodiversity.

    PubMed

    Blois, Jessica L; Williams, John W; Fitzpatrick, Matthew C; Jackson, Stephen T; Ferrier, Simon

    2013-06-04

    "Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption--that drivers of spatial gradients of species composition also drive temporal changes in diversity--rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  11. Space can substitute for time in predicting climate-change effects on biodiversity

    NASA Astrophysics Data System (ADS)

    Blois, Jessica L.; Williams, John W.; Fitzpatrick, Matthew C.; Jackson, Stephen T.; Ferrier, Simon

    2013-06-01

    "Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption-that drivers of spatial gradients of species composition also drive temporal changes in diversity-rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  12. The impact of stratospheric volcanic aerosol on decadal-scale climate predictions

    NASA Astrophysics Data System (ADS)

    Timmreck, Claudia; Pohlmann, Holger; Illing, Sebastian; Kadow, Christopher

    2016-04-01

    The possibility of a large future volcanic eruption provides arguably the largest uncertainty concerning the evolution of the climate system on the time scale of a few years; but also the greatest opportunity to learn about the behavior of the climate system, and our models thereof. So the question emerges how large will the uncertainty be for future decadal climate predictions if no volcanic aerosol is taken into account? And how strong has volcanic aerosol affected decadal prediction skill on annual and multi-year seasonal scales over the CMIP5 hindcast period? To understand the impact of volcanic aerosol on multi-year seasonal and decadal climate predictions we performed CMIP5-type hindcasts without volcanic aerosol using the German MiKlip prediction system system baseline 1 from 1961 to 1991 and compared them to the corresponding simulations including aerosols. Our results show that volcanic aerosol significantly affects the prediction skill for global mean surface air temperature in the first five years after strong volcanic eruptions. Also on the regional scale a volcanic imprint on decadal-scale variability is detectable. Neglecting volcanic aerosol leads to a reduced prediction skill over the tropical and subtropical Atlantic, Indic and West Pacific but to an improvement over the tropical East-Pacific, where the model has in general no skill. Multi-seasonal differences in the skill for seasonal-mean temperatures are evident over Continental Europe with significant skill loss due to neglection of volcanic aerosol in boreal winter over central Europe, Scandinavia and over south-eastern Europe and the East-Mediterranean in boreal summer.

  13. Combining climatic and soil properties better predicts covers of Brazilian biomes.

    PubMed

    Arruda, Daniel M; Fernandes-Filho, Elpídio I; Solar, Ricardo R C; Schaefer, Carlos E G R

    2017-04-01

    Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km(2) for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

  14. Combining climatic and soil properties better predicts covers of Brazilian biomes

    NASA Astrophysics Data System (ADS)

    Arruda, Daniel M.; Fernandes-Filho, Elpídio I.; Solar, Ricardo R. C.; Schaefer, Carlos E. G. R.

    2017-04-01

    Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

  15. Significant contribution of realistic vegetation representation to improved simulation and prediction of climate anomalies over land

    NASA Astrophysics Data System (ADS)

    Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Doblas-Reyes, Francisco; van den Hurk, Bart; Miller, Paul

    2015-04-01

    The EC-Earth earth system model has been recently developed to include the dynamics of vegetation through the coupling with the LPJ-Guess model. In its original formulation, the coupling between atmosphere and vegetation variability is simply operated by the vegetation Leaf Area Index (LAI), which affects climate by only changing the vegetation physiological resistance to evapotranspiration. This coupling with no implied change of the vegetation fractional coverage has been reported to have a weak effect on the surface climate modeled by EC-Earth (e.g.: also Weiss et al. 2012). The effective sub-grid vegetation fractional coverage can vary seasonally and at interannual time-scales as a function of leaf-canopy growth, phenology and senescence, and therefore affect biophysical parameters such as the surface roughness, albedo and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation densitiy to the LAI, based on a Lambert-Beer formulation. By comparing historical 20th century simulations and retrospective forecasts performed applying the new effective fractional-coverage parameterization with the respective reference simulations using the original constant vegetation-fraction, we showed an increased effect of vegetation on the EC-Earth surface climate. The analysis shows considerable sensitivity of EC-Earth surface climate at seasonal to interannual time-scales due to the variability of vegetation effective fractional coverage. Particularly large effects are shown over boreal winter middle-to-high latitudes, where the cooling effect of the new parameterization corrects the warm biases of the control simulations over land. For boreal winter, the realistic representation of vegetation variability leads to a significant improvement of the skill in predicting surface climate over land at seasonal time-scales. A potential predictability experiment extended to longer time-scales also indicates the

  16. Predicted changes in energy demands for heating and cooling due to climate change

    NASA Astrophysics Data System (ADS)

    Dolinar, Mojca; Vidrih, Boris; Kajfež-Bogataj, Lučka; Medved, Sašo

    In the last 3 years in Slovenia we experienced extremely hot summers and demand for cooling the buildings have risen significantly. Since climate change scenarios predict higher temperatures for the whole country and for all seasons, we expect that energy demand for heating would decrease while demand for cooling would increase. An analysis for building with permitted energy demand and for low-energy demand building in two typical urban climates in Slovenia was performed. The transient systems simulation program (TRNSYS) was used for simulation of the indoor conditions and the energy use for heating and cooling. Climate change scenarios were presented in form of “future” Test Reference Years (TRY). The time series of hourly data for all meteorological variables for different scenarios were chosen from actual measurements, using the method of highest likelihood. The climate change scenarios predicted temperature rise (+1 °C and +3 °C) and solar radiation increase (+3% and +6%). With the selection of these scenarios we covered the spectra of possible predicted climate changes in Slovenia. The results show that energy use for heating would decrease from 16% to 25% (depends on the intensity of warming) in subalpine region, while in Mediterranean region the rate of change would not be significant. In summer time we would need up to six times more energy for cooling in subalpine region and approximately two times more in Mediterranean region. low-energy building proved to be very economical in wintertime while on average higher energy consumption for cooling is expected in those buildings in summertime. In case of significant warmer and more solar energy intensive climate, the good isolated buildings are more efficient than standard buildings. TRY proved not to be efficient for studying extreme conditions like installed power of the cooling system.

  17. Predicted response of stem respiration in ponderosa pine to global climate change

    SciTech Connect

    Carey, E.V.; DeLucia, E.H.; Callaway, R.M. )

    1994-06-01

    We measured woody tissue respiration on boles of desert and montane populations of Pinus ponderosa growing in the Great Basin Desert and on the east-slope of the Sierra Nevada as part of a study of responses of P. ponderosa to global climate change. The differences in temperature and precipitation between desert and montane populations match changes in climate predicted from a doubling of atmospheric CO[sub 2]; therefore, these naturally occurring populations represent the difference between present and future climatic conditions for these trees. Allometric relationships derived previously, indicate that for trees of equal diameter, desert trees predicted that desert trees would have lower Q[sub 10] responses for respiration (increase in respiration with a 10[degrees] increase in temperature) volume was not different between populations (Desert: 3.24; Montane: 3.13 moles m[sup [minus]3] sec[sup [minus]1]). Moreover, between population differences in Q[sub 10] for respiration were not statistically significant (Desert: 2.27; Montane: 2.39). Results suggest that under predicted future climatic conditions increased respiratory losses from woody tissue resulting from increased allocation to sapwood may offset increases in carbon uptake due to enhanced photosynthesis from elevated CO[sub 2].

  18. Measuring the benefits of climate forecasts in predicting PV power production

    NASA Astrophysics Data System (ADS)

    De Felice, Matteo; Alessandri, Andrea; Pollino, Maurizio

    2016-04-01

    Surface solar radiation is an important variable to model and predict solar power output. Having accurate forecast may be of potential use for planning and operational tasks, both at short- and long-time scales. This study examines the predictability of seasonal surface solar radiation comparing ECMWF System4 Seasonal operational forecasts the SARAH Satellite Dataset on the period 1984-2007. This work tries to answer the following question: how useful are climate forecasts in predicting seasonal PV production? The "information layer" provided by climate information is overlapped with 1) the information about the land cover (CLC2006) to consider the potential amount of land available for PV panels and 2) the information about the solar power installed capacity for European region in order to define the areas where an improved forecast could have a bigger impact. All the information layers are summarised by using a simple scalar index (Index of Opportunity) computed for all the European regions for the four seasons. The results are very interesting, in fact the potential benefits of climate forecasts are not (only) related to their statistical skills (e.g. probabilistic scores) but also to the actual and potential installed capacity of solar power. Here, we show that to assess the usefulness of climate forecasts in the energy sector we should use all the relevant information layers, combining them according to the "needs" of the potential users.

  19. Predicting impacts of climate change on habitat connectivity of Kalopanax septemlobus in South Korea

    NASA Astrophysics Data System (ADS)

    Kang, Wanmo; Minor, Emily S.; Lee, Dowon; Park, Chan-Ryul

    2016-02-01

    Understanding the drivers of habitat distribution patterns and assessing habitat connectivity are crucial for conservation in the face of climate change. In this study, we examined a sparsely distributed tree species, Kalopanax septemlobus (Araliaceae), which has been heavily disturbed by human use in temperate forests of South Korea. We used maximum entropy distribution modeling (MaxEnt) to identify the climatic and topographic factors driving the distribution of the species. Then, we constructed habitat models under current and projected climate conditions for the year 2050 and evaluated changes in the extent and connectivity of the K. septemlobus habitat. Annual mean temperature and terrain slope were the two most important predictors of species distribution. Our models predicted the range shift of K. septemlobus toward higher elevations under medium-low and high emissions scenarios for 2050, with dramatic reductions in suitable habitat (51% and 85%, respectively). In addition, connectivity analysis indicated that climate change is expected to reduce future levels of habitat connectivity. Even under the Representative Construction Pathway (RCP) 4.5 medium-low warming scenario, the projected climate conditions will decrease habitat connectivity by 78%. Overall, suitable habitats for K. septemlobus populations will likely become more isolated depending on the severity of global warming. The approach presented here can be used to efficiently assess species and habitat vulnerability to climate change.

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

    SciTech Connect

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

    2006-08-04

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

  1. Psychosocial safety climate moderates the job demand-resource interaction in predicting workgroup distress.

    PubMed

    Dollard, Maureen F; Tuckey, Michelle R; Dormann, Christian

    2012-03-01

    Psychosocial safety climate (PSC) arises from workplace policies, practices, and procedures for the protection of worker psychological health and safety that are largely driven by management. Many work stress theories are based on the fundamental interaction hypothesis - that a high level of job demands (D) will lead to psychological distress and that this relationship will be offset when there are high job resources (R). However we proposed that this interaction really depends on the organizational context; in particular high levels of psychosocial safety climate will enable the safe utilization of resources to reduce demands. The study sample consisted of police constables from 23 police units (stations) with longitudinal survey responses at two time points separated by 14 months (Time 1, N=319, Time 2, N=139). We used hierarchical linear modeling to assess the effect of the proposed three-way interaction term (PSC×D×R) on change in workgroup distress variance over time. Specifically we confirmed the interaction between emotional demands and emotional resources (assessed at the individual level), in the context of unit psychosocial safety climate (aggregated individual data). As predicted, high emotional resources moderated the positive relationship between emotional demands and change in workgroup distress but only when there were high levels of unit psychosocial safety climate. Results were confirmed using a split-sample analysis. Results support psychosocial safety climate as a property of the organization and a target for higher order controls for reducing work stress. The 'right' climate enables resources to do their job.

  2. Projected climate change impacts and short term predictions on staple crops in Sub-Saharan Africa

    NASA Astrophysics Data System (ADS)

    Mereu, V.; Spano, D.; Gallo, A.; Carboni, G.

    2013-12-01

    . Multiple combinations of soils and climate conditions, crop management and varieties were considered for the different Agro-Ecological Zones. The climate impact was assessed using future climate prediction, statistically and/or dynamically downscaled, for specific areas. Direct and indirect effects of different CO2 concentrations projected for the future periods were separately explored to estimate their effects on crops. Several adaptation strategies (e.g., introduction of full irrigation, shift of the ordinary sowing/planting date, changes in the ordinary fertilization management) were also evaluated with the aim to reduce the negative impact of climate change on crop production. The results of the study, analyzed at local, AEZ and country level, will be discussed.

  3. Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations.

    PubMed

    Gerber, Brian D; Kendall, William L; Hooten, Mevin B; Dubovsky, James A; Drewien, Roderick C

    2015-09-01

    1. Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment. 2. Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression. 3. Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring-summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect. 4. Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are

  4. Assessment of the predictability of climate anomalies in connection with El Niño phenomena

    NASA Astrophysics Data System (ADS)

    Mokhov, I. I.; Timazhev, A. V.

    2015-10-01

    Using long-term observational data on the temperature, precipitation, and drought and moisture indices in European and Asian regions of Russia in May-July, the degree of predictability of climate anomalies in Russian regions in connection with different types of El Niño/La Niña phenomena is estimated with assessment of the probability of different regional climate anomalies under different phase transitions for the El Niño phenomena, which are characterized by various indices, including those observed in 2015.

  5. Understanding and predicting climate variations in the Middle East for sustainable water resource management and development

    NASA Astrophysics Data System (ADS)

    Samuels, Rana

    Water issues are a source of tension between Israelis and Palestinians. In the and region of the Middle East, water supply is not just scarce but also uncertain: It is not uncommon for annual rainfall to be as little as 60% or as much as 125% of the multiannual average. This combination of scarcity and uncertainty exacerbates the already strained economy and the already tensed political situation. The uncertainty could be alleviated if it were possible to better forecast water availability. Such forecasting is key not only for water planning and management, but also for economic policy and for political decision making. Water forecasts at multiple time scales are necessary for crop choice, aquifer operation and investments in desalination infrastructure. The unequivocal warming of the climate system adds another level of uncertainty as global and regional water cycles change. This makes the prediction of water availability an even greater challenge. Understanding the impact of climate change on precipitation can provide the information necessary for appropriate risk assessment and water planning. Unfortunately, current global circulation models (GCMs) are only able to predict long term climatic evolution at large scales but not local rainfall. The statistics of local precipitation are traditionally predicted using historical rainfall data. Obviously these data cannot anticipate changes that result from climate change. It is therefore clear that integration of the global information about climate evolution and local historical data is needed to provide the much needed predictions of regional water availability. Currently, there is no theoretical or computational framework that enables such integration for this region. In this dissertation both a conceptual framework and a computational platform for such integration are introduced. In particular, suite of models that link forecasts of climatic evolution under different CO2 emissions scenarios to observed rainfall

  6. Recent and Predicted Changes in Pan-Arctic Vegetation Properties and Their Climate Feedback Implications

    NASA Astrophysics Data System (ADS)

    Goetz, S. J.

    2014-12-01

    Arctic surface air temperatures have risen at approximately twice the global rate, generating a range of ecosystem responses and associated climate feedbacks. Well-documented examples include changes in vegetation productivity, fire disturbance, the expansion of woody shrubs into tundra, and associated changes in surface albedo and net surface shortwave radiative forcing. I will briefly review these and other changes across the pan-Arctic domain using a combination of field measurements and satellite remote sensing observations. I will examine the evidence for change that has already occurred and also discuss predictions of likely future ecosystem responses under different climate change scenarios. I will identify research and data needs that would help to resolve discrepancies and disparities that have been reported. In particular I will address the current potential and limitations of vegetation distribution models and the data sets that inform them. Notably, model predictions indicate rapid shifts to larger woody growth-forms, rapid colonization due to long-distance dispersal, and favorable conditions for recruitment following disturbances like tundra fire and permafrost degradation. Future albedo, evapotranspiration and aboveground biomass will change with the redistribution of Arctic vegetation, and the climate feedbacks of these ecosystem changes can be significant. Albedo and net surface shortwave radiation changes will dominate the influence on climate, largely due to the snow masking effects of taller vegetation. The carbon implications of ecosystem change will likely be dominated by processes that influence permafrost thaw vulnerability, but predictions also indicate that vegetation in the Arctic will affect climate primarily as a biophysical medium (i.e. via albedo change). As with thawing permafrost, predicted vegetation changes would exacerbate currently amplified rates of warming. New research efforts focused on the Arctic will address the research

  7. Major vessel occlusion may predict subtherapeutic anticoagulation intensity and feasibility of administration of intravenous thrombolytics

    PubMed Central

    Chang, Jun Young; Jung, Seunguk; Park, Hyun

    2017-01-01

    Objective We investigated the association between the presence of major vessel occlusion (MVO) and the intensity of the International Normalized Ratio (INR) in cardioembolic high-risk patients taking warfarin. We also evaluated whether the presence of MVO could predict the subtherapeutic range of INR ≤1.7 ensuring safe administration of intravenous thrombolytics. Methods The medical records of 177 cardioembolic stroke patients who were taking warfarin between April, 2008 and March, 2015 were retrospectively analyzed. Logistic regression analysis was performed to calculate the odds ratios (ORs) and 95% confidence intervals (95% CIs) for the association between vessel occlusion and intensity of INR. To predict INR ≤1.7, decision tree analysis was performed. Results INR was inversely associated with MVO in an unadjusted model (OR, 0.36; 95% CI, 0.17–0.76), and in a model adjusted for initial NIHSS score and time from symptom onset to arrival (OR, 0.28; 95% CI, 0.11–0.73). Fifty-two of 58 (89.7%) patients with MVO had an INR ≤1.7, compared with 83 of 119 (69.7%) patients without MVO. Indication for anticoagulation agent use was dichotomized into NVAF and others, and applied to the subgroup of patients with MVO. All patients with NVAF (31/31, 100%) had INR ≤1.7, while 21 of 27 of the other patients (77.8%) had INR ≤1.7. Conclusions Low INR at presentation in cardioembolic stroke patients during anticoagulation treatment was associated with occurrence of major vessel occlusive stroke. Presence of MVO and indications for anticoagulation may be utilized to ensure the feasibility of administration of intravenous thrombolytics. PMID:28158211

  8. Data-Driven Reduction and Climate Prediction by Nonlinear Stochastic Energy-Conserving Models

    NASA Astrophysics Data System (ADS)

    Kondrashov, D. A.

    2013-05-01

    Comprehensive dynamical climate models aim at simulating past, present and future climate and, more recently, at predicting it. These models, commonly known as general circulation models or global climate models (GCMs) represent a broad range of time and space scales and use a state vector that has many millions of degrees of freedom. Considerable work, both theoretical and data-based, has shown that much of the observed climate variability can be represented with a substantially smaller number of degrees of freedom. While detailed weather prediction out to a few days requires high numerical resolution, it is fairly clear that the dimension of the phase space in which a major fraction of climate variance can be predicted is likely to be much smaller. Low-dimensional models (LDMs) can simulate and predict that variability provided they are able to account for (i) linear and nonlinear interactions between the resolved high-variance climate components; and (ii) the interactions between the resolved components and the huge number of unresolved ones. Here we will present applications of a particular data-driven LDM approach, namely energy-conserving formulation of empirical model reduction (EMR). As an operational methodology, EMR attempts to construct a low-order nonlinear system of multi-level prognostic equations driven by stochastic forcing, and to estimate both the dynamical operator and the properties of the driving noise directly from observations or from a high-order model's simulation. The multi-level EMR structure for modeling the noise allows one to capture feedback between high- and low-frequency components of the variability, thus parameterizing the "fast" scales — often referred to as the "noise" — in terms of the memory of the "slow" scales, the "signal." EMR already proved to be highly competitive for real-time ENSO prediction among state-of-the art dynamical and statistical models. New opportunities for EMR prediction will be illustrated in the

  9. Non-linear Regression and Machine Learning for Streamflow Prediction and Climate Change Impact Analysis

    NASA Astrophysics Data System (ADS)

    Shortridge, J.; Guikema, S.; Zaitchik, B. F.

    2015-12-01

    In the past decade, machine-learning methods for empirical rainfall-runoff modeling have seen extensive development. However, the majority of research has focused on a small number of methods, such as artificial neural networks, while not considering other approaches for non-parametric regression that have been developed in recent years. These methods may be able to achieve comparable predictive accuracy to ANN's and more easily provide physical insights into the system of interest through evaluation of covariate influence. Additionally, these methods could provide a straightforward, computationally efficient way of evaluating climate change impacts in basins where data to support physical hydrologic models is limited. In this paper, we use multiple regression and machine-learning approaches to predict monthly streamflow in five highly-seasonal rivers in the highlands of Ethiopia. We find that generalized additive models, random forests, and cubist models achieve better predictive accuracy than ANNs in many basins assessed and are also able to outperform physical models developed for the same region. We discuss some challenges that could hinder the use of such models for climate impact assessment, such as biases resulting from model formulation and prediction under extreme climate conditions, and suggest methods for preventing and addressing these challenges. Finally, we demonstrate how predictor variable influence can be assessed to provide insights into the physical functioning of data-sparse watersheds.

  10. Using Prediction Markets to Generate Probability Density Functions for Climate Change Risk Assessment

    NASA Astrophysics Data System (ADS)

    Boslough, M.

    2011-12-01

    Climate-related uncertainty is traditionally presented as an error bar, but it is becoming increasingly common to express it in terms of a probability density function (PDF). PDFs are a necessary component of probabilistic risk assessments, for which simple "best estimate" values are insufficient. Many groups have generated PDFs for climate sensitivity using a variety of methods. These PDFs are broadly consistent, but vary significantly in their details. One axiom of the verification and validation community is, "codes don't make predictions, people make predictions." This is a statement of the fact that subject domain experts generate results using assumptions within a range of epistemic uncertainty and interpret them according to their expert opinion. Different experts with different methods will arrive at different PDFs. For effective decision support, a single consensus PDF would be useful. We suggest that market methods can be used to aggregate an ensemble of opinions into a single distribution that expresses the consensus. Prediction markets have been shown to be highly successful at forecasting the outcome of events ranging from elections to box office returns. In prediction markets, traders can take a position on whether some future event will or will not occur. These positions are expressed as contracts that are traded in a double-action market that aggregates price, which can be interpreted as a consensus probability that the event will take place. Since climate sensitivity cannot directly be measured, it cannot be predicted. However, the changes in global mean surface temperature are a direct consequence of climate sensitivity, changes in forcing, and internal variability. Viable prediction markets require an undisputed event outcome on a specific date. Climate-related markets exist on Intrade.com, an online trading exchange. One such contract is titled "Global Temperature Anomaly for Dec 2011 to be greater than 0.65 Degrees C." Settlement is based

  11. Evaluating Antarctic sea ice predictability at seasonal to interannual timescales in global climate models

    NASA Astrophysics Data System (ADS)

    Marchi, Sylvain; Fichefet, Thierry; Goosse, Hugues; Zunz, Violette; Tietsche, Steffen; Day, Jonny; Hawkins, Ed

    2016-04-01

    Unlike the rapid sea ice losses reported in the Arctic, satellite observations show an overall increase in Antarctic sea ice extent over recent decades. Although many processes have already been suggested to explain this positive trend, it remains the subject of current investigations. Understanding the evolution of the Antarctic sea ice turns out to be more complicated than for the Arctic for two reasons: the lack of observations and the well-known biases of climate models in the Southern Ocean. Irrespective of those issues, another one is to determine whether the positive trend in sea ice extent would have been predictable if adequate observations and models were available some decades ago. This study of Antarctic sea ice predictability is carried out using 6 global climate models (HadGEM1.2, MPI-ESM-LR, GFDL CM3, EC-Earth V2, MIROC 5.2 and ECHAM 6-FESOM) which are all part of the APPOSITE project. These models are used to perform hindcast simulations in a perfect model approach. The predictive skill is estimated thanks to the PPP (Potential Prognostic Predictability) and the ACC (Anomaly Correlation Coefficient). The former is a measure of the uncertainty of the ensemble while the latter assesses the accuracy of the prediction. These two indicators are applied to different variables related to sea ice, in particular the total sea ice extent and the ice edge location. This first model intercomparison study about sea ice predictability in the Southern Ocean aims at giving a general overview of Antarctic sea ice predictability in current global climate models.

  12. Bias reduction in decadal predictions of West African monsoon rainfall using regional climate models

    NASA Astrophysics Data System (ADS)

    Paxian, A.; Sein, D.; Panitz, H.-J.; Warscher, M.; Breil, M.; Engel, T.; Tödter, J.; Krause, A.; Cabos Narvaez, W. D.; Fink, A. H.; Ahrens, B.; Kunstmann, H.; Jacob, D.; Paeth, H.

    2016-02-01

    The West African monsoon rainfall is essential for regional food production, and decadal predictions are necessary for policy makers and farmers. However, predictions with global climate models reveal precipitation biases. This study addresses the hypotheses that global prediction biases can be reduced by dynamical downscaling with a multimodel ensemble of three regional climate models (RCMs), a RCM coupled to a global ocean model and a RCM applying more realistic soil initialization and boundary conditions, i.e., aerosols, sea surface temperatures (SSTs), vegetation, and land cover. Numerous RCM predictions have been performed with REMO, COSMO-CLM (CCLM), and Weather Research and Forecasting (WRF) in various versions and for different decades. Global predictions reveal typical positive and negative biases over the Guinea Coast and the Sahel, respectively, related to a southward shifted Intertropical Convergence Zone (ITCZ) and a positive tropical Atlantic SST bias. These rainfall biases are reduced by some regional predictions in the Sahel but aggravated by all RCMs over the Guinea Coast, resulting from the inherited SST bias, increased westerlies and evaporation over the tropical Atlantic and shifted African easterly waves. The coupled regional predictions simulate high-resolution atmosphere-ocean interactions strongly improving the SST bias, the ITCZ shift and the Guinea Coast and Central Sahel precipitation biases. Some added values in rainfall bias are found for more realistic SST and land cover boundary conditions over the Guinea Coast and improved vegetation in the Central Sahel. Thus, the ability of RCMs and improved boundary conditions to reduce rainfall biases for climate impact research depends on the considered West African region.

  13. Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts

    NASA Astrophysics Data System (ADS)

    Kim, Hye-Mi; Webster, Peter J.; Curry, Judith A.

    2012-05-01

    This study assesses the CMIP5 decadal hindcast/forecast simulations of seven state-of-the-art ocean-atmosphere coupled models. Each decadal prediction consists of simulations over a 10 year period each of which are initialized every five years from climate states of 1960/1961 to 2005/2006. Most of the models overestimate trends, whereby the models predict less warming or even cooling in the earlier decades compared to observations and too much warming in recent decades. All models show high prediction skill for surface temperature over the Indian, North Atlantic and western Pacific Oceans where the externally forced component and low-frequency climate variability is dominant. However, low prediction skill is found over the equatorial and North Pacific Ocean. The Atlantic Multidecadal Oscillation (AMO) index is predicted in most of the models with significant skill, while the Pacific Decadal Oscillation (PDO) index shows relatively low predictive skill. The multi-model ensemble has in general better-forecast quality than the single-model systems for global mean surface temperature, AMO and PDO.

  14. Observed and predicted responses of plant growth to climate across Canada

    NASA Astrophysics Data System (ADS)

    Bunn, Andrew G.; Goetz, Scott J.; Fiske, Gregory J.

    2005-08-01

    Using satellite observations from 1981-2000, and data interpolated from surface weather stations, we examined the association between gross photosynthetic activity (Pg) and climate across the boreal forest and tundra of Canada. The response of annual and interannual Pg was tightly coupled to climate, and seasonal associations between Pg and climate varied with plant functional types. The most important variable for modeling summer growth of conifer forests was the previous spring minimum temperature, whereas tundra responded primarily to summer maximum temperature. Using general circulation model predictors to 2050, we project that tundra will continue to grow vigorously in the coming decades while conifer forests will not. Increased tundra productivity will likely be associated with changes in vegetation composition (e.g., woody proliferation). If these biotic responses are stationary and persist as predicted, terrestrial carbon budgets will need to be modified.

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

    PubMed Central

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

    2012-01-01

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

  16. A drug-paired taste cue elicits withdrawal and predicts cocaine self-administration.

    PubMed

    Nyland, Jennifer E; Grigson, Patricia S

    2013-03-01

    Addiction is a chronic disease where periods of abstinence are riddled with instances of craving, withdrawal, and eventual relapse to escalated drug use. Cues previously associated with drug use can have a deleterious effect on this cycle by precipitating withdrawal symptoms. Here we focus specifically on the relationship between avoidance of a drug-paired taste cue and the ability of the drug-paired cue to elicit withdrawal and, ultimately, drug seeking and taking. We used a rat model of drug addiction and naloxone-induced loss of body weight to test whether a taste cue elicits withdrawal in anticipation of drug availability. Experiment 1 investigated the ability of a taste cue to elicit signs of withdrawal when it predicted experimenter-administered morphine (15 mg/kg, i.p.). In Experiment 2, a saccharin taste cue was paired with the opportunity to actively self-administer cocaine (0.167 mg/infusion, i.v.). The results show that presentation of a morphine- or cocaine-paired taste cue is sufficient to elicit naloxone-induced withdrawal symptoms, and greater withdrawal predicts greater cocaine self-administration in rats.

  17. Prediction of meningococcal meningitis epidemics in western Africa by using climate information

    NASA Astrophysics Data System (ADS)

    YAKA, D. P.; Sultan, B.; Tarbangdo, F.; Thiaw, W. M.

    2013-12-01

    The variations of certain climatic parameters and the degradation of ecosystems, can affect human's health by influencing the transmission, the spatiotemporal repartition and the intensity of infectious diseases. It is mainly the case of meningococcal meningitis (MCM) whose epidemics occur particularly in Sahelo-Soudanian climatic area of Western Africa under quite particular climatic conditions. Meningococcal Meningitis (MCM) is a contagious infection disease due to the bacteria Neisseria meningitis. MCM epidemics occur worldwide but the highest incidence is observed in the "meningitis belt" of sub-Saharan Africa, stretching from Senegal to Ethiopia. In spite of standards, strategies of prevention and control of MCS epidemic from World Health Organization (WHO) and States, African Sahelo-Soudanian countries remain frequently afflicted by disastrous epidemics. In fact, each year, during the dry season (February-April), 25 to 250 thousands of cases are observed. Children under 15 are particularly affected. Among favourable conditions for the resurgence and dispersion of the disease, climatic conditions may be important inducing seasonal fluctuations in disease incidence and contributing to explain the spatial pattern of the disease roughly circumscribed to the ecological Sahelo-Sudanian band. In this study, we tried to analyse the relationships between climatic factors, ecosystems degradation and MCM for a better understanding of MCM epidemic dynamic and their prediction. We have shown that MCM epidemics, whether at the regional, national or local level, occur in a specific period of the year, mainly from January to May characterised by a dry, hot and sandy weather. We have identified both in situ (meteorological synoptic stations) and satellitales climatic variables (NCEP reanalysis dataset) whose seasonal variability is dominating in MCM seasonal transmission. Statistical analysis have measured the links between seasonal variation of certain climatic parameters

  18. EPA Administrator Announces New Report on Impacts of Climate Change on Public Health

    EPA Pesticide Factsheets

    Today, on the first day of National Public Health Week, EPA and seven other federal agencies, as well as the White House Office of Science and Technology Policy, are releasing a new report summarizing the growing understanding of how climate change is affe

  19. A Study of the Perceived Relationships between the Leadership Style of Elementary Administrators and School Climate

    ERIC Educational Resources Information Center

    Ferree, Stephanie A.

    2013-01-01

    As national and state demands continue to mandate school improvement, leaders in schools have continued to seek answers from leadership theory and research to improve and sustain the culture and climate that has been created in order for diverse populations to meet academic excellence. The purpose of this research was to determine the relationship…

  20. A climate-based spatiotemporal prediction for dengue fever epidemics: a case study in southern Taiwan

    NASA Astrophysics Data System (ADS)

    Yu, H.-L.; Yang, S.-J.; Lin, Y.-C.

    2012-04-01

    Dengue Fever (DF) has been identified by the World Health organization (WHO) as one of the most serious vector-borne infectious diseases in tropical and sub-tropical areas. DF has been one of the most important epidemics in Taiwan which occur annually especially in southern Taiwan during summer and autumn. Most DF studies have focused mainly on temporal DF patterns and its close association with climatic covariates, whereas few studies have investigated the spatial DF patterns (spatial dependence and clustering) and composite space-time effects of the DF epidemics. The present study proposes a spatio-temporal DF prediction approach based on stochastic Bayesian Maximum Entropy (BME) analysis. Core and site-specific knowledge bases are considered, including climate and health datasets under conditions of uncertainty, space-time dependence functions, and a Poisson regression model of climatic variables contributing to DF occurrences in southern Taiwan during 2007, when the highest number of DF cases was recorded in the history of Taiwan epidemics (over 2000). The obtained results show that the DF outbreaks in the study area are highly influenced by climatic conditions. Furthermore, the analysis can provide the required "one-week-ahead" outbreak warnings based on spatio-temporal predictions of DF distributions. Therefore, the proposed analysis can provide the Taiwan Disease Control Agency with a valuable tool to timely identify, control, and even efficiently prevent DF spreading across space-time.

  1. Predicted habitat shifts of Pacific top predators in a changing climate

    NASA Astrophysics Data System (ADS)

    Hazen, Elliott L.; Jorgensen, Salvador; Rykaczewski, Ryan R.; Bograd, Steven J.; Foley, David G.; Jonsen, Ian D.; Shaffer, Scott A.; Dunne, John P.; Costa, Daniel P.; Crowder, Larry B.; Block, Barbara A.

    2013-03-01

    To manage marine ecosystems proactively, it is important to identify species at risk and habitats critical for conservation. Climate change scenarios have predicted an average sea surface temperature (SST) rise of 1-6°C by 2100 (refs , ), which could affect the distribution and habitat of many marine species. Here we examine top predator distribution and diversity in the light of climate change using a database of 4,300 electronic tags deployed on 23 marine species from the Tagging of Pacific Predators project, and output from a global climate model to 2100. On the basis of models of observed species distribution as a function of SST, chlorophyll a and bathymetry, we project changes in species-specific core habitat and basin-scale patterns of biodiversity. We predict up to a 35% change in core habitat for some species, significant differences in rates and patterns of habitat change across guilds, and a substantial northward displacement of biodiversity across the North Pacific. For already stressed species, increased migration times and loss of pelagic habitat could exacerbate population declines or inhibit recovery. The impending effects of climate change stress the urgency of adaptively managing ecosystems facing multiple threats.

  2. Prediction of Changes in Vegetation Distribution Under Climate Change Scenarios Using Modis Dataset

    NASA Astrophysics Data System (ADS)

    Hirayama, Hidetake; Tomita, Mizuki; Hara, Keitarou

    2016-06-01

    The distribution of vegetation is expected to change under the influence of climate change. This study utilizes vegetation maps derived from Terra/MODIS data to generate a model of current climate conditions suitable to beech-dominated deciduous forests, which are the typical vegetation of Japan's cool temperate zone. This model will then be coordinated with future climate change scenarios to predict the future distribution of beech forests. The model was developed by using the presence or absence of beech forest as the dependent variable. Four climatic variables; mean minimum daily temperature of the coldest month (TMC) warmth index (WI) winter precipitation (PRW) and summer precipitation (PRS): and five geophysical variables; topography (TOPO), surface geology (GEOL), soil (SOIL), slope aspect (ASP), and inclination (INCL); were adopted as independent variables. Previous vegetation distribution studies used point data derived from field surveys. The remote sensing data utilized in this study, however, should permit collecting of greater amounts of data, and also frequent updating of data and distribution maps. These results will hopefully show that use of remote sensing data can provide new insights into our understanding of how vegetation distribution will be influenced by climate change.

  3. Evaluation of GCMs in the context of regional predictive climate impact studies.

    NASA Astrophysics Data System (ADS)

    Kokorev, Vasily; Anisimov, Oleg

    2016-04-01

    Significant improvements in the structure, complexity, and general performance of earth system models (ESMs) have been made in the recent decade. Despite these efforts, the range of uncertainty in predicting regional climate impacts remains large. The problem is two-fold. Firstly, there is an intrinsic conflict between the local and regional scales of climate impacts and adaptation strategies, on one hand, and larger scales, at which ESMs demonstrate better performance, on the other. Secondly, there is a growing understanding that majority of the impacts involve thresholds, and are thus driven by extreme climate events, whereas accent in climate projections is conventionally made on gradual changes in means. In this study we assess the uncertainty in projecting extreme climatic events within a region-specific and process-oriented context by examining the skills and ranking of ESMs. We developed a synthetic regionalization of Northern Eurasia that accounts for the spatial features of modern climatic changes and major environmental and socio-economical impacts. Elements of such fragmentation could be considered as natural focus regions that bridge the gap between the spatial scales adopted in climate-impacts studies and patterns of climate change simulated by ESMs. In each focus region we selected several target meteorological variables that govern the key regional impacts, and examined the ability of the models to replicate their seasonal and annual means and trends by testing them against observations. We performed a similar evaluation with regard to extremes and statistics of the target variables. And lastly, we used the results of these analyses to select sets of models that demonstrate the best performance at selected focus regions with regard to selected sets of target meteorological parameters. Ultimately, we ranked the models according to their skills, identified top-end models that "better than average" reproduce the behavior of climatic parameters, and

  4. Regional climate change predictions from the Goddard Institute for Space Studies high resolution GCM

    NASA Technical Reports Server (NTRS)

    Crane, Robert G.; Hewitson, Bruce

    1990-01-01

    Model simulations of global climate change are seen as an essential component of any program aimed at understanding human impact on the global environment. A major weakness of current general circulation models (GCMs), however, is their inability to predict reliably the regional consequences of a global scale change, and it is these regional scale predictions that are necessary for studies of human/environmental response. This research is directed toward the development of a methodology for the validation of the synoptic scale climatology of GCMs. This is developed with regard to the Goddard Institute for Space Studies (GISS) GCM Model 2, with the specific objective of using the synoptic circulation form a doubles CO2 simulation to estimate regional climate change over North America, south of Hudson Bay. This progress report is specifically concerned with validating the synoptic climatology of the GISS GCM, and developing the transfer function to derive grid-point temperatures from the synoptic circulation. Principal Components Analysis is used to characterize the primary modes of the spatial and temporal variability in the observed and simulated climate, and the model validation is based on correlations between component loadings, and power spectral analysis of the component scores. The results show that the high resolution GISS model does an excellent job of simulating the synoptic circulation over the U.S., and that grid-point temperatures can be predicted with reasonable accuracy from the circulation patterns.

  5. Evaluation of Canadian Seasonal to Interannual Prediction System: seasonal hindcasts of the recent past climate.

    NASA Astrophysics Data System (ADS)

    Markovic, M.

    2015-12-01

    Canadian Seasonal to Interannual Prediction System (CanSIPS) has been operationally active within the Meteorological Service of Canada since the year of 2011. This coupled (atmosphere-land-ocean) system is in charge of producing seasonal forecasts of near surface temperature and precipitation for the following 12 months with respect to the forecast onset. CanSIPS comprises two coupled atmosphere-land-ocean models: CanCM3 and CanCM4 developed in Canadian Centre for Climate Modelling and Analysis. Each model produces ten-member ensemble forecasts which generate twenty member ensemble predictions. In this work we evaluate seasonal hindcasts of the recent past climate (1981-2010) simulated by the CanSIPS system. The importance of such evaluation stems from the fact that seasonal hindcasts can be used to calibrate the results of the seasonal predictions. Calibrated forecasts have in general more skill compared to the raw predictions. Moreover, verification of seasonal hindcasts enables an estimation of the expected performance of the prediction system over various regions and seasons (i.e. expected skill maps). Evaluation will be presented against reanalysis data. Near surface temperature and precipitation will be assessed over different geographical locations and various lead times.

  6. Breeding technique for initializing global coupled climate model for decadal climate prediction

    NASA Astrophysics Data System (ADS)

    Romanova, Vanya; Hense, Andreas

    2015-04-01

    Defining the uncertainties at the starting day of a hindcast is still problematic and there is no well established method from weather forecasting, which could be applied directly by the decadal forecast community. The success of proper initialization depend mainly on finding of the locations and strengths of uncertainties, which develop and grow fastest with the forecast time. One method widely used for a short and medium range forecast is the Bred Vectors (BV) technique. Here, we test the BV for a long-range, inter-annual, seasonal and decadal forecast. The applied a-priori random noise is bred over the time iteratively until the nonlinear fastest growing errors are extracted. The growth rates of these non-linearly filtered instabilities appear to approach the leading Lyapunov exponents (Toth and Kalnay, 1993) if the BV process is continued over a longer time period. Targeting decadal prediction we search only for the slowest modes of the ocean physical processes, and expect the disturbances to grow mainly in the Western Boundary Currents, in the ACC and ENSO regions. Therefore we implemented a BV variant which determines local BV around an almost fixed in time oceanic state. The breeding technique is build around the MPIOM-ESM coupled model at T31L31/GR30L40 resolution. Perturbations are applied on the ocean temperature, salinity, meridional and zonal components of the velocity. The metric used to scale the disturbed fields is taken to be the weighted total energy with its zonal, meridional kinetic and available potential energy terms having equal contributions. Also this weighted total energy norm is used to monitor the growths rates of the fastest growing error modes. The method and the breeding application are still in a testing phase. However, the first bred vectors are analyzed and the most sensitive regions in the ocean responsible for inter-annual to decadal variability are localized. A refinement of the scaling procedure of the amplitude of the

  7. Multiyear predictability of Northern Hemisphere surface air temperature in the Kiel Climate Model

    NASA Astrophysics Data System (ADS)

    Wu, Y.; Latif, M.; Park, W.

    2016-08-01

    The multiyear predictability of Northern Hemisphere surface air temperature (SAT) is examined in a multi-millennial control integration of the Kiel Climate Model, a coupled ocean-atmosphere-sea ice general circulation model. A statistical method maximizing average predictability time (APT) is used to identify the most predictable SAT patterns in the model. The two leading APT modes are much localized and the physics are discussed that give rise to the enhanced predictability of SAT in these limited regions. Multiyear SAT predictability exists near the sea ice margin in the North Atlantic and mid-latitude North Pacific sector. Enhanced predictability in the North Atlantic is linked to the Atlantic Multidecadal Oscillation and to the sea ice changes. In the North Pacific, the most predictable SAT pattern is characterized by a zonal band in the western and central mid-latitude Pacific. This pattern is linked to the Pacific Decadal Oscillation, which drives sea surface temperature anomalies. The temperature anomalies subduct into deeper ocean layers and re-emerge at the sea surface during the following winters, providing multiyear memory. Results obtained from the Coupled Model Intercomparison Project Phase 5 ensemble yield similar APT modes. Overall, the results stress the importance of ocean dynamics in enhancing predictability in the atmosphere.

  8. School Administrators' Perceptions of Trends, Issues, and Responsibilities Relating to the Modern Educational Climate.

    ERIC Educational Resources Information Center

    Sharp, William L.; Walter, James K.

    In 1995, a group of school administrators affiliated with the Indiana Executive Fellows Program identified important educational issues. This paper presents findings of a 1997 study that asked a different sample of superintendents to rank a list of educational issues on the basis of importance. Questionnaires were sent to 325 superintendents in…

  9. Social Climate and Administrative Decision-Making Research for Institutional Renewal.

    ERIC Educational Resources Information Center

    Rasheed, Mohammed A.

    An Arabic translation of the "Work Environment Scale" was administered to the employees of Riyadh University's College of Education in Saudi Arabia for the purpose of gathering data useful in administrative decision-making. The survey investigated the work environment of the college as it is perceived by three distinct groups: the…

  10. A perspective on sustained marine observations for climate modelling and prediction.

    PubMed

    Dunstone, Nick J

    2014-09-28

    Here, I examine some of the many varied ways in which sustained global ocean observations are used in numerical modelling activities. In particular, I focus on the use of ocean observations to initialize predictions in ocean and climate models. Examples are also shown of how models can be used to assess the impact of both current ocean observations and to simulate that of potential new ocean observing platforms. The ocean has never been better observed than it is today and similarly ocean models have never been as capable at representing the real ocean as they are now. However, there remain important unanswered questions that can likely only be addressed via future improvements in ocean observations. In particular, ocean observing systems need to respond to the needs of the burgeoning field of near-term climate predictions. Although new ocean observing platforms promise exciting new discoveries, there is a delicate balance to be made between their funding and that of the current ocean observing system. Here, I identify the need to secure long-term funding for ocean observing platforms as they mature, from a mainly research exercise to an operational system for sustained observation over climate change time scales. At the same time, considerable progress continues to be made via ship-based observing campaigns and I highlight some that are dedicated to addressing uncertainties in key ocean model parametrizations. The use of ocean observations to understand the prominent long time scale changes observed in the North Atlantic is another focus of this paper. The exciting first decade of monitoring of the Atlantic meridional overturning circulation by the RAPID-MOCHA array is highlighted. The use of ocean and climate models as tools to further probe the drivers of variability seen in such time series is another exciting development. I also discuss the need for a concerted combined effort from climate models and ocean observations in order to understand the current slow

  11. A perspective on sustained marine observations for climate modelling and prediction

    PubMed Central

    Dunstone, Nick J.

    2014-01-01

    Here, I examine some of the many varied ways in which sustained global ocean observations are used in numerical modelling activities. In particular, I focus on the use of ocean observations to initialize predictions in ocean and climate models. Examples are also shown of how models can be used to assess the impact of both current ocean observations and to simulate that of potential new ocean observing platforms. The ocean has never been better observed than it is today and similarly ocean models have never been as capable at representing the real ocean as they are now. However, there remain important unanswered questions that can likely only be addressed via future improvements in ocean observations. In particular, ocean observing systems need to respond to the needs of the burgeoning field of near-term climate predictions. Although new ocean observing platforms promise exciting new discoveries, there is a delicate balance to be made between their funding and that of the current ocean observing system. Here, I identify the need to secure long-term funding for ocean observing platforms as they mature, from a mainly research exercise to an operational system for sustained observation over climate change time scales. At the same time, considerable progress continues to be made via ship-based observing campaigns and I highlight some that are dedicated to addressing uncertainties in key ocean model parametrizations. The use of ocean observations to understand the prominent long time scale changes observed in the North Atlantic is another focus of this paper. The exciting first decade of monitoring of the Atlantic meridional overturning circulation by the RAPID-MOCHA array is highlighted. The use of ocean and climate models as tools to further probe the drivers of variability seen in such time series is another exciting development. I also discuss the need for a concerted combined effort from climate models and ocean observations in order to understand the current slow

  12. Enhancing the Predictive Potential of Personality: Isolating Multiple Components of Trait Expression via a Single Administration Design

    DTIC Science & Technology

    2015-03-01

    Technical Report 1351 Enhancing the Predictive Potential of Personality: Isolating Multiple Components of Trait Expression via a Single ...Components of Trait Expression via a Single Administration Design 5a. CONTRACT OR GRANT NUMBER W5J9CQ-12-C-011 5b. PROGRAM ELEMENT NUMBER 6...Components of Trait Expression via a Single Administration Design Dan J. Putka Human Resources Research Organization Matthew Fleisher

  13. Multi-year simulations and experimental seasonal predictions for rainy seasons in China by using a nested regional climate model (RegCM_NCC). Part I: Sensitivity study

    NASA Astrophysics Data System (ADS)

    Ding, Yihui; Shi, Xueli; Liu, Yiming; Liu, Yan; Li, Qingquan; Qian, Yongfu; Miao, Manqian; Zhai, Guoqing; Gao, Kun

    2006-05-01

    A modified version of the NCAR/RegCM2 has been developed at the National Climate Center (NCC), China Meteorological Administration, through a series of sensitivity experiments and multi-year simulations and hindcasts, with a special emphasis on the adequate choice of physical parameterization schemes suitable for the East Asian monsoon climate. This regional climate model is nested with the NCC/IAP (Institute of Atmospheric Physics) T63 coupled GCM to make an experimental seasonal prediction for China and East Asia. The four-year (2001 to 2004) prediction results are encouraging. This paper is the first part of a two-part paper, and it mainly describes the sensitivity study of the physical process parameterization represented in the model. The systematic errors produced by the different physical parameterization schemes such as the land surface processes, convective precipitation, cloud-radiation transfer process, boundary layer process and large-scale terrain features have been identified based on multi-year and extreme flooding event simulations. A number of comparative experiments has shown that the mass flux scheme (MFS) and Betts-Miller scheme (BM) for convective precipitation, the LPMI (land surface process model I) and LPMII (land surface process model II) for the land surface process, the CCM3 radiation transfer scheme for cloud-radiation transfer processes, the TKE (turbulent kinetic energy) scheme for the boundary layer processes and the topography treatment schemes for the Tibetan Plateau are suitable for simulations and prediction of the East Asia monsoon climate in rainy seasons. Based on the above sensitivity study, a modified version of the RegCM2 (RegCM_NCC) has been set up for climate simulations and seasonal predictions.

  14. Can phenological models predict tree phenology accurately under climate change conditions?

    NASA Astrophysics Data System (ADS)

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2014-05-01

    The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay

  15. Empirical prediction of climate dynamics: optimal models, derived from time series

    NASA Astrophysics Data System (ADS)

    Mukhin, D.; Loskutov, E. M.; Gavrilov, A.; Feigin, A. M.

    2013-12-01

    The new empirical method for prediction of climate indices by the analysis of climatic fields' time series is suggested. The method is based on construction of prognostic models of evolution operator (EO) in low-dimensional subspaces of system's phase space. One of the main points of suggested analysis is reconstruction of appropriate basis of dynamical variables (predictors) from spatially distributed data: different ways of data decomposition are discussed in the report including EOFs, MSSA and other relevant data rotations. We consider the models of different complexity for EO reconstruction, from linear statistical models of particular indices to more complex artificial neural network (ANN) models of climatic patterns dynamics, which take the form of discrete random dynamical systems [1]. Very important problem, that always arises in empirical modeling approaches, is optimal model selection criterium: appropriate regularization procedure is needed to avoid overfitted model. Particulary, it includes finding the optimal structural parameters of the model such as dimension of variables vector, i.e. number of principal components used for modeling, number of states used for prediction, and number of parameters determining quality of approximation. In this report the minimal descriptive length (MDL) approach [2] is proposed for this purpose: the model providing most data compression is chosen. Results of application of suggested method to analysis of SST and SLP fields' time series [3] covering last 30-50 years are presented: predictions of different climate indices time series including NINO 3.4, MEI, PDO, NOA are shown. References: 1. Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, Random dynamical models from time series, Phys. Rev. E 85, 036216, 2012 2. Molkov, Ya.I., D.N. Mukhin, E.M. Loskutov, A.M. Feigin, and G.A. Fidelin, Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series. Phys

  16. The predictive skill of species distribution models for plankton in a changing climate.

    PubMed

    Brun, Philipp; Kiørboe, Thomas; Licandro, Priscilla; Payne, Mark R

    2016-09-01

    Statistical species distribution models (SDMs) are increasingly used to project spatial relocations of marine taxa under future climate change scenarios. However, tests of their predictive skill in the real-world are rare. Here, we use data from the Continuous Plankton Recorder program, one of the longest running and most extensive marine biological monitoring programs, to investigate the reliability of predicted plankton distributions. We apply three commonly used SDMs to 20 representative plankton species, including copepods, diatoms, and dinoflagellates, all found in the North Atlantic and adjacent seas. We fit the models to decadal subsets of the full (1958-2012) dataset, and then use them to predict both forward and backward in time, comparing the model predictions against the corresponding observations. The probability of correctly predicting presence was low, peaking at 0.5 for copepods, and model skill typically did not outperform a null model assuming distributions to be constant in time. The predicted prevalence increasingly differed from the observed prevalence for predictions with more distance in time from their training dataset. More detailed investigations based on four focal species revealed that strong spatial variations in skill exist, with the least skill at the edges of the distributions, where prevalence is lowest. Furthermore, the scores of traditional single-value model performance metrics were contrasting and some implied overoptimistic conclusions about model skill. Plankton may be particularly challenging to model, due to its short life span and the dispersive effects of constant water movements on all spatial scales, however there are few other studies against which to compare these results. We conclude that rigorous model validation, including comparison against null models, is essential to assess the robustness of projections of marine planktonic species under climate change.

  17. Generating temporal model using climate variables for the prediction of dengue cases in Subang Jaya, Malaysia

    PubMed Central

    Dom, Nazri Che; Hassan, A Abu; Latif, Z Abd; Ismail, Rodziah

    2013-01-01

    Objective To develop a forecasting model for the incidence of dengue cases in Subang Jaya using time series analysis. Methods The model was performed using the Autoregressive Integrated Moving Average (ARIMA) based on data collected from 2005 to 2010. The fitted model was then used to predict dengue incidence for the year 2010 by extrapolating dengue patterns using three different approaches (i.e. 52, 13 and 4 weeks ahead). Finally cross correlation between dengue incidence and climate variable was computed over a range of lags in order to identify significant variables to be included as external regressor. Results The result of this study revealed that the ARIMA (2,0,0) (0,0,1)52 model developed, closely described the trends of dengue incidence and confirmed the existence of dengue fever cases in Subang Jaya for the year 2005 to 2010. The prediction per period of 4 weeks ahead for ARIMA (2,0,0)(0,0,1)52 was found to be best fit and consistent with the observed dengue incidence based on the training data from 2005 to 2010 (Root Mean Square Error=0.61). The predictive power of ARIMA (2,0,0) (0,0,1)52 is enhanced by the inclusion of climate variables as external regressor to forecast the dengue cases for the year 2010. Conclusions The ARIMA model with weekly variation is a useful tool for disease control and prevention program as it is able to effectively predict the number of dengue cases in Malaysia.

  18. Predicting impacts of climate change on medicinal asclepiads of Pakistan using Maxent modeling

    NASA Astrophysics Data System (ADS)

    Khanum, Rizwana; Mumtaz, A. S.; Kumar, Sunil

    2013-05-01

    Maximum entropy (Maxent) modeling was used to predict the potential climatic niches of three medicinally important Asclepiad species: Pentatropis spiralis, Tylophora hirsuta, and Vincetoxicum arnottianum. All three species are members of the Asclepiad plant family, yet they differ in ecological requirements, biogeographic importance, and conservation value. Occurrence data were collected from herbarium specimens held in major herbaria of Pakistan and two years (2010 and 2011) of field surveys. The Maxent model performed better than random for the three species with an average test AUC value of 0.74 for P. spiralis, 0.84 for V. arnottianum, and 0.59 for T. hirsuta. Under the future climate change scenario, the Maxent model predicted habitat gains for P. spiralis in southern Punjab and Balochistan, and loss of habitat in south-eastern Sindh. Vincetoxicum arnottianum as well as T. hirsuta would gain habitat in upper Peaks of northern parts of Pakistan. T. hirsuta is predicted to lose most of the habitats in northern Punjab and in parches from lower peaks of Galliat, Zhob, Qalat etc. The predictive modeling approach presented here may be applied to other rare Asclepiad species, especially those under constant extinction threat.

  19. The Argo Project: Global Ocean Observations for Understanding and Prediction of Climate Variability. Report for Calendar Year 2005

    DTIC Science & Technology

    2005-01-01

    environment of ocean ecosystems. Over 90% of the increased heat content due to global warming of the air/sea/ice climate system in the past 40...years occurred in the oceans. Climate stresses on ocean ecosystems have serious consequences, and sometimes dramatic ones, such as coral reef bleaching ...The Argo Project Global Ocean Observations for Understanding and Prediction of Climate Variability Report for Calendar Year 2005 Dean H

  20. The Ago Project: Global Ocean Observations for Understanding and Prediction of Climate Variability. Report for Calendar Year 2005

    DTIC Science & Technology

    2005-01-01

    defining the physical environment of ocean ecosystems. Over 90% of the increased heat content due to global warming of the air/sea/ice climate... coral reef bleaching . In the future, the impacts of a varying climate on the health of the seas and coastal ecosystems will become an increasingly...The Argo Project Global Ocean Observations for Understanding and Prediction of Climate Variability Report for Calendar Year 2005 Dean H

  1. The ARGO Project: Global Ocean Observations for Understanding and Prediction of Climate Variability. Report for Calendar Year 2004

    DTIC Science & Technology

    2004-01-01

    Over 90% of the increased heat content due to global warming of the air/sea/ice climate system in the past 40 years occurred in the oceans. Climate...stresses on ocean ecosystems have serious consequences, and sometimes dramatic ones, such as coral reef bleaching . In the future, the impacts of a...The Argo Project Global Ocean Observations for Understanding and Prediction of Climate Variability Report for Calendar Year 2004 Dean H

  2. Uncertainty and Risk in the Predictions of Global Climate Models. (Invited)

    NASA Astrophysics Data System (ADS)

    Winsberg, E.

    2009-12-01

    There has been a great deal of emphasis, in recent years, on developing methods for assigning probabilities, in the form of quantitative margins of uncertainty (QMUs) to the predictions of global climate models. In this paper, I will argue that a large part of the motivation for this activity has been misplaced. Rather than explicit QMUs, climate scientists ought to focus on risk mitigation: offering policy advice about what courses of action need to be taken in order to reduce the risk of negative outcomes to acceptable levels. The advantages of QMUs are clear. QMUs can be an extremely effective tool for dividing our intellectual labor into the epistemic and the normative. If scientists can manage to objectively assign probabilities to various outcomes given certain choices of action, then they can effectively leave decisions about the relative social value of these outcomes out of the work they do as experts. In this way, it is commonly thought, scientists can keep ethical questions—like questions about the relative value of environmental stability vs. the availability of fossil fuels for economic development—separate from the purely scientific questions about the workings of the climate system. It is this line of thinking, or so I argue, that has motivated the large quantity of intellectual labor that has recently been devoted, by both climate scientists and statisticians, to attaching QMUs to the predictions of global climate models. Such an approach, and the attendant division of labor that it affords between those who discover the facts and those who decide what we should value, has obvious advantages. Scientists, after all, are not elected leaders, and they lack the political legitimacy to make decisions on behalf of the public about what is socially valuable. Elected leaders, on the other hand, rarely have the expertise they would need to accurately forecast, for themselves, what the likely outcomes of their policy choices would be. Since it would be

  3. Using physiology to predict the responses of ants to climatic warming.

    PubMed

    Diamond, Sarah E; Penick, Clint A; Pelini, Shannon L; Ellison, Aaron M; Gotelli, Nicholas J; Sanders, Nathan J; Dunn, Robert R

    2013-12-01

    Physiological intolerance of high temperatures places limits on organismal responses to the temperature increases associated with global climatic change. Because ants are geographically widespread, ecologically diverse, and thermophilic, they are an ideal system for exploring the extent to which physiological tolerance can predict responses to environmental change. Here, we expand on simple models that use thermal tolerance to predict the responses of ants to climatic warming. We investigated the degree to which changes in the abundance of ants under warming reflect reductions in the thermal niche space for their foraging. In an eastern deciduous forest system in the United States with approximately 40 ant species, we found that for some species, the loss of thermal niche space for foraging was related to decreases in abundance with increasing experimental climatic warming. However, many ant species exhibited no loss of thermal niche space. For one well-studied species, Temnothorax curvispinosus, we examined both survival of workers and growth of colonies (a correlate of reproductive output) as functions of temperature in the laboratory, and found that the range of thermal tolerances for colony growth was much narrower than for survival of workers. We evaluated these functions in the context of experimental climatic warming and found that the difference in the responses of these two attributes to temperature generates differences in the means and especially the variances of expected fitness under warming. The expected mean growth of colonies was optimized at intermediate levels of warming (2-4°C above ambient); yet, the expected variance monotonically increased with warming. In contrast, the expected mean and variance of the survival of workers decreased when warming exceeded 4°C above ambient. Together, these results for T. curvispinosus emphasize the importance of measuring reproduction (colony growth) in the context of climatic change: indeed, our examination

  4. Modeling Spatial Recharge in the Arid Southern Okanagan Basin and Impacts of Future Predicted Climate Change

    NASA Astrophysics Data System (ADS)

    Allen, D. M.; Toews, M. W.

    2007-12-01

    Groundwater systems in arid regions will be particularly sensitive to climate change owing to the strong dependence of evapotranspiration rates on temperature, and potential shifts in the precipitation amounts and timing. In this study, future predicted climate change from three GCMs (CGCM1 GHG+A, CGCM3.1 A2, and HadCM3 A2) are used to evaluate the sensitivity of recharge in the Oliver region of the Okanagan Valley, south- central British Columbia, where annual precipitation is approximately 300~mm. Temperature data were downscaled using Statistical Downscaling Model (SDSM), while precipitation and solar radiation changes were estimated directly from the GCM data. Results for the region suggest that temperature will increase up to 4°C by the end of the century. Precipitation is expected to decrease in the spring, and increase in the fall. Solar radiation may decrease in the late summer. Shifts in climate, from present to future-predicted, were applied to the LARS-WG stochastic weather generator to generate daily stochastic weather series. Recharge was modeled spatially using output from the HELP hydrologic model applied to one-dimensional soil columns. An extensive valley-bottom soil database was used to determine both the spatial variation and vertical assemblage of soil horizons in the Oliver region. Soil hydraulic parameters were estimated from soil descriptions using pedotransfer functions through the ROSETTA program. Leaf area index (LAI) was estimated from ground-truthed Landsat 5 TM imagery, and surface slope was estimated from a digital elevation model. Irrigation application rates were modified for each climate scenario based on estimates of seasonal crop water demand. Daily irrigation was added to precipitation in irrigation districts using proportions of crop types along with daily climate and evapotranspiration data from LARS-WG. The two dominant crop classes are orchard (including peaches, cherries and apples) and vineyards (grapes). Recharge in

  5. Regional climate model downscaling may improve the prediction of alien plant species distributions

    NASA Astrophysics Data System (ADS)

    Liu, Shuyan; Liang, Xin-Zhong; Gao, Wei; Stohlgren, Thomas J.

    2014-12-01

    Distributions of invasive species are commonly predicted with species distribution models that build upon the statistical relationships between observed species presence data and climate data. We used field observations, climate station data, and Maximum Entropy species distribution models for 13 invasive plant species in the United States, and then compared the models with inputs from a General Circulation Model (hereafter GCM-based models) and a downscaled Regional Climate Model (hereafter, RCM-based models).We also compared species distributions based on either GCM-based or RCM-based models for the present (1990-1999) to the future (2046-2055). RCM-based species distribution models replicated observed distributions remarkably better than GCM-based models for all invasive species under the current climate. This was shown for the presence locations of the species, and by using four common statistical metrics to compare modeled distributions. For two widespread invasive taxa ( Bromus tectorum or cheatgrass, and Tamarix spp. or tamarisk), GCM-based models failed miserably to reproduce observed species distributions. In contrast, RCM-based species distribution models closely matched observations. Future species distributions may be significantly affected by using GCM-based inputs. Because invasive plants species often show high resilience and low rates of local extinction, RCM-based species distribution models may perform better than GCM-based species distribution models for planning containment programs for invasive species.

  6. Functional traits predict relationship between plant abundance dynamic and long-term climate warming

    PubMed Central

    Soudzilovskaia, Nadejda A.; Elumeeva, Tatiana G.; Onipchenko, Vladimir G.; Shidakov, Islam I.; Salpagarova, Fatima S.; Khubiev, Anzor B.; Tekeev, Dzhamal K.; Cornelissen, Johannes H. C.

    2013-01-01

    Predicting climate change impact on ecosystem structure and services is one of the most important challenges in ecology. Until now, plant species response to climate change has been described at the level of fixed plant functional types, an approach limited by its inflexibility as there is much interspecific functional variation within plant functional types. Considering a plant species as a set of functional traits greatly increases our possibilities for analysis of ecosystem functioning and carbon and nutrient fluxes associated therewith. Moreover, recently assembled large-scale databases hold comprehensive per-species data on plant functional traits, allowing a detailed functional description of many plant communities on Earth. Here, we show that plant functional traits can be used as predictors of vegetation response to climate warming, accounting in our test ecosystem (the species-rich alpine belt of Caucasus mountains, Russia) for 59% of variability in the per-species abundance relation to temperature. In this mountain belt, traits that promote conservative leaf water economy (higher leaf mass per area, thicker leaves) and large investments in belowground reserves to support next year’s shoot buds (root carbon content) were the best predictors of the species increase in abundance along with temperature increase. This finding demonstrates that plant functional traits constitute a highly useful concept for forecasting changes in plant communities, and their associated ecosystem services, in response to climate change. PMID:24145400

  7. Integrating seasonal climate prediction and agricultural models for insights into agricultural practice

    PubMed Central

    Hansen, James W

    2005-01-01

    Interest in integrating crop simulation models with dynamic seasonal climate forecast models is expanding in response to a perceived opportunity to add value to seasonal climate forecasts for agriculture. Integrated modelling may help to address some obstacles to effective agricultural use of climate information. First, modelling can address the mismatch between farmers' needs and available operational forecasts. Probabilistic crop yield forecasts are directly relevant to farmers' livelihood decisions and, at a different scale, to early warning and market applications. Second, credible ex ante evidence of livelihood benefits, using integrated climate–crop–economic modelling in a value-of-information framework, may assist in the challenge of obtaining institutional, financial and political support; and inform targeting for greatest benefit. Third, integrated modelling can reduce the risk and learning time associated with adaptation and adoption, and related uncertainty on the part of advisors and advocates. It can provide insights to advisors, and enhance site-specific interpretation of recommendations when driven by spatial data. Model-based ‘discussion support systems’ contribute to learning and farmer–researcher dialogue. Integrated climate–crop modelling may play a genuine, but limited role in efforts to support climate risk management in agriculture, but only if they are used appropriately, with understanding of their capabilities and limitations, and with cautious evaluation of model predictions and of the insights that arises from model-based decision analysis. PMID:16433092

  8. Climate change, phenological shifts, eco-evolutionary responses and population viability: toward a unifying predictive approach.

    PubMed

    Jenouvrier, Stéphanie; Visser, Marcel E

    2011-11-01

    The debate on emission targets of greenhouse gasses designed to limit global climate change has to take into account the ecological consequences. One of the clearest ecological consequences is shifts in phenology. Linking these shifts to changes in population viability under various greenhouse gasses emission scenarios requires a unifying framework. We propose a box-in-a-box modeling approach that couples population models to phenological change. This approach unifies population modeling with both ecological responses to climate change as well as evolutionary processes. We advocate a mechanistic embedded correlative approach, where the link from genes to population is established using a periodic matrix population model. This periodic model has several major advantages: (1) it can include complex seasonal behaviors allowing an easy link with phenological shifts; (2) it provides the structure of the population at each phase, including the distribution of genotypes and phenotypes, allowing a link with evolutionary processes; and (3) it can incorporate the effect of climate at different time periods. We believe that the way climatologists have approached the problem, using atmosphere-ocean coupled circulation models in which components are gradually included and linked to each other, can provide a valuable example to ecologists. We hope that ecologists will take up this challenge and that our preliminary modeling framework will stimulate research toward a unifying predictive model of the ecological consequences of climate change.

  9. Predicting the Affects of Climate Change on Evapotranspiration and Agricultural Productivity of Semi-arid Basins

    NASA Astrophysics Data System (ADS)

    Peri, L.; Tyler, S. W.; Zheng, C.; Pohll, G. M.; Yao, Y.

    2013-12-01

    Many arid and semi-arid regions around the world are experiencing water shortages that have become increasingly problematic. Since the late 1800s, upstream diversions in Nevada's Walker River have delivered irrigation supply to the surrounding agricultural fields resulting in a dramatic water level decline of the terminal Walker Lake. Salinity has also increased because the only outflow from the lake is evaporation from the lake surface. The Heihe River basin of northwestern China, a similar semi-arid catchment, is also facing losses from evaporation of terminal locations, agricultural diversions and evapotranspiration (ET) of crops. Irrigated agriculture is now experiencing increased competition for use of diminishing water resources while a demand for ecological conservation continues to grow. It is important to understand how the existing agriculture in these regions will respond as climate changes. Predicting the affects of climate change on groundwater flow, surface water flow, ET and agricultural productivity of the Walker and Heihe River basins is essential for future conservation of water resources. ET estimates from remote sensing techniques can provide estimates of crop water consumption. By determining similarities of both hydrologic cycles, critical components missing in both systems can be determined and predictions of impacts of climate change and human management strategies can be assessed.

  10. Predicting ecological changes on benthic estuarine assemblages through decadal climate trends along Brazilian Marine Ecoregions

    NASA Astrophysics Data System (ADS)

    Bernardino, Angelo F.; Netto, Sérgio A.; Pagliosa, Paulo R.; Barros, Francisco; Christofoletti, Ronaldo A.; Rosa Filho, José S.; Colling, André; Lana, Paulo C.

    2015-12-01

    Estuaries are threatened coastal ecosystems that support relevant ecological functions worldwide. The predicted global climate changes demand actions to understand, anticipate and avoid further damage to estuarine habitats. In this study we reviewed data on polychaete assemblages, as a surrogate for overall benthic communities, from 51 estuaries along five Marine Ecoregions of Brazil (Amazonia, NE Brazil, E Brazil, SE Brazil and Rio Grande). We critically evaluated the adaptive capacity and ultimately the resilience to decadal changes in temperature and rainfall of the polychaete assemblages. As a support for theoretical predictions on changes linked to global warming we compared the variability of benthic assemblages across the ecoregions with a 40-year time series of temperature and rainfall data. We found a significant upward trend in temperature during the last four decades at all marine ecoregions of Brazil, while rainfall increase was restricted to the SE Brazil ecoregion. Benthic assemblages and climate trends varied significantly among and within ecoregions. The high variability in climate patterns in estuaries within the same ecoregion may lead to correspondingly high levels of noise on the expected responses of benthic fauna. Nonetheless, we expect changes in community structure and productivity of benthic species at marine ecoregions under increasing influence of higher temperatures, extreme events and pollution.

  11. Theoretical basis for predicting climate-induced abrupt shifts in the oceans

    PubMed Central

    Beaugrand, Gregory

    2015-01-01

    Among the responses of marine species and their ecosystems to climate change, abrupt community shifts (ACSs), also called regime shifts, have often been observed. However, despite their effects for ecosystem functioning and both provisioning and regulating services, our understanding of the underlying mechanisms involved remains elusive. This paper proposes a theory showing that some ACSs originate from the interaction between climate-induced environmental changes and the species ecological niche. The theory predicts that a substantial stepwise shift in the thermal regime of a marine ecosystem leads indubitably to an ACS and explains why some species do not change during the phenomenon. It also explicates why the timing of ACSs may differ or why some studies may detect or not detect a shift in the same ecosystem, independently of the statistical method of detection and simply because they focus on different species or taxonomic groups. The present theory offers a way to predict future climate-induced community shifts and their potential associated trophic cascades and amplifications.

  12. Quantification of the uncertainties in soil and vegetation parameterizations for regional climate predictions

    NASA Astrophysics Data System (ADS)

    Breil, Marcus; Schädler, Gerd

    2016-04-01

    The aim of the german research program MiKlip II is the development of an operational climate prediction system that can provide reliable forecasts on a decadal time scale. Thereby, one goal of MiKlip II is to investigate the feasibility of regional climate predictions. Results of recent studies indicate that the regional climate is significantly affected by the interactions between the soil, the vegetation and the atmosphere. Thus, within the framework of MiKlip II a workpackage was established to assess the impact of these interactions on the regional decadal climate predictability. In a Regional Climate Model (RCM) the soil-vegetation-atmosphere interactions are represented in a Land Surface Model (LSM). Thereby, the LSM describes the current state of the land surface by calculating the soil temperature, the soil water content and the turbulent heat fluxes, serving the RCM as lower boundary condition. To be able to solve the corresponding equations, soil and vegetation processes are parameterized within the LSM. Such parameterizations are mainly derived from observations. But in most cases observations are temporally and spatially limited and consequently not able to represent the diversity of nature completely. Thus, soil and vegetation parameterizations always exhibit a certain degree of uncertainty. In the presented study, the uncertainties within a LSM are assessed by stochastic variations of the relevant parameterizations in VEG3D, a LSM developed at the Karlsruhe Institute of Technology (KIT). In a first step, stand-alone simulations of VEG3D are realized with varying soil and vegetation parameters, to identify sensitive model parameters. In a second step, VEG3D is coupled to the RCM COSMO-CLM. With this new model system regional decadal hindcast simulations, driven by global simulations of the Max-Planck-Institute for Meteorology Earth System Model (MPI-ESM), are performed for the CORDEX-EU domain in a resolution of 0.22°. The identified sensitive model

  13. Predicting High School Student Use of Learning Strategies: The Role of Preferred Learning Styles and Classroom Climate

    ERIC Educational Resources Information Center

    Cheema, Jehanzeb; Kitsantas, Anastasia

    2016-01-01

    This study investigated the predictiveness of preferred learning styles (competitive and cooperative) and classroom climate (teacher support and disciplinary climate) on learning strategy use in mathematics. The student survey part of the Programme for International Student Assessment 2003 comprising of 4633 US observations was used in a weighted…

  14. Predicting the Hydrologic Response of the Columbia River System to Climate Change

    NASA Astrophysics Data System (ADS)

    Chegwidden, O.; Hamman, J.; Xiao, M.; Ishottama, F.; Lee, S. Y.; Stumbaugh, M. R.; Mote, P.; Lettenmaier, D. P.; Nijssen, B.

    2014-12-01

    The Columbia River, located in the northwestern United States with headwaters in Canada (Pacific Northwest), is intensely managed for hydropower generation, irrigation, flood control, ecosystem services (particularly salmonids), navigation, and recreation. Effects of anthropogenic climate change already manifest themselves in the Pacific Northwest through reduced winter snow accumulation at lower elevations and earlier spring melt. As the climate warms, the Columbia River, whose flow regime is heavily dependent on seasonal snow melt, is likely to experience significant changes in the timing of its seasonal hydrograph and possibly in total flow volume. We report on a new study co-funded by the Bonneville Power Administration to update and enhance an existing climate change streamflow data set developed by the University of Washington Climate Impacts Group in 2009-2010. Our new study is based on the RCP4.5 and RCP8.5 climate projections from the Coupled Model Intercomparison Project Version 5 (CMIP5). In contrast to earlier studies, we are using a suite of three hydrologic models, the Variable Infiltration Capacity (VIC) model, the Unified Land Model and the Precipitation Runoff Modeling System, each implemented at 1/16 degree (~6 km) over the Pacific Northwest. In addition, we will use multiple statistical downscaling methods based on the output from a subset of 10 CMIP5 global climate models (GCMs). The use of multiple hydrologic models, downscaling methods and GCMs is motivated by the need to assess the impact of methodological choices in the modeling process on projected changes in Columbia River flows. We discuss the implementation of the three hydrologic models as well as our development of a glacier model for VIC, which is intended to better represent the effects of climate change on streamflows from the Columbia River headwaters region. Finally, we report on our application of a new auto-calibration method that uses an inverse routing scheme to develop

  15. Exploring the Ocean Through Climate Indicators: What Do Research, Predictions, and Decision-makers Need to Know?

    NASA Astrophysics Data System (ADS)

    Arrigo, J. S.

    2015-12-01

    There are several new and ongoing efforts around communicating climate and global change and variability by developing Climate Indicators (e.g. the US Global Change Research Project's Pilot Indicators Program, the US EPA's Climate Change Indicators, and the Ocean Observations Panel for Climate State of the Ocean indicators). Indicators provide information tailored to identified stakeholders and facilitate monitoring status, trends, extremes and variability of important climate features or processes. NOAA's Climate Monitoring program is in the middle of a three-year initiative toward supporting research toward the development of Ocean Climate Indicators for research, prediction, and decision makers. These indices combine ocean observations, climate data and products from platforms like (but not limited to) the drifting buoy, Argo, satellite, and buoy arrays that provide fundamental observations that contribute towards climate understanding, predictions, and projections. The program is supporting eight distinct projects that focus on primarily regional indices that target varied stakeholders and outreach strategies - from public awareness and education to targeted model performance improvement. This presentation will discuss the diverse set of projects, initial results, and discuss possibilities for and examples of using the indicators and processes for developing them for broader science outreach and education, with an eye toward the aim of organizing the ocean climate and observing community around developing a comprehensive ocean monitoring and indicators system.

  16. Predicted climate change alters the indirect effect of predators on an ecosystem process.

    PubMed

    Lensing, Janet R; Wise, David H

    2006-10-17

    Changes in rainfall predicted to occur with global climate change will likely alter rates of leaf-litter decomposition through direct effects on primary decomposers. In a field experiment replicated at two sites, we show that altered rainfall may also change how cascading trophic interactions initiated by arthropod predators in the leaf litter indirectly influence litter decomposition. On the drier site there was no interaction between rainfall and the indirect effect of predators on decomposition. In contrast, on the moister site spiders accelerated the disappearance rate of deciduous leaf litter under low rainfall, but had no, or possibly a negative, indirect effect under high rainfall. Thus, changes resulting from the more intense hydrological cycle expected to occur with climate change will likely influence how predators indirectly affect an essential ecosystem process.

  17. Climate change forecasts, long-term spatio-temporal prediction and the resilience of dry ecosystems

    NASA Astrophysics Data System (ADS)

    Shafran-Natan, Rakefet; Svoray, Tal; Avi, Perevolotsky

    2010-05-01

    Primary production is an important indicator to climatic changes in drylands, while reduction in productivity has many consequences on ecosystem functioning. We suggest that the response of dry ecosystems to climate change should lead to a change in spatial patterns of grasses without a substantial change in ecosystem resilience. We used field data and a recently published spatio-temporally explicit model to study factors affecting long-term variation in primary production in two dry ecosystems: semi-arid (SAE) and Mediterranean (DME) dominated by annual vegetation. The model was operated in both patch and landscape scales and was executed along 30 years (1979-2008) at SAE and along 21 years (1986-1990; 1993-2008) at DME. Model predictions were validated against samples that were harvested in each site at the end of the growing season, over 15 seasons (1994-2008) at SAE (0.63

  18. [Effects of sampling plot number on tree species distribution prediction under climate change].

    PubMed

    Liang, Yu; He, Hong-Shi; Wu, Zhi-Wei; Li, Xiao-Na; Luo, Xu

    2013-05-01

    Based on the neutral landscapes under different degrees of landscape fragmentation, this paper studied the effects of sampling plot number on the prediction of tree species distribution at landscape scale under climate change. The tree species distribution was predicted by the coupled modeling approach which linked an ecosystem process model with a forest landscape model, and three contingent scenarios and one reference scenario of sampling plot numbers were assumed. The differences between the three scenarios and the reference scenario under different degrees of landscape fragmentation were tested. The results indicated that the effects of sampling plot number on the prediction of tree species distribution depended on the tree species life history attributes. For the generalist species, the prediction of their distribution at landscape scale needed more plots. Except for the extreme specialist, landscape fragmentation degree also affected the effects of sampling plot number on the prediction. With the increase of simulation period, the effects of sampling plot number on the prediction of tree species distribution at landscape scale could be changed. For generalist species, more plots are needed for the long-term simulation.

  19. Land surface anomaly simulations and predictions with a climate model: an El Niño Southern Oscillation case study.

    PubMed

    Putt, Debbie; Haines, Keith; Gurney, Robert; Liu, Chunlei

    2009-03-13

    The ability of climate models to reproduce and predict land surface anomalies is an important but little-studied topic. In this study, an atmosphere and ocean assimilation scheme is used to determine whether HadCM3 can reproduce and predict snow water equivalent and soil moisture during the 1997-1998 El Niño Southern Oscillation event. Soil moisture is reproduced more successfully, though both snow and soil moisture show some predictability at 1- and 4-month lead times. This result suggests that land surface anomalies may be reasonably well initialized for climate model predictions and hydrological applications using atmospheric assimilation methods over a period of time.

  20. Seasonal Prediction of Hydro-Climatic Extremes in the Greater Horn of Africa Under Evolving Climate Conditions to Support Adaptation Strategies

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  1. Prediction of Arctic plant phenological sensitivity to climate change from historical records.

    PubMed

    Panchen, Zoe A; Gorelick, Root

    2017-03-01

    The pace of climate change in the Arctic is dramatic, with temperatures rising at a rate double the global average. The timing of flowering and fruiting (phenology) is often temperature dependent and tends to advance as the climate warms. Herbarium specimens, photographs, and field observations can provide historical phenology records and have been used, on a localised scale, to predict species' phenological sensitivity to climate change. Conducting similar localised studies in the Canadian Arctic, however, poses a challenge where the collection of herbarium specimens, photographs, and field observations have been temporally and spatially sporadic. We used flowering and seed dispersal times of 23 Arctic species from herbarium specimens, photographs, and field observations collected from across the 2.1 million km(2) area of Nunavut, Canada, to determine (1) which monthly temperatures influence flowering and seed dispersal times; (2) species' phenological sensitivity to temperature; and (3) whether flowering or seed dispersal times have advanced over the past 120 years. We tested this at different spatial scales and compared the sensitivity in different regions of Nunavut. Broadly speaking, this research serves as a proof of concept to assess whether phenology-climate change studies using historic data can be conducted at large spatial scales. Flowering times and seed dispersal time were most strongly correlated with June and July temperatures, respectively. Seed dispersal times have advanced at double the rate of flowering times over the past 120 years, reflecting greater late-summer temperature rises in Nunavut. There is great diversity in the flowering time sensitivity to temperature of Arctic plant species, suggesting climate change implications for Arctic ecological communities, including altered community composition, competition, and pollinator interactions. Intraspecific temperature sensitivity and warming trends varied markedly across Nunavut and could

  2. Back from a predicted climatic extinction of an island endemic: a future for the Corsican Nuthatch.

    PubMed

    Barbet-Massin, Morgane; Jiguet, Frédéric

    2011-03-25

    The Corsican Nuthatch (Sitta whiteheadi) is red-listed as vulnerable to extinction by the IUCN because of its endemism, reduced population size, and recent decline. A further cause is the fragmentation and loss of its spatially-restricted favourite habitat, the Corsican pine (Pinus nigra laricio) forest. In this study, we aimed at estimating the potential impact of climate change on the distribution of the Corsican Nuthatch using species distribution models. Because this species has a strong trophic association with the Corsican and Maritime pines (P. nigra laricio and P. pinaster), we first modelled the current and future potential distribution of both pine species in order to use them as habitat variables when modelling the nuthatch distribution. However, the Corsican pine has suffered large distribution losses in the past centuries due to the development of anthropogenic activities, and is now restricted to mountainous woodland. As a consequence, its realized niche is likely significantly smaller than its fundamental niche, so that a projection of the current distribution under future climatic conditions would produce misleading results. To obtain a predicted pine distribution at closest to the geographic projection of the fundamental niche, we used available information on the current pine distribution associated to information on the persistence of isolated natural pine coppices. While common thresholds (maximizing the sum of sensitivity and specificity) predicted a potential large loss of the Corsican Nuthatch distribution by 2100, the use of more appropriate thresholds aiming at getting closer to the fundamental distribution of the Corsican pine predicted that 98% of the current presence points should remain potentially suitable for the nuthatch and its range could be 10% larger in the future. The habitat of the endemic Corsican Nuthatch is therefore more likely threatened by an increasing frequency and intensity of wildfires or anthropogenic activities than

  3. Performance evaluation of NCEP climate forecast system for the prediction of winter temperatures over India

    NASA Astrophysics Data System (ADS)

    Nageswararao, M. M.; Mohanty, U. C.; Kiran Prasad, S.; Osuri, Krishna K.; Ramakrishna, S. S. V. S.

    2016-11-01

    The surface air temperature during the winter season (December-February) in India adversely affects agriculture as well as day-to-day life. Therefore, the accurate prediction of winter temperature in extended range is of utmost importance. The National Center for Environmental Prediction (NCEP) has been providing climatic variables from the fully coupled global climate model, known as Climate Forecast System version 1 (CFSv1) on monthly to seasonal scale since 2004, and it has been upgraded to CFSv2 subsequently in 2011. In the present study, the performance of CFSv1 and CFSv2 in simulating the winter 2 m maximum, minimum, and mean temperatures ( T max, T min, and T mean, respectively) over India is evaluated with respect to India Meteorological Department (IMD) 1° × 1° observations. The hindcast data obtained from both versions of CFS from 1982 to 2009 (27 years) with November initial conditions (lead-1) are used. The analyses of winter ( T max, T min, and T mean) temperatures revealed that CFSv1 and CFSv2 are able to replicate the patterns of observed climatology, interannual variability, and coefficient of variation with a slight negative bias. Of the two, CFSv2 is appreciable in capturing increasing trends of winter temperatures like observed. The T max, T min, and T mean correlations from CFSv2 is significantly high (0.35, 0.53, and 0.51, respectively), while CFSv1 correlations are less (0.29, 0.15, and 0.12) and insignificant. This performance of CFSv2 may be due to the better estimation of surface heat budget terms and realistic CO2 concentration, which were absent in CFSv1. CFSv2 proved to have a high probability of detection in predicting different categories (below, near, and above normal) for winter T min, which are required for crop yield and public utility services, over north India.

  4. Predicting plant invasions under climate change: are species distribution models validated by field trials?

    PubMed

    Sheppard, Christine S; Burns, Bruce R; Stanley, Margaret C

    2014-09-01

    Climate change may facilitate alien species invasion into new areas, particularly for species from warm native ranges introduced into areas currently marginal for temperature. Although conclusions from modelling approaches and experimental studies are generally similar, combining the two approaches has rarely occurred. The aim of this study was to validate species distribution models by conducting field trials in sites of differing suitability as predicted by the models, thus increasing confidence in their ability to assess invasion risk. Three recently naturalized alien plants in New Zealand were used as study species (Archontophoenix cunninghamiana, Psidium guajava and Schefflera actinophylla): they originate from warm native ranges, are woody bird-dispersed species and of concern as potential weeds. Seedlings were grown in six sites across the country, differing both in climate and suitability (as predicted by the species distribution models). Seedling growth and survival were recorded over two summers and one or two winter seasons, and temperature and precipitation were monitored hourly at each site. Additionally, alien seedling performances were compared to those of closely related native species (Rhopalostylis sapida, Lophomyrtus bullata and Schefflera digitata). Furthermore, half of the seedlings were sprayed with pesticide, to investigate whether enemy release may influence performance. The results showed large differences in growth and survival of the alien species among the six sites. In the more suitable sites, performance was frequently higher compared to the native species. Leaf damage from invertebrate herbivory was low for both alien and native seedlings, with little evidence that the alien species should have an advantage over the native species because of enemy release. Correlations between performance in the field and predicted suitability of species distribution models were generally high. The projected increase in minimum temperature and reduced

  5. The predicted influence of climate change on lesser prairie-chicken reproductive parameters

    USGS Publications Warehouse

    Grisham, Blake A.; Boal, Clint W.; Haukos, David A.; Davis, D.; Boydston, Kathy K.; Dixon, Charles; Heck, Willard R.

    2013-01-01

    The Southern High Plains is anticipated to experience significant changes in temperature and precipitation due to climate change. These changes may influence the lesser prairie-chicken (Tympanuchus pallidicinctus) in positive or negative ways. We assessed the potential changes in clutch size, incubation start date, and nest survival for lesser prairie-chickens for the years 2050 and 2080 based on modeled predictions of climate change and reproductive data for lesser prairie-chickens from 2001-2011 on the Southern High Plains of Texas and New Mexico. We developed 9 a priori models to assess the relationship between reproductive parameters and biologically relevant weather conditions. We selected weather variable(s) with the most model support and then obtained future predicted values from climatewizard.org. We conducted 1,000 simulations using each reproductive parameter's linear equation obtained from regression calculations, and the future predicted value for each weather variable to predict future reproductive parameter values for lesser prairie-chickens. There was a high degree of model uncertainty for each reproductive value. Winter temperature had the greatest effect size for all three parameters, suggesting a negative relationship between above-average winter temperature and reproductive output. The above-average winter temperatures are correlated to La Nina events, which negatively affect lesser prairie-chickens through resulting drought conditions. By 2050 and 2080, nest survival was predicted to be below levels considered viable for population persistence; however, our assessment did not consider annual survival of adults, chick survival, or the positive benefit of habitat management and conservation, which may ultimately offset the potentially negative effect of drought on nest survival.

  6. Analysis of the decadal predictability of the North Atlantic volume and heat transport in a future climate projection

    NASA Astrophysics Data System (ADS)

    Fischer, Matthias; Müller, Wolfgang A.; Domeisen, Daniela I. V.; Baehr, Johanna

    2015-04-01

    The North Atlantic ocean is predicted to change considerably with climate change. An analysis of the North Atlantic meridional overturning circulation (AMOC) and the meridional heat transport (OHT) in CMIP5 climate projections in the global coupled Max Planck Institute Earth System Model (MPI-ESM-LR) has shown potential changes in the AMOC's and OHT's seasonal cycle in a future climate. From the CMIP5 historical simulation to RCP4.5, both the AMOC and the OHT indicate latitude dependent temporal shifts of about 1 month until 2050. Based on these results, we here examine potential changes in the decadal predictability of the AMOC and OHT under climate change. In MPI-ESM-LR, we generate two hindcast ensembles with 20 start dates and 10 ensemble members per start date for (i) the current climate state in the CMIP5 historical simulation starting in 1995 and (ii) a future climate state in RCP4.5 starting in 2045. These two hindcast ensembles are compared against the historical simulation and RCP4.5 as control simulation, respectively, using anomaly correlation, root-mean-square error (RMSE) and the Brier skill score decomposition. We investigate whether the decadal predictability of the AMOC and OHT might change under future climate conditions both for the annual mean and individual seasons or climate indices (e.g. for the NAO).

  7. Predicting future changes in climate and its impact on change in land use: a case study of Cauvery Basin

    NASA Astrophysics Data System (ADS)

    Poyil, Rohith P.; Dhanalakshmi, S.; Goyal, Pramila

    2016-05-01

    The study involves the climate change prediction and its effects over a local sub grid scale of smaller area in Cauvery basin. The consequences of global warming due to anthropogenic activities are reflected in global as well as regional climate in terms of changes in key climatic variables such as temperature, precipitation, humidity and wind speed. The key objectives of the study are to define statistical relationships between different climate parameters, to estimate the sensitivities of climate variables to future climate scenarios by integrating with GIS and to predict the land use/ land cover change under the climate change scenarios. The historical data set was analyzed to predict the climate change and its impact on land use/land cover (LULC) is observed by correlating the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) values for two different times for the same area. It is so evident that due to the rise in temperature there is a considerable change in the land use affecting the vegetation index; increased temperature results in very low NDVI values or vegetation abundance.

  8. Improvement and Application of Atmospheric Radiative Transfer Models for Prediction of the Climatic Effects of Aerosol

    NASA Technical Reports Server (NTRS)

    Bergstrom, Robert W.; Mlawer, Eli J.; Sokolik, Irina N.; Clough, Shepard A.; Toon, Owen B.

    1998-01-01

    This paper presents a radiative transfer model that has been developed to accurately predict the atmospheric radiant flux in both the infrared and the solar spectrum with a minimum of computational effort. The model is designed to be included in numerical climate models. To assess the accuracy of the model, the results are compared to other more detailed models for several standard cases in the solar and thermal spectrum. As the thermal spectrum has been treated in other publications, we focus here on the solar part of the spectrum. We perform several example calculations focussing on the question of absorption of solar radiation by gases and aerosols.

  9. Improvement and Application of Atmospheric Radiative Transfer Models for Prediction of the Climatic Effects of Aerosol

    NASA Technical Reports Server (NTRS)

    Bergstrom, Robert W.

    1998-01-01

    This paper presents a radiative transfer model that has been developed to accurately predict the atmospheric radiant flux in both the infrared and the solar spectrum with a minimum of computational effort. The model is designed to be included in numerical climate models. To assess the accuracy of the model, the results are compared to other more detailed models for several standard cases in the solar and thermal spectrum. As the thermal spectrum has been treated in other publications we focus here on the solar part of the spectrum. We perform several example calculations focussing on the question of absorption of solar radiation by gases and aerosols.

  10. Climate downscaling effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States

    USGS Publications Warehouse

    Bucklin, David N.; Watling, James I.; Speroterra, Carolina; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.

    2013-01-01

    High-resolution (downscaled) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two different downscaling approaches (with and without the use of a regional climate model) affect climate envelope model predictions when all other sources of variation are held constant. We found that prediction maps differed spatially between downscaling approaches and that the variation attributable to downscaling technique was comparable to variation between maps generated using different general circulation models (GCMs). Precipitation variables tended to show greater discrepancies between downscaling techniques than temperature variables, and for one GCM, there was evidence that more poorly resolved precipitation variables contributed relatively more to model uncertainty than more well-resolved variables. Our work suggests that ecological modelers requiring high-resolution climate projections should carefully consider the type of downscaling applied to the climate projections prior to their use in predictive ecological modeling. The uncertainty associated with alternative downscaling methods may rival that of other, more widely appreciated sources of variation, such as the general circulation model or emissions scenario with which future climate projections are created.

  11. Modeling responses of large-river fish populations to global climate change through downscaling and incorporation of predictive uncertainty

    USGS Publications Warehouse

    Wildhaber, Mark L.; Wikle, Christopher K.; Anderson, Christopher J.; Franz, Kristie J.; Moran, Edward H.; Dey, Rima; Mader, Helmut; Kraml, Julia

    2012-01-01

    Climate change operates over a broad range of spatial and temporal scales. Understanding its effects on ecosystems requires multi-scale models. For understanding effects on fish populations of riverine ecosystems, climate predicted by coarse-resolution Global Climate Models must be downscaled to Regional Climate Models to watersheds to river hydrology to population response. An additional challenge is quantifying sources of uncertainty given the highly nonlinear nature of interactions between climate variables and community level processes. We present a modeling approach for understanding and accomodating uncertainty by applying multi-scale climate models and a hierarchical Bayesian modeling framework to Midwest fish population dynamics and by linking models for system components together by formal rules of probability. The proposed hierarchical modeling approach will account for sources of uncertainty in forecasts of community or population response. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. This understanding will aid evaluation of management options for coping with global climate change. In our initial analyses, we found that predicted pallid sturgeon population responses were dependent on the climate scenario considered.

  12. Estimating the limit of decadal-scale climate predictability using observational data

    NASA Astrophysics Data System (ADS)

    Ding, Ruiqiang; Li, Jianping; Zheng, Fei; Feng, Jie; Liu, Deqiang

    2016-03-01

    Current coupled atmosphere-ocean general circulation models can not simulate decadal variability well, and model errors would have a significant impact on the estimation of decadal predictability. In this study, the nonlinear local Lyapunov exponent method is adopted to estimate the limit of decadal predictability based on 9-year low-pass filtered sea surface temperature (SST) and sea level pressure (SLP) observations. The results show that the limit of decadal predictability of the SST field is relatively large in the North Atlantic, North Pacific, Southern Ocean, tropical Indian Ocean, and western North Pacific, exceeding 7 years at most locations in these regions. In contrast, the limit of the SST field is relatively small in the tropical central-eastern Pacific (4-6 years). Similar to the SST field, the SLP field has a relatively large limit of decadal predictability over the Antarctic, North Pacific, and tropical Indian Ocean (>6 years). In addition, a relatively large limit of decadal predictability of the SLP field also occurs over the land regions of Africa, India, and South America. Distributions of the limit of decadal predictability of both the SST and SLP fields are almost consistent with those of their intensity and persistence on decadal timescales. By examining the limit of decadal predictability of several major climate modes, we found that the limit of decadal predictability of the Pacific decadal oscillation (PDO) is about 9 years, slightly lower than that of the Atlantic multidecadal oscillation (AMO) (about 11 years). In contrast, the northern and southern annular modes have limits of decadal predictability of about 4 and 9 years, respectively. However, the above limits estimated using time-filtered data may overestimate the predictability of decadal variability due to the use of time filtering. Filtered noise with the same spectral characteristics as the PDO and AMO, has a predictability of about 3 years. Future work is required with a longer

  13. Estimating the limit of decadal-scale climate predictability using observational data

    NASA Astrophysics Data System (ADS)

    Ding, Ruiqiang; Li, Jianping

    2016-04-01

    Current coupled atmosphere-ocean general circulation models (CGCMs) can not simulate decadal variability well, and model errors would have a significant impact on the estimation of decadal predictability. In this study, the nonlinear local Lyapunov exponent (NLLE) method is adopted to estimate the limit of decadal predictability based on 9-yr low-pass filtered sea surface temperature (SST) and sea level pressure (SLP) observations. The results show that the limit of decadal predictability of the SST field is relatively large in the North Atlantic, North Pacific, Southern Ocean, tropical Indian Ocean, and western North Pacific, exceeding 7 years at most locations in these regions. In contrast, the limit of the SST field is relatively small in the tropical central-eastern Pacific (4-6 years). Similar to the SST field, the SLP field has a relatively large limit of decadal predictability over the Antarctic, North Pacific, and tropical Indian Ocean (>6 years). In addition, a relatively large limit of decadal predictability of the SLP field also occurs over the land regions of Africa, India, and South America. Distributions of the limit of decadal predictability of both the SST and SLP fields are almost consistent with those of their intensity and persistence on decadal timescales. By examining the limit of decadal predictability of several major climate modes, we found that the limit of decadal predictability of the Pacific Decadal Oscillation (PDO) is about 9 years, slightly lower than that of the Atlantic Multidecadal Oscillation (AMO) (about 11 years). In contrast, the Northern and Southern Annular Modes (NAM and SAM) have limits of decadal predictability of about 4 and 9 years, respectively. However, the above limits estimated using time-filtered data may overestimate the predictability of decadal variability due to the use of time filtering. Filtered noise with the same spectral characteristics as the PDO and AMO, has a predictability of about 3 years. Future work

  14. Long-Term Predictions of Global Climate Using the Ocean Conveyor

    SciTech Connect

    Ray, P.; Wilson, J.R.

    2003-01-01

    Many have attributed the Great Ocean Conveyor as a major driver of global climate change over millennia as well as a possible explanation for shorter (multidecadal) oscillations. The conveyor is thought to have a cycle time on the order of 1000 years, however recent research has suggested that it is much faster than previously believed (about 100 years). A faster conveyor leads to the possibility of the conveyor's role in even shorter oscillations such as the El Nino/Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). The conveyor is primarily density driven. In this study the salty outflow of the Red Sea is used to predict its behavior ten years into the future. A successful model could lead to a long-term prediction (ten years) of El Ninos, Atlantic hurricane season intensity, as well as global temperature and precipitation patterns.

  15. Topographically driven predictions for river food webs: responses to land cover and climate change

    NASA Astrophysics Data System (ADS)

    Power, M. E.; Dietrich, W. E.; Finlay, J. C.; Bode, C. A.; Hondzo, M.; Limm, M.; National CenterEarth Surface Dynamics

    2011-12-01

    Fluxes of materials and energy, as well as the performances and interactions of organisms in food webs are strongly influenced by topography and vegetation. We have been using a "predictive mapping" approach to investigate how resource fluxes and food web interactions change down the Eel River drainage network in Northwestern California. In this talk, I will focus on hydrologic and food web controls on the production and fate of dominant primary producers in the river (macroalgae, diatoms and cyanobacteria). Algal mediated processes (e.g. nitrogen-fixation) and processes that limit algal abundance (e.g. grazer control) change abruptly through the season and down drainage networks. Field surveys and mensurative experiments that map the drainage network positions of these changes, and manipulative field experiments that uncover their causes, set the stage for predictive mapping, which is necessary if not sufficient for forecasts of river ecosystem response to changes in climate, land use, or biota.

  16. Predicting climate change effects on wetland ecosystem services using species distribution modeling and plant functional traits.

    PubMed

    Moor, Helen; Hylander, Kristoffer; Norberg, Jon

    2015-01-01

    Wetlands provide multiple ecosystem services, the sustainable use of which requires knowledge of the underlying ecological mechanisms. Functional traits, particularly the community-weighted mean trait (CWMT), provide a strong link between species communities and ecosystem functioning. We here combine species distribution modeling and plant functional traits to estimate the direction of change of ecosystem processes under climate change. We model changes in CWMT values for traits relevant to three key services, focusing on the regional species pool in the Norrström area (central Sweden) and three main wetland types. Our method predicts proportional shifts toward faster growing, more productive and taller species, which tend to increase CWMT values of specific leaf area and canopy height, whereas changes in root depth vary. The predicted changes in CWMT values suggest a potential increase in flood attenuation services, a potential increase in short (but not long)-term nutrient retention, and ambiguous outcomes for carbon sequestration.

  17. Predicting weed migration from soil and climate maps. [Centaurea maculosa Lam

    SciTech Connect

    Chicoine, T.K.; Fay, P.K.; Nielsen, G.A.

    1985-01-01

    Soil characteristics, elevation, annual precipitation, potential evapotranspiration, length of frost-free season, and mean maximum July temperature were estimated for 116 established infestations of spotted knapweed (Centaurea maculosa Lam. number/sup 3/ CENMA) in Montana using basic land resource maps. Areas potentially vulnerable to invasion by the plant were delineated on the basis of representative edaphic and climatic characteristics. No single environmental variable was an effective predictor of sites vulnerable to invasion by spotted knapweed. Only a combination of variables was effective, indicating that the factors that regulate adaptability of this plant are complex. This technique provides a first approximation map of the regions most similar environmentally to infested sites and; therefore, most vulnerable to further invasion. This weed migration prediction technique shows promise for predicting suitable habitats of other invader species. 6 references, 4 figures, 1 table.

  18. Predicting malaria vector distribution under climate change scenarios in China: Challenges for malaria elimination.

    PubMed

    Ren, Zhoupeng; Wang, Duoquan; Ma, Aimin; Hwang, Jimee; Bennett, Adam; Sturrock, Hugh J W; Fan, Junfu; Zhang, Wenjie; Yang, Dian; Feng, Xinyu; Xia, Zhigui; Zhou, Xiao-Nong; Wang, Jinfeng

    2016-02-12

    Projecting the distribution of malaria vectors under climate change is essential for planning integrated vector control activities for sustaining elimination and preventing reintroduction of malaria. In China, however, little knowledge exists on the possible effects of climate change on malaria vectors. Here we assess the potential impact of climate change on four dominant malaria vectors (An. dirus, An. minimus, An. lesteri and An. sinensis) using species distribution models for two future decades: the 2030 s and the 2050 s. Simulation-based estimates suggest that the environmentally suitable area (ESA) for An. dirus and An. minimus would increase by an average of 49% and 16%, respectively, under all three scenarios for the 2030 s, but decrease by 11% and 16%, respectively in the 2050 s. By contrast, an increase of 36% and 11%, respectively, in ESA of An. lesteri and An. sinensis, was estimated under medium stabilizing (RCP4.5) and very heavy (RCP8.5) emission scenarios. in the 2050 s. In total, we predict a substantial net increase in the population exposed to the four dominant malaria vectors in the decades of the 2030 s and 2050 s, considering land use changes and urbanization simultaneously. Strategies to achieve and sustain malaria elimination in China will need to account for these potential changes in vector distributions and receptivity.

  19. Predicting malaria vector distribution under climate change scenarios in China: Challenges for malaria elimination

    NASA Astrophysics Data System (ADS)

    Ren, Zhoupeng; Wang, Duoquan; Ma, Aimin; Hwang, Jimee; Bennett, Adam; Sturrock, Hugh J. W.; Fan, Junfu; Zhang, Wenjie; Yang, Dian; Feng, Xinyu; Xia, Zhigui; Zhou, Xiao-Nong; Wang, Jinfeng

    2016-02-01

    Projecting the distribution of malaria vectors under climate change is essential for planning integrated vector control activities for sustaining elimination and preventing reintroduction of malaria. In China, however, little knowledge exists on the possible effects of climate change on malaria vectors. Here we assess the potential impact of climate change on four dominant malaria vectors (An. dirus, An. minimus, An. lesteri and An. sinensis) using species distribution models for two future decades: the 2030 s and the 2050 s. Simulation-based estimates suggest that the environmentally suitable area (ESA) for An. dirus and An. minimus would increase by an average of 49% and 16%, respectively, under all three scenarios for the 2030 s, but decrease by 11% and 16%, respectively in the 2050 s. By contrast, an increase of 36% and 11%, respectively, in ESA of An. lesteri and An. sinensis, was estimated under medium stabilizing (RCP4.5) and very heavy (RCP8.5) emission scenarios. in the 2050 s. In total, we predict a substantial net increase in the population exposed to the four dominant malaria vectors in the decades of the 2030 s and 2050 s, considering land use changes and urbanization simultaneously. Strategies to achieve and sustain malaria elimination in China will need to account for these potential changes in vector distributions and receptivity.

  20. Predicting the Distribution of Commercially Important Invertebrate Stocks under Future Climate

    PubMed Central

    Russell, Bayden D.; Connell, Sean D.; Mellin, Camille; Brook, Barry W.; Burnell, Owen W.; Fordham, Damien A.

    2012-01-01

    The future management of commercially exploited species is challenging because techniques used to predict the future distribution of stocks under climate change are currently inadequate. We projected the future distribution and abundance of two commercially harvested abalone species (blacklip abalone, Haliotis rubra and greenlip abalone, H. laevigata) inhabiting coastal South Australia, using multiple species distribution models (SDM) and for decadal time slices through to 2100. Projections are based on two contrasting global greenhouse gas emissions scenarios. The SDMs identified August (winter) Sea Surface Temperature (SST) as the best descriptor of abundance and forecast that warming of winter temperatures under both scenarios may be beneficial to both species by allowing increased abundance and expansion into previously uninhabited coasts. This range expansion is unlikely to be realised, however, as projected warming of March SST is projected to exceed temperatures which cause up to 10-fold increases in juvenile mortality. By linking fine-resolution forecasts of sea surface temperature under different climate change scenarios to SDMs and physiological experiments, we provide a practical first approximation of the potential impact of climate-induced change on two species of marine invertebrates in the same fishery. PMID:23251326

  1. Predicting malaria vector distribution under climate change scenarios in China: Challenges for malaria elimination

    PubMed Central

    Ren, Zhoupeng; Wang, Duoquan; Ma, Aimin; Hwang, Jimee; Bennett, Adam; Sturrock, Hugh J. W.; Fan, Junfu; Zhang, Wenjie; Yang, Dian; Feng, Xinyu; Xia, Zhigui; Zhou, Xiao-Nong; Wang, Jinfeng

    2016-01-01

    Projecting the distribution of malaria vectors under climate change is essential for planning integrated vector control activities for sustaining elimination and preventing reintroduction of malaria. In China, however, little knowledge exists on the possible effects of climate change on malaria vectors. Here we assess the potential impact of climate change on four dominant malaria vectors (An. dirus, An. minimus, An. lesteri and An. sinensis) using species distribution models for two future decades: the 2030 s and the 2050 s. Simulation-based estimates suggest that the environmentally suitable area (ESA) for An. dirus and An. minimus would increase by an average of 49% and 16%, respectively, under all three scenarios for the 2030 s, but decrease by 11% and 16%, respectively in the 2050 s. By contrast, an increase of 36% and 11%, respectively, in ESA of An. lesteri and An. sinensis, was estimated under medium stabilizing (RCP4.5) and very heavy (RCP8.5) emission scenarios. in the 2050 s. In total, we predict a substantial net increase in the population exposed to the four dominant malaria vectors in the decades of the 2030 s and 2050 s, considering land use changes and urbanization simultaneously. Strategies to achieve and sustain malaria elimination in China will need to account for these potential changes in vector distributions and receptivity. PMID:26868185

  2. Web Services at the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC)

    NASA Astrophysics Data System (ADS)

    Ansari, S.; Baldwin, R.; Del Greco, S.; Lott, N.; Rutledge, G.

    2007-12-01

    NOAA's National Climatic Data Center (NCDC) currently archives over 1.5 petabytes of climatological data from various networks and sources including in-situ, numerical models, radar and satellite. Access to these datasets is evolving from interactive web interfaces utilizing database technology to standardized web services in a Service Oriented Architecture (SOA). NCDC is currently offering several web services using Simple Object Access Protocol (SOAP), XML over Representational State Transfer (REST/XML), Open Geospatial Consortium (OGC) Web Map Service (WMS) / Web Feature Service (WFS) / Web Coverage Service (WCS) and OPeNDAP web service protocols. These services offer users a direct connection between their client applications and NCDC data servers. In addition, users may embed access to the services in custom applications to efficiently navigate and subset data in an automated fashion. NCDC currently provides gridded numerical model data through a THREDDS Data Server and GrADS Data Server which offers OPeNDAP and WCS access. In-situ network metadata are available through WMS and WFS while the corresponding time-series data are accessible through SOAP and REST web services. These in-situ services are a part of the Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI) WaterOneFlow services, a consolidated access system for hydrologic data, and comply with the WaterOneFlow specifications. NCDC's Severe Weather Data Inventory (SWDI), which provides user access to archives of several datasets critical to the detection and evaluation of severe weather, is also accessible through REST/XML services. Providing cataloging, access and search capabilities for many of NCDC's datasets using community driven standards is a top priority for the ever increasing data volumes being archived at NCDC. Providing interoperable access is critical to supporting data stewardship across multiple scientific disciplines and user types. This demonstration will

  3. Short Term Weather Forecasting and Long Term Climate Predictions in Mesoamerica

    NASA Astrophysics Data System (ADS)

    Hardin, D. M.; Daniel, I.; Mecikalski, J.; Graves, S.

    2008-05-01

    The SERVIR project utilizes several predictive models to support regional monitoring and decision support in Mesoamerica. Short term forecasts ranging from a few hours to several days produce more than 30 data products that are used daily by decision makers, as well as news organizations in the region. The forecast products can be visualized in both two and three dimensional viewers such as Google Maps and Google Earth. Other viewers developed specifically for the Mesoamerican region by the University of Alabama in Huntsville and the Institute for the Application of Geospatial Technologies in Auburn New York can also be employed. In collaboration with the NASA Short Term Prediction Research and Transition (SpoRT) Center SERVIR utilizes the Weather Research and Forecast (WRF) model to produce short-term (24 hr) regional weather forecasts twice a day. Temperature, precipitation, wind, and other variables are forecast in 10km and 30km grids over the Mesoamerica region. Using the PSU/NCAR Mesoscale Model, known as MM5, SERVIR produces 48 hour- forecasts of soil temperature, two meter surface temperature, three hour accumulated precipitation, winds at different heights, and other variables. These are forecast hourly in 9km grids. Working in collaboration with the Atmospheric Science Department of the University of Alabama in Huntsville produces a suite of short-term (0-6 hour) weather prediction products are generated. These "convective initiation" products predict the onset of thunderstorm rainfall and lightning within a 1-hour timeframe. Models are also employed for long term predictions. The SERVIR project, under USAID funding, has developed comprehensive regional climate change scenarios of Mesoamerica for future years: 2010, 2015, 2025, 2050, and 2099. These scenarios were created using the Pennsylvania State University/National Center for Atmospheric Research (MM5) model and processed on the Oak Ridge National Laboratory Cheetah supercomputer. The goal of these

  4. Plant physiological models of heat, water and photoinhibition stress for climate change modelling and agricultural prediction

    NASA Astrophysics Data System (ADS)

    Nicolas, B.; Gilbert, M. E.; Paw U, K. T.

    2015-12-01

    Soil-Vegetation-Atmosphere Transfer (SVAT) models are based upon well understood steady state photosynthetic physiology - the Farquhar-von Caemmerer-Berry model (FvCB). However, representations of physiological stress and damage have not been successfully integrated into SVAT models. Generally, it has been assumed that plants will strive to conserve water at higher temperatures by reducing stomatal conductance or adjusting osmotic balance, until potentially damaging temperatures and the need for evaporative cooling become more important than water conservation. A key point is that damage is the result of combined stresses: drought leads to stomatal closure, less evaporative cooling, high leaf temperature, less photosynthetic dissipation of absorbed energy, all coupled with high light (photosynthetic photon flux density; PPFD). This leads to excess absorbed energy by Photosystem II (PSII) and results in photoinhibition and damage, neither are included in SVAT models. Current representations of photoinhibition are treated as a function of PPFD, not as a function of constrained photosynthesis under heat or water. Thus, it seems unlikely that current models can predict responses of vegetation to climate variability and change. We propose a dynamic model of damage to Rubisco and RuBP-regeneration that accounts, mechanistically, for the interactions between high temperature, light, and constrained photosynthesis under drought. Further, these predictions are illustrated by key experiments allowing model validation. We also integrated this new framework within the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA). Preliminary results show that our approach can be used to predict reasonable photosynthetic dynamics. For instances, a leaf undergoing one day of drought stress will quickly decrease its maximum quantum yield of PSII (Fv/Fm), but it won't recover to unstressed levels for several days. Consequently, cumulative effect of photoinhibition on photosynthesis can cause

  5. A Practiced Basis for Predicting the Total Signal of Primary Climate Variables. Scientific Session U06

    NASA Astrophysics Data System (ADS)

    Suhler, G.

    2009-12-01

    From within Talmudic law came the counsel that for something to be real it must have real effects arising from its interactions. Then it may follow that a certain level of understanding of that which is real can be demonstrated by what is explained well in breadth and depth. An even stronger degree of understanding may be to fairly predict that which is real and to know through its future effects what turns out to be real. Indeed these two, explanatory power and predictive power, have been the first two measures of a science from the time of Galileo and Bacon and even prior. The third measure of a science has been the ability to prescribe a course of action and interaction that leads to desired results. We focus mainly on the first two. From such presentations as at American Association of State Climatologists beginning in 1998, AGU2002Fall Session H-061, and as organizer and presenters for AAAS2006 Symposium #127 (El Nino Predictability), the presenters have made known and placed into the public record predictions of monthly temperature and precipitation that are site-specific as well as regional. This session will take such examples of ‘total signal’ prediction over time frames up to now 12 years and counting and examine in terms of empirical observation and theoretical basis. That theoretical basis derives from Navier-Stokes primitive equations and can be shown to generate, among others, what have been called binary subharmonics that hold ‘period doubling’ as a special yet oft-obtained case for an interactive climate system at numerous time scales. The upshot is that from annual forcings Earth’s climate tends to repeat itself at or near up-time scale periods of 2,4,8, 16, 32, 64, 128, 256.…years. The interactive nature leads to modulation at all levels. Specifics of these forced system interactions will be examined from their theoretical basis through examples ranging from site-specific precipitation through Nino3 SST prediction to global

  6. A Preliminary Evaluation of Season-ahead Flood Prediction Conditioned on Large-scale Climate Drivers

    NASA Astrophysics Data System (ADS)

    Lee, Donghoon; Ward, Philip; Block, Paul

    2016-04-01

    Globally, flood disasters lead all natural hazards in terms of impacts on society, causing billions of dollars of damages each year. Typically, short-term forecasts emphasize immediate emergency actions, longer-range forecasts, on the order of months to seasons, however, can compliment short-term forecasts by focusing on disaster preparedness. In this study, the inter-annual variability of large-scale climate drivers (e.g. ENSO) is investigated to understand the prospects for skillful season-ahead flood prediction globally using PCR-GLOBWB modeled simulations. For example, global gridded correlations between discharge and Nino 3.4 are calculated, with notably strong correlations in the northwestern (-0.4~-0.6) and the southeastern (0.4~0.6) United States, and the Amazon river basin (-0.6~-0.8). Coupled interactions from multiple, simultaneous climate drivers are also evaluated. Skillful prediction has the potential to estimate season-ahead flood probabilities, flood extent, damages, and eventually integrate into early warning systems. This global approach is especially attractive for areas with limited observations and/or little capacity to develop early warning flood systems.

  7. 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.

  8. Predicting the Potential Distribution of Polygala tenuifolia Willd. under Climate Change in China

    PubMed Central

    Li, Lin; Zhao, Yao; Pei, Lin; Zhao, Jiancheng

    2016-01-01

    Global warming has created opportunities and challenges for the survival and development of species. Determining how climate change may impact multiple ecosystem levels and lead to various species adaptations is necessary for both biodiversity conservation and sustainable biological resource utilization. In this study, we employed Maxent to predict changes in the habitat range and altitude of Polygala tenuifolia Willd. under current and future climate scenarios in China. Four representative concentration pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) were modeled for two time periods (2050 and 2070). The model inputs included 732 presence points and nine sets of environmental variables under the current conditions and the four RCPs in 2050 and 2070. The area under the receiver-operating characteristic (ROC) curve (AUC) was used to evaluate model performance. All of the AUCs were greater than 0.80, thereby placing these models in the “very good” category. Using a jackknife analysis, the precipitation in the warmest quarter, annual mean temperature, and altitude were found to be the top three variables that affect the range of P. tenuifolia. Additionally, we found that the predicted highly suitable habitat was in reasonable agreement with its actual distribution. Furthermore, the highly suitable habitat area was slowly reduced over time. PMID:27661983

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

    NASA Astrophysics Data System (ADS)

    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.

  10. Origin of seasonal predictability for summer climate over the Northwestern Pacific

    PubMed Central

    Kosaka, Yu; Xie, Shang-Ping; Lau, Ngar-Cheung; Vecchi, Gabriel A.

    2013-01-01

    Summer climate in the Northwestern Pacific (NWP) displays large year-to-year variability, affecting densely populated Southeast and East Asia by impacting precipitation, temperature, and tropical cyclones. The Pacific–Japan (PJ) teleconnection pattern provides a crucial link of high predictability from the tropics to East Asia. Using coupled climate model experiments, we show that the PJ pattern is the atmospheric manifestation of an air–sea coupled mode spanning the Indo-NWP warm pool. The PJ pattern forces the Indian Ocean (IO) via a westward propagating atmospheric Rossby wave. In response, IO sea surface temperature feeds back and reinforces the PJ pattern via a tropospheric Kelvin wave. Ocean coupling increases both the amplitude and temporal persistence of the PJ pattern. Cross-correlation of ocean–atmospheric anomalies confirms the coupled nature of this PJIO mode. The ocean–atmosphere feedback explains why the last echoes of El Niño–Southern Oscillation are found in the IO-NWP in the form of the PJIO mode. We demonstrate that the PJIO mode is indeed highly predictable; a characteristic that can enable benefits to society. PMID:23610388

  11. Relative role of parameter vs. climate uncertainty for predictions of future Southeastern U.S. pine carbon cycling

    NASA Astrophysics Data System (ADS)

    Jersild, A.; Thomas, R. Q.; Brooks, E.; Teskey, R. O.; Wynne, R. H.; Arthur, D.; Gonzalez, C.; Thomas, V. A.; Fox, T. D.; Smallman, L.

    2015-12-01

    Predictions of the how forest productivity and carbon sequestration will respond to climate change are essential for assisting land managers in adapting to future climate. However, current predictions can include considerable uncertainty that is often not well quantified. To address the need for better quantification of uncertainty, we calculated and compared parameter and climate prediction uncertainty for predictions of Southeastern U.S. pine forest productivity. We used a Metropolis-Hastings Markov Chain Monte Carlo-based data assimilation technique to fuse regionally widespread and diverse datasets with the Physiological Principles Predicting Growth model (3PG) model. The datasets incorporated include biomass observations from forest research plots that are part of the Pine Integrated Network: Education, Mitigation, and Adaptation project (PINEMAP) project, photosynthesis and evaporation observations from loblolly pine Ameriflux sites, and productivity responses to elevated CO2 from the Duke Free Air C site. These spatially and temporally diverse data sets give our unique analysis a more accurately measured uncertainty by constraining complimentary components of the model. In our analysis, parameter uncertainty was quantified using simulations that integrate across the posterior parameter distributions, while climate model uncertainty was quantified using downscaled RCP 8.5 simulations from twenty different CMIP5 climate models. Overall, we found that the uncertainty in future productivity of Southeastern U.S. managed pine forests that was associated with parameterization is comparable to the uncertainty associated with climate simulations. Our results indicate that reducing parameterization in ecosystem model development can improve future predictions of forest productivity and carbon sequestration, but uncertainties in future climate predictions also need to be properly quantified and communicated to forest owners and managers.

  12. 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

  13. Climate-Driven Range Extension of Amphistegina (Protista, Foraminiferida): Models of Current and Predicted Future Ranges

    PubMed Central

    Langer, Martin R.; Weinmann, Anna E.; Lötters, Stefan; Bernhard, Joan M.; Rödder, Dennis

    2013-01-01

    Species-range expansions are a predicted and realized consequence of global climate change. Climate warming and the poleward widening of the tropical belt have induced range shifts in a variety of marine and terrestrial species. Range expansions may have broad implications on native biota and ecosystem functioning as shifting species may perturb recipient communities. Larger symbiont-bearing foraminifera constitute ubiquitous and prominent components of shallow water ecosystems, and range shifts of these important protists are likely to trigger changes in ecosystem functioning. We have used historical and newly acquired occurrence records to compute current range shifts of Amphistegina spp., a larger symbiont-bearing foraminifera, along the eastern coastline of Africa and compare them to analogous range shifts currently observed in the Mediterranean Sea. The study provides new evidence that amphisteginid foraminifera are rapidly progressing southwestward, closely approaching Port Edward (South Africa) at 31°S. To project future species distributions, we applied a species distribution model (SDM) based on ecological niche constraints of current distribution ranges. Our model indicates that further warming is likely to cause a continued range extension, and predicts dispersal along nearly the entire southeastern coast of Africa. The average rates of amphisteginid range shift were computed between 8 and 2.7 km year−1, and are projected to lead to a total southward range expansion of 267 km, or 2.4° latitude, in the year 2100. Our results corroborate findings from the fossil record that some larger symbiont-bearing foraminifera cope well with rising water temperatures and are beneficiaries of global climate change. PMID:23405081

  14. Prediction-Market-Based Quantification of Climate Change Consensus and Uncertainty

    NASA Astrophysics Data System (ADS)

    Boslough, M.

    2012-12-01

    Intrade is an online trading exchange that includes climate prediction markets. One such family of contracts can be described as "Global temperature anomaly for 2012 to be greater than x °C or more," where the figure x ranges in increments of .05 from .30 to 1.10 (relative to the 1951-1980 base period), based on data published by NASA GISS. Each market will settle at 10.00 if the published global temperature anomaly for 2012 is equal to or greater than x, and will otherwise settle at 0.00. Similar contracts will be available for 2013. Global warming hypotheses can be cast as probabilistic predictions for future temperatures. The first modern such climate prediction is that of Broecker (1975), whose temperatures are easily separable from his CO2 growth scenario—which he overestimated—by interpolating his table of temperature as a function of CO2 concentration and projecting the current trend into the near future. For the current concentration of 395 ppm, Broecker's equilibrium temperature anomaly prediction relative to pre-industrial is 1.05 °C, or about 0.75 °C relative to the GISS base period. His neglect of lag in response to the changes in radiative forcing was partially compensated by his low sensitivity of 2.4 °C, leading to a slight overestimate. Simple linear extrapolation of the current trend since 1975 yields an estimate of .65 ± .09 °C (net warming of .95 °C) for anthropogenic global warming with a normal distribution of random natural variability. To evaluate an extreme case, we can estimate the prediction Broecker would have made if he had used the Lindzen & Choi (2009) climate sensitivity of 0.5 °C. The net post-industrial warming by 2012 would have been 0.21 °C, for an expected change of -0.09 from the GISS base period. This is the temperature to which the Earth would be expected to revert if the observed warming since the 19th century was merely due to random natural variability that coincidentally mimicked Broecker's anthropogenic

  15. Collaborative Proposal: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic System Model (RASM)

    SciTech Connect

    Maslowski, Wieslaw

    2016-10-17

    This project aims to develop, apply and evaluate a regional Arctic System model (RASM) for enhanced decadal predictions. Its overarching goal is to advance understanding of the past and present states of arctic climate and to facilitate improvements in seasonal to decadal predictions. In particular, it will focus on variability and long-term change of energy and freshwater flows through the arctic climate system. The project will also address modes of natural climate variability as well as extreme and rapid climate change in a region of the Earth that is: (i) a key indicator of the state of global climate through polar amplification and (ii) which is undergoing environmental transitions not seen in instrumental records. RASM will readily allow the addition of other earth system components, such as ecosystem or biochemistry models, thus allowing it to facilitate studies of climate impacts (e.g., droughts and fires) and of ecosystem adaptations to these impacts. As such, RASM is expected to become a foundation for more complete Arctic System models and part of a model hierarchy important for improving climate modeling and predictions.

  16. Predicting fire activity in the US over the next 50 years using new IPCC climate projections

    NASA Astrophysics Data System (ADS)

    Wang, D.; Morton, D. C.; Collatz, G. J.

    2012-12-01

    Fire is an integral part of the Earth system with both direct and indirect effects on terrestrial ecosystems, the atmosphere, and human societies (Bowman et al. 2009). Climate conditions regulate fire activities through a variety of ways, e.g., influencing the conditions for ignition and fire spread, changing vegetation growth and decay and thus the accumulation of fuels for combustion (Arora and Boer 2005). Our recent study disclosed the burned area (BA) in US is strongly correlated with potential evaporation (PE), a measurement of climatic dryness derived from National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) climate data (Morton et al. 2012). The correlation varies spatially and temporally. With regard to fire of peak fire seasons, Northwestern US, Great Plains and Alaska have the strongest BA/PE relationship. Using the recently released the Global Fire Emissions Database (GFED) Version 3 (van der Werf et al. 2010), we showed increasing BA in the last decade in most of NCA regions. Longer time series of Monitoring Trends in Burn Severity (MTBS) (Eidenshink et al. 2007) data showed the increasing trends occurred in all NCA regions from 1984 to 2010. This relationship between BA and PE provides us the basis to predict the future fire activities in the projected climate conditions. In this study, we build spatially explicit predictors using the historic PE/BA relationship. PE from 2011 to 2060 is calculated from the Coupled Model Intercomparison Project Phase 5 (CMIP5) data and the historic PE/BA relationship is then used to estimate BA. This study examines the spatial pattern and temporal dynamics of the future US fires driven by new climate predictions for the next 50 years. Reference: Arora, V.K., & Boer, G.J. (2005). Fire as an interactive component of dynamic vegetation models. Journal of Geophysical Research-Biogeosciences, 110 Bowman, D.M.J.S., Balch, J.K., Artaxo, P., Bond, W.J., Carlson, J.M., Cochrane, M.A., D

  17. Effects of lateral boundary condition resolution and update frequency on regional climate model predictions

    NASA Astrophysics Data System (ADS)

    Pankatz, Klaus; Kerkweg, Astrid

    2015-04-01

    The work presented is part of the joint project "DecReg" ("Regional decadal predictability") which is in turn part of the project "MiKlip" ("Decadal predictions"), an effort funded by the German Federal Ministry of Education and Research to improve decadal predictions on a global and regional scale. In MiKlip, one big question is if regional climate modeling shows "added value", i.e. to evaluate, if regional climate models (RCM) produce better results than the driving models. However, the scope of this study is to look more closely at the setup specific details of regional climate modeling. As regional models only simulate a small domain, they have to inherit information about the state of the atmosphere at their lateral boundaries from external data sets. There are many unresolved questions concerning the setup of lateral boundary conditions (LBC). External data sets come from global models or from global reanalysis data-sets. A temporal resolution of six hours is common for this kind of data. This is mainly due to the fact, that storage space is a limiting factor, especially for climate simulations. However, theoretically, the coupling frequency could be as high as the time step of the driving model. Meanwhile, it is unclear if a more frequent update of the LBCs has a significant effect on the climate in the domain of the RCM. The first study examines how the RCM reacts to a higher update frequency. The study is based on a 30 year time slice experiment for three update frequencies of the LBC, namely six hours, one hour and six minutes. The evaluation of means, standard deviations and statistics of the climate in the regional domain shows only small deviations, some statistically significant though, of 2m temperature, sea level pressure and precipitation. The second part of the first study assesses parameters linked to cyclone activity, which is affected by the LBC update frequency. Differences in track density and strength are found when comparing the simulations

  18. Climate-Based Models for Pulsed Resources Improve Predictability of Consumer Population Dynamics: Outbreaks of House Mice in Forest Ecosystems

    PubMed Central

    Holland, E. Penelope; James, Alex; Ruscoe, Wendy A.; Pech, Roger P.; Byrom, Andrea E.

    2015-01-01

    Accurate predictions of the timing and magnitude of consumer responses to episodic seeding events (masts) are important for understanding ecosystem dynamics and for managing outbreaks of invasive species generated by masts. While models relating consumer populations to resource fluctuations have been developed successfully for a range of natural and modified ecosystems, a critical gap that needs addressing is better prediction of resource pulses. A recent model used change in summer temperature from one year to the next (ΔT) for predicting masts for forest and grassland plants in New Zealand. We extend this climate-based method in the framework of a model for consumer–resource dynamics to predict invasive house mouse (Mus musculus) outbreaks in forest ecosystems. Compared with previous mast models based on absolute temperature, the ΔT method for predicting masts resulted in an improved model for mouse population dynamics. There was also a threshold effect of ΔT on the likelihood of an outbreak occurring. The improved climate-based method for predicting resource pulses and consumer responses provides a straightforward rule of thumb for determining, with one year’s advance warning, whether management intervention might be required in invaded ecosystems. The approach could be applied to consumer–resource systems worldwide where climatic variables are used to model the size and duration of resource pulses, and may have particular relevance for ecosystems where global change scenarios predict increased variability in climatic events. PMID:25785866

  19. Climate-based models for pulsed resources improve predictability of consumer population dynamics: outbreaks of house mice in forest ecosystems.

    PubMed

    Holland, E Penelope; James, Alex; Ruscoe, Wendy A; Pech, Roger P; Byrom, Andrea E

    2015-01-01

    Accurate predictions of the timing and magnitude of consumer responses to episodic seeding events (masts) are important for understanding ecosystem dynamics and for managing outbreaks of invasive species generated by masts. While models relating consumer populations to resource fluctuations have been developed successfully for a range of natural and modified ecosystems, a critical gap that needs addressing is better prediction of resource pulses. A recent model used change in summer temperature from one year to the next (ΔT) for predicting masts for forest and grassland plants in New Zealand. We extend this climate-based method in the framework of a model for consumer-resource dynamics to predict invasive house mouse (Mus musculus) outbreaks in forest ecosystems. Compared with previous mast models based on absolute temperature, the ΔT method for predicting masts resulted in an improved model for mouse population dynamics. There was also a threshold effect of ΔT on the likelihood of an outbreak occurring. The improved climate-based method for predicting resource pulses and consumer responses provides a straightforward rule of thumb for determining, with one year's advance warning, whether management intervention might be required in invaded ecosystems. The approach could be applied to consumer-resource systems worldwide where climatic variables are used to model the size and duration of resource pulses, and may have particular relevance for ecosystems where global change scenarios predict increased variability in climatic events.

  20. Mechanistic variables can enhance predictive models of endotherm distributions: The American pika under current, past, and future climates

    USGS Publications Warehouse

    Mathewson, Paul; Moyer-Horner, Lucas; Beever, Erik; Briscoe, Natalie; Kearney, Michael T; Yahn, Jeremiah; Porter, Warren P.

    2017-01-01

    How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect

  1. Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates.

    PubMed

    Mathewson, Paul D; Moyer-Horner, Lucas; Beever, Erik A; Briscoe, Natalie J; Kearney, Michael; Yahn, Jeremiah M; Porter, Warren P

    2017-03-01

    How climate constrains species' distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8-19% less habitat loss in response to annual temperature increases of ~3-5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect

  2. Prediction of Soil Erosion from Uplands under Climate Change Scenarios in Korea

    NASA Astrophysics Data System (ADS)

    Kim, Min-Kyeong; Ko, Byong-Gu; Hur, Seung-Oh; Kim, Min-Young; Lee, Deog-Bae

    2010-05-01

    Major impacts of climate change expect that soil erosion rate may increase during the 21st century. This study was conducted to assess the potential impacts of climate change on soil erosion by water in Korea. The soil loss was estimated for regions with the potential risk of soil erosion on a national scale. For computation, Universal soil loss equation (USLE) with rainfall and runoff erosivity factors (R), cover management factors (C), support practice factors (P) and revised USLE with soil erodibility factors (K) and topographic factors (LS) were used. RUSLE, the revised version of USLE, was modified for Korean conditions and re-evaluated to estimate the national-scale of soil loss based on the digital soil maps for Korea. The changes of precipitation for 2010 to 2090s were predicted under A1B scenarios made by National Institute of Meteorological Research in Korea. Future soil loss was predicted based on a change of R factor. As results, the predicted precipitations were increased by 6.7% for 2010 to 2030, 9.5% for 2040 to 2060s and 190% for 2070 to 2090s, respectively. The total soil loss from uplands in 2005 was estimated approximately 28ⅹ106 ton. Total soil losses were estimated as 31ⅹ106 ton in 2010 to 2030s, 31ⅹ106 ton in 2040 to 2060s and 33ⅹ106 ton in 2070 to 2090s, respectively. As precipitation increased by 17% in the end of 21st century, the total soil loss was increased by 12.9%. Overall, these results emphasize the significance of precipitation. However, it should be noted that when precipitation becomes insignificant, the results may turn out to be complex due to the large interaction among plant biomass, runoff and erosion. This may cause increase or decrease the overall erosion.

  3. Effects of time-averaging climate parameters on predicted multicompartmental fate of pesticides and POPs.

    PubMed

    Lammel, Gerhard

    2004-01-01

    With the aim to investigate the justification of time-averaging of climate parameters in multicompartment modelling the effects of various climate parameters and different modes of entry on the predicted substances' total environmental burdens and the compartmental fractions were studied. A simple, non-steady state zero-dimensional (box) mass-balance model of intercompartmental mass exchange which comprises four compartments was used for this purpose. Each two runs were performed, one temporally unresolved (time-averaged conditions) and a time-resolved (hourly or higher) control run. In many cases significant discrepancies are predicted, depending on the substance and on the parameter. We find discrepancies exceeding 10% relative to the control run and up to an order of magnitude for prediction of the total environmental burden from neglecting seasonalities of the soil and ocean temperatures and the hydroxyl radical concentration in the atmosphere and diurnalities of atmospheric mixing depth and the hydroxyl radical concentration in the atmosphere. Under some conditions it was indicated that substance sensitivity could be explained by the magnitude of the sink terms in the compartment(s) with parameters varying. In general, however, any key for understanding substance sensitivity seems not be linked in an easy manner to the properties of the substance, to the fractions of its burden or to the sink terms in either of the compartments with parameters varying. Averaging of diurnal variability was found to cause errors of total environmental residence time of different sign for different substances. The effects of time-averaging of several parameters are in general not additive but synergistic as well as compensatory effects occur. An implication of these findings is that the ranking of substances according to persistence is sensitive to time resolution on the scale of hours to months. As a conclusion it is recommended to use high temporal resolution in multi

  4. Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns

    PubMed Central

    Mickley, Loretta J.

    2017-01-01

    We develop a statistical model to predict June–July–August (JJA) daily maximum 8-h average (MDA8) ozone concentrations in the eastern United States based on large-scale climate patterns during the previous spring. We find that anomalously high JJA ozone in the East is correlated with these springtime patterns: warm tropical Atlantic and cold northeast Pacific sea surface temperatures (SSTs), as well as positive sea level pressure (SLP) anomalies over Hawaii and negative SLP anomalies over the Atlantic and North America. We then develop a linear regression model to predict JJA MDA8 ozone from 1980 to 2013, using the identified SST and SLP patterns from the previous spring. The model explains ∼45% of the variability in JJA MDA8 ozone concentrations and ∼30% variability in the number of JJA ozone episodes (>70 ppbv) when averaged over the eastern United States. This seasonal predictability results from large-scale ocean–atmosphere interactions. Warm tropical Atlantic SSTs can trigger diabatic heating in the atmosphere and influence the extratropical climate through stationary wave propagation, leading to greater subsidence, less precipitation, and higher temperatures in the East, which increases surface ozone concentrations there. Cooler SSTs in the northeast Pacific are also associated with more summertime heatwaves and high ozone in the East. On average, models participating in the Atmospheric Model Intercomparison Project fail to capture the influence of this ocean–atmosphere interaction on temperatures in the eastern United States, implying that such models would have difficulty simulating the interannual variability of surface ozone in this region. PMID:28223483

  5. Predicting and attributing recent East African Spring droughts with dynamical-statistical climate model ensembles

    NASA Astrophysics Data System (ADS)

    Funk, C. C.; Shukla, S.; Hoerling, M. P.; Robertson, F. R.; Hoell, A.; Liebmann, B.

    2013-12-01

    During boreal spring, eastern portions of Kenya and Somalia have experienced more frequent droughts since 1999. Given the region's high levels of food insecurity, better predictions of these droughts could provide substantial humanitarian benefits. We show that dynamical-statistical seasonal climate forecasts, based on the latest generation of coupled atmosphere-ocean and uncoupled atmospheric models, effectively predict boreal spring rainfall in this area. Skill sources are assessed by comparing ensembles driven with full-ocean forcing with ensembles driven with ENSO-only sea surface temperatures (SSTs). Our analysis suggests that both ENSO and non-ENSO Indo-Pacific SST forcing have played an important role in the increase in drought frequencies. Over the past 30 years, La Niña drought teleconnections have strengthened, while non-ENSO Indo-Pacific convection patterns have also supported increased (decreased) Western Pacific (East African) rainfall. To further examine the relative contribution of ENSO, low frequency warming and the Pacific Decadal Oscillation, we present decompositions of ECHAM5, GFS, CAM4 and GMAO AMIP simulations. These decompositions suggest that rapid warming in the western Pacific and steeper western-to-central Pacific SST gradients have likely played an important role in the recent intensification of the Walker circulation, and the associated increase in East African aridity. A linear combination of time series describing the Pacific Decadal Oscillation and the strength of Indo-Pacific warming are shown to track East African rainfall reasonably well. The talk concludes with a few thoughts linking the potentially important interplay of attribution and prediction. At least for recent East African droughts, it appears that a characteristic Indo-Pacific SST and precipitation anomaly pattern can be linked statistically to support forecasts and attribution analyses. The combination of traditional AGCM attribution analyses with simple yet

  6. Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change.

    PubMed

    Morin, Xavier; Thuiller, Wilfried

    2009-05-01

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

  7. From field to region yield predictions in response to pedo-climatic variations in Eastern Canada

    NASA Astrophysics Data System (ADS)

    JÉGO, G.; Pattey, E.; Liu, J.

    2013-12-01

    The increase in global population coupled with new pressures to produce energy and bioproducts from agricultural land requires an increase in crop productivity. However, the influence of climate and soil variations on crop production and environmental performance is not fully understood and accounted for to define more sustainable and economical management strategies. Regional crop modeling can be a great tool for understanding the impact of climate variations on crop production, for planning grain handling and for assessing the impact of agriculture on the environment, but it is often limited by the availability of input data. The STICS ("Simulateur mulTIdisciplinaire pour les Cultures Standard") crop model, developed by INRA (France) is a functional crop model which has a built-in module to optimize several input parameters by minimizing the difference between calculated and measured output variables, such as Leaf Area Index (LAI). STICS crop model was adapted to the short growing season of the Mixedwood Plains Ecozone using field experiments results, to predict biomass and yield of soybean, spring wheat and corn. To minimize the numbers of inference required for regional applications, 'generic' cultivars rather than specific ones have been calibrated in STICS. After the calibration of several model parameters, the root mean square error (RMSE) of yield and biomass predictions ranged from 10% to 30% for the three crops. A bit more scattering was obtained for LAI (20%prediction to climate variations. Using RS data to re-initialize input parameters that are not readily available (e.g. seeding date) is considered an effective way

  8. Predicting University Preference and Attendance: Applied Marketing in Higher Education Administration.

    ERIC Educational Resources Information Center

    Cook, Robert W.; Zallocco, Ronald L.

    1983-01-01

    A multi-attribute attitude model was used to determine whether a multicriteria scale can be used to predict student preferences for and attendance at universities. Data were gathered from freshmen attending five state universities in Ohio. The results indicate a high level of predictability. (Author/MLW)

  9. The First Pan-WCRP Workshop on Monsoon Climate Systems: Toward Better Prediction of the Monsoons

    SciTech Connect

    Sperber, K R; Yasunari, T

    2005-07-27

    In 2004 the Joint Scientific Committee (JSC) that provides scientific guidance to the World Climate Research Programme (WCRP) requested an assessment of (1) WCRP monsoon related activities and (2) the range of available observations and analyses in monsoon regions. The purpose of the assessment was to (a) define the essential elements of a pan-WCRP monsoon modeling strategy, (b) identify the procedures for producing this strategy, and (c) promote improvements in monsoon observations and analyses with a view toward their adequacy, and addressing any undue redundancy or duplication. As such, the WCRP sponsored the ''1st Pan-WCRP Workshop on Monsoon Climate Systems: Toward Better Prediction of the Monsoons'' at the University of California, Irvine, CA, USA from 15-17 June 2005. Experts from the two WCRP programs directly relevant to monsoon studies, the Climate Variability and Predictability Programme (CLIVAR) and the Global Energy and Water Cycle Experiment (GEWEX), gathered to assess the current understanding of the fundamental physical processes governing monsoon variability and to highlight outstanding problems in simulating the monsoon that can be tackled through enhanced cooperation between CLIVAR and GEWEX. The agenda with links to the presentations can be found at: http://www.clivar.org/organization/aamon/WCRPmonsoonWS/agenda.htm. Scientific motivation for a joint CLIVAR-GEWEX approach to investigating monsoons includes the potential for improved medium-range to seasonal prediction through better simulation of intraseasonal (30-60 day) oscillations (ISO's). ISO's are important for the onset of monsoons, as well as the development of active and break periods of rainfall during the monsoon season. Foreknowledge of the active and break phases of the monsoon is important for crop selection, the determination of planting times and mitigation of potential flooding and short-term drought. With a few exceptions simulations of ISO are typically poor in all classes of

  10. The Argo Project: Global Ocean Observations for Understanding and Prediction of Climate Variability. Report for Calendar Year 2006

    DTIC Science & Technology

    2006-01-01

    physical environment of ocean ecosystems. Over 90% of the increased heat content due to global warming of the air/sea/ice climate system in the...The Argo Project Global Ocean Observations for Understanding and Prediction of Climate Variability Report for Calendar Year 2006 Dean H...1. REPORT DATE 2006 2. REPORT TYPE 3. DATES COVERED 00-00-2006 to 00-00-2006 4. TITLE AND SUBTITLE The Argo Project Global Ocean

  11. Seasonal drought ensemble predictions based on multiple climate models in the upper Han River Basin, China

    NASA Astrophysics Data System (ADS)

    Ma, Feng; Ye, Aizhong; Duan, Qingyun

    2017-03-01

    An experimental seasonal drought forecasting system is developed based on 29-year (1982-2010) seasonal meteorological hindcasts generated by the climate models from the North American Multi-Model Ensemble (NMME) project. This system made use of a bias correction and spatial downscaling method, and a distributed time-variant gain model (DTVGM) hydrologic model. DTVGM was calibrated using observed daily hydrological data and its streamflow simulations achieved Nash-Sutcliffe efficiency values of 0.727 and 0.724 during calibration (1978-1995) and validation (1996-2005) periods, respectively, at the Danjiangkou reservoir station. The experimental seasonal drought forecasting system (known as NMME-DTVGM) is used to generate seasonal drought forecasts. The forecasts were evaluated against the reference forecasts (i.e., persistence forecast and climatological forecast). The NMME-DTVGM drought forecasts have higher detectability and accuracy and lower false alarm rate than the reference forecasts at different lead times (from 1 to 4 months) during the cold-dry season. No apparent advantage is shown in drought predictions during spring and summer seasons because of a long memory of the initial conditions in spring and a lower predictive skill for precipitation in summer. Overall, the NMME-based seasonal drought forecasting system has meaningful skill in predicting drought several months in advance, which can provide critical information for drought preparedness and response planning as well as the sustainable practice of water resource conservation over the basin.

  12. Predicting Plant Diversity Patterns in Madagascar: Understanding the Effects of Climate and Land Cover Change in a Biodiversity Hotspot

    PubMed Central

    Brown, Kerry A.; Parks, Katherine E.; Bethell, Colin A.; Johnson, Steig E.; Mulligan, Mark

    2015-01-01

    Climate and land cover change are driving a major reorganization of terrestrial biotic communities in tropical ecosystems. In an effort to understand how biodiversity patterns in the tropics will respond to individual and combined effects of these two drivers of environmental change, we use species distribution models (SDMs) calibrated for recent climate and land cover variables and projected to future scenarios to predict changes in diversity patterns in Madagascar. We collected occurrence records for 828 plant genera and 2186 plant species. We developed three scenarios, (i.e., climate only, land cover only and combined climate-land cover) based on recent and future climate and land cover variables. We used this modelling framework to investigate how the impacts of changes to climate and land cover influenced biodiversity across ecoregions and elevation bands. There were large-scale climate- and land cover-driven changes in plant biodiversity across Madagascar, including both losses and gains in diversity. The sharpest declines in biodiversity were projected for the eastern escarpment and high elevation ecosystems. Sharp declines in diversity were driven by the combined climate-land cover scenarios; however, there were subtle, region-specific differences in model outputs for each scenario, where certain regions experienced relatively higher species loss under climate or land cover only models. We strongly caution that predicted future gains in plant diversity will depend on the development and maintenance of dispersal pathways that connect current and future suitable habitats. The forecast for Madagascar’s plant diversity in the face of future environmental change is worrying: regional diversity will continue to decrease in response to the combined effects of climate and land cover change, with habitats such as ericoid thickets and eastern lowland and sub-humid forests particularly vulnerable into the future. PMID:25856241

  13. Predicting plant diversity patterns in Madagascar: understanding the effects of climate and land cover change in a biodiversity hotspot.

    PubMed

    Brown, Kerry A; Parks, Katherine E; Bethell, Colin A; Johnson, Steig E; Mulligan, Mark

    2015-01-01

    Climate and land cover change are driving a major reorganization of terrestrial biotic communities in tropical ecosystems. In an effort to understand how biodiversity patterns in the tropics will respond to individual and combined effects of these two drivers of environmental change, we use species distribution models (SDMs) calibrated for recent climate and land cover variables and projected to future scenarios to predict changes in diversity patterns in Madagascar. We collected occurrence records for 828 plant genera and 2186 plant species. We developed three scenarios, (i.e., climate only, land cover only and combined climate-land cover) based on recent and future climate and land cover variables. We used this modelling framework to investigate how the impacts of changes to climate and land cover influenced biodiversity across ecoregions and elevation bands. There were large-scale climate- and land cover-driven changes in plant biodiversity across Madagascar, including both losses and gains in diversity. The sharpest declines in biodiversity were projected for the eastern escarpment and high elevation ecosystems. Sharp declines in diversity were driven by the combined climate-land cover scenarios; however, there were subtle, region-specific differences in model outputs for each scenario, where certain regions experienced relatively higher species loss under climate or land cover only models. We strongly caution that predicted future gains in plant diversity will depend on the development and maintenance of dispersal pathways that connect current and future suitable habitats. The forecast for Madagascar's plant diversity in the face of future environmental change is worrying: regional diversity will continue to decrease in response to the combined effects of climate and land cover change, with habitats such as ericoid thickets and eastern lowland and sub-humid forests particularly vulnerable into the future.

  14. Functional trade-offs in succulent stems predict responses to climate change in columnar cacti.

    PubMed

    Williams, David G; Hultine, Kevin R; Dettman, David L

    2014-07-01

    Columnar cacti occur naturally in many habitats and environments in the Americas but are conspicuously dominant in very dry desert regions. These majestic plants are widely regarded for their cultural, economic, and ecological value and, in many ecosystems, support highly diverse communities of pollinators, seed dispersers, and frugivores. Massive amounts of water and other resources stored in the succulent photosynthetic stems of these species confer a remarkable ability to grow and reproduce during intensely hot and dry periods. Yet many columnar cacti are potentially under severe threat from environmental global changes, including climate change and loss of habitat. Stems in columnar cacti and other cylindrical-stemmed cacti are morphologically diverse; stem volume-to-surface area ratio (V:S) across these taxa varies by almost two orders of magnitude. Intrinsic functional trade-offs are examined here across a broad range of V:S in species of columnar cacti. It is proposed that variation in photosynthetic gas exchange, growth, and response to stress is highly constrained by stem V:S, establishing a mechanistic framework for understanding the sensitivity of columnar cacti to climate change and drought. Specifically, species that develop stems with low V:S, and thus have little storage capacity, are expected to express high mass specific photosynthesis and growth rates under favourable conditions compared with species with high V:S. But the trade-off of having little storage capacity is that low V:S species are likely to be less tolerant of intense or long-duration drought compared with high V:S species. The application of stable isotope measurements of cactus spines as recorders of growth, water relations, and metabolic responses to the environment across species of columnar cacti that vary in V:S is also reviewed. Taken together, our approach provides a coherent theory and required set of observations needed for predicting the responses of columnar cacti to

  15. Effects of European land use on contemporary tree-climate relationships in the northeastern United States: Implications for predictive models

    NASA Astrophysics Data System (ADS)

    Goring, S. J.; Cogbill, C. V.; Dawson, A.; Hooten, M.; McLachlan, J. S.; Mladenoff, D. J.; Paciorek, C. J.; Ruid, M.; Tipton, J.; Williams, J. W.; Record, S.; Matthes, J. H.; Dietze, M.

    2014-12-01

    Much of our understanding of the climatic controls on tree species distributions is based on contemporary observational datasets. For example, forest inventory analysis (FIA) and other spatial datasets are used to build correlative models of climate suitability for plant taxa for use in environmental niche models. More complex dynamic models rely on species interactions, physiological processes, and competition, among other processes, that are also parameterized against contemporary data. However, as much as a quarter of the forested region in the upper Midwestern United States may be considered novel relative to pre-settlement baselines (Goring et al. submitted). Hence, modern surveys or even long-term datasets may represent only a portion of the ecological or climate space taxa might occupy. Using gridded datasets of pre-settlement vegetation for the northeastern United States from Town Propritor Suveys and the Public Land Survey, we examine the effects of European land-use conversion - logging, agricultural conversion and re-establishment - on climate-vegetation relationships. We show that in regions where land-use change is climatically biased, such as conversion to agriculture along the prairie-forest boundary, impacts on the realized climatic niches for various tree taxa can be significant. Improving predicted distributions of taxa is critical for planning and mitigating the effects of widespread shifts in forest composition resulting from climate change. Using pre-settlement data can improve our understanding of the potential niches occupied by major forest taxa, improving the predictive abilities of environmental niche and mechanistic models.

  16. Multi-Scale Predictions of the Asian Monsoons in the NCEP Climate Forecast System

    NASA Astrophysics Data System (ADS)

    Yang, S.

    2013-12-01

    A comprehensive analysis of the major features of the Asian monsoon system in the NCEP Climate Forecast System version 2 (CFSv2) and predictions of the monsoon by the model has been conducted. The intraseasonal-to-interannual variations of both summer monsoon and winter monsoon, as well as the annual cycles of monsoon climate, are focused. Features of regional monsoons including the monsoon phenomena over South Asia, East Asia, and Southeast Asia are discussed. The quasi-biweekly oscillation over tropical Asia and the Mei-yu climate over East Asia are also investigated. Several aspects of monsoon features including the relationships between monsoon and ENSO (including different types of ENSO: eastern Pacific warming and central Pacific warming), extratropical effects, dependence on time leads (initial conditions), regional monsoon features, and comparison between CFSv2 and CFS version 1 (CFSv1) are particularly emphasized. Large-scale characteristics of the Asian summer monsoon including several major dynamical monsoon indices and their associated precipitation patterns can be predicted several months in advance. The skill of predictions of the monsoon originates mostly from the impact of ENSO. It is found that large predictability errors occur in first three lead months and they only change slightly as lead time increases. The large errors in the first three lead months are associated with the large errors in surface thermal condition and atmospheric circulation in the central and eastern Pacific and the African continent. In addition, the response of the summer monsoon to ENSO becomes stronger with increase in lead time. The CFSv2 successfully simulates several major features of the East Asian winter monsoon and its relationships with the Arctic Oscillation, the East Asian subtropical jet, the East Asian trough, the Siberian high, and the lower-tropospheric winds. Surprisingly, the upper-tropospheric winds over the middle-high latitudes can be better simulated

  17. Predictive analysis of landslide susceptibility in the Kao-Ping watershed, Taiwan under climate change conditions

    NASA Astrophysics Data System (ADS)

    Shou, K. J.; Wu, C. C.; Lin, J. F.

    2015-01-01

    Among the most critical issues, climatic abnormalities caused by global warming also affect Taiwan significantly for the past decade. The increasing frequency of extreme rainfall events, in which concentrated and intensive rainfalls generally cause geohazards including landslides and debris flows. The extraordinary Typhoon Morakot hit Southern Taiwan on 8 August 2009 and induced serious flooding and landslides. In this study, the Kao-Ping River watershed was adopted as the study area, and the typical events 2007 Krosa Typhoon and 2009 Morakot Typhoon were adopted to train the susceptibility model. This study employs rainfall frequency analysis together with the atmospheric general circulation model (AGCM) downscaling estimation to understand the temporal rainfall trends, distributions, and intensities in the Kao-Ping River watershed. The rainfall estimates were introduced in the landslide susceptibility model to produce the predictive landslide susceptibility for various rainfall scenarios, including abnormal climate conditions. These results can be used for hazard remediation, mitigation, and prevention plans for the Kao-Ping River watershed.

  18. Genetic and physiological bases for phenological responses to current and predicted climates

    PubMed Central

    Wilczek, A. M.; Burghardt, L. T.; Cobb, A. R.; Cooper, M. D.; Welch, S. M.; Schmitt, J.

    2010-01-01

    We are now reaching the stage at which specific genetic factors with known physiological effects can be tied directly and quantitatively to variation in phenology. With such a mechanistic understanding, scientists can better predict phenological responses to novel seasonal climates. Using the widespread model species Arabidopsis thaliana, we explore how variation in different genetic pathways can be linked to phenology and life-history variation across geographical regions and seasons. We show that the expression of phenological traits including flowering depends critically on the growth season, and we outline an integrated life-history approach to phenology in which the timing of later life-history events can be contingent on the environmental cues regulating earlier life stages. As flowering time in many plants is determined by the integration of multiple environmentally sensitive gene pathways, the novel combinations of important seasonal cues in projected future climates will alter how phenology responds to variation in the flowering time gene network with important consequences for plant life history. We discuss how phenology models in other systems—both natural and agricultural—could employ a similar framework to explore the potential contribution of genetic variation to the physiological integration of cues determining phenology. PMID:20819808

  19. Uncertainties of seasonal surface climate predictions induced by soil moisture biases in the La Plata Basin

    NASA Astrophysics Data System (ADS)

    Sorensson, Anna; Berbery, E. Hugo

    2015-04-01

    This work examines the evolution of soil moisture initialization biases and their effects on seasonal forecasts depending on the season and vegetation type for a regional model over the La Plata Basin in South America. WRF/Noah model simulations covering multiple cases during a two-year period are designed to emphasize the conceptual nature of the simulations at the expense of statistical significance of the results. Analysis of the surface climate shows that the seasonal predictive skill is higher when the model is initialized during the wet season and the initial soil moisture differences are small. Large soil moisture biases introduce large surface temperature biases, particularly for Savanna, Grassland and Cropland vegetation covers at any time of the year, thus introducing uncertainty in the surface climate. Regions with Evergreen Broadleaf Forest have roots that extend to the deep layer whose moisture content affects the surface temperature through changes in the partitioning of the surface fluxes. The uncertainties of monthly maximum temperature can reach several degrees during the dry season in cases when: (a) the soil is much wetter in the reanalysis than in the WRF/Noah equilibrium soil moisture, and (b) the memory of the initial value is long due to scarce rainfall and low temperatures. This study suggests that responses of the atmosphere to soil moisture initialization depend on how the initial wet and dry conditions are defined, stressing the need to take into account the characteristics of a particular region and season when defining soil moisture initialization experiments.

  20. Sensitivity of an energy balance climate model with predicted snowfall rates

    NASA Technical Reports Server (NTRS)

    Bowman, K. P.

    1985-01-01

    A snowfall parameterization and a polar-ice-sheet model are developed and applied to the two-level zonally averaged seasonal energy-balance climate model of Held and Suarez (1979), and sensitivity experiments involving changes in insolation are performed both with and without ice sheets. The results are presented in tables and graphs, and the hydrological-cycle response to insolation changes is found to be similar to that predicted by global-circulation models employing prescribed precipitation levels, with a somewhat higher sensitivity in the snow line. The area covered by ice sheets in the ice-sheet models is shown to be greater than that covered by permanent snow in the models without ice sheets, an effect attributed to lower surface temperatures over the ice. It is inferred that an increase in the solar constant can cause increased high-latitude precipitation but not an ice age.

  1. Global climate changes recorded in coastal wetland sediments: Empirical observations linked to theoretical predictions

    NASA Astrophysics Data System (ADS)

    Kolker, Alexander S.; Kirwan, Matthew L.; Goodbred, Steven L.; Cochran, J. Kirk

    2010-07-01

    Whether coastal areas are experiencing, and responding to, an accelerated rate of global sea-level rise (GSLR) is critically important for the ˜2 billion people living near Earth's oceans. Accretion rates from a suite of physiographically diverse coastal wetlands surrounding Long Island, NY accelerated during the 20th century at 2.3 ± 0.2 × 10-2 mm yr-2, which is comparable to reported rates of GSLR acceleration and global temperature changes. Wetlands varied in tidal range, salinity and geomorphic setting, and were located in embayments with limited human impacts in a region with limited and constant rates of subsidence. From geochronologies with temporal resolutions of 2-5 yr, we constructed new composite histories of sediment accretion and mineral deposition. Wetland dynamics are consistent with predictions from sedimentology and a numerical model of ecogeomorphic response, suggesting that these systems, and likely others worldwide, are responding to accelerated GSLR and related climatic changes.

  2. Predicting the likely response of data-poor ecosystems to climate change using space-for-time substitution across domains.

    PubMed

    Lester, Rebecca E; Close, Paul G; Barton, Jan L; Pope, Adam J; Brown, Stuart C

    2014-11-01

    Predicting ecological response to climate change is often limited by a lack of relevant local data from which directly applicable mechanistic models can be developed. This limits predictions to qualitative assessments or simplistic rules of thumb in data-poor regions, making management of the relevant systems difficult. We demonstrate a method for developing quantitative predictions of ecological response in data-poor ecosystems based on a space-for-time substitution, using distant, well-studied systems across an inherent climatic gradient to predict ecological response. Changes in biophysical data across the spatial gradient are used to generate quantitative hypotheses of temporal ecological responses that are then tested in a target region. Transferability of predictions among distant locations, the novel outcome of this method, is demonstrated via simple quantitative relationships that identify direct and indirect impacts of climate change on physical, chemical and ecological variables using commonly available data sources. Based on a limited subset of data, these relationships were demonstrably plausible in similar yet distant (>2000 km) ecosystems. Quantitative forecasts of ecological change based on climate-ecosystem relationships from distant regions provides a basis for research planning and informed management decisions, especially in the many ecosystems for which there are few data. This application of gradient studies across domains - to investigate ecological response to climate change - allows for the quantification of effects on potentially numerous, interacting and complex ecosystem components and how they may vary, especially over long time periods (e.g. decades). These quantitative and integrated long-term predictions will be of significant value to natural resource practitioners attempting to manage data-poor ecosystems to prevent or limit the loss of ecological value. The method is likely to be applicable to many ecosystem types, providing a

  3. Darcy’s law predicts widespread forest mortality under climate warming

    USGS Publications Warehouse

    McDowell, Nate G.; Allen, Craig D.

    2015-01-01

    Drought and heat-induced tree mortality is accelerating in many forest biomes as a consequence of a warming climate, resulting in a threat to global forests unlike any in recorded history1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12. Forests store the majority of terrestrial carbon, thus their loss may have significant and sustained impacts on the global carbon cycle11,12. We use a hydraulic corollary to Darcy’s law, a core principle of vascular plant physiology13, to predict characteristics of plants that will survive and die during drought under warmer future climates. Plants that are tall with isohydric stomatal regulation, low hydraulic conductance, and high leaf area are most likely to die from future drought stress. Thus, tall trees of old-growth forests are at the greatest risk of loss, which has ominous implications for terrestrial carbon storage. This application of Darcy’s law indicates today’s forests generally should be replaced by shorter and more xeric plants, owing to future warmer droughts and associated wildfires and pest attacks. The Darcy’s corollary also provides a simple, robust framework for informing forest management interventions needed to promote the survival of current forests. Given the robustness of Darcy’s law for predictions of vascular plant function, we conclude with high certainty that today’s forests are going to be subject to continued increases in mortality rates that will result in substantial reorganization of their structure and carbon storage.

  4. Sensitivity of soil moisture initialization for decadal predictions under different regional climatic conditions in Europe

    NASA Astrophysics Data System (ADS)

    Khodayar, S.; Sehlinger, A.; Feldmann, H.; Kottmeier, C.

    2015-12-01

    The impact of soil initialization is investigated through perturbation simulations with the regional climate model COSMO-CLM. The focus of the investigation is to assess the sensitivity of simulated extreme periods, dry and wet, to soil moisture initialization in different climatic regions over Europe and to establish the necessary spin up time within the framework of decadal predictions for these regions. Sensitivity experiments consisted of a reference simulation from 1968 to 1999 and 5 simulations from 1972 to 1983. The Effective Drought Index (EDI) is used to select and quantify drought status in the reference run to establish the simulation time period for the sensitivity experiments. Different soil initialization procedures are investigated. The sensitivity of the decadal predictions to soil moisture initial conditions is investigated through the analysis of water cycle components' (WCC) variability. In an episodic time scale the local effects of soil moisture on the boundary-layer and the propagated effects on the large-scale dynamics are analysed. The results show: (a) COSMO-CLM reproduces the observed features of the drought index. (b) Soil moisture initialization exerts a relevant impact on WCC, e.g., precipitation distribution and intensity. (c) Regional characteristics strongly impact the response of the WCC. Precipitation and evapotranspiration deviations are larger for humid regions. (d) The initial soil conditions (wet/dry), the regional characteristics (humid/dry) and the annual period (wet/dry) play a key role in the time that soil needs to restore quasi-equilibrium and the impact on the atmospheric conditions. Humid areas, and for all regions, a humid initialization, exhibit shorter spin up times, also soil reacts more sensitive when initialised during dry periods. (e) The initial soil perturbation may markedly modify atmospheric pressure field, wind circulation systems and atmospheric water vapour distribution affecting atmospheric stability

  5. Recent Shift in Climate Relationship Enables Prediction of the Timing of Bird Breeding.

    PubMed

    Hinsley, Shelley A; Bellamy, Paul E; Hill, Ross A; Ferns, Peter N

    2016-01-01

    Large-scale climate processes influence many aspects of ecology including breeding phenology, reproductive success and survival across a wide range of taxa. Some effects are direct, for example, in temperate-zone birds, ambient temperature is an important cue enabling breeding effort to coincide with maximum food availability, and earlier breeding in response to warmer springs has been documented in many species. In other cases, time-lags of up to several years in ecological responses have been reported, with effects mediated through biotic mechanisms such as growth rates or abundance of food supplies. Here we use 23 years of data for a temperate woodland bird species, the great tit (Parus major), breeding in deciduous woodland in eastern England to demonstrate a time-lagged linear relationship between the on-set of egg laying and the winter index of the North Atlantic Oscillation such that timing can be predicted from the winter index for the previous year. Thus the timing of bird breeding (and, by inference, the timing of spring events in general) can be predicted one year in advance. We also show that the relationship with the winter index appears to arise through an abiotic time-lag with local spring warmth in our study area. Examining this link between local conditions and larger-scale processes in the longer-term showed that, in the past, significant relationships with the immediately preceding winter index were more common than those with the time-lagged index, and especially so from the late 1930s to the early 1970s. However, from the mid 1970s onwards, the time-lagged relationship has become the most significant, suggesting a recent change in climate patterns. The strength of the current time-lagged relationship suggests that it might have relevance for other temperature-dependent ecological relationships.

  6. Assimilation of temperature and salinity profile data in the Norwegian Climate Prediction Model

    NASA Astrophysics Data System (ADS)

    Wang, Yiguo; Counillon, Francois; Bertino, Laurent; Bethke, Ingo; Keenlyside, Noel

    2016-04-01

    Assimilating temperature and salinity profile data is promising to constrain the ocean component of Earth system models for the purpose of seasonal-to-dedacal climate predictions. However, assimilating temperature and salinity profiles that are measured in standard depth coordinate (z-coordinate) into isopycnic coordinate ocean models that are discretised by water densities is challenging. Prior studies (Thacker and Esenkov, 2002; Xie and Zhu, 2010) suggested that converting observations to the model coordinate (i.e. innovations in isopycnic coordinate) performs better than interpolating model state to observation coordinate (i.e. innovations in z-coordinate). This problem is revisited here with the Norwegian Climate Prediction Model, which applies the ensemble Kalman filter (EnKF) into the ocean isopycnic model (MICOM) of the Norwegian Earth System Model. We perform Observing System Simulation Experiments (OSSEs) to compare two schemes (the EnKF-z and EnKF-ρ). In OSSEs, the truth is set to the EN4 objective analyses and observations are perturbations of the truth with white noises. Unlike in previous studies, it is found that EnKF-z outperforms EnKF-ρ for different observed vertical resolution, inhomogeneous sampling (e.g. upper 1000 meter observations only), or lack of salinity measurements. That is mostly because the operator converting observations into isopycnic coordinate is strongly non-linear. We also study the horizontal localisation radius at certain arbitrary grid points. Finally, we perform the EnKF-z with the chosen localisation radius in a realistic framework with NorCPM over a 5-year analysis period. The analysis is validated by different independent datasets.

  7. Predictions of Flow Duration Curve Shifts Due to Anthropogenic and Climatic Changes

    NASA Astrophysics Data System (ADS)

    Henry, N. F.; Kroll, C. N.; Endreny, T. A.

    2014-12-01

    Methods are needed to understand and predict streamflows in systems undergoing anthropogenic and climatic alteration. This study is motivated by a need to develop methods to accurately estimate historical and future flow regimes of the Delaware River to inform management decisions for the endangered dwarf wedgemussel (Alasmidonta heterodon). Many streamflow regimes in this system have undergone substantial alteration within the past 100 years. Here, flow duration curves (FDCs), a common hydrologic tool used to assess flow regimes, are created and examined at 145 Delaware River Basin catchments. These catchments have experienced various hydrologic alterations, including land use changes, water withdrawals, and river regulation due to dams and reservoirs. Linear regression models are developed for various percentile flows across a FDC. These models use watershed characteristics that describe observed flow regimes in altered as well as unaltered systems. The characteristics that have the most significant influence on the shape of the FDCs are then identified and isolated as descriptors of the alteration. Once these models are developed to include these key variables, given a specific alteration (e.g. fresh water withdrawals, change in annual precipitation, etc.), a new flow regime can be estimated. Preliminary results indicate that certain watershed characteristics related to alteration (e.g. magnitude of land fragmentation, water withdrawals, hydrologic disturbance index) are significant in our models and influence FDC patterns. The results of this study may prove to have broader applications in regards to water resources management as the methods developed here may serve as a predictive tool as human interference and climatic changes continue to alter flow regimes.

  8. Recent Shift in Climate Relationship Enables Prediction of the Timing of Bird Breeding

    PubMed Central

    Bellamy, Paul E.; Hill, Ross A.; Ferns, Peter N.

    2016-01-01

    Large-scale climate processes influence many aspects of ecology including breeding phenology, reproductive success and survival across a wide range of taxa. Some effects are direct, for example, in temperate-zone birds, ambient temperature is an important cue enabling breeding effort to coincide with maximum food availability, and earlier breeding in response to warmer springs has been documented in many species. In other cases, time-lags of up to several years in ecological responses have been reported, with effects mediated through biotic mechanisms such as growth rates or abundance of food supplies. Here we use 23 years of data for a temperate woodland bird species, the great tit (Parus major), breeding in deciduous woodland in eastern England to demonstrate a time-lagged linear relationship between the on-set of egg laying and the winter index of the North Atlantic Oscillation such that timing can be predicted from the winter index for the previous year. Thus the timing of bird breeding (and, by inference, the timing of spring events in general) can be predicted one year in advance. We also show that the relationship with the winter index appears to arise through an abiotic time-lag with local spring warmth in our study area. Examining this link between local conditions and larger-scale processes in the longer-term showed that, in the past, significant relationships with the immediately preceding winter index were more common than those with the time-lagged index, and especially so from the late 1930s to the early 1970s. However, from the mid 1970s onwards, the time-lagged relationship has become the most significant, suggesting a recent change in climate patterns. The strength of the current time-lagged relationship suggests that it might have relevance for other temperature-dependent ecological relationships. PMID:27182711

  9. Scaling Process Studies and Observations in the Arctic for Improved Climate Predictability

    NASA Astrophysics Data System (ADS)

    Wilson, C. J.; Graham, D. E.; Hinzman, L. D.; Hubbard, S. S.; Liang, L.; Norby, R. J.; Riley, W. J.; Rogers, A.; Rowland, J. C.; Thornton, P. E.; Torn, M. S.; Wullschleger, S. D.

    2012-12-01

    A fundamental goal of the Next-Generation Ecosystem Experiments (NGEE-Arctic) project is to improve climate prediction through process understanding and representation of that knowledge in Earth System models. Geomorphological units, including thaw lakes, drained thaw lake basins, and ice-rich polygonal ground provide the organizing framework for our model scaling approach for the coastal plains of the North Slope of Alaska. A comprehensive suite of process studies and observations of hydrology, geomorphology, biogeochemistry, vegetation patterns, and energy exchange and their couplings will be undertaken across nested scales to populate the NGEE hierarchical modeling framework and to achieve a broader goal of optimally informing process representations in a global-scale model. A central focus of this challenge is to advance process understanding and prediction of the evolution of permafrost degradation and its impact on topography and thermal conditions and how these changes control the spatial and temporal availability of water for biogeochemical, ecological, and physical feedbacks to the climate system. Field activities to inform model development is being carried out across a gradient of polygonal ground nested within a drained thaw lake basin age gradient near Barrow, Alaska. Co-analysis of in-situ observations with ground based geophysical and airborne and satellite based remote sensing products from the single polygon to multiple drained lake basin scale is revealing surface-subsurface variability and interactions that influence or control local hydrology, greenhouse gas production, vegetation and the energy balance. We are using a range of data assimilation and fusion techniques to combine spatially extensive data sets developed from multi-scale field data with intensive data being collected from both controlled laboratory experiments using field cores and in-situ thermal, hydrologic, biogeochemical and ecologic observations to improve process understanding

  10. Investigating the potential of SST assimilation for ocean state estimation and climate prediction

    NASA Astrophysics Data System (ADS)

    Keenlyside, Noel; Counillon, Francois; Bethke, Ingo; Wang, Yiguo; Billeau, Sebastien; Shen, Mao-Lin; Bentsen, Mats

    2016-04-01

    The Norwegian Climate Prediction Model (NorCPM) assimilates the stochastic HadISST2 product with the ensemble Kalman Filter data assimilation method into the ocean part the Norwegian Earth System model. We document a pilot stochastic reanalysis for the period 1950-2010 and use it to perform seasonal-to-decadal (s2d) predictions. The accuracy, reliability and drift is investigated using both assimilated and independent observations. NorCPM is found slightly over-dispersive against assimilated observations but shows stable performance through the analysis period (˜0.4K). It demonstrates skill against independent measurements: SSH, heat and salt content, in particular in the ENSO, the North Pacific, the North Atlantic subpolar gyre (SPG) regions and the Nordic Seas. Furthermore, NorCPM provides a reliable monitoring of the SPG index and represents the variability of the temperature vertical structure there in good agreement with observations. The monitoring of the Atlantic meridional overturning circulation is also encouraging. The benefit of using flow dependent assimilation method and constructing the covariance in isopycnal coordinate are investigated in the SPG region. Isopycnal coordinate discretisation is found to better captures the vertical structure than standard depth-coordinate discretisation, which can deepen the influence of assimilation when assimilating surface observations. The vertical covariance shows a pronounced seasonal and decadal variability, which highlights the benefit of flow dependent data assimilation method. This study demonstrates the potential of NorCPM for providing a long reanalysis for the 19-20 century when SST observations are available. The results of s2d predictions carried out will be presented, and the potential to use this method to assess decadal predictability over the historical period will be discussed.

  11. Polar predictability: exploring the influence of GCM and regional model uncertainty on future ice sheet climates

    NASA Astrophysics Data System (ADS)

    Reusch, D. B.

    2015-12-01

    Evaluating uncertainty in GCMs and regional-scale forecast models is an essential step in the development of climate change predictions. Polar-region skill is particularly important due to the potential for changes affecting both local (ice sheet) and global (sea level) environments through more frequent/intense surface melting and changes in precipitation type/amount. High-resolution, regional-scale models also use GCMs as a source of boundary/initial conditions in future scenarios, thus inheriting a measure of GCM-derived externally-driven uncertainty. We examine inter- and intramodel uncertainty through statistics from decadal climatologies and analyses of variability based on self-organizing maps (SOMs), a nonlinear data analysis tool. We evaluate a 19-member CMIP5 subset and the 30-member CESM1.0-CAM5-BGC Large Ensemble (CESMLE) during polar melt seasons (boreal/austral summer) for recent (1981-2000) and future (2081-2100, RCP 8.5) decades. Regional-model uncertainty is examined with a subset of these GCMs driving Polar WRF simulations. Decadal climatologies relative to a reference (recent: the ERA-Interim reanalysis; future: a skillful modern GCM) identify model uncertainty in bulk, e.g., BNU-ESM is too warm, CMCC-CM too cold. While quite useful for model screening, diagnostic benefit is often indirect. SOMs extend our diagnostics by providing a concise, objective summary of model variability as a set of generalized patterns. Joint analysis of reference and test models summarizes the variability of multiple realizations of climate (all the models), benchmarks each model versus the reference (frequency analysis helps identify the patterns behind GCM bias), and places each GCM in a common context. Joint SOM analysis of CESMLE members shows how initial conditions contribute to differences in modeled climates, providing useful information about internal variability, such as contributions from each member to overall uncertainty using pattern frequencies. In the

  12. Population differentiation in tree-ring growth response of white fir (Abies concolor) to climate: Implications for predicting forest responses to climate change

    SciTech Connect

    Jensen, Deborah Bowne

    1993-01-01

    Forest succession models and correlative models have predicted 200--650 kilometer shifts in the geographic range of temperate forests and forest species as one response to global climate change. Few studies have investigated whether population differences may effect the response of forest species to climate change. This study examines differences in tree-ring growth, and in the phenotypic plasticity of tree-ring growth in 16-year old white fir, Abies concolor, from ten populations grown in four common gardens in the Sierra Nevada of California. For each population, tree-ring growth was modelled as a function of precipitation and degree-day sums. Tree-ring growth under three scenarios of doubled CO2 climates was estimated.

  13. Predicting the impacts of climate change on the distribution of threatened forest-restricted birds in Madagascar.

    PubMed

    Andriamasimanana, Rado H; Cameron, Alison

    2013-04-01

    The greatest common threat to birds in Madagascar has historically been from anthropogenic deforestation. During recent decades, global climate change is now also regarded as a significant threat to biodiversity. This study uses Maximum Entropy species distribution modeling to explore how potential climate change could affect the distribution of 17 threatened forest endemic bird species, using a range of climate variables from the Hadley Center's HadCM3 climate change model, for IPCC scenario B2a, for 2050. We explore the importance of forest cover as a modeling variable and we test the use of pseudo-presences drawn from extent of occurrence distributions. Inclusion of the forest cover variable improves the models and models derived from real-presence data with forest layer are better predictors than those from pseudo-presence data. Using real-presence data, we analyzed the impacts of climate change on the distribution of nine species. We could not predict the impact of climate change on eight species because of low numbers of occurrences. All nine species were predicted to experience reductions in their total range areas, and their maximum modeled probabilities of occurrence. In general, species range and altitudinal contractions follow the reductive trend of the Maximum presence probability. Only two species (Tyto soumagnei and Newtonia fanovanae) are expected to expand their altitude range. These results indicate that future availability of suitable habitat at different elevations is likely to be critical for species persistence through climate change. Five species (Eutriorchis astur, Neodrepanis hypoxantha, Mesitornis unicolor, Euryceros prevostii, and Oriola bernieri) are probably the most vulnerable to climate change. Four of them (E. astur, M. unicolor, E. prevostii, and O. bernieri) were found vulnerable to the forest fragmentation during previous research. Combination of these two threats in the future could negatively affect these species in a drastic way

  14. The potential for predicted climate shifts to impact genetic landscapes of lizards in the South African Cape Floristic Region.

    PubMed

    Tolley, Krystal A; Makokha, Jane Sakwa; Houniet, Darren T; Swart, Belinda L; Matthee, Conrad A

    2009-04-01

    The Cape Floristic Region (CFR) is well-known for its floral diversity, yet also contains a rich herpetofauna with >180 species, 28% of which are endemic. Recent studies conducted on CFR lizards indicated that phylogeographic patterns show some congruency, and that the western CFR shows higher overall diversity in the form of population and/or clade turnover. Here, we combine mitochondrial sequence data from two published (Bradypodion spp. and Agama atra) and one new dataset (Pedioplanis burchelli) to investigate whether geographic patterns of genetic diversity could be influenced by predicted climatic changes. We utilised Bayesian methodology and spatial genetic landscapes to establish broad-scale patterns and show that the western CFR is a contact zone for several clades in all three taxa, supporting the hypothesis of phylogeographic congruence. Current levels of gene flow are virtually zero between the western and eastern CFR. In the east, gene flow between populations is negligible at present but was probably stronger in the past given the present lack of strong genetic structure. Bioclimatic modelling predicted that climatically suitable areas within the CFR will decline for Bradypodion spp. and P. burchelli, with areas high in clade turnover loosing more climatically suitable areas than areas with low clade turnover. The models also predict that loss of climatic suitability may result in highly fragmented and patchy distributions, resulting in a greater loss of connectivity. In contrast, A. atra does not show significant climatic suitability losses overall, although it may experience localised losses (and gains). This species is not predicted to loose suitability in areas of high clade turnover. Thus, the incorporation of genetic data into climatic models has extended our knowledge on the vulnerability of these species given the predicted threat of landscape change.

  15. Using Bayesian methods to predict climate impacts on groundwater availability and agricultural production in Punjab, India

    NASA Astrophysics Data System (ADS)

    Russo, T. A.; Devineni, N.; Lall, U.

    2015-12-01

    Lasting success of the Green Revolution in Punjab, India relies on continued availability of local water resources. Supplying primarily rice and wheat for the rest of India, Punjab supports crop irrigation with a canal system and groundwater, which is vastly over-exploited. The detailed data required to physically model future impacts on water supplies agricultural production is not readily available for this region, therefore we use Bayesian methods to estimate hydrologic properties and irrigation requirements for an under-constrained mass balance model. Using measured values of historical precipitation, total canal water delivery, crop yield, and water table elevation, we present a method using a Markov chain Monte Carlo (MCMC) algorithm to solve for a distribution of values for each unknown parameter in a conceptual mass balance model. Due to heterogeneity across the state, and the resolution of input data, we estimate model parameters at the district-scale using spatial pooling. The resulting model is used to predict the impact of precipitation change scenarios on groundwater availability under multiple cropping options. Predicted groundwater declines vary across the state, suggesting that crop selection and water management strategies should be determined at a local scale. This computational method can be applied in data-scarce regions across the world, where water resource management is required to resolve competition between food security and available resources in a changing climate.

  16. Historical citizen science to understand and predict climate-driven trout decline.

    PubMed

    Clavero, Miguel; Ninyerola, Miquel; Hermoso, Virgilio; Filipe, Ana Filipa; Pla, Magda; Villero, Daniel; Brotons, Lluís; Delibes, Miguel

    2017-01-11

    Historical species records offer an excellent opportunity to test the predictive ability of range forecasts under climate change, but researchers often consider that historical records are scarce and unreliable, besides the datasets collected by renowned naturalists. Here, we demonstrate the relevance of biodiversity records developed through citizen-science initiatives generated outside the natural sciences academia. We used a Spanish geographical dictionary from the mid-nineteenth century to compile over 10 000 freshwater fish records, including almost 4 000 brown trout (Salmo trutta) citations, and constructed a historical presence-absence dataset covering over 2 000 10 × 10 km cells, which is comparable to present-day data. There has been a clear reduction in trout range in the past 150 years, coinciding with a generalized warming. We show that current trout distribution can be accurately predicted based on historical records and past and present values of three air temperature variables. The models indicate a consistent decline of average suitability of around 25% between 1850s and 2000s, which is expected to surpass 40% by the 2050s. We stress the largely unexplored potential of historical species records from non-academic sources to open new pathways for long-term global change science.

  17. Seasonally Varying Predation Behavior and Climate Shifts Are Predicted to Affect Predator-Prey Cycles.

    PubMed

    Tyson, Rebecca; Lutscher, Frithjof

    2016-11-01

    The functional response of some predator species changes from a pattern characteristic for a generalist to that for a specialist according to seasonally varying prey availability. Current theory does not address the dynamic consequences of this phenomenon. Since season length correlates strongly with altitude and latitude and is predicted to change under future climate scenarios, including this phenomenon in theoretical models seems essential for correct prediction of future ecosystem dynamics. We develop and analyze a two-season model for the great horned owl (Bubo virginialis) and snowshoe hare (Lepus americanus). These species form a predator-prey system in which the generalist to specialist shift in predation pattern has been documented empirically. We study the qualitative behavior of this predator-prey model community as summer season length changes. We find that relatively small changes in summer season length can have a profound impact on the system. In particular, when the predator has sufficient alternative resources available during the summer season, it can drive the prey to extinction, there can be coexisting stable states, and there can be stable large-amplitude limit cycles coexisting with a stable steady state. Our results illustrate that the impacts of global change on local ecosystems can be driven by internal system dynamics and can potentially have catastrophic consequences.

  18. Idiosyncratic species effects confound size-based predictions of responses to climate change

    PubMed Central

    Twomey, Marion; Brodte, Eva; Jacob, Ute; Brose, Ulrich; Crowe, Tasman P.; Emmerson, Mark C.

    2012-01-01

    Understanding and predicting the consequences of warming for complex ecosystems and indeed individual species remains a major ecological challenge. Here, we investigated the effect of increased seawater temperatures on the metabolic and consumption rates of five distinct marine species. The experimental species reflected different trophic positions within a typical benthic East Atlantic food web, and included a herbivorous gastropod, a scavenging decapod, a predatory echinoderm, a decapod and a benthic-feeding fish. We examined the metabolism–body mass and consumption–body mass scaling for each species, and assessed changes in their consumption efficiencies. Our results indicate that body mass and temperature effects on metabolism were inconsistent across species and that some species were unable to meet metabolic demand at higher temperatures, thus highlighting the vulnerability of individual species to warming. While body size explains a large proportion of the variation in species' physiological responses to warming, it is clear that idiosyncratic species responses, irrespective of body size, complicate predictions of population and ecosystem level response to future scenarios of climate change. PMID:23007085

  19. Predicting splenic abnormality in Hodgkin disease using volume response to epinephrine administration

    SciTech Connect

    Rosen, P.R.; Lasher, J.C.; Weiland, F.L.; Kopp, D.T.

    1982-06-01

    The change in relative splenic volume following epinephrine administration was used to determine splenic abnormality noninvasively. Selective splenic imaging was accomplished with /sup 99m/Tc-labeled heat-treated red blood cells, an LFOV gamma camera, a 30 degrees bilateral rotating slant hole collimator, and bilateral slant hole tomographic software. Relative splenic volumes were plotted over time, and correlated with clinical and histologic data. Eight patients with Hodgkin disease and other lymphomas were examined. Volume response to epinephrine appears to be significantly less in abnormal spleens, and may serve to identify patients with unequivocally normal spleens prior to treatment.

  20. Predicting splenic abnormality in Hodgkin disease using volume response to epinephrine administration

    SciTech Connect

    Rosen P.R.; Lasher, J.C.; Weiland, F.L.; Kopp, D.T.

    1982-06-01

    The change in relative splenic volume following epinephrine administration was used to determine splenic abnormality noninvasively. Selective splenic imaging was accomplished with /sup 99m/Tc-labeled heat-treated red blood cells, an LFOV gamma camera, a 30/sup 0/ bilateral rotating slant hole collimator, and bilateral slant hole tomographic software. Relative splenic volumes were plotted over time, and correlated with clinical and histologic data. Eight patients with Hodgkin disease and other lymphomas were examined. Volume response to epinephrine appears to be significantly less in abnormal spleens, and may serve to identify patients with unequivocally normal spleens prior to treatment.

  1. Predicting body temperature and activity of adult Polyommatus icarus using neural network models under current and projected climate scenarios.

    PubMed

    Howe, P D; Bryant, S R; Shreeve, T G

    2007-10-01

    We use field observations in two geographic regions within the British Isles and regression and neural network models to examine the relationship between microhabitat use, thoracic temperatures and activity in a widespread lycaenid butterfly, Polyommatus icarus. We also make predictions for future activity under climate change scenarios. Individuals from a univoltine northern population initiated flight with significantly lower thoracic temperatures than individuals from a bivoltine southern population. Activity is dependent on body temperature and neural network models of body temperature are better at predicting body temperature than generalized linear models. Neural network models of activity with a sole input of predicted body temperature (using weather and microclimate variables) are good predictors of observed activity and were better predictors than generalized linear models. By modelling activity under climate change scenarios for 2080 we predict differences in activity in relation to both regional differences of climate change and differing body temperature requirements for activity in different populations. Under average conditions for low-emission scenarios there will be little change in the activity of individuals from central-southern Britain and a reduction in northwest Scotland from 2003 activity levels. Under high-emission scenarios, flight-dependent activity in northwest Scotland will increase the greatest, despite smaller predicted increases in temperature and decreases in cloud cover. We suggest that neural network models are an effective way of predicting future activity in changing climates for microhabitat-specialist butterflies and that regional differences in the thermoregulatory response of populations will have profound effects on how they respond to climate change.

  2. Atmospheric CO2 concentrations during ancient greenhouse climates were similar to those predicted for A.D. 2100.

    PubMed

    Breecker, D O; Sharp, Z D; McFadden, L D

    2010-01-12

    Quantifying atmospheric CO(2) concentrations ([CO(2)](atm)) during Earth's ancient greenhouse episodes is essential for accurately predicting the response of future climate to elevated CO(2) levels. Empirical estimates of [CO(2)](atm) during Paleozoic and Mesozoic greenhouse climates are based primarily on the carbon isotope composition of calcium carbonate in fossil soils. We report that greenhouse [CO(2)](atm) have been significantly overestimated because previously assumed soil CO(2) concentrations during carbonate formation are too high. More accurate [CO(2)](atm), resulting from better constraints on soil CO(2), indicate that large (1,000s of ppmV) fluctuations in [CO(2)](atm) did not characterize ancient climates and that past greenhouse climates were accompanied by concentrations similar to those projected for A.D. 2100.

  3. Impact of the assimilated sea ice data product on seasonal climate predictions with MPI-ESM

    NASA Astrophysics Data System (ADS)

    Bunzel, Felix; Notz, Dirk; Baehr, Johanna; Müller, Wolfgang; Fröhlich, Kristina

    2015-04-01

    data product used for model initialisation, and evaluate possible links to the predictability of mid- and low-latitude climate.

  4. Satellite Observations and Chemistry Climate Models - A Meandering Path Towards Better Predictions

    NASA Technical Reports Server (NTRS)

    Douglass, Anne R.

    2011-01-01

    Knowledge of the chemical and dynamical processes that control the stratospheric ozone layer has grown rapidly since the 1970s, when ideas that depletion of the ozone layer due to human activity were put forth. The concept of ozone depletion due to anthropogenic chlorine increase is simple; quantification of the effect is much more difficult. The future of stratospheric ozone is complicated because ozone is expected to increase for two reasons: the slow decrease in anthropogenic chlorine due to the Montreal Protocol and its amendments and stratospheric cooling caused by increases in carbon dioxide and other greenhouse gases. Prediction of future ozone levels requires three-dimensional models that represent physical, photochemical and radiative processes, i.e., chemistry climate models (CCMs). While laboratory kinetic and photochemical data are necessary inputs for a CCM, atmospheric measurements are needed both to reveal physical and chemical processes and for comparison with simulations to test the conceptual model that CCMs represent. Global measurements are available from various satellites including but not limited to the LIMS and TOMS instruments on Nimbus 7 (1979 - 1993), and various instruments on the Upper Atmosphere Research Satellite (1991 - 2005), Envisat (2002 - ongoing), Sci-Sat (2003 - ongoing) and Aura (2004 - ongoing). Every successful satellite instrument requires a physical concept for the measurement, knowledge of physical chemical properties of the molecules to be measured, and stellar engineering to design an instrument that will survive launch and operate for years with no opportunity for repair but providing enough information that trend information can be separated from any instrument change. The on-going challenge is to use observations to decrease uncertainty in prediction. This talk will focus on two applications. The first considers transport diagnostics and implications for prediction of the eventual demise of the Antarctic ozone hole

  5. Future global and regional climate change: From near-term prediction to long-term projections (Invited)

    NASA Astrophysics Data System (ADS)

    Knutti, R.; Collins, M.; Power, S.; Kirtman, B. P.; Christensen, J. H.; Krishna Kumar, K.

    2013-12-01

    The IPCC AR5 assessed results from a hierarchy of different climate models on how climate might change in the future from decades to millennia. The projections are based on a series of new climate models and for new scenarios. They are very consistent with projections in AR4 and confirm widespread changes in the atmosphere, ocean, sea ice and land under emission scenarios without mitigation. In the late 21st century and beyond, the warming is dominated by the total emissions of CO2, and many changes will persist for centuries even if emissions were stopped. Stabilization of global temperature at 2°C above the preindustrial value for example, requires strong emission reductions over the 21st century. In the near term and locally however, interannual and decadal climate variability remains a large and mostly irreducible component of the uncertainty in projections. Improving the quality of information on regional climate change and improving the ability of the scientific community to perform near-term climate predictions are key challenges for the future. The development of a consensus in the climate science community on (i) the major directions for future model development and (ii) the scope of future coordinated model experiments will help serve the needs of both future IPCC assessments and the wider research community.

  6. Scaling up from traits to communities to ecosystems across broad climate gradients: Testing Metabolic Scaling Theories predictions for forests

    NASA Astrophysics Data System (ADS)

    Enquist, B. J.; Michaletz, S. T.; Buzzard, V.

    2015-12-01

    Key insights in global ecology will come from mechanistically linking pattern and process across scales. Macrosystems ecology specifically attempts to link ecological processes across spatiotemporal scales. The goal s to link the processing of energy and nutrients from cells all the way ecosystems and to understand how shifting climate influences ecosystem processes. Using new data collected from NSF funded Macrosystems project we report on new findings from forests sites across a broad temperature gradient. Our study sites span tropical, temperate, and high elevation forests we assess several key predictions and assumptions of Metabolic Scaling Theory (MST) as well as several other competing hypotheses for the role of climate, light, and plant traits on influencing forest demography and forest ecosystems. Specifically, we assess the importance of plant size, light limitation, size structure, and various climatic factors on forest growth, demography, and ecosystem functioning. We provide some of the first systematic tests of several key predictions from MST. We show that MST predictions are largely upheld and that new insights from assessing theories predictions yields new observations and findings that help modify and extend MST's predictions and applicability. We discuss how theory is critically needed to further our understanding of how to scale pattern and process in ecology - from traits to ecosystems - in order to develop a more predictive global change biology.

  7. Predicting non-familial major physical violent crime perpetration in the U.S. Army from administrative data

    PubMed Central

    Rosellini, Anthony J.; Monahan, John; Street, Amy E.; Heeringa, Steven G.; Hill, Eric D.; Petukhova, Maria; Reis, Ben Y.; Sampson, Nancy A.; Bliese, Paul; Schoenbaum, Michael; Stein, Murray B.; Ursano, Robert; Kessler, Ronald C.

    2016-01-01

    BACKGROUND Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among U.S. Army soldiers. METHODS A consolidated administrative database for all 975,057 soldiers in the U.S. Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). 5,771 of these soldiers committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression; random forests; penalized regressions). The model was then validated in an independent 2011-2013 sample. RESULTS Key predictors were indicators of disadvantaged social/socio-economic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver operating characteristic curve was .80-.82 in 2004-2009 and .77 in a 2011-2013 validation sample. 36.2-33.1% (male-female) of all administratively-recorded crimes were committed by the 5% of soldiers having highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample. CONCLUSIONS Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks. PMID:26436603

  8. Long-term fluctuations of Pelagia noctiluca (Cnidaria, Scyphomedusa) in the western Mediterranean Sea. Prediction by climatic variables

    NASA Astrophysics Data System (ADS)

    Goy, Jacquelinn; Morand, Pierre; Etienne, Michéle

    1989-02-01

    The archives of the Station Zoologique at Villefranche-sur-Mer contain records of "years with Pelagia noctiluca" and 'years without Pelagia". These records, plus additional data, indicate that over the past 200 years (1785-1985) outburst of Pelagia have occured about every 12 years. Using a forecasting model, climatic variables, notably temperature, rainfall and atmospheric pressure, appear to predict "years with Pelagia".

  9. Using biogeographic distributions and natural history to predict marine/estuarine species at risk to climate change

    EPA Science Inventory

    Effects of climate change on marine and estuarine species will vary with attributes of the species and the spatial patterns of environmental change at the habitat and global scales. To better predict which species are at greatest risk, we are developing a knowledge base of specie...

  10. Does an understanding of ecosystems responses to rainfall pulses improve predictions of responses of drylands to climate change?

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Drylands will experience more intense and frequent droughts and floods. Ten-year field experiments manipulating the amount and variability of precipitation suggest that we cannot predict responses of drylands to climate change based on pulse experimentation. Long-term drought experiments showed no e...

  11. Evaluation of reanalysis climate simulations for the prediction of extreme runoff characteristics

    NASA Astrophysics Data System (ADS)

    Coskun, Mehmet; Samaniego, Luis; Kumar, Rohini

    2010-05-01

    Discharge regimes of river basins are expected to be altered due to possible effects of global warming. For planning and water resources management, it is fundamental to estimate the probability of occurrence of extreme hydrological events such as magnitude and frequency of floods and droughts. So far, it is a matter of debate whether actual Global and Regional Climate Model outputs or their reanalysis products (bias corrected) are able to provide a reasonable estimate of the meteorological variables that are required to force a distributed hydrologic model. In this study, we will evaluate various climate simulations for their reliability to predict extreme runoff characteristics in three German mesoscale river basins with various sizes and hydro-meteorological conditions: Neckar (12 700 km2), Bode (3 300 km2), and Mulde (2 700 km2). Reanalysis of the global atmosphere and surface conditions were obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis (ERA-40) for the period from 1957 to 2002. These data will be used to force a grid based mesoscale hydrologic model calibrated with past meteorological and discharge observations. Several runoff characteristics will be estimated based on daily discharge simulations and then compared with their corresponding estimates derived from daily streamflow observations. Finally, nonparametric statistical test (e.g. Kolmogorov-Smirnov test) and Tukey's depth function will be employed to test two null hypotheses: 1) Meteorological observations and the reanalysis data are realisations from a common generating process, and 2) The probability of occurrence of extreme runoff characteristics obtained from both data sets is similar.

  12. [Current distribution of Schisandra chinensis in China and its predicted responses to climate change].

    PubMed

    Hu, Li-Le; Zhang, Hai-Ying; Qin, Ling; Yan, Bo-Qian

    2012-09-01

    With integration of literature data, specimens records, and field surveys, the current distribution map of Schisandra chinensis in China was drawn, and, based on this map and considering 21 environmental factors, the future distribution of S. chinensis in China in the 2050s and 2080s under the IPCC A2 and A1B climate change scenarios was predicted by using Maxent software. Currently, the S. chinensis in China occurred in 15 provinces, involving 151 counties, and its distribution area decreased with decreasing latitude and longitude. The main distribution area included Heilongjiang, Liaoning, Inner Mongolia, and Jilin. The potential distribution area of S. chinensis in China was 145.12 x 10(4) km2, 48.6% of which were the favorable habitat area, mainly distributed in Changbai Mountains, Xiaoxing'anling Mountains, Daxing'anling Mountains, and the regions between Hebei and Liaoning provinces. The most favorable habitat area only accounted for 0.3%, and was mainly in the Kuandian Manchu Autonomous County, Benxi Manchu Autonomous County, and Huanren Manchu Autonomous County of Liaoning Province, the Antu County and Helong County of Jilin Province, and the Yakeshi City of Inner Mongolia. Under the two climate change scenarios, the potential future distribution area of S. chinensis in China would have a gradual decrease, and the decrement would be larger under A2 than under A1B scenario. By 2050, the distribution area of the S. chinensis under A1B and A2 scenarios would be moderately decreased to 84.0% and 81.5% of the current distribution area, respectively; by 2080, the distribution of S. chinensis under A2 scenario would be dramatically decreased to only 0.5% of the current range, and that under A1B scenario would be decreased to 1/2 of the current range.

  13. Long-range Weather Prediction and Prevention of Climate Catastrophes: A Status Report

    DOE R&D Accomplishments Database

    Caldeira, K.; Caravan, G.; Govindasamy, B.; Grossman, A.; Hyde, R.; Ishikawa, M.; Ledebuhr, A.; Leith, C.; Molenkamp, C.; Teller, E.; Wood, L.

    1999-08-18

    As the human population of Earth continues to expand and to demand an ever-higher quality-of-life, requirements for ever-greater knowledge--and then control--of the future of the state of the terrestrial biosphere grow apace. Convenience of living--and, indeed, reliability of life itself--become ever more highly ''tuned'' to the future physical condition of the biosphere being knowable and not markedly different than the present one. Two years ago, we reported at a quantitative albeit conceptual level on technical ways-and-means of forestalling large-scale changes in the present climate, employing practical means of modulating insolation and/or the Earth's mean albedo. Last year, we reported on early work aimed at developing means for creating detailed, high-fidelity, all-Earth weather forecasts of two weeks duration, exploiting recent and anticipated advances in extremely high-performance digital computing and in atmosphere-observing Earth satellites bearing high-technology instrumentation. This year, we report on recent progress in both of these areas of endeavor. Preventing the commencement of large-scale changes in the current climate presently appears to be a considerably more interesting prospect than initially realized, as modest insolation reductions are model-predicted to offset the anticipated impacts of ''global warming'' surprisingly precisely, in both space and time. Also, continued study has not revealed any fundamental difficulties in any of the means proposed for insolation modulation and, indeed, applicability of some of these techniques to other planets in the inner Solar system seems promising. Implementation of the high-fidelity, long-range weather-forecasting capability presently appears substantially easier with respect to required populations of Earth satellites and atmospheric transponders and data-processing systems, and more complicated with respect to transponder lifetimes in the actual atmosphere; overall, the enterprise seems more

  14. Long-range weather prediction and prevention of climate catastrophes: a status report

    SciTech Connect

    Caldeira, K; Caravan, G; Govindasamy, B; Grossman, A; Hyde, R; Ishikawa, M; Ledebuhr, A; Leith, C; Molenkamp, C; Teller, E; Wood, L

    1999-08-18

    As the human population of Earth continues to expand and to demand an ever-higher quality-of-life, requirements for ever-greater knowledge--and then control--of the future of the state of the terrestrial biosphere grow apace. Convenience of living--and, indeed, reliability of life itself--become ever more highly ''tuned'' to the future physical condition of the biosphere being knowable and not markedly different than the present one, Two years ago, we reported at a quantitative albeit conceptual level on technical ways-and-means of forestalling large-scale changes in the present climate, employing practical means of modulating insolation and/or the Earth's mean albedo. Last year, we reported on early work aimed at developing means for creating detailed, high-fidelity, all-Earth weather forecasts of two weeks duration, exploiting recent and anticipated advances in extremely high-performance digital computing and in atmosphere-observing Earth satellites bearing high-technology instrumentation. This year, we report on recent progress in both of these areas of endeavor. Preventing the commencement of large-scale changes in the current climate presently appears to be a considerably more interesting prospect than initially realized, as modest insolation reductions are model-predicted to offset the anticipated impacts of ''global warming'' surprisingly precisely, in both space and time. Also, continued study has not revealed any fundamental difficulties in any of the means proposed for insolation modulation and, indeed, applicability of some of these techniques to other planets in the inner Solar system seems promising. Implementation of the high-fidelity, long-range weather-forecasting capability presently appears substantially easier with respect to required populations of Earth satellites and atmospheric transponders and data-processing systems, and more complicated with respect to transponder lifetimes in the actual atmosphere; overall, the enterprise seems more

  15. Climate sensitivity runs and regional hydrologic modeling for predicting the response of the greater Florida Everglades ecosystem to climate change.

    PubMed

    Obeysekera, Jayantha; Barnes, Jenifer; Nungesser, Martha

    2015-04-01

    It is important to understand the vulnerability of the water management system in south Florida and to determine the resilience and robustness of greater Everglades restoration plans under future climate change. The current climate models, at both global and regional scales, are not ready to deliver specific climatic datasets for water resources investigations involving future plans and therefore a scenario based approach was adopted for this first study in restoration planning. We focused on the general implications of potential changes in future temperature and associated changes in evapotranspiration, precipitation, and sea levels at the regional boundary. From these, we developed a set of six climate and sea level scenarios, used them to simulate the hydrologic response of the greater Everglades region including agricultural, urban, and natural areas, and compared the results to those from a base run of current conditions. The scenarios included a 1.5 °C increase in temperature, ±10 % change in precipitation, and a 0.46 m (1.5 feet) increase in sea level for the 50-year planning horizon. The results suggested that, depending on the rainfall and temperature scenario, there would be significant changes in water budgets, ecosystem performance, and in water supply demands met. The increased sea level scenarios also show that the ground water levels would increase significantly with associated implications for flood protection in the urbanized areas of southeastern Florida.

  16. The Impact of Climate Change on Indigenous Arabica Coffee (Coffea arabica): Predicting Future Trends and Identifying Priorities

    PubMed Central

    Gole, Tadesse Woldemariam; Baena, Susana

    2012-01-01

    Precise modelling of the influence of climate change on Arabica coffee is limited; there are no data available for indigenous populations of this species. In this study we model the present and future predicted distribution of indigenous Arabica, and identify priorities in order to facilitate appropriate decision making for conservation, monitoring and future research. Using distribution data we perform bioclimatic modelling and examine future distribution with the HadCM3 climate model for three emission scenarios (A1B, A2A, B2A) over three time intervals (2020, 2050, 2080). The models show a profoundly negative influence on indigenous Arabica. In a locality analysis the most favourable outcome is a c. 65% reduction in the number of pre-existing bioclimatically suitable localities, and at worst an almost 100% reduction, by 2080. In an area analysis the most favourable outcome is a 38% reduction in suitable bioclimatic space, and the least favourable a c. 90% reduction, by 2080. Based on known occurrences and ecological tolerances of Arabica, bioclimatic unsuitability would place populations in peril, leading to severe stress and a high risk of extinction. This study establishes a fundamental baseline for assessing the consequences of climate change on wild populations of Arabica coffee. Specifically, it: (1) identifies and categorizes localities and areas that are predicted to be under threat from climate change now and in the short- to medium-term (2020–2050), representing assessment priorities for ex situ conservation; (2) identifies ‘core localities’ that could have the potential to withstand climate change until at least 2080, and therefore serve as long-term in situ storehouses for coffee genetic resources; (3) provides the location and characterization of target locations (populations) for on-the-ground monitoring of climate change influence. Arabica coffee is confimed as a climate sensitivite species, supporting data and inference that existing

  17. Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis

    PubMed Central

    Srimath-Tirumula-Peddinti, Ravi Chandra Pavan Kumar; Neelapu, Nageswara Rao Reddy; Sidagam, Naresh

    2015-01-01

    Background Malarial incidence, severity, dynamics and distribution of malaria are strongly determined by climatic factors, i.e., temperature, precipitation, and relative humidity. The objectives of the current study were to analyse and model the relationships among climate, vector and malaria disease in district of Visakhapatnam, India to understand malaria transmission mechanism (MTM). Methodology Epidemiological, vector and climate data were analysed for the years 2005 to 2011 in Visakhapatnam to understand the magnitude, trends and seasonal patterns of the malarial disease. Statistical software MINITAB ver. 14 was used for performing correlation, linear and multiple regression analysis. Results/Findings Perennial malaria disease incidence and mosquito population was observed in the district of Visakhapatnam with peaks in seasons. All the climatic variables have a significant influence on disease incidence as well as on mosquito populations. Correlation coefficient analysis, seasonal index and seasonal analysis demonstrated significant relationships among climatic factors, mosquito population and malaria disease incidence in the district of Visakhapatnam, India. Multiple regression and ARIMA (I) models are best suited models for modeling and prediction of disease incidences and mosquito population. Predicted values of average temperature, mosquito population and malarial cases increased along with the year. Developed MTM algorithm observed a major MTM cycle following the June to August rains and occurring between June to September and minor MTM cycles following March to April rains and occurring between March to April in the district of Visakhapatnam. Fluctuations in climatic factors favored an increase in mosquito populations and thereby increasing the number of malarial cases. Rainfall, temperatures (20°C to 33°C) and humidity (66% to 81%) maintained a warmer, wetter climate for mosquito growth, parasite development and malaria transmission. Conclusions

  18. On the use and potential use of seasonal to decadal climate predictions for decision-making in Europe

    NASA Astrophysics Data System (ADS)

    Soares, Marta Bruno; Dessai, Suraje

    2014-05-01

    The need for climate information to help inform decision-making in sectors susceptible to climate events and impacts is widely recognised. In Europe, developments in the science and models underpinning the study of climate variability and change have led to an increased interest in seasonal to decadal climate predictions (S2DCP). While seasonal climate forecasts are now routinely produced operationally by a number of centres around the world, decadal climate predictions are still in its infancy restricted to the realm of research. Contrary to other regions of the world, where the use of these types of forecasts, particularly at seasonal timescales, has been pursued in recent years due to higher levels of predictability, little is known about the uptake and climate information needs of end-users regarding S2DCP in Europe. To fill this gap we conducted in-depth interviews with experts and decision-makers across a range of European sectors, a workshop with European climate services providers, and a systematic literature review on the use of S2DCP in Europe. This study is part of the EUropean Provision Of Regional Impact Assessment on a Seasonal-to-decadal timescale (EUPORIAS) project which aims to develop semi-operational prototypes of impact prediction systems in Europe on seasonal to decadal timescales. We found that the emerging landscape of users and potential users of S2DCP in Europe is complex and heterogeneous. Differences in S2DCP information needs across and within organisations and sectors are largely underpinned by factors such as the institutional and regulatory context of the organisations, the plethora of activities and decision-making processes involved, the level of expertise and capacity of the users, and the availability of resources within the organisations. In addition, although the use of S2DCP across Europe is still fairly limited, particular sectors such as agriculture, health, energy, water, (re)insurance, and transport are taking the lead on

  19. Determining the sensitivity of New Mexico biomes to predicted climate change scenarios of the Southwest

    NASA Astrophysics Data System (ADS)

    Litvak, M. E.; Anderson-Teixeira, K.

    2009-12-01

    The southwestern U.S. stands to transition to a warmer, more arid climate in the coming decades, and it is critical for regional carbon balance to understand how these changes will affect Southwest ecosystems. In order to quantify how increased temperatures and prolonged drought are likely to affect regional carbon flux and storage, we have focused specifically on six dominant upland terrestrial biomes in the Southwest ranging from desert grassland and shrubland in low elevations, to juniper savanna and pinon-juniper woodland at mid-elevation and ponderosa pine and mixed conifer forest at the highest elevations. In all six biomes, we quantified aboveground carbon pools and used eddy-covariance to continuously measure net ecosystem exchange of carbon, water and energy from 2006-2009. Here we quantify how carbon storage, water use efficiency, and sensitivity to both precipitation variability and temperature vary across these six biomes. We use measured functional responses to variability in temperature and soil water content over the three year period to estimate how carbon storage in these biomes will likely respond to predicted climate change scenarios for the Southwest. Aboveground carbon storage increased dramatically with elevation, ranging from 371 g/m2 in the desert grassland to 53,382 g/m2 in the mixed conifer forest. Gross primary production increased more rapidly with elevation than did ecosystem respiration, such that net ecosystem productivity increased from a source of ~30 g C m-2 yr-1 in the desert grassland to a sink of approximately 350 g C m-2 yr-1 in the mixed conifer forest. Within sites, daily carbon uptake tended to peak at intermediate temperatures, often becoming negative on the hottest days, and this pattern strengthened with elevation. The fraction of gross primary productivity lost as respiration increased with temperature in all six biomes, but was most striking in both the pinon-juniper woodland and ponderosa pine forest. Carbon

  20. Organizational Climate, Faculty Trust: Predicting Student Bullying--An Elementary School Study

    ERIC Educational Resources Information Center

    Anderton, Tenna

    2012-01-01

    Bullying is a serious problem among students. Research linking school climate and trust as to bullying is minimal. This study examined elements of school climate and trust in relation to bullying and protection using Hoy and Smith's (2004) climate study and Smith and Birney's (2005) trust study. Trust was found to be the significant predictor of…

  1. GFDL's unified regional-global weather-climate modeling system with variable resolution capability for severe weather predictions and regional climate simulations

    NASA Astrophysics Data System (ADS)

    Lin, S. J.

    2015-12-01

    The NOAA/Geophysical Fluid Dynamics Laboratory has been developing a unified regional-global modeling system with variable resolution capabilities that can be used for severe weather predictions (e.g., tornado outbreak events and cat-5 hurricanes) and ultra-high-resolution (1-km) regional climate simulations within a consistent global modeling framework. The fundation of this flexible regional-global modeling system is the non-hydrostatic extension of the vertically Lagrangian dynamical core (Lin 2004, Monthly Weather Review) known in the community as FV3 (finite-volume on the cubed-sphere). Because of its flexability and computational efficiency, the FV3 is one of the final candidates of NOAA's Next Generation Global Prediction System (NGGPS). We have built into the modeling system a stretched (single) grid capability, a two-way (regional-global) multiple nested grid capability, and the combination of the stretched and two-way nests, so as to make convection-resolving regional climate simulation within a consistent global modeling system feasible using today's High Performance Computing System. One of our main scientific goals is to enable simulations of high impact weather phenomena (such as tornadoes, thunderstorms, category-5 hurricanes) within an IPCC-class climate modeling system previously regarded as impossible. In this presentation I will demonstrate that it is computationally feasible to simulate not only super-cell thunderstorms, but also the subsequent genesis of tornadoes using a global model that was originally designed for century long climate simulations. As a unified weather-climate modeling system, we evaluated the performance of the model with horizontal resolution ranging from 1 km to as low as 200 km. In particular, for downscaling studies, we have developed various tests to ensure that the large-scale circulation within the global varaible resolution system is well simulated while at the same time the small-scale can be accurately captured

  2. Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya.

    PubMed

    Aryal, Achyut; Shrestha, Uttam Babu; Ji, Weihong; Ale, Som B; Shrestha, Sujata; Ingty, Tenzing; Maraseni, Tek; Cockfield, Geoff; Raubenheimer, David

    2016-06-01

    Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator-prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy-deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate-only model shows that only 11.64% (17,190 km(2)) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km(2) (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate-only model. It is predicted that future climate may alter the predator-prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards - a species already facing energetic constraints due to the

  3. Forecasting phenological responses to climate change: Using hierarchical models to bridge local processes and regional predictions (Invited)

    NASA Astrophysics Data System (ADS)

    Diez, J.; Ibanez, I.

    2010-12-01

    Species’ phenological responses to climate change have large implications for future species distributions, trophic interactions, and ecosystem processes. Analyses of historical databases have shown that these responses are often species-specific and spatially variable. This variability makes predicting future responses more challenging. At the root of this challenge is the fundamental problem in ecology of how locally variable processes scale up to yield regional patterns. In this study, we show how hierarchical models of species phenological responses to climate may help address this challenge. Using long-term datasets (1953-2005) from the Japanese Meteorological Service for Morus bombysis (mulberry) at 100 sites distributed across Japan, we developed models using both monthly and daily climate data to predict bud burst dates. In both cases, hierarchical models were used to translate the different local responses among sites into more realistic predictions across the region and at unmeasured locations. The daily models represent a new approach to predicting phenology that is flexible enough to incorporate different mechanisms that may be important for some species, including forcing, chilling, photoperiod, and extreme events such as frosts. We use the daily models to show how spatial variability in bud burst dates results in part from different mechanisms being more important in different parts of the country. We compare these results to the monthly models to contrast the predictive value of the more detailed models. Our results emphasize the general utility of hierarchical models for understanding and forecasting regional changes in phenology, regardless of the specific model employed.

  4. Fine-spatial scale predictions of understory species using climate- and LiDAR-derived terrain and canopy metrics

    NASA Astrophysics Data System (ADS)

    Nijland, Wiebe; Nielsen, Scott E.; Coops, Nicholas C.; Wulder, Michael A.; Stenhouse, Gordon B.

    2014-01-01

    Food and habitat resources are critical components of wildlife management and conservation efforts. The grizzly bear (Ursus arctos) has diverse diets and habitat requirements particularly for understory plant species, which are impacted by human developments and forest management activities. We use light detection and ranging (LiDAR) data to predict the occurrence of 14 understory plant species relevant to bear forage and compare our predictions with more conventional climate- and land cover-based models. We use boosted regression trees to model each of the 14 understory species across 4435 km2 using occurrence (presence-absence) data from 1941 field plots. Three sets of models were fitted: climate only, climate and basic land and forest covers from Landsat 30-m imagery, and a climate- and LiDAR-derived model describing both the terrain and forest canopy. Resulting model accuracies varied widely among species. Overall, 8 of 14 species models were improved by including the LiDAR-derived variables. For climate-only models, mean annual precipitation and frost-free periods were the most important variables. With inclusion of LiDAR-derived attributes, depth-to-water table, terrain-intercepted annual radiation, and elevation were most often selected. This suggests that fine-scale terrain conditions affect the distribution of the studied species more than canopy conditions.

  5. Climate, Demography, and Zoogeography Predict Introgression Thresholds in Salmonid Hybrid Zones in Rocky Mountain Streams.

    PubMed

    Young, Michael K; Isaak, Daniel J; McKelvey, Kevin S; Wilcox, Taylor M; Bingham, Daniel M; Pilgrim, Kristine L; Carim, Kellie J; Campbell, Matthew R; Corsi, Matthew P; Horan, Dona L; Nagel, David E; Schwartz, Michael K

    2016-01-01

    Among the many threats posed by invasions of nonnative species is introgressive hybridization, which can lead to the genomic extinction of native taxa. This phenomenon is regarded as common and perhaps inevitable among native cutthroat trout and introduced rainbow trout in western North America, despite that these taxa naturally co-occur in some locations. We conducted a synthetic analysis of 13,315 genotyped fish from 558 sites by building logistic regression models using data from geospatial stream databases and from 12 published studies of hybridization to assess whether environmental covariates could explain levels of introgression between westslope cutthroat trout and rainbow trout in the U.S. northern Rocky Mountains. A consensus model performed well (AUC, 0.78-0.86; classification success, 72-82%; 10-fold cross validation, 70-82%) and predicted that rainbow trout introgression was significantly associated with warmer water temperatures, larger streams, proximity to warmer habitats and to recent sources of rainbow trout propagules, presence within the historical range of rainbow trout, and locations further east. Assuming that water temperatures will continue to rise in response to climate change and that levels of introgression outside the historical range of rainbow trout will equilibrate with those inside that range, we applied six scenarios across a 55,234-km stream network that forecast 9.5-74.7% declines in the amount of habitat occupied by westslope cutthroat trout populations of conservation value, but not the wholesale loss of such populations. We conclude that introgression between these taxa is predictably related to environmental conditions, many of which can be manipulated to foster largely genetically intact populations of westslope cutthroat trout and help managers prioritize conservation activities.

  6. Climate, Demography, and Zoogeography Predict Introgression Thresholds in Salmonid Hybrid Zones in Rocky Mountain Streams

    PubMed Central

    Young, Michael K.; Isaak, Daniel J.; McKelvey, Kevin S.; Wilcox, Taylor M.; Pilgrim, Kristine L.; Carim, Kellie J.; Campbell, Matthew R.; Corsi, Matthew P.; Horan, Dona L.; Nagel, David E.; Schwartz, Michael K.

    2016-01-01

    Among the many threats posed by invasions of nonnative species is introgressive hybridization, which can lead to the genomic extinction of native taxa. This phenomenon is regarded as common and perhaps inevitable among native cutthroat trout and introduced rainbow trout in western North America, despite that these taxa naturally co-occur in some locations. We conducted a synthetic analysis of 13,315 genotyped fish from 558 sites by building logistic regression models using data from geospatial stream databases and from 12 published studies of hybridization to assess whether environmental covariates could explain levels of introgression between westslope cutthroat trout and rainbow trout in the U.S. northern Rocky Mountains. A consensus model performed well (AUC, 0.78–0.86; classification success, 72–82%; 10-fold cross validation, 70–82%) and predicted that rainbow trout introgression was significantly associated with warmer water temperatures, larger streams, proximity to warmer habitats and to recent sources of rainbow trout propagules, presence within the historical range of rainbow trout, and locations further east. Assuming that water temperatures will continue to rise in response to climate change and that levels of introgression outside the historical range of rainbow trout will equilibrate with those inside that range, we applied six scenarios across a 55,234-km stream network that forecast 9.5–74.7% declines in the amount of habitat occupied by westslope cutthroat trout populations of conservation value, but not the wholesale loss of such populations. We conclude that introgression between these taxa is predictably related to environmental conditions, many of which can be manipulated to foster largely genetically intact populations of westslope cutthroat trout and help managers prioritize conservation activities. PMID:27828980

  7. Amphibian breeding phenology trends under climate change: predicting the past to forecast the future.

    PubMed

    Green, David M

    2017-02-01

    Global climate warming is predicted to hasten the onset of spring breeding by anuran amphibians in seasonal environments. Previous data had indicated that the breeding phenology of a population of Fowler's Toads (Anaxyrus fowleri) at their northern range limit had been progressively later in spring, contrary to generally observed trends in other species. Although these animals are known to respond to environmental temperature and the lunar cycle to commence breeding, the timing of breeding should also be influenced by the onset of overwintering animals' prior upward movement through the soil column from beneath the frost line as winter becomes spring. I used recorded weather data to identify four factors of temperature, rainfall and snowfall in late winter and early spring that correlated with the toads' eventual date of emergence aboveground. Estimated dates of spring emergence of the toads calculated using a predictive model based on these factors, as well as the illumination of the moon, were highly correlated with observed dates of emergence over 24 consecutive years. Using the model to estimate of past dates of spring breeding (i.e. retrodiction) indicated that even three decades of data were insufficient to discern any appreciable phenological trend in these toads. However, by employing weather data dating back to 1876, I detected a significant trend over 140 years towards earlier spring emergence by the toads by less than half a day/decade, while, over the same period of time, average annual air temperature and annual precipitation had both increased. Changes in the springtime breeding phenology for late-breeding species, such as Fowler's Toads, therefore may conform to expectations of earlier breeding under global warming. Improved understanding of the environmental cues that bring organisms out of winter dormancy will enable better interpretation of long-term phenological trends.

  8. Predicted responses of invasive mammal communities to climate-related changes in mast frequency in forest ecosystems.

    PubMed

    Tompkins, Daniel M; Byrom, Andrea E; Pech, Roger P

    2013-07-01

    Predicting the dynamics and impacts of multiple invasive species can be complex because ecological relationships, which occur among several trophic levels, are often incompletely understood. Further, the complexity of these trophic relationships exacerbates our inability to predict climate change effects on invaded ecosystems. We explore the hypothesis that interactions between two global change drivers, invasive vertebrates and climate change, will potentially make matters worse for native biodiversity. In New Zealand beech (Nothofagus spp.) forests, a highly irruptive invasive mammal community is driven by multi-annual resource pulses of beech seed (masting). Because mast frequency is predicted to increase with climate change, we use this as a model system to explore the extent to which such effects may influence invasive vertebrate communities, and the implications of such interactions for native biodiversity and its management. We build on an established model of trophic interactions in the system, combining it with a logistic probability mast function, the parameters of which were altered to simulate either contemporary conditions or conditions of more or less frequent masting. The model predicts that increased mast frequency will lead to populations of a top predator (the stoat) and a mesopredator (the ship rat) becoming less irruptive and being maintained at appreciably higher average abundances in this forest type. In addition, the ability of both current and in-development management approaches to suppress invasive mammals is predicted to be compromised. Because invasive mammals are key drivers of native fauna extinction in New Zealand, with the additional loss of associated functions such as pollination and seed dispersal, these predictions imply potentially serious adverse impacts of climate change for the conservation of biodiversity and ecosystem function. Our study also highlights the importance of long-term monitoring data for assessing and managing

  9. New developments in geostrophic turbulence and its implications for climate modeling and weather predictability

    NASA Astrophysics Data System (ADS)

    Tribbia, Joseph

    2012-10-01

    One of the many areas in geophysical fluid dynamics that impacts how we model dissipation in the climate system is the theory of two-dimensional and quasi geostrophic turbulence and its impact on atmospheric flow. Upscale energy and and down scale enstrophy cascades have been observed in the atmosphere along with the -3 power law predicted in two-dimensional turbulence theory put forward by Batchelor and Kraichnan in the late 1960s. A consequence of this observational finding is the fact that, unlike three-dimensional turbulence in which the eddy turnover time decreases with eddy length scale, in two dimensional and quasi-geostrophic turbulence the eddy turnover time is constant independent of eddy length scale in the enstrophy cascading range. A further consequence of this is that the Rossby number is constant through the enstrophy cascade. This implies that instabilities which depend on ageostrophic processes are restricted because the scaling laws which imply balanced, quasi-geostrophic dynamics are valid at all length scales. Recent results show, however, even given that all of the above statements are true and maintained in the dynamics, there is a mechanism through which quasi-geostrophic turbulence becomes inconsistent and develops the seeds of its own destruction at small scales.

  10. Modelling convective severe weather occurrence using observations, reanalysis data and decadal climate predictions

    NASA Astrophysics Data System (ADS)

    Pistotnik, Georg; Groenemeijer, Pieter

    2014-05-01

    Observations of local severe convective events can be combined with atmospheric reanalyses to compute severe weather probability as a function of parameters characterizing the local state of the atmosphere. Using ERA-Interim reanalysis data and observations from the European Severe Weather Database, we have investigated several ways to express the probability of large hail, tornadoes, flash floods or wind gusts as a function of parameters such as convective available potential energy, vertical wind shear and precipitation. Our attempts include fitting analytic functions, using smoothers of various kinds, and binning the data within the multidimensional parameter space according to various algorithms. We imposed that any difference between binned observations and the modelled probability function be insignificant at the 95% confidence level. Further tests of robustness of the model were conducted. A probability function fulfilling this criterion was selected and subsequently applied to the ERA-Interim data as well as to predictions of the decadal forecasting system developed in the MiKlip programme. We investigated climatic and modelled past and future trends of severe convective weather. We will present the (preliminary) results of that effort.

  11. Predicting equilibrium vegetation responses to global climate change using coupled biogeography and ecosystem models

    SciTech Connect

    Borchers, J.G.; Nielson, R.P.

    1995-06-01

    Much current uncertainty surrounding the sensitivity to climatic change of natural vegetation in the USA is related to widely-varying approaches taken in constructing simulation models. Our goal was to reduce this uncertainty by coupling the biogeography model MAPSS (Mapped Atmosphere-Plant-Soil System) with critical ecosystem processes as simulated by TEM (Terrestrial Ecosystem Model). MAPSS predicts changes in leaf-area index (LAI) and vegetation biome boundaries using a site water balance model in conjunction with a physiologically-conceived rule-base model. On the other hand, TEM simulates equilibrium fluxes and pools of carbon (C) and nitrogen (N) such as net primary productivity (NPP) and available N without redistributing vegetation. In the coupled version of MAPSS presented here, these hydrological and biogeochemical processes are mutually constrained. For example, N availability may limit maximum LAI, and therefore, site water balance. Alternatively, actual evapotranspiration and soil water availability may modulate NPP via photosynthesis and net N mineralization. Initial results with this TEM-coupled version of MAPSS reveal significantly different patterns of NPP and vegetation distribution for the conterminous USA compared to those from uncoupled models, particularly at thermal and hydric extremes.

  12. The impact of climate change on the accuracy of streamflow predictions in California

    NASA Astrophysics Data System (ADS)

    Leonardson, R.; Vicuna, S.; Dracup, J. A.; Dale, L. L.

    2005-12-01

    Every spring, California's Department of Water Resources (DWR) forecasts streamflow for the upcoming irrigation season (April-July) for rivers fed by the Western Sierra snowpack. The DWR forecasts influence water allocations, cropping strategy, and flood and drought management plans; forecast error can impact California's economy, especially the agricultural sector. Demand for water is expected to increase with population growth and climate change effects, in which the summers will be longer and hotter. At the same time, the percentage of precipitation falling as snow will shrink, and the snowpack is expected to melt earlier. We expect both of these factors to reduce forecast accuracy, as forecasting rainfall is less precise than measuring snowpack and because less of the snowpack will remain after April 1st. In this study, we examine relationships between historical forecast error and watershed elevation, snowpack size, snowmelt timing (as hydrograph center-of-mass), and other factors. We quantify expected future error using the predictions of multiple global circulation models and various emissions scenarios.

  13. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation

    PubMed Central

    Huey, Raymond B.; Kearney, Michael R.; Krockenberger, Andrew; Holtum, Joseph A. M.; Jess, Mellissa; Williams, Stephen E.

    2012-01-01

    A recently developed integrative framework proposes that the vulnerability of a species to environmental change depends on the species' exposure and sensitivity to environmental change, its resilience to perturbations and its potential to adapt to change. These vulnerability criteria require behavioural, physiological and genetic data. With this information in hand, biologists can predict organisms most at risk from environmental change. Biologists and managers can then target organisms and habitats most at risk. Unfortunately, the required data (e.g. optimal physiological temperatures) are rarely available. Here, we evaluate the reliability of potential proxies (e.g. critical temperatures) that are often available for some groups. Several proxies for ectotherms are promising, but analogous ones for endotherms are lacking. We also develop a simple graphical model of how behavioural thermoregulation, acclimation and adaptation may interact to influence vulnerability over time. After considering this model together with the proxies available for physiological sensitivity to climate change, we conclude that ectotherms sharing vulnerability traits seem concentrated in lowland tropical forests. Their vulnerability may be exacerbated by negative biotic interactions. Whether tropical forest (or other) species can adapt to warming environments is unclear, as genetic and selective data are scant. Nevertheless, the prospects for tropical forest ectotherms appear grim. PMID:22566674

  14. Predicting competitive shifts and responses to climate change based on latitudinal distributions of species assemblages.

    PubMed

    Lord, Joshua; Whitlatch, Robert

    2015-05-01

    Many terrestrial plant and marine benthic communities involve intense competition for space as a means to survive and reproduce. Superior competitors can dominate other species numerically with high reproductive rates, indirectly with high growth rates that facilitate space acquisition, or directly with competitive overgrowth. To assess how climate change could affect competitive interactions, we examined latitudinal patterns in growth rates and overgrowth competition via field surveys and experiments with marine epibenthic communities. Epibenthic fouling communities are dominated by invasive tunicates, bryozoans, and other species that grow on docks, boats, and other artificial structures. Fouling communities are space limited, so growth rate and overgrowth competition play an important role in shaping abundance patterns. We experimentally assessed temperature-dependent growth rates of several tunicates and bryozoans in eight regions spanning the U.S. east and west coasts. Several species displayed positive growth responses to warmer temperature in the northern portions of their latitudinal ranges, and vice versa. We used photo surveys of floating docks in at least 16 harbors in each region to compare communities and overgrowth competition. There was a strong correlation across species and regions between growth rate and competitive ability, indicating that growth plays an important role in competitive outcomes. Because growth rates are typically temperature dependent for organisms that compete for space, including terrestrial plants, fungi, algae, bacteria, and sessile benthic organisms, global warming could affect competitive outcomes. Our results suggest that these competitive shifts can be predicted by species' relative growth rates and latitudinal ranges.

  15. Pathogen-Host Associations and Predicted Range Shifts of Human Monkeypox in Response to Climate Change in Central Africa

    PubMed Central

    Thomassen, Henri A.; Fuller, Trevon; Asefi-Najafabady, Salvi; Shiplacoff, Julia A. G.; Mulembakani, Prime M.; Blumberg, Seth; Johnston, Sara C.; Kisalu, Neville K.; Kinkela, Timothée L.; Fair, Joseph N.; Wolfe, Nathan D.; Shongo, Robert L.; LeBreton, Matthew; Meyer, Hermann; Wright, Linda L.; Muyembe, Jean-Jacques; Buermann, Wolfgang; Okitolonda, Emile; Hensley, Lisa E.; Lloyd-Smith, James O.; Smith, Thomas B.; Rimoin, Anne W.

    2013-01-01

    Climate change is predicted to result in changes in the geographic ranges and local prevalence of infectious diseases, either through direct effects on the pathogen, or indirectly through range shifts in vector and reservoir species. To better understand the occurrence of monkeypox virus (MPXV), an emerging Orthopoxvirus in humans, under contemporary and future climate conditions, we used ecological niche modeling techniques in conjunction with climate and remote-sensing variables. We first created spatially explicit probability distributions of its candidate reservoir species in Africa's Congo Basin. Reservoir species distributions were subsequently used to model current and projected future distributions of human monkeypox (MPX). Results indicate that forest clearing and climate are significant driving factors of the transmission of MPX from wildlife to humans under current climate conditions. Models under contemporary climate conditions performed well, as indicated by high values for the area under the receiver operator curve (AUC), and tests on spatially randomly and non-randomly omitted test data. Future projections were made on IPCC 4th Assessment climate change scenarios for 2050 and 2080, ranging from more conservative to more aggressive, and representing the potential variation within which range shifts can be expected to occur. Future projections showed range shifts into regions where MPX has not been recorded previously. Increased suitability for MPX was predicted in eastern Democratic Republic of Congo. Models developed here are useful for identifying areas where environmental conditions may become more suitable for human MPX; targeting candidate reservoir species for future screening efforts; and prioritizing regions for future MPX surveillance efforts. PMID:23935820

  16. Predicting Impacts of Climate Change on the Aboveground Carbon Sequestration Rate of a Temperate Forest in Northeastern China

    PubMed Central

    Ma, Jun; Hu, Yuanman; Bu, Rencang; Chang, Yu; Deng, Huawei; Qin, Qin

    2014-01-01

    The aboveground carbon sequestration rate (ACSR) reflects the influence of climate change on forest dynamics. To reveal the long-term effects of climate change on forest succession and carbon sequestration, a forest landscape succession and disturbance model (LANDIS Pro7.0) was used to simulate the ACSR of a temperate forest at the community and species levels in northeastern China based on both current and predicted climatic data. On the community level, the ACSR of mixed Korean pine hardwood forests and mixed larch hardwood forests, fluctuated during the entire simulation, while a large decline of ACSR emerged in interim of simulation in spruce-fir forest and aspen-white birch forests, respectively. On the species level, the ACSR of all conifers declined greatly around 2070s except for Korean pine. The ACSR of dominant hardwoods in the Lesser Khingan Mountains area, such as Manchurian ash, Amur cork, black elm, and ribbed birch fluctuated with broad ranges, respectively. Pioneer species experienced a sharp decline around 2080s, and they would finally disappear in the simulation. The differences of the ACSR among various climates were mainly identified in mixed Korean pine hardwood forests, in all conifers, and in a few hardwoods in the last quarter of simulation. These results indicate that climate warming can influence the ACSR in the Lesser Khingan Mountains area, and the largest impact commonly emerged in the A2 scenario. The ACSR of coniferous species experienced higher impact by climate change than that of deciduous species. PMID:24763409

  17. Predicting impacts of climate change on the aboveground carbon sequestration rate of a temperate forest in northeastern China.

    PubMed

    Ma, Jun; Hu, Yuanman; Bu, Rencang; Chang, Yu; Deng, Huawei; Qin, Qin

    2014-01-01

    The aboveground carbon sequestration rate (ACSR) reflects the influence of climate change on forest dynamics. To reveal the long-term effects of climate change on forest succession and carbon sequestration, a forest landscape succession and disturbance model (LANDIS Pro7.0) was used to simulate the ACSR of a temperate forest at the community and species levels in northeastern China based on both current and predicted climatic data. On the community level, the ACSR of mixed Korean pine hardwood forests and mixed larch hardwood forests, fluctuated during the entire simulation, while a large decline of ACSR emerged in interim of simulation in spruce-fir forest and aspen-white birch forests, respectively. On the species level, the ACSR of all conifers declined greatly around 2070s except for Korean pine. The ACSR of dominant hardwoods in the Lesser Khingan Mountains area, such as Manchurian ash, Amur cork, black elm, and ribbed birch fluctuated with broad ranges, respectively. Pioneer species experienced a sharp decline around 2080s, and they would finally disappear in the simulation. The differences of the ACSR among various climates were mainly identified in mixed Korean pine hardwood forests, in all conifers, and in a few hardwoods in the last quarter of simulation. These results indicate that climate warming can influence the ACSR in the Lesser Khingan Mountains area, and the largest impact commonly emerged in the A2 scenario. The ACSR of coniferous species experienced higher impact by climate change than that of deciduous species.

  18. Recent and predicted changes in atmospheric composition over the United States from climate, emissions, and pine beetles

    NASA Astrophysics Data System (ADS)

    Heald, C. L.; Berg, A.; Val Martin, M.; Meddens, A. J.; Hicke, J. A.; Huff Hartz, K. E.; Lamarque, J.; Tilmes, S.; Emmons, L. K.

    2012-12-01

    Changes in emissions, climate and land use all play a key role in modulating the composition of the troposphere. In this talk I will cover two topics related to this theme. First, to examine the relative impacts of these effects, I will discuss predicted changes in air quality (PM and ozone) by 2050 over the United States following the latest RCP scenarios in the Community Earth System Model. Second, as an example of climate-biosphere-atmosphere interactions, I will discuss the impact of the recent mountain pine beetle outbreak on VOC emissions and organic aerosol concentrations in Western North America over the last decade.

  19. Modelling the influence of predicted future climate change on the risk of wind damage within New Zealand's planted forests.

    PubMed

    Moore, John R; Watt, Michael S

    2015-08-01

    Wind is the major abiotic disturbance in New Zealand's planted forests, but little is known about how the risk of wind damage may be affected by future climate change. We linked a mechanistic wind damage model (ForestGALES) to an empirical growth model for radiata pine (Pinus radiata D. Don) and a process-based growth model (cenw) to predict the risk of wind damage under different future emissions scenarios and assumptions about the future wind climate. The cenw model was used to estimate site productivity for constant CO2 concentration at 1990 values and for assumed increases in CO2 concentration from current values to those expected during 2040 and 2090 under the B1 (low), A1B (mid-range) and A2 (high) emission scenarios. Stand development was modelled for different levels of site productivity, contrasting silvicultural regimes and sites across New Zealand. The risk of wind damage was predicted for each regime and emission scenario combination using the ForestGALES model. The sensitivity to changes in the intensity of the future wind climate was also examined. Results showed that increased tree growth rates under the different emissions scenarios had the greatest impact on the risk of wind damage. The increase in risk was greatest for stands growing at high stand density under the A2 emissions scenario with increased CO2 concentration. The increased productivity under this scenario resulted in increased tree height, without a corresponding increase in diameter, leading to more slender trees that were predicted to be at greater risk from wind damage. The risk of wind damage was further increased by the modest increases in the extreme wind climate that are predicted to occur. These results have implications for the development of silvicultural regimes that are resilient to climate change and also indicate that future productivity gains may be offset by greater losses from disturbances.

  20. Predicted effects of climate warming on the distribution of 50 stream fishes in Wisconsin, U.S.A.

    USGS Publications Warehouse

    Stewart, Jana S.; Lyons, John D.; Matt Mitro,

    2010-01-01

    Summer air and stream water temperatures are expected to rise in the state of Wisconsin, U.S.A., over the next 50 years. To assess potential climate warming effects on stream fishes, predictive models were developed for 50 common fish species using classification-tree analysis of 69 environmental variables in a geographic information system. Model accuracy was 56·0–93·5% in validation tests. Models were applied to all 86 898 km of stream in the state under four different climate scenarios: current conditions, limited climate warming (summer air temperatures increase 1° C and water 0·8° C), moderate warming (air 3° C and water 2·4° C) and major warming (air 5° C and water 4° C). With climate warming, 23 fishes were predicted to decline in distribution (three to extirpation under the major warming scenario), 23 to increase and four to have no change. Overall, declining species lost substantially more stream length than increasing species gained. All three cold-water and 16 cool-water fishes and four of 31 warm-water fishes were predicted to decline, four warm-water fishes to remain the same and 23 warm-water fishes to increase in distribution. Species changes were predicted to be most dramatic in small streams in northern Wisconsin that currently have cold to cool summer water temperatures and are dominated by cold-water and cool-water fishes, and least in larger and warmer streams and rivers in southern Wisconsin that are currently dominated by warm-water fishes. Results of this study suggest that even small increases in summer air and water temperatures owing to climate warming will have major effects on the distribution of stream fishes in Wisconsin.

  1. Predicted effects of climate warming on the distribution of 50 stream fishes in Wisconsin, U.S.A.

    USGS Publications Warehouse

    Lyons, J.; Stewart, J.S.; Mitro, M.

    2010-01-01

    Summer air and stream water temperatures are expected to rise in the state of Wisconsin, U.S.A., over the next 50 years. To assess potential climate warming effects on stream fishes, predictive models were developed for 50 common fish species using classification-tree analysis of 69 environmental variables in a geographic information system. Model accuracy was 56.0-93.5% in validation tests. Models were applied to all 86 898 km of stream in the state under four different climate scenarios: current conditions, limited climate warming (summer air temperatures increase 1?? C and water 0.8?? C), moderate warming (air 3?? C and water 2.4?? C) and major warming (air 5?? C and water 4?? C). With climate warming, 23 fishes were predicted to decline in distribution (three to extirpation under the major warming scenario), 23 to increase and four to have no change. Overall, declining species lost substantially more stream length than increasing species gained. All three cold-water and 16 cool-water fishes and four of 31 warm-water fishes were predicted to decline, four warm-water fishes to remain the same and 23 warm-water fishes to increase in distribution. Species changes were predicted to be most dramatic in small streams in northern Wisconsin that currently have cold to cool summer water temperatures and are dominated by cold-water and cool-water fishes, and least in larger and warmer streams and rivers in southern Wisconsin that are currently dominated by warm-water fishes. Results of this study suggest that even small increases in summer air and water temperatures owing to climate warming will have major effects on the distribution of stream fishes in Wisconsin. ?? 2010 The Authors. Journal of Fish Biology ?? 2010 The Fisheries Society of the British Isles.

  2. The PRONE score: an algorithm for predicting doctors’ risks of formal patient complaints using routinely collected administrative data

    PubMed Central

    Spittal, Matthew J; Bismark, Marie M; Studdert, David M

    2015-01-01

    Background Medicolegal agencies—such as malpractice insurers, medical boards and complaints bodies—are mostly passive regulators; they react to episodes of substandard care, rather than intervening to prevent them. At least part of the explanation for this reactive role lies in the widely recognised difficulty of making robust predictions about medicolegal risk at the individual clinician level. We aimed to develop a simple, reliable scoring system for predicting Australian doctors’ risks of becoming the subject of repeated patient complaints. Methods Using routinely collected administrative data, we constructed a national sample of 13 849 formal complaints against 8424 doctors. The complaints were lodged by patients with state health service commissions in Australia over a 12-year period. We used multivariate logistic regression analysis to identify predictors of subsequent complaints, defined as another complaint occurring within 2 years of an index complaint. Model estimates were then used to derive a simple predictive algorithm, designed for application at the doctor level. Results The PRONE (Predicted Risk Of New Event) score is a 22-point scoring system that indicates a doctor's future complaint risk based on four variables: a doctor's specialty and sex, the number of previous complaints and the time since the last complaint. The PRONE score performed well in predicting subsequent complaints, exhibiting strong validity and reliability and reasonable goodness of fit (c-statistic=0.70). Conclusions The PRONE score appears to be a valid method for assessing individual doctors’ risks of attracting recurrent complaints. Regulators could harness such information to target quality improvement interventions, and prevent substandard care and patient dissatisfaction. The approach we describe should be replicable in other agencies that handle large numbers of patient complaints or malpractice claims. PMID:25855664

  3. 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

  4. 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

  5. Time Series of Aerosol Column Optical Depth at the Barrow, Alaska, ARM Climate Research Facility for 2008 Fourth Quarter 2009 ARM and Climate Change Prediction Program Metric Report

    SciTech Connect

    C Flynn; AS Koontz; JH Mather

    2009-09-01

    The uncertainties in current estimates of anthropogenic radiative forcing are dominated by the effects of aerosols, both in relation to the direct absorption and scattering of radiation by aerosols and also with respect to aerosol-related changes in cloud formation, longevity, and microphysics (See Figure 1; Intergovernmental Panel on Climate Change, Assessment Report 4, 2008). Moreover, the Arctic region in particular is especially sensitive to changes in climate with the magnitude of temperature changes (both observed and predicted) being several times larger than global averages (Kaufman et al. 2009). Recent studies confirm that aerosol-cloud interactions in the arctic generate climatologically significant radiative effects equivalent in magnitude to that of green house gases (Lubin and Vogelmann 2006, 2007). The aerosol optical depth is the most immediate representation of the aerosol direct effect and is also important for consideration of aerosol-cloud interactions, and thus this quantity is essential for studies of aerosol radiative forcing.

  6. Using Climate Variability to Predict Annual Precipitation and Estimate the Persistence of Climate Extremes for Major Urban Areas and Regions within the United States

    NASA Astrophysics Data System (ADS)

    Giovannettone, J. P.

    2015-12-01

    Relationships between climate variability and precipitation in several urban areas throughout the United States are developed using various global climate indices. Precipitation data for over 1200 stations are obtained from the United States Historical Climatology Network maintained by the National Climate Data Center, NOAA. All data are averaged over an extended period (up to five years) and correlated to several climate indices averaged over a period of equal length using lag times also up to five years. The period length and lag time are optimized in order to produce the highest correlation. The index that best correlates with precipitation for each urban area analyzed in the current study is identified and used to create regions within the United States that are predominantly affected by a particular index; strong correlations (r2 values > 0.70) were found in all regions. The final result is a map of the United States that displays the spatial distribution of each region. These results, which include the specific relationships developed for each region and urban area, will not only allow a greater understanding of the major mechanisms that are responsible for rainfall variability throughout the United States, but will also result in improved predictability of precipitation over multiple time scales, including seasonal and annual. In addition, the ability to predict total rainfall for periods greater than one year will allow an estimate of the persistence of trends and extreme events, such as periods of drought or above-average rainfall, to be made in advance; how far these projections can be made in advance depends on the lag times used to create each site-specific and regional correlation. An example related to the California Drought is given.

  7. Using Chemoinformatics, Bioinformatics, and Bioassay to Predict and Explain the Antibacterial Activity of Nonantibiotic Food and Drug Administration Drugs.

    PubMed

    Kahlous, Nour Aldin; Bawarish, Muhammad Al Mohdi; Sarhan, Muhammad Arabi; Küpper, Manfred; Hasaba, Ali; Rajab, Mazen

    2017-03-27

    Discovering of new and effective antibiotics is a major issue facing scientists today. Luckily, the development of computer science offers new methods to overcome this issue. In this study, a set of computer software was used to predict the antibacterial activity of nonantibiotic Food and Drug Administration (FDA)-approved drugs, and to explain their action by possible binding to well-known bacterial protein targets, along with testing their antibacterial activity against Gram-positive and Gram-negative bacteria. A three-dimensional virtual screening method that relies on chemical and shape similarity was applied using rapid overlay of chemical structures (ROCS) software to select candidate compounds from the FDA-approved drugs database that share similarity with 17 known antibiotics. Then, to check their antibacterial activity, disk diffusion test was applied on Staphylococcus aureus and Escherichia coli. Finally, a protein docking method was applied using HYBRID software to predict the binding of the active candidate to the target receptor of its similar antibiotic. Of the 1,991 drugs that were screened, 34 had been selected and among them 10 drugs showed antibacterial activity, whereby drotaverine and metoclopramide activities were without precedent reports. Furthermore, the docking process predicted that diclofenac, drotaverine, (S)-flurbiprofen, (S)-ibuprofen, and indomethacin could bind to the protein target of their similar antibiotics. Nevertheless, their antibacterial activities are weak compared with those of their similar antibiotics, which can be potentiated further by performing chemical modifications on their structure.

  8. Characterization of the Dynamics of Climate Systems and Identification of Missing Mechanisms Impacting the Long Term Predictive Capabilities of Global Climate Models Utilizing Dynamical Systems Approaches to the Analysis of Observed and Modeled Climate

    SciTech Connect

    Bhatt, Uma S.; Wackerbauer, Renate; Polyakov, Igor V.; Newman, David E.; Sanchez, Raul E.

    2015-11-13

    The goal of this research was to apply fractional and non-linear analysis techniques in order to develop a more complete characterization of climate change and variability for the oceanic, sea ice and atmospheric components of the Earth System. This research applied two measures of dynamical characteristics of time series, the R/S method of calculating the Hurst exponent and Renyi entropy, to observational and modeled climate data in order to evaluate how well climate models capture the long-term dynamics evident in observations. Fractional diffusion analysis was applied to ARGO ocean buoy data to quantify ocean transport. Self organized maps were applied to North Pacific sea level pressure and analyzed in ways to improve seasonal predictability for Alaska fire weather. This body of research shows that these methods can be used to evaluate climate models and shed light on climate mechanisms (i.e., understanding why something happens). With further research, these methods show promise for improving seasonal to longer time scale forecasts of climate.

  9. Factors predicting incremental administration of antihypertensive boluses during deep brain stimulator placement for Parkinson's disease.

    PubMed

    Rajan, Shobana; Deogaonkar, Milind; Kaw, Roop; Nada, Eman Ms; Hernandez, Adrian V; Ebrahim, Zeyd; Avitsian, Rafi

    2014-10-01

    Hypertension is common in deep brain stimulator (DBS) placement predisposing to intracranial hemorrhage. This retrospective review evaluates factors predicting incremental antihypertensive use intraoperatively. Medical records of Parkinson's disease (PD) patients undergoing DBS procedure between 2008-2011 were reviewed after Institutional Review Board approval. Anesthesia medication, preoperative levodopa dose, age, preoperative use of antihypertensive medications, diabetes mellitus, anxiety, motor part of the Unified Parkinson's