Science.gov

Sample records for administration climate prediction

  1. ADMINISTRATIVE CLIMATE.

    ERIC Educational Resources Information Center

    BRUCE, ROBERT L.; CARTER, G.L., JR.

    IN THE COOPERATIVE EXTENSION SERVICE, STYLES OF LEADERSHIP PROFOUNDLY AFFECT THE QUALITY OF THE SERVICE RENDERED. ACCORDINGLY, MAJOR INFLUENCES ON ADMINISTRATIVE CLIMATE AND EMPLOYEE PRODUCTIVITY ARE EXAMINED IN ESSAYS ON (1) SOURCES OF JOB SATISFACTION AND DISSATISFACTION, (2) MOTIVATIONAL THEORIES BASED ON JOB-RELATED SATISFACTIONS AND NEEDS,…

  2. 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.

  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. 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.

  5. Decadal climate prediction (project GCEP).

    PubMed

    Haines, Keith; Hermanson, Leon; Liu, Chunlei; Putt, Debbie; Sutton, Rowan; Iwi, Alan; Smith, Doug

    2009-03-13

    Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability. PMID:19087944

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. 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

  11. Decadal Climate Prediction: Challenges and Opportunities

    NASA Astrophysics Data System (ADS)

    Hurrell, J. W.

    2010-12-01

    The scientific understanding of climate change is now sufficiently clear to show that climate change from global warming is already upon us, and the rate of change as projected exceeds anything seen in nature in the past 10,000 years. Uncertainties remain, however, especially regarding how climate will change at regional and local scales where the signal of natural variability is large. Decision makers in diverse arenas, from water mangers in the U.S. Southwest to public health experts in Asia, need to know if the climate events they are seeing are the product of natural variability, and hence can be expected to reverse at some point, or are the result of potentially irreversible anthropogenic climate change. The climate science community will not be able to answer these questions and reduce the uncertainties in near-term climate projections without moving toward high resolution climate system predictions, with a blurring of the distinction between shorter-term predictions and longer-term climate projections. The key is the realization that climate system predictions of natural and forced change, regardless of timescale, will require initialization of coupled general circulation models with the best estimates of the current observed state of the atmosphere, oceans, cryosphere, and land surface, a state influenced both by the current phases of modes of natural variability and by the accumulated impacts to date of anthropogenic radiative forcing. Formidable challenges exist: for instance, what is the best method of initialization given imperfect observations and systematic errors in models, what effect does initialization have on climate predictions, what predictions should be attempted and how would they be verified? Accurate initial conditions for the global oceans are especially important and could conceivably be provided by ARGO floats and existing ocean data assimilation exercises. However, performing hindcasts prior to the ARGO float era of near-global upper

  12. Decadal Climate Prediction: Opportunities and Challenges

    NASA Astrophysics Data System (ADS)

    Hurrell, J. W.

    2011-12-01

    The scientific understanding of climate change is sufficiently clear to show that climate change from global warming is already upon us, and the rate of change as projected exceeds anything seen in nature in the past 10,000 years. Uncertainties remain, however, especially regarding how climate will change at regional and local scales where the signal of natural variability is large. Decision makers in diverse arenas, from water managers in the U.S. Southwest to public health experts in Asia, need to know if the climate events they are seeing are the product of natural variability, and hence can be expected to reverse at some point, or are the result of potentially irreversible anthropogenic climate change. The climate science community will not be able to answer these questions and reduce the uncertainties in near-term climate projections without moving toward high resolution climate system predictions, with a blurring of the distinction between shorter-term predictions and longer-term climate projections. The key is the realization that climate system predictions of natural and forced change, regardless of timescale, will require initialization of coupled general circulation models with the best estimates of the current observed state of the atmosphere, oceans, cryosphere, and land surface, a state influenced both by the current phases of modes of natural variability and by the accumulated impacts to date of anthropogenic radiative forcing. Formidable challenges exist: for instance, what is the best method of initialization given imperfect observations and systematic errors in models, what effect does initialization have on climate predictions, what predictions should be attempted and how would they be verified? Accurate initial conditions for the global oceans are especially important and could conceivably be provided by ARGO floats and existing ocean data assimilation exercises. However, performing hindcasts prior to the ARGO float era of near-global upper ocean

  13. 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…

  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. El Nino and climate prediction

    SciTech Connect

    1994-12-31

    This booklet describes how winds that flow from east to west across the equatorial Pacific Ocean are driven by the atmospheric-pressure differential between eastern and western Pacific, and goes on to discuss how this affects the ENSO cycle. Advances and successes in prediction of the ENSO are briefly describe in this NOAA publication.

  17. Prediction of climate variability and projection of climate change

    SciTech Connect

    Grassl, H.

    1996-12-31

    The years since 1985 have seen rapid progress in climate research. By the implementation of a new observing system in the Tropical Pacific Ocean combined with the development of adapted coupled ocean-atmosphere models the Tropical Ocean-Global Atmosphere (TOGA) project of the World Climate Research Programme (WCRP) led to the breakthrough to physically-based climate predictions. For most of the tropics and partly extending to mid-latitudes, climate anomalies can now be predicted for the next season and in some places even for the next year. On the other hand, global coupled ocean-atmosphere-land models have recently approached natural climate variability on time-scales to several decades to such an extent, that these models, partly validated with data from the past, became useful for answering the following two questions: Has mankind already changed global climate? Is anthropogenic global climate change, in the coming century, surmounting at least all variability observed during the last 10,000 years? Both questions are answered by yes. For the first question, the observed patterns of warming and cooling with respect to geographical, seasonal and vertical dependence can only be explained by a combined action of global greenhouse gas increase, regional sulfate aerosol load and stratospheric ozone depletion. For the second, even low climate sensitivity and low economic growth, will lead, if no measures are taken, to a mean global warming of 1.0 C, thus surmounting the warmest phase of the holocene. Implications of these findings for the implementation of the UN Framework Convention on Climate Change will also be discussed.

  18. 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

  19. Climate predictability in the second year.

    PubMed

    Hermanson, Leon; Sutton, Rowan T

    2009-03-13

    In this paper, the predictability of climate arising from ocean heat content (OHC) anomalies is investigated in the HadCM3 coupled atmosphere-ocean model. An ensemble of simulations of the twentieth century are used to provide initial conditions for a case study. The case study consists of two ensembles started from initial conditions with large differences in regional OHC in the North Atlantic, the Southern Ocean and parts of the West Pacific. Surface temperatures and precipitation are on average not predictable beyond seasonal time scales, but for certain initial conditions there may be longer predictability. It is shown that, for the case study examined here, some aspects of tropical precipitation, European surface temperatures and North Atlantic sea-level pressure are potentially predictable 2 years ahead. Predictability also exists in the other case studies, but the climate variables and regions, which are potentially predictable, differ. This work was done as part of the Grid for Coupled Ensemble Prediction (GCEP) eScience project. PMID:19087941

  20. Values and uncertainties in climate prediction, revisited.

    PubMed

    Parker, Wendy

    2014-06-01

    Philosophers continue to debate both the actual and the ideal roles of values in science. Recently, Eric Winsberg has offered a novel, model-based challenge to those who argue that the internal workings of science can and should be kept free from the influence of social values. He contends that model-based assignments of probability to hypotheses about future climate change are unavoidably influenced by social values. I raise two objections to Winsberg's argument, neither of which can wholly undermine its conclusion but each of which suggests that his argument exaggerates the influence of social values on estimates of uncertainty in climate prediction. I then show how a more traditional challenge to the value-free ideal seems tailor-made for the climate context. PMID:25051868

  1. Regional Climate Predictability in the Extratropics

    SciTech Connect

    Robertson,A.W.:Ghil,M.

    2001-08-09

    The goal of this project was to develop a dynamical framework for extratropical climate predictability on decade-to-century timescales and subcontinental spatial scales,besed on the intraseasonal dynamics of the midlatitude atmosphere and their interaction with the ocean's longer timescales.A two-pronged approach was taken,based on (a)idealized,quasi-geostrophic,coupled models of the midlatitude ocean-atmosphere system,and(b)analysis of GCM results.

  2. 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.

  3. Using simple chaotic models to interpret climate under climate change: Implications for probabilistic climate prediction

    NASA Astrophysics Data System (ADS)

    Daron, Joseph

    2010-05-01

    Exploring the reliability of model based projections is an important pre-cursor to evaluating their societal relevance. In order to better inform decisions concerning adaptation (and mitigation) to climate change, we must investigate whether or not our models are capable of replicating the dynamic nature of the climate system. Whilst uncertainty is inherent within climate prediction, establishing and communicating what is plausible as opposed to what is likely is the first step to ensuring that climate sensitive systems are robust to climate change. Climate prediction centers are moving towards probabilistic projections of climate change at regional and local scales (Murphy et al., 2009). It is therefore important to understand what a probabilistic forecast means for a chaotic nonlinear dynamic system that is subject to changing forcings. It is in this context that we present the results of experiments using simple models that can be considered analogous to the more complex climate system, namely the Lorenz 1963 and Lorenz 1984 models (Lorenz, 1963; Lorenz, 1984). Whilst the search for a low-dimensional climate attractor remains illusive (Fraedrich, 1986; Sahay and Sreenivasan, 1996) the characterization of the climate system in such terms can be useful for conceptual and computational simplicity. Recognising that a change in climate is manifest in a change in the distribution of a particular climate variable (Stainforth et al., 2007), we first establish the equilibrium distributions of the Lorenz systems for certain parameter settings. Allowing the parameters to vary in time, we investigate the dependency of such distributions to initial conditions and discuss the implications for climate prediction. We argue that the role of chaos and nonlinear dynamic behaviour ought to have more prominence in the discussion of the forecasting capabilities in climate prediction. References: Fraedrich, K. Estimating the dimensions of weather and climate attractors. J. Atmos. Sci

  4. 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.

  5. 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

  6. 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. PMID:23536299

  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. 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...

  9. 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...

  10. 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.

  11. 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.

  12. Validating Predictions from Climate Envelope Models

    PubMed Central

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

    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. PMID

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Predicting Climate Change: Lessons From Reductionism, Emergence, and the Past

    NASA Astrophysics Data System (ADS)

    Harrison, Stephan; Stainforth, Dave

    2009-03-01

    Climate and Earth system models are the only tools used to make predictions of future climate change. Such predictions are subject to considerable uncertainties, and understanding these uncertainties has clear and important policy implications. This Forum highlights the concepts of reductionism and emergence, and past climate variability, to illuminate some of the uncertainties faced by those wishing to model the future evolution of global climate. General circulation models (GCMs) of the atmosphere-ocean system are scientists' principal tools for providing information about future climate. GCMs consequently have considerable influence on climate change-related policy questions. Over the past decade, there have been significant attempts, mainly by statisticians and mathematicians, to explore the uncertainties in model simulations of possible futures, accompanied by growing debate about the interpretation of these simulations as aids in societal decisions. In this Forum, we discuss atmosphere-ocean GCMs in the context of reductionist and emergent approaches to scientific study.

  18. Predicting Dengue Fever Outbreaks in French Guiana Using Climate Indicators

    PubMed Central

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

    2016-01-01

    Background Dengue fever epidemic dynamics are driven by complex interactions between hosts, vectors and viruses. Associations between climate and dengue have been studied around the world, but the results have shown that the impact of the climate can vary widely from one study site to another. In French Guiana, climate-based models are not available to assist in developing an early warning system. This study aims to evaluate the potential of using oceanic and atmospheric conditions to help predict dengue fever outbreaks in French Guiana. Methodology/Principal Findings Lagged correlations and composite analyses were performed to identify the climatic conditions that characterized a typical epidemic year and to define the best indices for predicting dengue fever outbreaks during the period 1991–2013. A logistic regression was then performed to build a forecast model. We demonstrate that a model based on summer Equatorial Pacific Ocean sea surface temperatures and Azores High sea-level pressure had predictive value and was able to predict 80% of the outbreaks while incorrectly predicting only 15% of the non-epidemic years. Predictions for 2014–2015 were consistent with the observed non-epidemic conditions, and an outbreak in early 2016 was predicted. Conclusions/Significance These findings indicate that outbreak resurgence can be modeled using a simple combination of climate indicators. This might be useful for anticipating public health actions to mitigate the effects of major outbreaks, particularly in areas where resources are limited and medical infrastructures are generally insufficient. PMID:27128312

  19. 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.

  20. 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.

  1. 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.

  2. Do We Need Better Climate Predictions to Adapt to a Changing Climate? (Invited)

    NASA Astrophysics Data System (ADS)

    Dessai, S.; Hulme, M.; Lempert, R.; Pielke, R., Jr.

    2009-12-01

    Based on a series of international scientific assessments, climate change has been presented to society as a major problem that needs urgently to be tackled. The science that underpins these assessments has been pre-dominantly from the realm of the natural sciences and central to this framing have been ‘projections’ of future climate change (and its impacts on environment and society) under various greenhouse gas emissions scenarios and using a variety of climate model predictions with embedded assumptions. Central to much of the discussion surrounding adaptation to climate change is the claim - explicit or implicit - that decision makers need accurate and increasingly precise assessments of future impacts of climate change in order to adapt successfully. If true, this claim places a high premium on accurate and precise climate predictions at a range of geographical and temporal scales; such predictions therefore become indispensable, and indeed a prerequisite for, effective adaptation decision-making. But is effective adaptation tied to the ability of the scientific enterprise to predict future climate with accuracy and precision? If so, this may impose a serious and intractable limit on adaptation. This paper proceeds in three sections. It first gathers evidence of claims that climate prediction is necessary for adaptation decision-making. This evidence is drawn from peer-reviewed literature and from published science funding strategies and government policy in a number of different countries. The second part discusses the challenges of climate prediction and why science will consistently be unable to provide accurate and precise predictions of future climate relevant for adaptation (usually at the local/regional level). Section three discusses whether these limits to future foresight represent a limit to adaptation, arguing that effective adaptation need not be limited by a general inability to predict future climate. Given the deep uncertainties involved in

  3. The origins of computer weather prediction and climate modeling

    SciTech Connect

    Lynch, Peter

    2008-03-20

    Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understanding of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reaching. Earth System Models are capable of replicating climate regimes of past millennia and are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer was constructed. There were several major practical obstacles to be overcome before numerical prediction could be put into practice. A fuller understanding of atmospheric dynamics allowed the development of simplified systems of equations; regular radiosonde observations of the free atmosphere and, later, satellite data, provided the initial conditions; stable finite difference schemes were developed; and powerful electronic computers provided a practical means of carrying out the prodigious calculations required to predict the changes in the weather. Progress in weather forecasting and in climate modeling over the past 50 years has been dramatic. In this presentation, we will trace the history of computer forecasting through the ENIAC integrations to the present day. The useful range of deterministic prediction is increasing by about one day each decade, and our understanding of climate change is growing rapidly as Earth System Models of ever-increasing sophistication are developed.

  4. 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... notice sets forth the schedule of a forthcoming meeting of the DoC NOAA National Climate Assessment and... the call. Please check the National Climate Assessment Web site for additional information at...

  5. 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.

  6. Evapotranspiration in Subtropical Climate: Measurements and predictions

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Evapotranspiration (ET) loss is estimated at about 80-85% of annual precipitation in South Florida. Accurate prediction of ET is an important part of the implementation of the Comprehensive Everglades Restoration Plan (CERP). In the USDA's Everglades Agro-Hydrology Model (EAHM), the daily soil root...

  7. Dynamic bifurcations for predictability of climate tipping events

    NASA Astrophysics Data System (ADS)

    Surovyatkina, Elena; Kurths, Juergen

    2014-05-01

    Despite recent advances in understanding of the nonlinear processes responsible for changes in the climate system predicting the future abrupt climate changes remains an outstanding scientific challenge of special importance for the society. Better understanding of nonlinear mechanisms of tipping points is a major goal in treating this problem. Existed approaches to examine climatic tipping points allow identifying the climate-tipping events in the past but very limited to predict them in advance. Our recent theoretical findings suggest that for predicting tipping points it is crucial to distinguish which scenario of a bifurcation transition dominates: dynamic (predictable) or stochastic (unpredictable). In order to illustrate suggested approach we compare the features of dynamic and stochastic bifurcations in most common scenario of abrupt transition between two climate states. This scenario is associated with a saddle-node and a transcritical bifurcations. Such scenario has been used, in particularly, in the analysis of stability of the thermohaline circulation against freshwater flux, Indian summer monsoon against global change. We demonstrate the effect of the rate of change of the bifurcation parameter at dynamic bifurcation and noise effect at stochastic bifurcation transitions by theoretical estimates. We analyse pre-bifurcation noise-dependent and rate-dependent phenomena, and distinguish its roles as "precursors" of impending bifurcations for dynamic and stochastic transitions. We show that appropriate choice of "precursors" might lead to improving predictability of climate tipping points. Additionally, the evaluation of the role of dynamic and stochastic factors might be also useful for the assessment of the vulnerability of tipping elements to noise-induced changes. Finally, our preliminary investigations suggest that a hitherto neglected dynamic effect induced by such small parameter as rate of change of the bifurcation parameter may have had important

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

    PubMed

    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.

  9. 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.

  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. 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.

  12. Climate change and predicted trend of fungal keratitis in Egypt.

    PubMed

    Saad-Hussein, A; El-Mofty, H M; Hassanien, M A

    2011-06-01

    Rising rates of invasive fungal infections may be linked to global climate change. A study was made of the trend of ophthalmic fungal corneal keratitis in the greater Cairo area of Egypt and its association with climate records during the same period. Data on diagnosed cases of fungal keratitis were collected from records of ophthalmic departments of Cairo University hospital and atmospheric temperature and humidity for the greater Cairo area were obtained from online records. Statistical analysis showed a significant increase in the relative frequency of keratomycosis during 1997-2007. The rise correlated significantly with rises n min,mum temperature and the maximum atmospheric humidity in the greater Cairo area over the same period (after exclusion of the effect of the maximum atmos pheric temperature). The predicted increase in keratomycosis up to the year 2030 corresponds to predicted increases in CO2 emissions and surface temperature from climate change models for Egypt.

  13. 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.

  14. 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

  15. 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.

  16. 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. PMID:24119205

  17. 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.

  18. 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

  19. The National Oceanic and Atmospheric Administration (NOAA) Climate Services Portal: A New Centralized Resource for Distributed Climate Information

    NASA Astrophysics Data System (ADS)

    Burroughs, J.; Baldwin, R.; Herring, D.; Lott, N.; Boyd, J.; Handel, S.; Niepold, F.; Shea, E.

    2010-09-01

    With the rapid rise in the development of Web technologies and climate services across NOAA, there has been an increasing need for greater collaboration regarding NOAA's online climate services. The drivers include the need to enhance NOAA's Web presence in response to customer requirements, emerging needs for improved decision-making capabilities across all sectors of society facing impacts from climate variability and change, and the importance of leveraging climate data and services to support research and public education. To address these needs, NOAA (during fiscal year 2009) embarked upon an ambitious program to develop a NOAA Climate Services Portal (NCS Portal). Four NOAA offices are leading the effort: 1) the NOAA Climate Program Office (CPO), 2) the National Ocean Service's Coastal Services Center (CSC), 3) the National Weather Service's Climate Prediction Center (CPC), and 4) the National Environmental Satellite, Data, and Information Service's (NESDIS) National Climatic Data Center (NCDC). Other offices and programs are also contributing in many ways to the effort. A prototype NCS Portal is being placed online for public access in January 2010, http://www.climate.gov. This website only scratches the surface of the many climate services across NOAA, but this effort, via direct user engagement, will gradually expand the scope and breadth of the NCS Portal to greatly enhance the accessibility and usefulness of NOAA's climate data and services.

  20. The Campus Climate Revisited: Chilly for Women Faculty, Administrators, and Graduate Students.

    ERIC Educational Resources Information Center

    Sandler, Bernice R.; Hall, Roberta M.

    The professional climate often experienced by women faculty and administrators is reported, along with some consideration to the experiences of graduate and professional students. Attention is focused on subtle ways in which women are treated differently and common behaviors that create a chilly professional climate. The information was obtained…

  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. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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…

  7. The Effects of Collective Bargaining on the Climate of Administration and Supervision.

    ERIC Educational Resources Information Center

    Kraig, Glen M.

    Collective bargaining between teachers and educational administrators has frequently had negative effects on the climate of school supervision and administration, but this need not always be the case. Before collective bargaining, teachers as a group were powerless over their pay and working conditions. Now many teachers feel that collective…

  8. 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

    2011-01-01

    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. PMID:21249228

  9. 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...

  10. 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.

  11. Predicting Vegetation Patterning across Climate, Soil, and Topographic Gradients

    NASA Astrophysics Data System (ADS)

    Axelsson, C.; Hanan, N. P.

    2014-12-01

    Vegetation communities in water-limited systems sometimes form periodic patterns, e.g. banded, spotted and labyrinthine distributions of woody and herbaceous plants. Pattern formation is commonly linked to competition and facilitation among plants, and variation in runoff and infiltration capacity in the landscape. Based on previous studies, we expect that climate, soil type, and slope to a large degree influence the type of vegetation pattern found at a specific site. We have analyzed to what extent vegetation patterns on the African continent can be predicted based on available climatic, topographic, and soil data. Our focus is not restricted to periodic patterns in drylands, but encompasses a range of tropical ecosystems from arid to humid. Vegetation patterns observed in remote sensing data can be informative regarding the underlying ecological processes that shape the landscape, not only in strikingly periodic vegetation but also in savannas with randomly located or dispersed vegetation. We use high-resolution multispectral and panchromatic remote sensing data classified into woody, herbaceous, and bare ground components. From these images we extract spatial statistical metrics that define type and degree of vegetation patterning. We then relate variables from climate, soil and topographic datasets to the observed patterns in order to determine how well we can predict vegetation patterning and which climatic and edaphic variables are most informative. We discuss the results and the possible sources of uncertainty in the relationships.

  12. 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. PMID:27062059

  13. 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

  14. 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.

  15. 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).

  16. 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.

  17. 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

  18. Feedbacks in Climate - Permafrost - Vegetation System: Predictive Modeling Approach.

    NASA Astrophysics Data System (ADS)

    Anisimov, O.; Beloloutskaia, M.

    2003-12-01

    Permafrost models driven by scenarios of climate change predict that reduction of the total (continuous) permafrost area in the northern hemisphere by 2030, 2050, and 2080 is likely to be 10%-18% (15%-25%); 15%-30% (20%-40%), and 20%-35% (25%-50%), respectively. Predicted changes of the seasonal thaw depth in the following three decades are relatively small, typically within 10%-15%. By the middle of the century thaw depth may increase on average by 15%-25%, and by 50% and more in the northernmost locations. By 2080 layer of seasonal thawing will become markedly thicker (by 30%-50% and more) all over the permafrost area. Vegetation above permafrost plays important role in regulating ground temperature and depth of seasonal thawing. In the warm period organic layer of peat, mosses, and lichens has low thermal conductivity and protects permafrost from thawing. Results from coupled climate - permafrost - vegetation model suggest that 5cm, 10cm, 15cm, and 20cm thick organic layer reduces seasonal thaw depth by 10%, 25%, 40%, and 60%, respectively, compared to bare ground. Enhanced growth of non-vascular plants under warmer climatic conditions may thus mitigate the effects of climatic warming on permafrost. Controlled experiments involving continuous localized warming at selected sites in the northern Europe and in Alaska indicate a multiyear tendency towards the replacement of mosses and lichens by vascular plants. In the long term climate-induced changes of vegetation may thus cause enhances warming and deeper seasonal thawing of the frozen ground ultimately leading to degradation of permafrost. Acknowledgement. This work is supported by the National Science Foundation of the Netherlands, grant # 047.011.2001.003.

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. 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.

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

    SciTech Connect

    Acker, J.G.; Busalacchi, A. |

    1995-02-01

    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.

  5. 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.

  6. 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.

  7. Predicting climate fluctuations for water management by applying neural network

    SciTech Connect

    Zhang, E.Y.

    1996-12-31

    The ability to forecast climate fluctuations would be a valuable asset to regional water management authorities such as the South Florida Water Management District. These forecasts may provide advanced warnings of possible extended periods of deficits or surpluses of water availability allowing better regional water management for flood protection, water supply, and environmental enhancement. In order to achieve this goal, it is necessary to have a global perspective of the oceanic and atmospheric phenomena which may affect regional water resources. However, the complexity involved may hinder traditional analytical approaches in forecasting because such approaches are based on many simplified assumptions about the natural phenomena. This paper investigates the applicability of neural networks in climate forecasting for regional water resources management. This paper applies the most widely used Back Propagation model to the climate forecasting. In this study, issues such as selecting a best fit neural network configuration, deploying a proper training algorithm, and preprocessing input data are addressed. The effects of various global oceanic and atmospheric variables to the regional water resources are also discussed. The study is focused on the prediction of water storage for Lake Okeechobee, the liquid heart for south Florida. Several global weather parameters over the past several decades are used as input data for training and testing. Different combinations of the variables are explored. Preliminary results show that the neural networks are promising tools in this type of forecasting.

  8. 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.

  9. Understanding and Predicting Water and Energy Cycle Changes in NOAA Climate Program

    NASA Astrophysics Data System (ADS)

    Koblinsky, C. J.

    2008-05-01

    The NOAA Climate Program leads and coordinates climate activities across all line offices in NOAA. The objectives of NOAA Climate Program are: 1) to describe and understand the state of the climate system through integrated observations, monitoring, and data management, 2) to understand and predict climate variability and change from weeks to decades to a century, and 3) to improve the ability of society to plan for and respond to climate variability and change. The NOAA Climate Program consists of three major programs: Climate Observation and Monitoring, Climate Research and Modeling and Climate Service Development. Understanding and predicting water & energy cycle variability and changes and their consequences to the society have been major undertaking within NOAA Climate Program. Climate variability and change profoundly influence the health, prosperity, and well-being of the people of the United States, as well as all other nations of the world, with vital global economic and security implications. NOAA Climate Program is currently working on a new strategy to develop an improved capability and better climate services to plan for and adapt to climate variability and change. Understanding and predicting water & energy cycle variability and changes will be an important component in NOAA's new strategy for improved climate services. NOAA is willing to work with national and international partners to improve climate services in the changing climate.

  10. Darcy's law predicts widespread forest mortality under climate warming

    NASA Astrophysics Data System (ADS)

    McDowell, Nathan G.; Allen, Craig D.

    2015-07-01

    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 use 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. 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.

  11. Extreme hydrometeorological events and climate change predictions in Europe

    NASA Astrophysics Data System (ADS)

    Millán, Millán M.

    2014-10-01

    Field meteorological data collected in several European Commission projects (from 1974 to 2011) were re-analysed in the context of a perceived reduction in summer storms around the Western Mediterranean Basin (WMB). The findings reveal some hitherto overlooked processes that raise questions about direct impacts on European hydrological cycles, e.g., extreme hydrometeorological events, and about the role of feedbacks on climate models and climate predictions. For instance, the summer storms are affected by land-use changes along the coasts and mountain slopes. Their loss triggers a chain of events that leads to an Accumulation Mode (AM) where water vapour and air pollutants (ozone) become stacked in layers, up to 4000(+) m, over the WMB. The AM cycle can last 3-5 consecutive days, and recur several times each month from mid May to late August. At the end of each cycle the accumulated water vapour can feed Vb track events and generate intense rainfall and summer floods in Central Europe. Venting out of the water vapour that should have precipitated within the WMB increases the salinity of the sea and affects the Atlantic-Mediterranean Salinity valve at Gibraltar. This, in turn, can alter the tracks of Atlantic Depressions and their frontal systems over Atlantic Europe. Another effect is the greenhouse heating by water vapour and photo-oxidants (e.g., O3) when layered over the Basin during the AM cycle. This increases the Sea Surface Temperature (SST), and the higher SST intensifies torrential rain events over the Mediterranean coasts in autumn. All these processes raise research questions that must be addressed to improve the meteorological forecasting of extreme events, as well as climate model predictions.

  12. 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.

  13. Factors Predicting Women's Upward Career Mobility in School Administration.

    ERIC Educational Resources Information Center

    Jones, Effie H.; Montenegro, Xenia P.

    1983-01-01

    Contrasted characteristics and career mobility of female participants in a 1977 American Association of School Administrators (AASA) training program with those of 41 nonparticipants. Found no differences between the two groups in degree of "internal" barriers (role conflict, etc), but found that the AASA trainees encountered fewer "external"…

  14. 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…

  15. Impact of in-consistency between the climate model and its initial conditions on climate prediction

    NASA Astrophysics Data System (ADS)

    Liu, Xueyuan; Köhl, Armin; Stammer, Detlef; Masuda, Shuhei; Ishikawa, Yoichi; Mochizuki, Takashi

    2016-05-01

    We investigated the influence of dynamical in-consistency of initial conditions on the predictive skill of decadal climate predictions. The investigation builds on the fully coupled global model "Coupled GCM for Earth Simulator" (CFES). In two separate experiments, the ocean component of the coupled model is full-field initialized with two different initial fields from either the same coupled model CFES or the GECCO2 Ocean Synthesis while the atmosphere is initialized from CFES in both cases. Differences between both experiments show that higher SST forecast skill is obtained when initializing with coupled data assimilation initial conditions (CIH) instead of those from GECCO2 (GIH), with the most significant difference in skill obtained over the tropical Pacific at lead year one. High predictive skill of SST over the tropical Pacific seen in CIH reflects the good reproduction of El Niño events at lead year one. In contrast, GIH produces additional erroneous El Niño events. The tropical Pacific skill differences between both runs can be rationalized in terms of the zonal momentum balance between the wind stress and pressure gradient force, which characterizes the upper equatorial Pacific. In GIH, the differences between the oceanic and atmospheric state at initial time leads to imbalance between the zonal wind stress and pressure gradient force over the equatorial Pacific, which leads to the additional pseudo El Niño events and explains reduced predictive skill. The balance can be reestablished if anomaly initialization strategy is applied with GECCO2 initial conditions and improved predictive skill in the tropical Pacific is observed at lead year one. However, initializing the coupled model with self-consistent initial conditions leads to the highest skill of climate prediction in the tropical Pacific by preserving the momentum balance between zonal wind stress and pressure gradient force along the equatorial Pacific.

  16. 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

  17. Probabilistic prediction of climate using multi-model ensembles: from basics to applications

    PubMed Central

    Palmer, T.N; Doblas-Reyes, F.J; Hagedorn, R; Weisheimer, A

    2005-01-01

    The development of multi-model ensembles for reliable predictions of inter-annual climate fluctuations and climate change, and their application to health, agronomy and water management, are discussed. PMID:16433088

  18. 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...

  19. 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.

  20. 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.

  1. Discriminating between climate observations in terms of their ability to improve an ensemble of climate predictions

    PubMed Central

    Huang, Yi; Leroy, Stephen; Goody, Richard M.

    2011-01-01

    In view of the cost and complexity of climate-observing systems, it is a matter of concern to know which measurements, by satellite or in situ, can best improve the accuracy and precision of long-term ensembles of climate projections. We follow a statistical procedure to evaluate the relative capabilities of a wide variety of observable data types for improving the accuracy and precision of an ensemble of Intergovernmental Panel on Climate Change (IPCC) models. Thirty-two data types are evaluated for their potential for improving a 50-y surface air temperature trend prediction with data from earlier periods, with an emphasis on 20 y. Data types can be ordered in terms of their ability to increase the precision of a forecast. Results show that important conclusions can follow from this ordering. The small size of the IPCC model ensemble (20 members) creates uncertainties in these conclusions, which need to be substantiated with the larger ensembles expected in the future. But the larger issue of whether the methodology can provide useful answers is demonstrated. PMID:21670245

  2. Tracking Expected Improvements of Decadal Prediction in Climate Services

    NASA Astrophysics Data System (ADS)

    Suckling, E.; Thompson, E.; Smith, L. A.

    2013-12-01

    Physics-based simulation models are ultimately expected to provide the best available (decision-relevant) probabilistic climate predictions, as they can capture the dynamics of the Earth System across a range of situations, situations for which observations for the construction of empirical models are scant if not nonexistent. This fact in itself provides neither evidence that predictions from today's Earth Systems Models will outperform today's empirical models, nor a guide to the space and time scales on which today's model predictions are adequate for a given purpose. Empirical (data-based) models are employed to make probability forecasts on decadal timescales. The skill of these forecasts is contrasted with that of state-of-the-art climate models, and the challenges faced by each approach are discussed. The focus is on providing decision-relevant probability forecasts for decision support. An empirical model, known as Dynamic Climatology is shown to be competitive with CMIP5 climate models on decadal scale probability forecasts. Contrasting the skill of simulation models not only with each other but also with empirical models can reveal the space and time scales on which a generation of simulation models exploits their physical basis effectively. It can also quantify their ability to add information in the formation of operational forecasts. Difficulties (i) of information contamination (ii) of the interpretation of probabilistic skill and (iii) of artificial skill complicate each modelling approach, and are discussed. "Physics free" empirical models provide fixed, quantitative benchmarks for the evaluation of ever more complex climate models, that is not available from (inter)comparisons restricted to only complex models. At present, empirical models can also provide a background term for blending in the formation of probability forecasts from ensembles of simulation models. In weather forecasting this role is filled by the climatological distribution, and

  3. Predictions of a Global Climate Change and Cycle on Jupiter

    NASA Astrophysics Data System (ADS)

    Marcus, P. S.

    2003-12-01

    We predict that most of Jupiter's large vortices, similar to (but not including) the Great Red Spot, will soon disappear due to vortex mergers. This will cause global temperature changes of ˜10oK. Within a decade, several of Jupiter's westward jet streams (there are 12) will form waves. They will grow, break, roll-up and re-populate Jupiter with new vortices. These dynamics should be visible from earth as the break-up of a circumferential band of clouds into ``spots''. The new vortices will be similar to those that were observed during most of the 20th century. For ˜60 years they will change only slowly, then abruptly bunch together. Shortly afterward, most will disappear by merging with other vortices. The cycle described above will repeat with a ˜70-year time scale, with many of the events detectable from earth or by satellite. The formation of the White Oval ``spots'' in 1939 began the current global climate cycle, and their mergers in 1997--2000 signaled the beginning of its end. Our predictions are based on fundamental vortex dynamics rather than global circulation models.

  4. 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.

  5. 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.

  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. 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.

  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. 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

  10. 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.

  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. 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

  14. Climate simulation and numerical weather prediction using GPUs

    NASA Astrophysics Data System (ADS)

    Lapillonne, Xavier; Fuhrer, Oliver; Ruedisuehli, Stefan; Arteaga, Andrea; Osuna, Carlos; Walser, Andre; Leuenberger, Daniel

    2015-04-01

    After the successful development of a prototype GPU version of the atmospheric model COSMO, the COSMO Consortium has decided to bring these developments back to the official version in order to have an operational GPU-capable model for climate and weather prediction. The implementation is designed so as to avoid costly data transfer between the GPU and the CPU and achieve best performance. To this end, most parts of model are ported to GPU. Furthermore, the implementation has been specifically targeted for hardware architectures with fat nodes (nodes with multiple GPUs), which is very favourable in terms of minimizing the energy-to-solution metric. The dynamical core has been completely rewritten using a GPU-enabled domain-specific language. The rest of the model namely the physical parametrizations and the data assimilation are ported to GPU using the OpenACC compiler directives. In this contribution, we present the overall porting strategy as well as new features available on GPU in the latest version of the model in particular concerning the data assimilation. Performance and verification results obtained on several hybrid Cray systems are presented and compared against the current operational model version used at MeteoSwiss.

  15. Uncertainty levels in predicted patterns of anthropogenic climate change

    NASA Astrophysics Data System (ADS)

    Barnett, Tim P.; Hegerl, Gabriele; Knutson, Tom; Tett, Simon

    2000-06-01

    This paper investigates the uncertainties in different model estimates of an expected anthropogenic signal in the near-surface air temperature field. We first consider nine coupled global climate models (CGCMs) forced by CO2 increasing at the rate of 1%/yr. Averaged over years 71-80 of their integrations, the approximate time of CO2 doubling, the models produce a global mean temperature change that agrees to within about 25% of the nine model average. However, the spatial patterns of change can be rather different. This is likely to be due to different representations of various physical processes in the respective models, especially those associated with land and sea ice processes. We next analyzed 11 different runs from three different CGCMs, each forced by observed/projected greenhouse gases (GHG) and estimated direct sulfate aerosol effects. Concentrating on the patterns of trend of near-surface air temperature change over the period 1945-1995, we found that the raw individual model simulations often bore little resemblance to each other or to the observations. This was due partially to large magnitude, small-scale spatial noise that characterized all the model runs, a feature resulting mainly from internal model variability. Heavy spatial smoothing and ensemble averaging improved the intermodel agreement. The existence of substantial differences between different realizations of an ensemble produced by identical forcing almost requires that detection and attribution work be done with ensembles of scenario runs, as single runs can be misleading. Application of recent detection and attribution methods, coupled with ensemble averaging, produced a reasonably consistent match between model predictions of expected patterns of temperature trends due to a combination of GHG and direct sulfate aerosols and those observed. This statement is provisional since the runs studied here did not include other anthropogenic pollutants thought to be important (e.g., indirect

  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. PMID:27140640

  17. 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

  18. Climate predictability experiments with a general circulation model

    NASA Astrophysics Data System (ADS)

    Bengtsson, L.; Arpe, K.; Roeckner, E.; Schulzweida, U.

    1996-03-01

    The atmospheric response to the evolution of the global sea surface temperatures from 1979 to 1992 is studied using the Max-Planck-Institut 19 level atmospheric general circulation model, ECHAM3 at T 42 resolution. Five separate 14-year integrations are performed and results are presented for each individual realization and for the ensemble-averaged response. The results are compared to a 30-year control integration using a climate monthly mean state of the sea surface temperatures and to analysis data. It is found that the ECHAM3 model, by and large, does reproduce the observed response pattern to El Nino and La Niña. During the El Nino events, the subtropical jet streams in both hemispheres are intensified and displaced equatorward, and there is a tendency towards weak upper easterlies over the equator. The Southern Oscillation is a very stable feature of the integrations and is accurately reproduced in all experiments. The inter-annual variability at middle- and high-latitudes, on the other hand, is strongly dominated by chaotic dynamics, and the tropical SST forcing only modulates the atmospheric circulation. The potential predictability of the model is investigated for six different regions. Signal to noise ratio is large in most parts of the tropical belt, of medium strength in the western hemisphere and generally small over the European area. The ENSO signal is most pronounced during the boreal spring. A particularly strong signal in the precipitation field in the extratropics during spring can be found over the southern United States. Western Canada is normally warmer during the warm ENSO phase, while northern Europe is warmer than normal during the ENSO cold phase. The reason is advection of warm air due to a more intense Pacific low than normal during the warm ENSO phase and a more intense Icelandic low than normal during the cold ENSO phase, respectively.

  19. The predictive validity of the rat self-administration model for abuse liability.

    PubMed

    O'Connor, Eoin C; Chapman, Kathryn; Butler, Paul; Mead, Andy N

    2011-01-01

    The self-administration model is the primary non-clinical approach for assessing the reinforcing properties of novel compounds. Given the now frequent use of rats in self-administration studies, it is important to understand the predictive validity of the rat self-administration model for use in abuse liability assessments. This review of 71 drugs identifies high concordance between findings from rat self-administration studies and two clinical indicators of abuse liability, namely reports of positive subjective-effects and the DEA drug scheduling status. To understand the influence of species on concordance we compare rodent and non-human primate (NHP) self-administration data. In the few instances where discrepancies are observed between rat data and the clinical indicators of abuse liability, rat self-administration data corresponds with NHP data in the majority of these cases. We discuss the influence of genetic factors (sex and strain), food deprivation state and the study design (acquisition or drug substitution) on self-administration study outcomes and highlight opportunities to improve the predictive validity of the self-administration model.

  20. 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.

  1. Climate and Species Richness Predict the Phylogenetic Structure of African Mammal Communities

    PubMed Central

    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. PMID:25875361

  2. 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

  3. 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.

  4. Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia.

    PubMed

    Briscoe, Natalie J; Kearney, Michael R; Taylor, Chris A; Wintle, Brendan A

    2016-07-01

    Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models (SDMs) using long-term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat-induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long-term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range - with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot-spells, in driving species-climate relationships and distributions. By unpacking the mechanisms

  5. Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia.

    PubMed

    Briscoe, Natalie J; Kearney, Michael R; Taylor, Chris A; Wintle, Brendan A

    2016-07-01

    Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models (SDMs) using long-term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat-induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long-term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range - with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot-spells, in driving species-climate relationships and distributions. By unpacking the mechanisms

  6. 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.

  7. Climatic patterns predict the elaboration of song displays in songbirds

    PubMed Central

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

    2012-01-01

    Summary Climatic variability and unpredictability [1] 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 [2–5]. This hypothesis is supported by larger brain sizes [6], higher foraging innovation rates [7–9], higher reproductive flexibility [10–12], and higher sociality [13] in species living in more variable climates. Male songbirds sing to attract females and repel rivals [14]. 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 [15, 16]. First, stronger selection in more variable and unpredictable climates could lead to the elaboration of signals of quality [14, 17–20]. 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 [14, 21–23]. PMID:19464180

  8. 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.

  9. Correlating CCM upper atmosphere parameters to surface observations for regional climate change predictions

    SciTech Connect

    Li, Xiangshang; Sailor, D.J.

    1997-11-01

    This paper explores the use of statistical downscaling of General Circulation Model (GCM) results for the purpose of regional climate change analysis. The strong correlation between surface observations and GCM upper air predictions is used in an approach very similar to the Model Output Statistics approach used in numerical weather prediction. The primary assumption in this analysis is that the statistical relationships remain unchanged under conditions of climatic change. These relations are applied to GCM upper atmosphere predictions for future (2*CO{sub 2}) climate predictions. The result is a set of regional climate change predictions conceptually valid at the scale of cities. The downscaling for specific cities within a GCM grid cell reveals some of the anticipated variability within the grid cell. In addition, multiple linear regression analysis may indicate warming that is significantly higher or lower for a particular region than the raw data from the GCM runs. 3 refs., 3 figs., 2 tabs.

  10. A new framework for climate sensitivity and prediction: a modelling perspective

    NASA Astrophysics Data System (ADS)

    Ragone, Francesco; Lucarini, Valerio; Lunkeit, Frank

    2016-03-01

    The sensitivity of climate models to increasing CO2 concentration and the climate response at decadal time-scales are still major factors of uncertainty for the assessment of the long and short term effects of anthropogenic climate change. While the relative slow progress on these issues is partly due to the inherent inaccuracies of numerical climate models, this also hints at the need for stronger theoretical foundations to the problem of studying climate sensitivity and performing climate change predictions with numerical models. Here we demonstrate that it is possible to use Ruelle's response theory to predict the impact of an arbitrary CO2 forcing scenario on the global surface temperature of a general circulation model. Response theory puts the concept of climate sensitivity on firm theoretical grounds, and addresses rigorously the problem of predictability at different time-scales. Conceptually, these results show that performing climate change experiments with general circulation models is a well defined problem from a physical and mathematical point of view. Practically, these results show that considering one single CO2 forcing scenario is enough to construct operators able to predict the response of climatic observables to any other CO2 forcing scenario, without the need to perform additional numerical simulations. We also introduce a general relationship between climate sensitivity and climate response at different time scales, thus providing an explicit definition of the inertia of the system at different time scales. This technique allows also for studying systematically, for a large variety of forcing scenarios, the time horizon at which the climate change signal (in an ensemble sense) becomes statistically significant. While what we report here refers to the linear response, the general theory allows for treating nonlinear effects as well. These results pave the way for redesigning and interpreting climate change experiments from a radically new

  11. Climate science: Water's past revisited to predict its future

    NASA Astrophysics Data System (ADS)

    Kirby, Matthew E.

    2016-04-01

    A reconstruction of 1,200 years of water's history in the Northern Hemisphere, based on proxy data, fuels the debate about whether anthropogenic climate change affected twentieth-century precipitation. See Letter p.94

  12. Visual Analysis of time-dependent 2D Uncertainties in Decadal Climate Predictions

    NASA Astrophysics Data System (ADS)

    Böttinger, Michael; Röber, Niklas; Meier-Fleischer, Karin; Pohlmann, Holger

    2016-04-01

    Climate prediction systems used today for investigating the climate predictability on a decadal time scale are based on coupled global climate models. First, ensembles of hindcast experiments are carried out in order to derive the predictive skill of the prediction system. Then, in a second step, the prediction system is initialized with observations and actual future predictions are computed. The ensemble simulation techniques applied enable issuing of probabilistic information along with the quantities predicted. Different aspects of the uncertainty can be derived: The ensemble standard deviation (or ensemble spread) is a measure for the internal variability of the simulation, while the predictive skill is an inverse measure for the uncertainty in the prediction. In this work, we focus on the concurrent visualization of three related time-dependent 2D fields: the forecast variable itself, here the 2m temperature anomaly, along with the corresponding predictive skill and the ensemble spread which is given through the ensemble standard deviation. On the basis of temporally filtered data, animations are used to visualize the mean spatio-temporal development of the three quantities. Furthermore, seasonal analyses are similarly visualized in order to identify seasonal patterns. We show exemplary solutions produced with three different visualization systems: NCL, Avizo Green and ParaView. As example data set, we have used a decadal climate prediction carried out within the German research project "MiKlip - Decadal Predictions" using the MPI-M Earth System Model (MPI-ESM) from the Max Planck Institute for Meteorology in Hamburg.

  13. 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.

  14. 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...

  15. 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.

  16. 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…

  17. Western Mountain Initiative: predicting ecosystem responses to climate change

    USGS Publications Warehouse

    Baron, Jill S.; Peterson, David L.; Wilson, J.T.

    2008-01-01

    Mountain ecosystems of the western United States provide irreplaceable goods and services such as water, timber, biodiversity, and recreational opportunities, but their responses to climatic changes are complex and not well understood. The Western Mountain Initiative (WMI), a collaboration between USGS and U.S. Forest Service scientists, catalyzes assessment and synthesis of the effects of disturbance and climate change across western mountain areas, focusing on national parks and surrounding national forests. The WMI takes an ecosystem approach to science, integrating research across science disciplines at scales ranging from field studies to global trends.

  18. Evaluating Climate Predictability Signals in Response to Forcing: Mount Pinatubo as a Case Study

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

    Volcanic eruptions serve as a benchmark for assessing aspects of predictability because they provide a sudden global impulse to the Earth's climate system. In particular, the 1991 eruption of Mt. Pinatubo in the Philippines is of interest since it was the largest aerosol perturbation to the stratosphere in the twentieth century and the most intensely observed eruption on record. Using instrumental measurements during the eruption, the predictive capabilities of climate models may be evaluated and the predictability of climate variables under forcing may be inferred. In this study, the evolution of the climate response to volcanic forcing is simulated in the Community Earth System Model, CESM1.0, using ensembles of forced and unforced global climate model runs. The predictability signals of all atmospheric climate model variables are systematically calculated using a unitless signal to noise ratio. The signals are compared to determine the most sensitive variables. Additionally, this work examines spatial predictability patterns, important for regional predictability, by mapping the signals globally and using empirical orthogonal functions. Phase plots are employed to highlight the response of variables to forcing, and comparisons with observations of the eruption are made to provide validation of the results.

  19. Behavioral and electrophysiological indices of negative affect predict cocaine self-administration.

    PubMed

    Wheeler, Robert A; Twining, Robert C; Jones, Joshua L; Slater, Jennifer M; Grigson, Patricia S; Carelli, Regina M

    2008-03-13

    The motivation to seek cocaine comes in part from a dysregulation of reward processing manifested in dysphoria, or affective withdrawal. Learning is a critical aspect of drug abuse; however, it remains unclear whether drug-associated cues can elicit the emotional withdrawal symptoms that promote cocaine use. Here we report that a cocaine-associated taste cue elicited a conditioned aversive state that was behaviorally and neurophysiologically quantifiable and predicted subsequent cocaine self-administration behavior. Specifically, brief intraoral infusions of a cocaine-predictive flavored saccharin solution elicited aversive orofacial responses that predicted early-session cocaine taking in rats. The expression of aversive taste reactivity also was associated with a shift in the predominant pattern of electrophysiological activity of nucleus accumbens (NAc) neurons from inhibitory to excitatory. The dynamic nature of this conditioned switch in affect and the neural code reveals a mechanism by which cues may exert control over drug self-administration. PMID:18341996

  20. 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.

  1. 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. PMID:10677464

  2. 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

  3. Interactive Land Use-Climate Change Predictions in West Africa: Preliminary Results

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    Land use changes constitute an important regional climate change forcing that modifies the greenhouse gas induced future climate changes. At the same time, climate change is an important driver for land use changes, although it is unclear how important this impact might be relative to the impact of socio-economic factors on future land use. Using West Africa as an example, this study examines the importance of considering land use-climate change interactions in decadal predictions for future land use and climate changes, and thus assess whether there is a strong need to incorporate land use modeling into earth system models. Specifically, we evaluate the impact of projected climate changes from a regional climate model (RegCM4-CLM4) on crop yields using the crop model DSSAT, and assess the need for future land use changes by combining crop yield changes with demand for local productions predicted based on socio-economic drivers using an economic model (IFPRI's IMPACT model). For this preliminary assessment, a simple land use allocation approach is used, which favors agricultural expansion over intensification in order to provide an upper limit for land use changes. As a first test, the RCP8.5 mid-century climate projected by the NCAR CESM model is used as the future climate boundary conditions to drive the regional climate model. The impact of considering the land use-climate change interactions will be evaluated based on the differences in projected climate changes between two types of simulations: one that considers land use changes driven by both climate-induced crop yield changes and socioeconomic factors, and one that considers land use changes driven solely by socioeconomic factors.

  4. 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.

  5. Climate change and infectious diseases: from evidence to a predictive framework.

    PubMed

    Altizer, Sonia; Ostfeld, Richard S; Johnson, Pieter T J; Kutz, Susan; Harvell, C Drew

    2013-08-01

    Scientists have long predicted large-scale responses of infectious diseases to climate change, giving rise to a polarizing debate, especially concerning human pathogens for which socioeconomic drivers and control measures can limit the detection of climate-mediated changes. Climate change has already increased the occurrence of diseases in some natural and agricultural systems, but in many cases, outcomes depend on the form of climate change and details of the host-pathogen system. In this review, we highlight research progress and gaps that have emerged during the past decade and develop a predictive framework that integrates knowledge from ecophysiology and community ecology with modeling approaches. Future work must continue to anticipate and monitor pathogen biodiversity and disease trends in natural ecosystems and identify opportunities to mitigate the impacts of climate-driven disease emergence. PMID:23908230

  6. Climate change and infectious diseases: from evidence to a predictive framework.

    PubMed

    Altizer, Sonia; Ostfeld, Richard S; Johnson, Pieter T J; Kutz, Susan; Harvell, C Drew

    2013-08-01

    Scientists have long predicted large-scale responses of infectious diseases to climate change, giving rise to a polarizing debate, especially concerning human pathogens for which socioeconomic drivers and control measures can limit the detection of climate-mediated changes. Climate change has already increased the occurrence of diseases in some natural and agricultural systems, but in many cases, outcomes depend on the form of climate change and details of the host-pathogen system. In this review, we highlight research progress and gaps that have emerged during the past decade and develop a predictive framework that integrates knowledge from ecophysiology and community ecology with modeling approaches. Future work must continue to anticipate and monitor pathogen biodiversity and disease trends in natural ecosystems and identify opportunities to mitigate the impacts of climate-driven disease emergence.

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. 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. PMID:26496438

  15. 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

  16. 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.

  17. 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 ...

  18. 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.

  19. 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.

  20. Uncertainties in future climate predictions due to convection parameterisations

    NASA Astrophysics Data System (ADS)

    Rybka, H.; Tost, H.

    2013-10-01

    In the last decades several convection parameterisations have been developed to consider the impact of small-scale unresolved processes in Earth System Models associated with convective clouds. Global model simulations, which have been performed under current climate conditions with different convection schemes, significantly differ among each other in the simulated transport of trace gases and precipitation patterns due to the parameterisation assumptions and formulations, e.g. the simplified treatment of the cloud microphysics. Here we address sensitivity studies comparing four different convection schemes under alternative climate conditions (doubling of the CO2 concentrations) to identify uncertainties related to convective processes. The increase in surface temperature reveals regional differences up to 4 K dependent on the chosen convection parameterisation. The increase in upper tropospheric temperature affects the amount of water vapour transported to the lower stratosphere. Furthermore, the change in transporting short-lived pollutants within the atmosphere is highly ambiguous for the lower and upper troposphere. Finally, cloud radiative effects have been analysed uncovering a shift in different cloud types in the tropics.

  1. 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.

  2. 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.

  3. 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

  4. Advancing decadal-scale climate prediction in the North Atlantic sector.

    PubMed

    Keenlyside, N S; Latif, M; Jungclaus, J; Kornblueh, L; Roeckner, E

    2008-05-01

    The climate of the North Atlantic region exhibits fluctuations on decadal timescales that have large societal consequences. Prominent examples include hurricane activity in the Atlantic, and surface-temperature and rainfall variations over North America, Europe and northern Africa. Although these multidecadal variations are potentially predictable if the current state of the ocean is known, the lack of subsurface ocean observations that constrain this state has been a limiting factor for realizing the full skill potential of such predictions. Here we apply a simple approach-that uses only sea surface temperature (SST) observations-to partly overcome this difficulty and perform retrospective decadal predictions with a climate model. Skill is improved significantly relative to predictions made with incomplete knowledge of the ocean state, particularly in the North Atlantic and tropical Pacific oceans. Thus these results point towards the possibility of routine decadal climate predictions. Using this method, and by considering both internal natural climate variations and projected future anthropogenic forcing, we make the following forecast: over the next decade, the current Atlantic meridional overturning circulation will weaken to its long-term mean; moreover, North Atlantic SST and European and North American surface temperatures will cool slightly, whereas tropical Pacific SST will remain almost unchanged. Our results suggest that global surface temperature may not increase over the next decade, as natural climate variations in the North Atlantic and tropical Pacific temporarily offset the projected anthropogenic warming.

  5. Sea surface temperature and short term climate predictability

    NASA Astrophysics Data System (ADS)

    Andronache, Constantin

    2013-03-01

    Atmospheric processes have a relatively short memory of initial conditions of about two weeks for detailed daily weather prediction. Nevertheless, skilful seasonal forecast is possible in the presence of slow varying boundary conditions (BC) of the atmosphere, such as sea surface temperature anomalies (SSTA) over large oceanic regions. These conditions typically evolve on a much slower time scale than daily weather events and atmospheric predictability can be increased as long as the future evolution of such BC can be predicted. Given the importance of SSTA in the interaction between the ocean and atmosphere, it is of interest to investigate the nature of temporal persistence of large-scale SSTA in the global ocean. We use the global SSTA and investigate possible sources of predictability at seasonal time scale and its impact in various regions of the ocean. Data used are the NOAA Extended Reconstructed Sea Surface Temperature (SST). We show that: 1) SSTA has a persistence that depends largely on regional location in the global ocean; 2) A given SSTA distribution from a particular month, can have corresponding similar configurations in the past, largely due to the recurrence of ENSO events which affect SSTA distribution over vast regions of the global ocean.

  6. 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.

  7. 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. PMID:24677422

  8. 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.

  9. A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles.

    PubMed

    Murphy, J M; Booth, B B B; Collins, M; Harris, G R; Sexton, D M H; Webb, M J

    2007-08-15

    A methodology is described for probabilistic predictions of future climate. This is based on a set of ensemble simulations of equilibrium and time-dependent changes, carried out by perturbing poorly constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean-atmosphere global climate model. These (ongoing) experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century climate at large regional scales. A further ensemble of regional climate simulations at 25km resolution is being produced for Europe, allowing the specification of probabilistic predictions at spatial scales required for studies of climate impacts. The ensemble simulations are processed using a set of statistical procedures, the centrepiece of which is a Bayesian statistical framework designed for use with complex but imperfect models. This supports the generation of probabilities constrained by a wide range of observational metrics, and also by expert-specified prior distributions defining the model parameter space. The Bayesian framework also accounts for additional uncertainty introduced by structural modelling errors, which are estimated using our ensembles to predict the results of alternative climate models containing different structural assumptions. This facilitates the generation of probabilistic predictions combining information from perturbed physics and multi-model ensemble simulations. The methodology makes extensive use of emulation and scaling techniques trained on climate model results. These are used to sample the equilibrium response to doubled carbon dioxide at any required point in the parameter space of surface and atmospheric processes, to sample time-dependent changes by combining this information with ensembles sampling uncertainties in the transient response of a wider set of earth system processes

  10. 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.

  11. 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

  12. 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

  13. 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

  14. Complex networks as a unified framework for descriptive analysis and predictive modeling in climate

    SciTech Connect

    Steinhaeuser, Karsten J K; Chawla, Nitesh; Ganguly, Auroop R

    2010-01-01

    The analysis of climate data has relied heavily on hypothesis-driven statistical methods, while projections of future climate are based primarily on physics-based computational models. However, in recent years a wealth of new datasets has become available. Therefore, we take a more data-centric approach and propose a unified framework for studying climate, with an aim towards characterizing observed phenomena as well as discovering new knowledge in the climate domain. Specifically, we posit that complex networks are well-suited for both descriptive analysis and predictive modeling tasks. We show that the structural properties of climate networks have useful interpretation within the domain. Further, we extract clusters from these networks and demonstrate their predictive power as climate indices. Our experimental results establish that the network clusters are statistically significantly better predictors than clusters derived using a more traditional clustering approach. Using complex networks as data representation thus enables the unique opportunity for descriptive and predictive modeling to inform each other.

  15. Evaluating predictability in nonlinear climate systems using the Mount Pinatubo eruption

    NASA Astrophysics Data System (ADS)

    Gaddis, A.; Drake, J.; Evans, K. J.; Gentry, R. W.

    2010-12-01

    Volcanic eruptions serve as a benchmark for assessing aspects of predictability since they provide a sudden global impulse to the Earth’s climate system. In particular, the 1991 eruption of Mt. Pinatubo in the Philippines is of interest since it was the largest aerosol perturbation to the stratosphere in this century, and also it is the most intensely observed eruption on record. Using this information, the predictive skill of climate models may be evaluated and information about the earth’s climate sensitivity may be inferred. In this study, weather records from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis project are used consisting of monthly averages of climate variables. This data is then compared to high-resolution data for the same time period from the newest Community Climate System Model, CCSM4. Empirical orthogonal function analysis of both data sets is performed to generate a trajectory in phase space with respect to aerosol optical depth for the Pinatubo event. This characterizes the sensitivity of the system. The results are presented for various climate variables, such as surface air temperature, precipitable water, and sea level pressure.

  16. 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.

  17. 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.

  18. Observed and predicted effects of climate change on species abundance in protected areas

    NASA Astrophysics Data System (ADS)

    Johnston, Alison; Ausden, Malcolm; Dodd, Andrew M.; Bradbury, Richard B.; Chamberlain, Dan E.; Jiguet, Frédéric; Thomas, Chris D.; Cook, Aonghais S. C. P.; Newson, Stuart E.; Ockendon, Nancy; Rehfisch, Mark M.; Roos, Staffan; Thaxter, Chris B.; Brown, Andy; Crick, Humphrey Q. P.; Douse, Andrew; McCall, Rob A.; Pontier, Helen; Stroud, David A.; Cadiou, Bernard; Crowe, Olivia; Deceuninck, Bernard; Hornman, Menno; Pearce-Higgins, James W.

    2013-12-01

    The dynamic nature and diversity of species' responses to climate change poses significant difficulties for developing robust, long-term conservation strategies. One key question is whether existing protected area networks will remain effective in a changing climate. To test this, we developed statistical models that link climate to the abundance of internationally important bird populations in northwestern Europe. Spatial climate-abundance models were able to predict 56% of the variation in recent 30-year population trends. Using these models, future climate change resulting in 4.0°C global warming was projected to cause declines of at least 25% for more than half of the internationally important populations considered. Nonetheless, most EU Special Protection Areas in the UK were projected to retain species in sufficient abundances to maintain their legal status, and generally sites that are important now were projected to be important in the future. The biological and legal resilience of this network of protected areas is derived from the capacity for turnover in the important species at each site as species' distributions and abundances alter in response to climate. Current protected areas are therefore predicted to remain important for future conservation in a changing climate.

  19. Complex Networks Reveal Persistent Global / Regional Structure and Predictive Information Content in Climate Data

    NASA Astrophysics Data System (ADS)

    Steinhaeuser, K.; Chawla, N. V.; Ganguly, A. R.

    2010-12-01

    Recent articles have posited that the skills of climate model projections, particularly for variables and scales of interest to decision makers, may need to be significantly improved. Here we hypothesize that there is information content in variables that are projected more reliably, for example, sea surface temperatures, which is relevant for improving predictions of other variables at scales which may be more crucial, for example, regional land temperature and precipitation anomalies. While this hypothesis may be partially supported based on conceptual understanding, a key question to explore is whether the relevant information content can be meaningfully extracted from observations and model simulations. Here we use climate reconstructions from reanalysis datasets to examine the question in detail. Our tool of choice is complex networks, which have provided useful insights in the context of descriptive analysis and change detection for climate in the recent literature. We describe a new adaptation of complex networks based on computational approaches which provide additional descriptive insights at both global and regional scales, specifically sea surface variables, and provide a unified framework for data-guided predictive modeling, specifically for regional temperature and precipitation over land. Complex networks were constructed from historical data to study the properties of the global climate system and characterize behavior at the global scale. Clusters based on community detection, which leverage the network distance, were used to identify regional structures. Persistence and stability of these features over time were evaluated. Predictive information content of ocean indicators with respect to land climate was extracted using a suite of regression models and validated on held-out data. Our results suggest that the new adaptation of complex networks may be well-suited to provide a unified framework for exploring climate teleconnections or long

  20. 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.

  1. 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.

  2. 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

  3. The Climate Variability & Predictability (CVP) Program at NOAA - DYNAMO Recent Project Advancements

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    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 Geosphere-Biosphere Programme (IGBP), and the U.S. Global Change Research Program (USGCRP). The CVP program sits within the Earth System Science (ESS) Division at NOAA's Climate Program Office. Dynamics of the Madden-Julian Oscillation (DYNAMO): The Indian Ocean is one of Earth's most sensitive regions because the interactions between ocean and atmosphere there have a discernable effect on global climate patterns. The tropical weather that brews in that region can move eastward along the equator and reverberate around the globe, shaping weather and climate in far-off places. The vehicle for this variability is a phenomenon called the Madden-Julian Oscillation, or MJO. The MJO, which originates over the Indian Ocean roughly every 30 to 90 days, is known to influence the Asian and Australian monsoons. It can also enhance hurricane activity in the northeast Pacific and Gulf of Mexico, trigger torrential rainfall along the west coast of North America, and affect the onset of El Niño. CVP-funded scientists participated in the DYNAMO field campaign in 2011-12. Results from this international campaign are expected to improve researcher's insights into this influential phenomenon. A better understanding of the processes governing MJO is an essential step toward

  4. 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

    2015-02-01

    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.

  5. 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.

  6. 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.

  7. Statistical and dynamical climate predictions to guide water resources in Ethiopia

    NASA Astrophysics Data System (ADS)

    Block, P. J.; Goddard, L. M.

    2010-12-01

    Climate predictions with lead times of one season or more often provide prospects for exploiting climate-related risks and opportunities. This motivates the evaluation of precipitation prediction techniques from statistical and dynamical models and their combination (multi-model) to potentially augment prediction skill over the Blue Nile basin in Ethiopia. Subsequently, this work considers to what degree greater skill or reliability in a particular prediction technique translates through hydropower management models given their nonlinear response. One hundred precipitation series from the period 1981-2000 are generated to compare prediction techniques. The linked multi-model ensemble forecast - hydropower system proves superior to the statistical and dynamical prediction technique linked systems across a range of metrics; an increase in annual benefits by $2-5 million dollars on average, with equivalent or better reliability, is also evident. The forecast - hydropower system is sufficiently flexible to allow water managers to attain an optimal balance between benefits and reliability, by varying exceedance probability and target energy thresholds, with the added benefit of forecast guidance. Ideally this provides decision-makers with incentives to integrate improved prediction techniques into sectoral management models, and further justifies expanding efforts into climate forecast improvement.

  8. Australian Tropical Cyclone Activity: Interannual Prediction and Climate Change

    NASA Astrophysics Data System (ADS)

    Nicholls, N.

    2014-12-01

    It is 35 years since it was first demonstrated that interannual variations in seasonal Australian region tropical cyclone (TC) activity could be predicted using simple indices of the El Niño - Southern Oscillation (ENSO). That demonstration (Nicholls, 1979), which was surprising and unexpected at the time, relied on only 25 years of data (1950-1975), but its later confirmation eventually led to the introduction of operational seasonal tropical cyclone activity. It is worth examining how well the ENSO-TC relationship has performed, over the period since 1975. Changes in observational technology, and even how a tropical cyclone is defined, have affected the empirical relationships between ENSO and seasonal activity, and ways to overcome this in forecasting seasonal activity will be discussed. Such changes also complicate the investigation of long-term trends in cyclone activity. The early work linked cyclone activity to local sea surface temperature thereby leading to the expectation that global warming would result in an increase in cyclone activity. But studies in the 1990s (eg., Nicholls et al., 1998) suggested that such an increase in activity was not occurring, neither in the Australian region nor elsewhere. Trends in Australian tropical cyclone activity will be discussed, and the confounding influence of factors such as changes in observational technologies will be examined. Nicholls, N. 1979. A possible method for predicting seasonal tropical cyclone activity in the Australian region. Mon. Weath. Rev., 107, 1221-1224 Nicholls, N., Landsea, C., and Gill, J., 1998. Recent trends in Australian region tropical cyclone activity. Meteorology and Atmospheric Physics, 65, 197-205.

  9. 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.

  10. 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.

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

    PubMed

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

    2013-01-19

    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.

  12. 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…

  13. Evaluation and attribution of vegetation contribution to seasonal climate predictability

    NASA Astrophysics Data System (ADS)

    Catalano, Franco; Alessandri, Andrea; De Felice, Matteo

    2015-04-01

    The land surface model of EC-Earth has been modified to include dependence of vegetation densities on the Leaf Area Index (LAI), based on the Lambert-Beer formulation. Effective vegetation fractional coverage can now vary at seasonal and interannual time-scales and therefore affect biophysical parameters such as the surface roughness, albedo and soil field capacity. The modified model is used to perform a real predictability seasonal hindcast experiment. LAI is prescribed using a recent observational dataset based on the third generation GIMMS and MODIS satellite data. Hindcast setup is: 7 months forecast length, 2 start dates (1st May and 1st November), 10 members, 28 years (1982-2009). The effect of the realistic LAI prescribed from observation is evaluated with respect to a control experiment where LAI does not vary. Hindcast results demonstrate that a realistic representation of vegetation significantly improves the forecasts of temperature and precipitation. The sensitivity is particularly large for temperature during boreal winter over central North America and Central Asia. This may be attributed in particular to the effect of the high vegetation component on the snow cover. Summer forecasts are improved in particular for precipitation over Europe, Sahel, North America, West Russia and Nordeste. Correlation improvements depends on the links between targets (temperature and precipitation) and drivers (surface heat fluxes, albedo, soil moisture, evapotranspiration, moisture divergence) which varies from region to region.

  14. Predicting effects of global climate change on reservoir water quality and fish habitat

    SciTech Connect

    Chang, L H; Railsback, S F

    1989-01-01

    This paper demonstrates the use of general circulation models (GCMs) for assessing global climate change effects on reservoir water quality and illustrates that general conclusions about the effects of increased carbon dioxide (CO{sub 2}) concentrations on water resources can be made by using GCMs. These conclusions are based on GCM predictions of the climatic effects of doubling CO{sub 2} concentrations (the 2 {times} CO{sub 2} scenario). We also point out inadequacies in using information from GCM output alone to simulate reservoir water quality effects of climate change. Our investigation used Douglas Lake, a large multipurpose reservoir in eastern Tennessee, as an example. We studied water temperature and dissolved oxygen (DO), important water quality parameters that are expected to respond to a changed climate. Finally, we used the temperature and DO requirements of striped bass as an indicator of biological effects of combined changes in temperature and DO. 3 refs., 1 fig.

  15. 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...

  16. 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...

  17. Predicting the effect of climate change on African trypanosomiasis: integrating epidemiology with parasite and vector biology.

    PubMed

    Moore, Sean; Shrestha, Sourya; Tomlinson, Kyle W; Vuong, Holly

    2012-05-01

    Climate warming over the next century is expected to have a large impact on the interactions between pathogens and their animal and human hosts. Vector-borne diseases are particularly sensitive to warming because temperature changes can alter vector development rates, shift their geographical distribution and alter transmission dynamics. For this reason, African trypanosomiasis (sleeping sickness), a vector-borne disease of humans and animals, was recently identified as one of the 12 infectious diseases likely to spread owing to climate change. We combine a variety of direct effects of temperature on vector ecology, vector biology and vector-parasite interactions via a disease transmission model and extrapolate the potential compounding effects of projected warming on the epidemiology of African trypanosomiasis. The model predicts that epidemics can occur when mean temperatures are between 20.7°C and 26.1°C. Our model does not predict a large-range expansion, but rather a large shift of up to 60 per cent in the geographical extent of the range. The model also predicts that 46-77 million additional people may be at risk of exposure by 2090. Future research could expand our analysis to include other environmental factors that influence tsetse populations and disease transmission such as humidity, as well as changes to human, livestock and wildlife distributions. The modelling approach presented here provides a framework for using the climate-sensitive aspects of vector and pathogen biology to predict changes in disease prevalence and risk owing to climate change.

  18. 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…

  19. 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.

  20. 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.

  1. An integrated climate service strategy for African drought monitoring and prediction: linking information to action

    NASA Astrophysics Data System (ADS)

    Funk, C.; Verdin, J. P.; Rowland, J.; Budde, M.

    2008-12-01

    For 23 years, the US Agency for International Development's Famine Early Warning Systems Network (FEWS NET) has applied climate data analysis in support of timely food insecurity mitigation and adaptation in Africa. FEWS NET, therefore, provides a compelling example of a sector-specific climate service. We briefly review the phases, successes and shortcomings of the FEWS NET climate service, describe an improved long term climate service strategy, and present new research supporting an improved, integrated drought monitoring approach. Our new monitoring system emphasizes seamless links between historical precipitation archives, near real-time rainfall estimates, and 1-to-4 month statistical predictions. Assessment of forecast skill shows useful levels of accuracy for many regions during key periods of the growing season. Integrating these forecasts with near real time blended satellite-gauge precipitation observations facilitates early identification of mid-season agricultural drought. Integrated historical climate archives (1979-present) permit analysis of observed and forecast climate conditions in terms of historical probabilities and analogs. Tools specific to staple crops and pastoralist settings are then used to assess the likely impacts of hydrometeorological anomalies. These are geographically integrated with livelihoods information and interpreted in terms of current food security conditions and timelines to determine human consequences. A client-server web-mapping data portal will allow users to dynamically access the climate anomaly information, and visualize the results in conjunction with livelihood information.

  2. Patterns of Hydrologic Sensitivity to Climate in the Western US: Implications for Future Predictions

    NASA Astrophysics Data System (ADS)

    Safeeq, M.; Grant, G.

    2015-12-01

    A key challenge for resource and land managers is predicting the consequences of climate warming on streamflow and water resources. During the last century in the western United States, significant reductions in snowpack and earlier snowmelt have led to an increase in the fraction of annual streamflow during winter and a decline in the summer. However, this increase and decrease in streamflow is mediated by the climate and landscape. Here we explore key landscape and climate metrics for interpreting hydrologic sensitivity to climate using observed flow from a range of watersheds across the western United States. Our results indicate that the recession constant and fraction of precipitation falling as snow are the two primary controls on hydrologic sensitivity to climate in this region. Dry season flows in watersheds that drain slowly from deep groundwater and receive precipitation as snow are most sensitive to climate warming. In terms of peak flow, watersheds are most sensitivity to the consistency (i.e. signal-to-noise ratio) in fraction of precipitation falling as snow. Our results also indicate that not all trends in western United States are associated with changes in snowpack dynamics; we observe declining flow in late fall and winter in rain-dominated watersheds as well. These empirical findings support both theory and hydrologic modeling and have implications for how hydrologic sensitivity to climate change is evaluated and interpreted across broad regions.

  3. 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.

  4. Uncertainty in predicting range dynamics of endemic alpine plants under climate warming.

    PubMed

    Hülber, Karl; Wessely, Johannes; Gattringer, Andreas; Moser, Dietmar; Kuttner, Michael; Essl, Franz; Leitner, Michael; Winkler, Manuela; Ertl, Siegrun; Willner, Wolfgang; Kleinbauer, Ingrid; Sauberer, Norbert; Mang, Thomas; Zimmermann, Niklaus E; Dullinger, Stefan

    2016-07-01

    Correlative species distribution models have long been the predominant approach to predict species' range responses to climate change. Recently, the use of dynamic models is increasingly advocated for because these models better represent the main processes involved in range shifts and also simulate transient dynamics. A well-known problem with the application of these models is the lack of data for estimating necessary parameters of demographic and dispersal processes. However, what has been hardly considered so far is the fact that simulating transient dynamics potentially implies additional uncertainty arising from our ignorance of short-term climate variability in future climatic trends. Here, we use endemic mountain plants of Austria as a case study to assess how the integration of decadal variability in future climate affects outcomes of dynamic range models as compared to projected long-term trends and uncertainty in demographic and dispersal parameters. We do so by contrasting simulations of a so-called hybrid model run under fluctuating climatic conditions with those based on a linear interpolation of climatic conditions between current values and those predicted for the end of the 21st century. We find that accounting for short-term climate variability modifies model results nearly as differences in projected long-term trends and much more than uncertainty in demographic/dispersal parameters. In particular, range loss and extinction rates are much higher when simulations are run under fluctuating conditions. These results highlight the importance of considering the appropriate temporal resolution when parameterizing and applying range-dynamic models, and hybrid models in particular. In case of our endemic mountain plants, we hypothesize that smoothed linear time series deliver more reliable results because these long-lived species are primarily responsive to long-term climate averages. PMID:27061825

  5. 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).

  6. 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.

  7. 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.

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

    PubMed

    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

    2010-02-01

    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.

  9. 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). PMID:23349829

  10. 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

  11. 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).

  12. 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.

  13. A new estimator of heat periods for decadal climate predictions - a complex network approach

    NASA Astrophysics Data System (ADS)

    Weimer, Michael; Mieruch, Sebastian; Schädler, Gerd; Kottmeier, Christoph

    2016-08-01

    Regional decadal predictions have emerged in the past few years as a research field with high application potential, especially for extremes like heat and drought periods. However, up to now the prediction skill of decadal hindcasts, as evaluated with standard methods, is moderate and for extreme values even rarely investigated. In this study, we use hindcast data from a regional climate model (CCLM) for eight regions in Europe and quantify the skill of the model alternatively by constructing time-evolving climate networks and use the network correlation threshold (link strength) as a predictor for heat periods. We show that the skill of the network measure to estimate the low-frequency dynamics of heat periods is superior for decadal predictions with respect to the typical approach of using a fixed temperature threshold for estimating the number of heat periods in Europe.

  14. 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.

  15. 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

    2013-06-01

    "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.

  16. Predicting stratigraphy by evaluating depositional response to climate change and basin evolution

    SciTech Connect

    Perlmutter, M.A.; Matthews, M.D. )

    1992-01-01

    Continental and marine stratigraphy can be predicted by integrating sediment flux as a function of the climatic succession, caused by orbital (Milankovitch) oscillations, with the long-term evolution of accommodation space. Total sediment volume of an interval can be calculated from seismic data. Variation in sediment flux is then estimated by evaluating climatic succession in concert with drainage area and elevation. Flux from drainage areas can vary by up to seventy times during an orbital cycle, depending on the succession and topography, creating a sediment supply cycle. Overall, highest yields occur during shifts from arid to subhumid climates. Climatic succession and, therefore, the phase relationships of lake level and sediment supply cycles to orbital cycles are functions of geographic position, with maximum yield and lake level occurring at any phase of an orbital cycle depending on succession. Maximum yield and lake level may or may not occur synchronously in any single climate belt. In addition, because sea level tends to be in phase with orbital cycles while sediment supply may not be, the phase relationship between sediment and sea level cycles also varied with basin location. After these relationships are determined, clastic and carbonate stratigraphy can be reliably forecast by integrating sediment flux with long-term, tectonically controlled evolution of accommodation space. This technique, called global cyclostratigraphy, has been used to predict the generalized stratigraphy of basins ranging in age from the Devonian to the Pleistocene.

  17. 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.

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

    PubMed Central

    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. PMID:23690569

  19. 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

    2013-06-01

    "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. PMID:23690569

  20. RCWIM - an improved global water isotope pattern prediction model using fuzzy climatic clustering regionalization

    NASA Astrophysics Data System (ADS)

    Terzer, Stefan; Araguás-Araguás, Luis; Wassenaar, Leonard I.; Aggarwal, Pradeep K.

    2013-04-01

    Prediction of geospatial H and O isotopic patterns in precipitation has become increasingly important to diverse disciplines beyond hydrology, such as climatology, ecology, food authenticity, and criminal forensics, because these two isotopes of rainwater often control the terrestrial isotopic spatial patterns that facilitate the linkage of products (food, wildlife, water) to origin or movement (food, criminalistics). Currently, spatial water isotopic pattern prediction relies on combined regression and interpolation techniques to create gridded datasets by using data obtained from the Global Network of Isotopes In Precipitation (GNIP). However, current models suffer from two shortcomings: (a) models may have limited covariates and/or parameterization fitted to a global domain, which results in poor predictive outcomes at regional scales, or (b) the spatial domain is intentionally restricted to regional settings, and thereby of little use in providing information at global geospatial scales. Here we present a new global climatically regionalized isotope prediction model which overcomes these limitations through the use of fuzzy clustering of climatic data subsets, allowing us to better identify and customize appropriate covariates and their multiple regression coefficients instead of aiming for a one-size-fits-all global fit (RCWIM - Regionalized Climate Cluster Water Isotope Model). The new model significantly reduces the point-based regression residuals and results in much lower overall isotopic prediction uncertainty, since residuals are interpolated onto the regression surface. The new precipitation δ2H and δ18O isoscape model is available on a global scale at 10 arc-minutes spatial and at monthly, seasonal and annual temporal resolution, and will provide improved predicted stable isotope values used for a growing number of applications. The model further provides a flexible framework for future improvements using regional climatic clustering.

  1. 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.

  2. 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

  3. 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.

  4. 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].

  5. 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

  6. 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

  7. 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. PMID:22269559

  8. 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.

  9. 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.

  10. 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.

  11. CMIP: a study of climate variability and predictability according to general circulation models

    SciTech Connect

    Covey, C.; Santer, B.D.; Cohen-Solal, E.

    1996-09-01

    Coupled ocean-atmosphere general circulation models are used to predict future global changes, such as warming due to anthropogenic greenhouse gases (Houghton et al., 1996). In addition, coupled-GCM simulations of the natural climate (without human interference) can be compared with observations over the past century. Recent work along such lines concludes that an anthropogenic signal of global warming is emerging from natural variability `noise` (ibid.). More careful and systematic examination of the models seems warranted, however. Toward that end the World Climate Research Program has begun the Coupled ocean-atmosphere Model Intercomparison Project.

  12. Prediction of seasonal climate-induced variations in global food production

    NASA Astrophysics Data System (ADS)

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

    2013-10-01

    Consumers, including the poor in many countries, are increasingly dependent on food imports and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attention to the cropping forecasts of important 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 found that moderate-to-marked yield loss over a substantial percentage (26-33%) of the harvested area of these crops is reliably predictable if climatic forecasts are near perfect. However, only rice and wheat production are reliably predictable at three months before the harvest using within-season hindcasts. The reliabilities of estimates varied substantially by crop--rice and wheat yields were the most predictable, followed by soybean and maize. The reasons for variation in the reliability of the estimates included the differences in crop sensitivity to the climate and the technology used by the crop-producing regions. Our findings reveal that the use of seasonal climatic forecasts to predict crop failures will be useful for monitoring global food production and will encourage the adaptation of food systems toclimatic extremes.

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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

  20. 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

  1. Multiyear Predictability of Surface Air Temperature in the Kiel Climate Model

    NASA Astrophysics Data System (ADS)

    Wu, Yanling; Latif, Mojib; Park, Wonsun

    2015-04-01

    The multiyear predictability of unforced surface air temperature (SAT) variability is examined in the Kiel Climate Model (KCM), a coupled ocean-atmosphere-sea ice general circulation model. A statistical method that maximizes Average Predictability Time (APT) is used to find the most predictable patterns in the model. Multiyear SAT predictability is detected in the North Atlantic and North Pacific sectors. In both regions, ocean dynamics enhances predictability, while the net heat flux is a damping factor. Enhanced predictability in the North Atlantic sector is concentrated near the sea ice margin. The multiyear predictability there is linked to the Atlantic Multidecadal Oscillation/Variability (AMO/V) and also associated with variability of the subpolar gyre. In the North Pacific, the most predictable pattern is characterized by a zonal band in the western and central mid-latitude Pacific. It is linked to the Pacific Decadal Oscillation (PDO) which produces temperature anomalies in the surface layer during winter. These are subducted into deeper layers and re-emerge during the following winters, giving rise to multiyear predictability. The results are consistent with those obtained from the CMIP5 ensemble.

  2. 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.

  3. 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.

  4. Testable Predictions for Large-Scale Coastline-Shape Change in Response to Changing Storm Climate

    NASA Astrophysics Data System (ADS)

    Murray, A. B.; Moore, L. J.; McNamara, D.; Brenner, O.; Slott, J.

    2008-12-01

    Recent modeling (Ashton et al. 2001; Ashton and Murray, 2006a) and observations (Ashton and Murray 2006b) suggest that sandy coastlines self-organize into large-scale, plan-view shapes that depend sensitively on the regional wave climate-the distribution of influences on alongshore sediment transport from different deep-water wave-approach angles. Subsequent modeling (Slott et al., 2007) shows that even moderate changes in wave climate, as are likely to arise as storm behaviors shift in the coming century, will cause coastlines to change shape rapidly, compared to a steady-wave-climate scenario. Such large-scale shape changes involve greatly accentuated rates of local erosion, and highly variable erosion/accretion rates. A recent analysis of wave records from the Southeastern US (Komar and Allen, 2007) indicates that wave climates have already been changing over the past three decades; the heights of waves attributable to tropical storms have been increasing, changing the angular distribution of wave influences. Modeling based on these observations leads to predictions of how coastlines in this region should already be changing shape (McNamara et al., in prep.). As a case study, we are examining historical shorelines for the Carolina coastline, to test whether the predicted alongshore patterns of shoreline change can already be detected.

  5. Multiyear climate prediction with initialization based on 4D-Var data assimilation

    NASA Astrophysics Data System (ADS)

    Mochizuki, Takashi; Masuda, Shuhei; Ishikawa, Yoichi; Awaji, Toshiyuki

    2016-04-01

    An initialization relevant to interannual-to-decadal climate prediction has usually used a simple restoring approach for oceanic variables. Here we demonstrate the potential use of four-dimensional variational (4D-Var) data assimilation on the leading edge of initialization approach particularly in multiyear (5 year long) climate prediction. We perform full-field initialization rather than anomaly initialization and assimilate the atmosphere states together with the ocean states to an atmosphere-ocean coupled climate model. In particular, it is noteworthy that ensembles of multiyear hindcasts using our assimilation results as initial conditions exhibit an improved skill in hindcasting the multiyear changes of the upper ocean heat content (OHC) over the central North Pacific. The 4D-Var approach enables us to directly assimilate a time trajectory of slow changes of the Aleutian Low that are compatible with the sea surface height and the OHC. Consequently, we can estimate a coupled climate state suitable for hindcasting dynamical changes over the extratropical North Pacific as observed.

  6. 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.

  7. Use of provenance tests to predict response to climate change: loblolly pine and Norway spruce.

    PubMed

    Schmidtling, R. C.

    1994-01-01

    Provenance tests are often used to determine genetic responses of seed sources to transfer to different climates. This study was undertaken to determine whether provenance tests can be used to predict tree response to rapid climate changes in situ. Data from provenance tests of loblolly pines (Pinus taeda L.), Norway spruce (Picea abies L. Karst) and other southern pines (subsect. AUSTRALES Loud.) were interpreted using regression models to relate growth to temperature variables. Results of different plantings were combined by expressing growth as a percent deviation from the "local" source, and expressing temperature at the source as a deviation from that of the planting site. The results of the loblolly pine and Norway spruce models predicted a loss of about 5 to 10% in height growth below that expected for a genetically adapted seed source, if the average yearly temperature increases by 4 degrees C. PMID:14967650

  8. Evaluating the uncertainty of predicting future climate time series at the hourly time scale

    NASA Astrophysics Data System (ADS)

    Caporali, E.; Fatichi, S.; Ivanov, V. Y.

    2011-12-01

    A stochastic downscaling methodology is developed to generate hourly, point-scale time series for several meteorological variables, such as precipitation, cloud cover, shortwave radiation, air temperature, relative humidity, wind speed, and atmospheric pressure. The methodology uses multi-model General Circulation Model (GCM) realizations and an hourly weather generator, AWE-GEN. Probabilistic descriptions of factors of change (a measure of climate change with respect to historic conditions) are computed for several climate statistics and different aggregation times using a Bayesian approach that weights the individual GCM contributions. The Monte Carlo method is applied to sample the factors of change from their respective distributions thereby permitting the generation of time series in an ensemble fashion, which reflects the uncertainty of climate projections of future as well as the uncertainty of the downscaling procedure. Applications of the methodology and probabilistic expressions of certainty in reproducing future climates for the periods, 2000 - 2009, 2046 - 2065 and 2081 - 2100, using the 1962 - 1992 period as the baseline, are discussed for the location of Firenze (Italy). The climate predictions for the period of 2000 - 2009 are tested against observations permitting to assess the reliability and uncertainties of the methodology in reproducing statistics of meteorological variables at different time scales.

  9. 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.

  10. On the potential of extratropical SST anomalies for improving climate predictions

    NASA Astrophysics Data System (ADS)

    Kumar, Arun; Wang, Hui

    2015-05-01

    Skill for initialized decadal predictions for atmospheric and terrestrial variability is posited to reside in successful prediction of sea surface temperatures (SSTs) associated with the low-frequency modes of coupled ocean-atmosphere variability, for example, Pacific Decadal Oscillation (PDO) or Atlantic Multi-decadal Oscillation (AMO). So far, assessments of the skill of atmospheric and terrestrial variability in decadal predictions, however, have not been encouraging. Similarly, in the context of seasonal climate variability, teleconnections between SSTs associated with PDO and AMO and terrestrial climate have also been noted, but the same SST information used in predictive mode has failed to demonstrate convincing gains in skill. Are these results an artifact of model biases, or more a consequence of some fundamental property of coupled evolution of ocean-atmosphere system in extratropical latitudes, and the manner in which extratropical SST anomalies modulate (or constrain) atmospheric variability? Based on revisiting an analysis of a simple model that replicates the essential characteristics of coupled ocean-atmosphere interaction in extratropical latitudes, it is demonstrated that lack of additional skill in predicting atmospheric and terrestrial variability is more a consequence of fundamental characteristics of coupled evolution of ocean-atmosphere system. The results based on simple models are also substantiated following an analysis of a set of seasonal hindcasts with a fully coupled model.

  11. Does including physiology improve species distribution model predictions of responses to recent climate change?

    PubMed

    Buckley, Lauren B; Waaser, Stephanie A; MacLean, Heidi J; Fox, Richard

    2011-12-01

    Thermal constraints on development are often invoked to predict insect distributions. These constraints tend to be characterized in species distribution models (SDMs) by calculating development time based on a constant lower development temperature (LDT). Here, we assessed whether species-specific estimates of LDT based on laboratory experiments can improve the ability of SDMs to predict the distribution shifts of six U.K. butterflies in response to recent climate warming. We find that species-specific and constant (5 degrees C) LDT degree-day models perform similarly at predicting distributions during the period of 1970-1982. However, when the models for the 1970-1982 period are projected to predict distributions in 1995-1999 and 2000-2004, species-specific LDT degree-day models modestly outperform constant LDT degree-day models. Our results suggest that, while including species-specific physiology in correlative models may enhance predictions of species' distribution responses to climate change, more detailed models may be needed to adequately account for interspecific physiological differences. PMID:22352161

  12. 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

  13. Fragile transmission cycles of tick-borne encephalitis virus may be disrupted by predicted climate change.

    PubMed

    Randolph, S E; Rogers, D J

    2000-09-01

    Repeated predictions that vector-borne disease prevalence will increase with global warming are usually based on univariate models. To accommodate the full range of constraints, the present-day distribution of tick-borne encephalitis virus (TBEv) was matched statistically to current climatic variables, to provide a multivariate description of present-day areas of disease risk. This was then applied to outputs of a general circulation model that predicts how climatic variables may change in the future, and future distributions of TBEv were predicted for them. The expected summer rise in temperature and decrease in moisture appears to drive the distribution of TBEv into higher-latitude and higher-altitude regions progressively through the 2020s, 2050s and 2080s. The final toe-hold in the 2080s may be confined to a small part of Scandinavia, including new foci in southern Finland. The reason for this apparent contraction of the range of TBEv is that its transmission cycles depend on a particular pattern of tick seasonal dynamics, which may be disrupted by climate change. The observed marked increase in incidence of tick-borne encephalitis in most parts of Europe since 1993 may be due to non-biological causes, such as political and sociological changes.

  14. 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.

  15. Evaluating predictability in nonlinear climate systems using the Mount Pinatubo eruption

    NASA Astrophysics Data System (ADS)

    Gaddis, A. L.; Drake, J.; Evans, K. J.; Gentry, R. W.

    2011-12-01

    Volcanic eruptions serve as a benchmark for assessing aspects of predictability because they provide a sudden global impulse to the Earth's climate system. In particular, the 1991 eruption of Mt. Pinatubo in the Philippines is of interest since it was the largest aerosol perturbation to the stratosphere in the twentieth century and the most intensely observed eruption on record. Using instrumental measurements during the eruption, the predictive skill of climate models may be evaluated and information about the earth's climate sensitivity may be inferred. In this study, weather records from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis project and the European Centre for Medium-Range Weather Forecasts 40 Year Re-analysis (ERA-40) are compared to simulations from the atmospheric component of the Community Earth System Model, CESM1.0. For all data sets, monthly averages of 2m air temperature are evaluated for the years preceding and following the eruption. The time series of the temperature response for all data sets are plotted against aerosol optical depth. This method generates a trajectory in phase space that characterizes the evolution of the system under forcing. The temporal pattern of response to forcing for the model and reanalysis data is discussed qualitatively and quantified statistically.

  16. 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.

  17. Scale-dependent regional climate predictability over North America inferred from CMIP3 and CMIP5 ensemble simulations

    NASA Astrophysics Data System (ADS)

    Zhang, Fuqing; Li, Wei; Mann, Michael E.

    2016-08-01

    Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement of climate model predictions and projections may not be possible. That limit varies among climate variables and from region to region. We show that the uncertainty (noise) in surface temperature predictions (represented by the spread among an ensemble of global climate model simulations) generally exceeds the ensemble mean (signal) at horizontal scales below 1000 km throughout North America, implying poor predictability at those scales. More limited skill is shown for the predictability of regional precipitation. The ensemble spread in this case tends to exceed or equal the ensemble mean for scales below 2000 km. These findings highlight the challenges in predicting regionally specific future climate anomalies, especially for hydroclimatic impacts such as drought and wetness.

  18. The Relative Contribution of Internal and Model Variabilities to the Uncertainty in Decadal Climate Predictions

    NASA Astrophysics Data System (ADS)

    Strobach, Ehud; Bel, Golan

    2016-04-01

    Decadal climate predictions, which are initialized with observed conditions, are characterized by two main sources of uncertainties--internal and model variabilities. The former is due to the sensitivity of the models to the initial conditions, and the latter is due to the different predictions of different models. There is not much that can be done to reduce the internal variability; however, there are several methods for reducing the model variability--for example, using an ensemble weighted according to the past performance of the models rather than an equally weighted ensemble. Quantifying the contribution of each of these sources can help in assessing the potential reduction of the total uncertainty of these climate predictions. We used an ensemble of climate model simulations, from the CMIP5 decadal experiments, that includes different climate models and several initializations for each of the models, to analyze the uncertainties on a decadal time scale. Time series of the monthly and annual means of the surface temperature and wind components were established for the variability analysis. The analysis focused on the contributions of the internal and model variabilities and the total uncertainty. We found that different definitions of the anomaly resulted in different conclusions regarding the variability of the ensemble. However, some features of the uncertainty were common to all the anomalies we considered. In particular, we found that (i) over decadal time scales, there is no considerable increase in the uncertainty with time; (ii) the model variability is more sensitive to the annual cycle than the internal variability (this, in turn, results in a maximal uncertainty during the winter in the northern hemisphere); (iii) the uncertainty of the surface temperature prediction is dominated by the model variability, whereas the uncertainty of the surface wind components is determined by both the model and the internal variabilities. Analysis of the spatial

  19. 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

  20. 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

  1. Sub-grid Parameterization of Cumulus Vertical Velocities for Climate and Numerical Weather Prediction Models

    NASA Astrophysics Data System (ADS)

    Cooke, William; Donner, Leo

    2015-04-01

    Microphysical and aerosol processes determine the magnitude of climate forcing by aerosol-cloud interactions, are central aspects of cloud-climate feedback, and are important elements in weather systems for which accurate forecasting is a major goal of numerical weather prediction. Realistic simulation of these processes demands not only accurate microphysical and aerosol process representations but also realistic simulation of the vertical motions in which the aerosols and microphysics act. Aerosol activation, for example, is a strong function of vertical velocity. Cumulus parameterizations for climate and numerical weather prediction models have recently begun to include vertical velocities among the statistics they predict. These vertical velocities have been subject to only limited evaluation using observed vertical velocities. Deployments of multi-Doppler radars and dual-frequency profilers in recent field campaigns have substantially increased the observational base of cumulus vertical velocities, which for decades had been restricted mostly to GATE observations. Observations from TWP-ICE (Darwin, Australia) and MC3E (central United States) provide previously unavailable information on the vertical structure of cumulus vertical velocities and observations in differing synoptic contexts from those available in the past. They also provide an opportunity to independently evaluate cumulus parameterizations with vertical velocities tuned to earlier GATE observations. This presentation will compare vertical velocities observed in TWP-ICE and MC3E with cumulus vertical velocities using the parameterization in the GFDL CM3 climate model. Single-column results indicate parameterized vertical velocities are frequently greater than observed. Errors in parameterized vertical velocities exhibit similarities to vertical velocities explicitly simulated by cloud-system resolving models, and underlying issues in the treatment of microphysics may be important for both. The

  2. Diurnal simulation models of weather data for improved predictions of global climate changes

    SciTech Connect

    Loukidou-Kafatou, T.

    1992-01-01

    Most of our knowledge about the Earth has been assembled by those in Earth-science disciplines. Each of these disciplines has traditionally operated within its own frame of reference with little or no interaction. We now recognize global connections between the physical dynamics of the Earth system, and that knowledge from all Earth science disciplines is needed to describe this system. We begin to gain a new awareness of the common destiny of humanity beyond geographical and political boundaries. The need to be able to predict climate changes is imperative and the need to formulate policies to regulate the effects of human activities on global climate is compelling and critical at this point in human history. Yet the ability to do so requires an understanding of the highly complex and interactive mechanisms of climate. One essential ingredient in achieving this understanding is climatological data. Climatological data of the past are available, in the best case, every six hours per day, resolution that is not adequate for the study of the natural variability of climate. The picture of the past record of the Earth's history is incomplete and fragmentary as we look further back in time. Yet snapshots of past conditions can provide an important test bed for evolving models of Earth system processes operating on time-scales of decades to centuries. This research contributes to the reconstruction of the paleoclimate, the climate of the past, which links long and short timescales. In this research project three diurnal models are developed. They require four equally spaced data per day as a basis for simulating hourly data. The models use mathematical techniques, such as Fourier Transform, Fast Fourier Transform, and cubic's Spline. All models perform at an error rate of less than 10%. The models can be used to recreate past records in climate, in GCMs, in agriculture and all Earth Sciences.

  3. 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.

  4. Adapting SOYGRO V5.42 for prediction under climate change conditions

    SciTech Connect

    Pickering, N.B.; Jones, J.W.; Boote, K.J.

    1995-12-31

    In some studies of the impacts of climate change on global crop production, crop growth models were empirically adapted to improve their response to increased CO{sub 2} concentration and air temperature. This chapter evaluates the empirical adaptations of the photosynthesis and evapotranspiration (ET) algorithms used in the soybean [Glycine max (L.) Merr.] model, SOYGRO V5.42, by comparing it with a new model that includes mechanistic approaches for these two processes. The new evapotranspiration-photosynthesis sub-model (ETPHOT) uses a hedgerow light interception algorithm, a C{sub 3}-leaf biochemical photosynthesis submodel, and predicts canopy ET and temperatures using a three-zone energy balance. ETPHOT uses daily weather data, has an internal hourly time step, and sums hourly predictions to obtain daily gross photosynthesis and ET. The empirical ET and photosynthesis curves included in SOYGRO V5.42 for climate change prediction were similar to those predicted by the ETPHOT model. Under extreme conditions that promote high leaf temperatures, like in the humid tropics. SOYGRO V5.42 overestimated daily gross photosynthesis response to CO{sub 2} compared with the ETPHOT model. SOYGRO V5.42 also slightly overestimated daily gross photosynthesis at intermediate air temperatures and ambient CO{sub 2} concentrations. 80 refs., 12 figs.

  5. 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.

  6. Predicted impact of mass drug administration on the development of protective immunity against Schistosoma haematobium.

    PubMed

    Mitchell, Kate M; Mutapi, Francisca; Mduluza, Takafira; Midzi, Nicholas; Savill, Nicholas J; Woolhouse, Mark E J

    2014-01-01

    Previous studies suggest that protective immunity against Schistosoma haematobium is primarily stimulated by antigens from dying worms. Praziquantel treatment kills adult worms, boosting antigen exposure and protective antibody levels. Current schistosomiasis control efforts use repeated mass drug administration (MDA) of praziquantel to reduce morbidity, and may also reduce transmission. The long-term impact of MDA upon protective immunity, and subsequent effects on infection dynamics, are not known. A stochastic individual-based model describing levels of S. haematobium worm burden, egg output and protective parasite-specific antibody, which has previously been fitted to cross-sectional and short-term post-treatment egg count and antibody patterns, was used to predict dynamics of measured egg output and antibody during and after a 5-year MDA campaign. Different treatment schedules based on current World Health Organisation recommendations as well as different assumptions about reductions in transmission were investigated. We found that antibody levels were initially boosted by MDA, but declined below pre-intervention levels during or after MDA if protective immunity was short-lived. Following cessation of MDA, our models predicted that measured egg counts could sometimes overshoot pre-intervention levels, even if MDA had had no effect on transmission. With no reduction in transmission, this overshoot occurred if protective immunity was short-lived. This implies that disease burden may temporarily increase following discontinuation of treatment, even in the absence of any reduction in the overall transmission rate. If MDA was additionally assumed to reduce transmission, a larger overshoot was seen across a wide range of parameter combinations, including those with longer-lived protective immunity. MDA may reduce population levels of immunity to urogenital schistosomiasis in the long-term (3-10 years), particularly if transmission is reduced. If MDA is stopped while

  7. 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

  8. 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

  9. 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

  10. Communication Climate and Administrative Burnout: A Technique for Relieving Some of the Pressures.

    ERIC Educational Resources Information Center

    Pood, Elliott; Jellicorse, John Lee

    1984-01-01

    Reports on how a communication audit was used as a technique to reduce burnout. Covers designing and administrating the instruments and then analyzing the results through group discussion. Includes a sample Communication Audit Instrument. (PD)

  11. Climate change simulations predict altered biotic response in a thermally heterogeneous stream system.

    PubMed

    Westhoff, Jacob T; Paukert, Craig P

    2014-01-01

    Climate change is predicted to increase water temperatures in many lotic systems, but little is known about how changes in air temperature affect lotic systems heavily influenced by groundwater. Our objectives were to document spatial variation in temperature for spring-fed Ozark streams in Southern Missouri USA, create a spatially explicit model of mean daily water temperature, and use downscaled climate models to predict the number of days meeting suitable stream temperature for three aquatic species of concern to conservation and management. Longitudinal temperature transects and stationary temperature loggers were used in the Current and Jacks Fork Rivers during 2012 to determine spatial and temporal variability of water temperature. Groundwater spring influence affected river water temperatures in both winter and summer, but springs that contributed less than 5% of the main stem discharge did not affect river temperatures beyond a few hundred meters downstream. A multiple regression model using variables related to season, mean daily air temperature, and a spatial influence factor (metric to account for groundwater influence) was a strong predictor of mean daily water temperature (r2 = 0.98; RMSE = 0.82). Data from two downscaled climate simulations under the A2 emissions scenario were used to predict daily water temperatures for time steps of 1995, 2040, 2060, and 2080. By 2080, peak numbers of optimal growth temperature days for smallmouth bass are expected to shift to areas with more spring influence, largemouth bass are expected to experience more optimal growth days (21-317% increase) regardless of spring influence, and Ozark hellbenders may experience a reduction in the number of optimal growth days in areas with the highest spring influence. Our results provide a framework for assessing fine-scale (10 s m) thermal heterogeneity and predict shifts in thermal conditions at the watershed and reach scale. PMID:25356982

  12. Climate change simulations predict altered biotic response in a thermally heterogeneous stream system.

    PubMed

    Westhoff, Jacob T; Paukert, Craig P

    2014-01-01

    Climate change is predicted to increase water temperatures in many lotic systems, but little is known about how changes in air temperature affect lotic systems heavily influenced by groundwater. Our objectives were to document spatial variation in temperature for spring-fed Ozark streams in Southern Missouri USA, create a spatially explicit model of mean daily water temperature, and use downscaled climate models to predict the number of days meeting suitable stream temperature for three aquatic species of concern to conservation and management. Longitudinal temperature transects and stationary temperature loggers were used in the Current and Jacks Fork Rivers during 2012 to determine spatial and temporal variability of water temperature. Groundwater spring influence affected river water temperatures in both winter and summer, but springs that contributed less than 5% of the main stem discharge did not affect river temperatures beyond a few hundred meters downstream. A multiple regression model using variables related to season, mean daily air temperature, and a spatial influence factor (metric to account for groundwater influence) was a strong predictor of mean daily water temperature (r2 = 0.98; RMSE = 0.82). Data from two downscaled climate simulations under the A2 emissions scenario were used to predict daily water temperatures for time steps of 1995, 2040, 2060, and 2080. By 2080, peak numbers of optimal growth temperature days for smallmouth bass are expected to shift to areas with more spring influence, largemouth bass are expected to experience more optimal growth days (21-317% increase) regardless of spring influence, and Ozark hellbenders may experience a reduction in the number of optimal growth days in areas with the highest spring influence. Our results provide a framework for assessing fine-scale (10 s m) thermal heterogeneity and predict shifts in thermal conditions at the watershed and reach scale.

  13. Climate Change Simulations Predict Altered Biotic Response in a Thermally Heterogeneous Stream System

    PubMed Central

    Westhoff, Jacob T.; Paukert, Craig P.

    2014-01-01

    Climate change is predicted to increase water temperatures in many lotic systems, but little is known about how changes in air temperature affect lotic systems heavily influenced by groundwater. Our objectives were to document spatial variation in temperature for spring-fed Ozark streams in Southern Missouri USA, create a spatially explicit model of mean daily water temperature, and use downscaled climate models to predict the number of days meeting suitable stream temperature for three aquatic species of concern to conservation and management. Longitudinal temperature transects and stationary temperature loggers were used in the Current and Jacks Fork Rivers during 2012 to determine spatial and temporal variability of water temperature. Groundwater spring influence affected river water temperatures in both winter and summer, but springs that contributed less than 5% of the main stem discharge did not affect river temperatures beyond a few hundred meters downstream. A multiple regression model using variables related to season, mean daily air temperature, and a spatial influence factor (metric to account for groundwater influence) was a strong predictor of mean daily water temperature (r2 = 0.98; RMSE = 0.82). Data from two downscaled climate simulations under the A2 emissions scenario were used to predict daily water temperatures for time steps of 1995, 2040, 2060, and 2080. By 2080, peak numbers of optimal growth temperature days for smallmouth bass are expected to shift to areas with more spring influence, largemouth bass are expected to experience more optimal growth days (21 – 317% increase) regardless of spring influence, and Ozark hellbenders may experience a reduction in the number of optimal growth days in areas with the highest spring influence. Our results provide a framework for assessing fine-scale (10 s m) thermal heterogeneity and predict shifts in thermal conditions at the watershed and reach scale. PMID:25356982

  14. 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

  15. 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.

  16. 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.

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

    PubMed

    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

    2013-11-01

    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

  18. 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.

  19. 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. PMID:21710282

  20. 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

  1. Influence and predictive capacity of climate anomalies on daily to decadal extremes in canopy photosynthesis.

    PubMed

    Desai, Ankur R

    2014-02-01

    Significant advances have been made over the past decades in capabilities to simulate diurnal and seasonal variation of leaf-level and canopy-scale photosynthesis in temperate and boreal forests. However, long-term prediction of future forest productivity in a changing climate may be more dependent on how climate and biological anomalies influence extremes in interannual to decadal variability of canopy ecosystem carbon exchanges. These exchanges can differ markedly from leaf level responses, especially owing to the prevalence of long lags in nutrient and water cycling. Until recently, multiple long-term (10+ year) high temporal frequency (daily) observations of canopy exchange were not available to reliably assess this claim. An analysis of one of the longest running North American eddy covariance flux towers reveals that single climate variables do not adequately explain carbon exchange anomalies beyond the seasonal timescale. Daily to weekly lagged anomalies of photosynthesis positively autocorrelate with daily photosynthesis. This effect suggests a negative feedback in photosynthetic response to climate extremes, such as anomalies in evapotranspiration and maximum temperature. Moisture stress in the prior season did inhibit photosynthesis, but mechanisms are difficult to assess. A complex interplay of integrated and lagged productivity and moisture-limiting factors indicate a critical role of seasonal thresholds that limit growing season length and peak productivity. These results lead toward a new conceptual framework for improving earth system models with long-term flux tower observations.

  2. 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

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

    PubMed

    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

    2013-11-01

    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.

  4. Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan

    NASA Astrophysics Data System (ADS)

    Kuo, Chun-Chao; Gan, Thian Yew; Yu, Pao-Shan

    2010-06-01

    SummaryA combined, climate-hydrologic system with three components to predict the streamflow of two river basins of Taiwan at one season (3-month) lead time for the NDJ and JFM seasons was developed. The first component consists of the wavelet-based, ANN-GA model (Artificial Neural Network calibrated by Genetic Algorithm) which predicts the seasonal rainfall by using selected sea surface temperature (SST) as predictors, given that SST are generally predictable by climate models up to 6-month lead time. For the second component, three disaggregation models, Valencia and Schaake (VS), Lane, and Canonical Random Cascade Model (CRCM), were tested to compare the accuracy of seasonal rainfall disaggregated by these three models to 3-day time scale rainfall data. The third component consists of the continuous rainfall-runoff model modified from HBV (called the MHBV) and calibrated by a global optimization algorithm against the observed rainfall and streamflow data of the Shihmen and Tsengwen river basins of Taiwan. The proposed system was tested, first by disaggregating the predicted seasonal rainfall of ANN-GA to rainfall of 3-day time step using the Lane model; then the disaggregated rainfall data was used to drive the calibrated MHBV to predict the streamflow for both river basins at 3-day time step up to a season's lead time. Overall, the streamflow predicted by this combined system for the NDJ season, which is better than that of the JFM season, will be useful for the seasonal planning and management of water resources of these two river basins of Taiwan.

  5. 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…

  6. 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…

  7. [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.

  8. 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. PMID:17023538

  9. Decadal climate variability over the North Pacific and North America: Dynamics and predictability

    SciTech Connect

    Latif, M.; Barnett, T.P.

    1996-10-01

    The dynamics and predictability of decadal climate variability over the North Pacific and North America are investigated by analyzing various observation datasets and the output of a state of the art coupled ocean-atmosphere general circulation model that was integrated for 125 years. Both the observations and model results support the picture that the decadal variability in the regional of interest is based on a cycle involving unstable ocean-atmosphere interactions over the North Pacific. The period of this cycle is of the order of a few decades. The cycle involves the two major circulation regimes in the North Pacific climate system, the subtropical ocean gyre, and the Aleutian low. 41 refs., 18 figs.

  10. 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.

  11. The Florida Water and Climate Alliance: A Collaborative Working Group for the Development of Climate Predictions for Improved Water Management, Operations and Planning. (Invited)

    NASA Astrophysics Data System (ADS)

    Graham, W. D.; Hwang, S.; Adams, A.

    2013-12-01

    The Florida Water and Climate Alliance (FloridaWCA) is a stakeholder-scientist partnership focused on increasing the relevance of climate science data and tools for water resource planning and supply operations in Florida. To date the FloridaWCA has (1) developed a collaborative working group comprised of public water suppliers, water resource managers, climate scientists, and hydrologic scientists focused on understanding how climate variability/change may impact planning and operations of Florida's public water supply utilities, (2) identified the appropriate spatio-temporal scales, climatic indices, and events that drive utilities' and water managers' decisions, (3) evaluated the accuracy of statistically and dynamically downscaled General Circulation Model (GCM) predictions for the region, and (4) used the downscaled climate model predictions in Working Group members' hydrologic models to evaluate the usefulness of these data for minimizing current and future risks associated with climate variability/climate change. This presentation will highlight technical results that show the variable accuracy with which alternative statistical and dynamic downscaling techniques reproduce the small-scale spatiotemporal variability of precipitation in Florida, and the importance of this small-scale variability for predicting hydrologic response to changes in climatic forcing. The implications of uncertainty produced by alternative future scenarios, GCM projections, and downscaling techniques for water resource decision-making will be explored. The benefits of convening scientists and practitioners in an iterative process of knowledge co-production will be discussed, and lessons learned that may be applicable to other groups involved in multi-stakeholder process development will be presented.

  12. Predicting leaf physiology from simple plant and climate attributes: a global GLOPNET analysis.

    PubMed

    Reich, Peter B; Wright, Ian J; Lusk, Christopher H

    2007-10-01

    Knowledge of leaf chemistry, physiology, and life span is essential for global vegetation modeling, but such data are scarce or lacking for some regions, especially in developing countries. Here we use data from 2021 species at 175 sites around the world from the GLOPNET compilation to show that key physiological traits that are difficult to measure (such as photosynthetic capacity) can be predicted from simple qualitative plant characteristics, climate information, easily measured ("soft") leaf traits, or all of these in combination. The qualitative plant functional type (PFT) attributes examined are phylogeny (angiosperm or gymnosperm), growth form (grass, herb, shrub, or tree), and leaf phenology (deciduous vs. evergreen). These three PFT attributes explain between one-third and two-thirds of the variation in each of five quantitative leaf ecophysiological traits: specific leaf area (SLA), leaf life span, mass-based net photosynthetic capacity (Amass), nitrogen content (N(mass)), and phosphorus content (P(mass)). Alternatively, the combination of four simple, widely available climate metrics (mean annual temperature, mean annual precipitation, mean vapor pressure deficit, and solar irradiance) explain only 5-20% of the variation in those same five leaf traits. Adding the climate metrics to the qualitative PFTs as independent factors in the model increases explanatory power by 3-11% for the five traits. If a single easily measured leaf trait (SLA) is also included in the model along with qualitative plant traits and climate metrics, an additional 5-25% of the variation in the other four other leaf traits is explained, with the models accounting for 62%, 65%, 66%, and 73% of global variation in N(mass), P(mass), A(mass), and leaf life span, respectively. Given the wide availability of the summary climate data and qualitative PFT data used in these analyses, they could be used to explain roughly half of global variation in the less accessible leaf traits (A

  13. Modeled impacts of predicted climate change on recharge and groundwater levels

    NASA Astrophysics Data System (ADS)

    Scibek, J.; Allen, D. M.

    2006-11-01

    A methodology is developed for linking climate models and groundwater models to investigate future impacts of climate change on groundwater resources. An unconfined aquifer, situated near Grand Forks in south central British Columbia, Canada, is used to test the methodology. Climate change scenarios from the Canadian Global Coupled Model 1 (CGCM1) model runs are downscaled to local conditions using Statistical Downscaling Model (SDSM), and the change factors are extracted and applied in LARS-WG stochastic weather generator and then input to the recharge model. The recharge model simulated the direct recharge to the aquifer from infiltration of precipitation and consisted of spatially distributed recharge zones, represented in the Hydrologic Evaluation of Landfill Performance (HELP) hydrologic model linked to a geographic information system (GIS). A three-dimensional transient groundwater flow model, implemented in MODFLOW, is then used to simulate four climate scenarios in 1-year runs (1961-1999 present, 2010-2039, 2040-2069, and 2070-2099) and compare groundwater levels to present. The effect of spatial distribution of recharge on groundwater levels, compared to that of a single uniform recharge zone, is much larger than that of temporal variation in recharge, compared to a mean annual recharge representation. The predicted future climate for the Grand Forks area from the downscaled CGCM1 model will result in more recharge to the unconfined aquifer from spring to the summer season. However, the overall effect of recharge on the water balance is small because of dominant river-aquifer interactions and river water recharge.

  14. The sensitivity of global ozone predictions to dry deposition schemes and their response to climate change

    NASA Astrophysics Data System (ADS)

    Centoni, Federico; Stevenson, David; Fowler, David; Nemitz, Eiko; Coyle, Mhairi

    2015-04-01

    Concentrations of ozone at the surface are strongly affected by deposition to the surface. Deposition processes are very sensitive to temperature and relative humidity at the surface and are expected to respond to global change, with implications for both air quality and ecosystem services. Many studies have shown that ozone stomatal uptake by vegetation typically accounts for 40-60% of total deposition on average and the other part which occurs through non-stomatal pathways is not constant. Flux measurements show that non-stomatal removal increases with temperature and under wet conditions. There are large uncertainties in parameterising the non-stomatal ozone deposition term in climate chemistry models and model predictions vary greatly. In addition, different model treatments of dry deposition constitute a source of inter-model variability in surface ozone predictions. The main features of the original Unified Model-UK Chemistry and Aerosols (UM-UKCA) dry deposition scheme and the Zhang et al. 2003 scheme, which introduces in UM-UKCA a more developed non-stomatal deposition approach, are presented. This study also estimates the relative contributions of ozone flux via stomatal and non-stomatal uptakes at the global scale, and explores the sensitivity of simulated surface ozone and ozone deposition flux by implementing different non-stomatal parameterization terms. With a view to exploring the potential influence of future climate, we present results showing the effects of variations in some meteorological parameters on present day (2000) global ozone predictions. In particular, this study revealed that the implementation of a more mechanistic representation of the non-stomatal deposition in UM-UKCA model along with a decreased stomatal uptake due to the effect of blocking under wet conditions, accounted for a substantial reduction of ozone fluxes to broadleaf trees in the tropics with an increase of annual mean surface ozone. On the contrary, a large increase of

  15. 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, Dawn M.; 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 Niña 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.

  16. 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.

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

    PubMed

    Grisham, Blake A; Boal, Clint W; Haukos, David A; Davis, Dawn M; 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 Niña 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.

  18. 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.

    2015-08-01

    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.

  19. 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

  20. Predicting Nitrogen Loading in Streams Under Climate Change Scenarios in the Continental United States.

    NASA Astrophysics Data System (ADS)

    Sinha, E.; Michalak, A. M.

    2014-12-01

    Human actions have doubled the amount of nitrogen in the terrestrial biosphere. Although nitrogen application in the form of fertilizers increases food production, excess nitrogen can be harmful to the environment and to human well-being. Excess nitrogen in streams is transported into downstream water bodies, which leads to increased eutrophication and associated problems such as harmful algal blooms and/or hypoxic conditions. The amount of nitrogen exported to streams depends on several factors, including nitrogen input to the watershed, land use, and precipitation. Previous studies have developed models for predicting nitrate load using stream discharge, in order to estimate the contribution of various factors to total nitrogen load, to identify strategies for reducing nitrogen load, and to assess future changes in nitrogen load resulting from anticipated changes in precipitation patterns. Applying these models to estimate future nitrogen loads thus requires running a rainfall-runoff model driven by climate model predictions before nitrogen loading can, in turn, be estimated, thereby compounding uncertainties. In this study, we present a statistical modeling approach that circumvents this two-step process by estimating nitrogen loading directly from precipitation predictions that can be obtained from climate model outputs. The proposed model uses net anthropogenic nitrogen input (NANI), land use type, and precipitation as input parameters. Preliminary results show that the model explains greater than 65% of the variance in the observed annual log transformed nitrogen loads across various catchments throughout the United States. The model is applied to the watersheds comprising the Mississippi river basin to identify the spatial distribution of the sources of nitrogen loading and the inter-annual variation in nitrogen loads under current conditions. Additionally, the model is used to examine changes in magnitude and spatial patterns of nitrogen loading for

  1. 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

  2. Improving Sea Ice Prediction in the NCEP Climate Forecast System Model

    NASA Astrophysics Data System (ADS)

    Collow, T. W.; Wang, W.; Kumar, A.

    2015-12-01

    Skillful prediction of Arctic sea ice is important for the wide variety of interests focused in that region. However, the current operational system used by the NOAA Climate Prediction Center does not adequately predict the seasonal climatology of sea ice extent and maintains too high sea ice coverage across the Arctic. It is thought that the primary reasoning for this lies in the initialization of sea ice thickness. Experiments are carried out using the Climate Forecast System (CFSv2) model with an improved sea ice thickness initialization from the Pan-Arctic Ice Ocean Analysis and Assimilation System (PIOMAS) rather than the default Climate Forecast System Reanalysis (CFSR) sea ice thickness data. All other variables are initialized from CFSR. In addition, physics parameterizations are adjusted to better simulate real world conditions. Here we focus on hindcasts initialized from 2005-2014. Although the seasonal cycle of sea ice is generally better captured in runs that use PIOMAS sea ice thickness initialization, local sea ice freeze in early winter in the Bering Strait and Chukchi Sea is delayed when both sea ice thickness configurations are used. In addition ice freeze in the North Atlantic is more pronounced than in the observations. This shows that simply changing initial sea ice thickness is not enough to improve forecasts for all locations. Modeled atmospheric and oceanic parameters are investigated including the radiation budget, land surface temperature advection, and sub-surface oceanic heat flow to diagnose possible reasons for the modeling deficiencies, and further modifications to the model will be discussed.

  3. 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

  4. RegCM4 Dynamical Downscaling of Seasonal Climate Predictions over the Southeast of Brazil

    NASA Astrophysics Data System (ADS)

    Reboita, Michelle S.; Dutra, Lívia M. M.; da Rocha, Rosmeri P.

    2013-04-01

    In this study the Regional Climate Model version 4 (RegCM4) was nested in the General Circulation Model from the Brazilian Center for Weather Forecasts and Climate Studies (CPTEC) to produce three month (seasonal) predictions to the southeast of Brazil (SB). The predictions for MAM (March-April-May), AMJ (April-May-June) and SON (September-October-November) of 2012 used six different parameterizations of convection: 1) Grell with Arakawa-Schubert (GAS) closure, 2) Grell with Fritsch-Chappell (GFC) closure, 3) Kuo, 4) Emanuel (EM), 5) Mixed-1 with GFC and Emanuel schemes over the land and ocean, respectively and 6) Mixed-2 with Emanuel and GFC over the land and ocean, respectively. The simulations started 48 days before the seasons of interest to permit a spin-up period. The predicted precipitation was compared with observation from Climate Prediction Center (CPC). For MAM/2012, the experiment with Kuo scheme presented the seasonal precipitation similar to CPC, while GAS, GFC and Mixed-1 experiments underestimated the precipitation (~1-2 mm/day) over the center of SB and EM and Mixed-2 schemes overestimated it (~4 mm/day) over most part of SB. For AMJ/2012 all experiments underestimated the precipitation (~2-3 mm/day) in the central-south part of SB, but they simulated the precipitation intensity close to the observation over the center-north SB (except the EM which shows overestimation in this area). For this season, Mixed-1 presented the smaller bias compared to the other convective schemes. For AMJ/2012, the experiments underestimated the precipitation (~2-4 mm/day) over the center-north of SB and overestimated it (~2-4 mm/day) in the eastern sector. In this period, the EM and Mixed-1 predictions presented the smaller bias compared to CPC. Considering the three seasons, this study suggests that the best convective scheme to seasonal predictions for SB is Mixed-1, while GFC, GAS and Kuo also produce satisfactory seasonal precipitation.

  5. 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.

  6. 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.

  7. The Campus Climate Revisited: Chilly for Women Faculty, Administrators, and Graduate Students. [Final Report.

    ERIC Educational Resources Information Center

    Sandler, Bernice R.

    The Project on the Status and Education of Women (PSEW) of the Association of American Colleges describes ways in which women faculty, administrators, and graduate students in higher education are often treated differently from men. Under a grant from the Fund for the Improvement of Postsecondary Education (FIPSE), PSEW investigated negative…

  8. 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.

  9. 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

  10. 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

  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. 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.

  13. Positive and negative family emotional climate differentially predict youth anxiety and depression via distinct affective pathways.

    PubMed

    Luebbe, Aaron M; Bell, Debora J

    2014-08-01

    A socioaffective specificity model was tested in which positive and negative affect differentially mediated relations of family emotional climate to youth internalizing symptoms. Participants were 134 7(th)-9(th) grade adolescents (65 girls; 86 % Caucasian) and mothers who completed measures of emotion-related family processes, experienced affect, anxiety, and depression. Results suggested that a family environment characterized by maternal psychological control and family negative emotion expressiveness predicted greater anxiety and depression, and was mediated by experienced negative affect. Conversely, a family emotional environment characterized by low maternal warmth and low positive emotion expressiveness predicted only depression, and was mediated through lowered experienced positive affect. This study synthesizes a theoretical model of typical family emotion socialization with an extant affect-based model of shared and unique aspects of anxiety and depression symptom expression.

  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. 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.

  16. 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

  17. 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

    2016-01-01

    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

  18. 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

  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

    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. 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

  2. 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.

  3. Sexual selection predicts advancement of avian spring migration in response to climate change

    PubMed Central

    Spottiswoode, Claire N; Tøttrup, Anders P; Coppack, Timothy

    2006-01-01

    Global warming has led to earlier spring arrival of migratory birds, but the extent of this advancement varies greatly among species, and it remains uncertain to what degree these changes are phenotypically plastic responses or microevolutionary adaptations to changing environmental conditions. We suggest that sexual selection could help to understand this variation, since early spring arrival of males is favoured by female choice. Climate change could weaken the strength of natural selection opposing sexual selection for early migration, which would predict greatest advancement in species with stronger female choice. We test this hypothesis comparatively by investigating the degree of long-term change in spring passage at two ringing stations in northern Europe in relation to a synthetic estimate of the strength of female choice, composed of degree of extra-pair paternity, relative testes size and degree of sexually dichromatic plumage colouration. We found that species with a stronger index of sexual selection have indeed advanced their date of spring passage to a greater extent. This relationship was stronger for the changes in the median passage date of the whole population than for changes in the timing of first-arriving individuals, suggesting that selection has not only acted on protandrous males. These results suggest that sexual selection may have an impact on the responses of organisms to climate change, and knowledge of a species' mating system might help to inform attempts at predicting these. PMID:17015341

  4. 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

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

    PubMed

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

    2013-05-01

    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.

  6. 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

  7. 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.

  8. 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. PMID:26489417

  9. ADMINISTRATORS FOR AMERICA'S JUNIOR COLLEGES--PREDICTIONS OF NEED 1965-1980.

    ERIC Educational Resources Information Center

    SCHULTZ, RAYMOND E.

    THE MAJOR SOURCES ON WHICH THIS NEED SURVEY IS BASED INCLUDE AN ADMINISTRATORS' INFORMATION FORM DEVELOPED SPECIFICALLY FOR THIS STUDY, THE JUNIOR COLLEGE DIRECTORY, AND THE EDUCATION DIRECTORY, 1963-64, PART III. THERE WILL BE A NEED FOR NEARLY 3,000 NEW ADMINISTRATORS IN MAJOR POSITIONS OVER THE NEXT 15 YEARS, INCLUDING 1,403 PRESIDENTS, 1,507…

  10. Diurnal Simulation Models of Weather Data for Improved Predictions of Global Climate Changes

    NASA Astrophysics Data System (ADS)

    Loukidou-Kafatou, Thalia

    Most of our knowledge about the Earth has been assembled by those in Earth-science disciplines. Each of these disciplines has traditionally operated within its own frame of reference with little or no interaction. This situation is now changing rapidly as a new view of the Earth forces members of the scientific community to transcend disciplinary boundaries. We now recognize global connections between the physical dynamics of the Earth system, and that knowledge from all Earth science disciplines is needed to describe this system. The need for an interdisciplinary approach to Earth science has been accentuated by advances in space sciences. We begin to gain a new awareness of the common destiny of humanity beyond geographical and political boundaries. The need to be able to predict climate changes is imperative and the need to formulate policies to regulate the effects of human activities on global climate is compelling and critical at this point in human history. Yet the ability to do so requires an understanding of the highly complex and interactive mechanisms of climate. One essential ingredient in achieving this understanding is climatological data. Climatological data of the past are available, in the best case, every six hours per day, resolution that is not adequate for the study of the natural variability of climate. The picture of the past record of the Earth's history is incomplete and fragmentary as we look further back in time. Yet snapshots of past conditions can provide an important test bed for evolving models of Earth system processes operating on time-scales of decades to centuries. This research contributes to the reconstruction of the paleoclimate, the climate of the past, which links long and short timescales. In this research project three diurnal models are developed. They require four equally spaced data per day as a basis for simulating hourly data. The models use mathematical techniques, such as Fourier Transform, Fast Fourier Transform

  11. Predicted Hydrological Impacts of Climate Change for Harp Lake and Catchment, South- Central Ontario

    NASA Astrophysics Data System (ADS)

    Yao, H.

    2009-05-01

    Potential hydrological impacts of two climate change scenarios were analyzed for a typical inland lake (Harp Lake) and its catchment in the boreal ecozone of Ontario, Canada. The first scenario was created by extrapolation of long-term trends of monthly temperature and precipitation over a 129-year data record, into a future target year 2050. This scenario predicts a slight temperature increase (0.6 0C per 50 years on average) in each of 12 months and a slight increase in monthly precipitation (on average 2.8 mm/m per 50 years). The second scenario was based on a Canadian general circulation model (GCM) prediction, which predicts larger changes and more differences among months. It predicts that the temperature of each month would increase in 2050 by 3.1 0C on average, and monthly precipitation would increase or decrease depending on the month by 1.7 mm/m on average compared to the present. The hydrological responses of the catchment and the lake were calculated using a USGS catchment water balance model and a proposed lake water balance model respectively, and the two models were calibrated with 26-years of hydrological and meteorological data from the Harp catchment. The USGS model has six parameters which were calibrated. Model-estimated runoff and observed runoff have a high correlation coefficient of 0.88. The model was then used to calculate catchment's responses in evapotranspiration and runoff under each of the two climate change scenarios. The lake's water balance model includes a relationship of lake outflow and inflow (i.e. catchment runoff) which was regressed using the 26-year data for each month, and a monthly water budget accounting. The change of lake-water level within a month was calculated as the difference of input term (precipitation, inflow) and output term (outflow, evaporation). The first scenario with a warmer and wetter climate predicted a smaller magnitude of hydrological impact than the second scenario. The first scenario showed an

  12. 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

  13. Climate-driven range extension of Amphistegina (protista, foraminiferida): models of current and predicted future ranges.

    PubMed

    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.

  14. 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

  15. Regional intercomparisons of General Circulation Model predictions and historical climate data: CO/sub 2/

    SciTech Connect

    Grotch, S.L.

    1988-04-01

    This study is a detailed intercomparsion of the results produced by four different General Circulation Models (GCMs) that have been used to project the climatic consequences of a doubling of the atmospheric CO/sub 2/ concentration. The results for the models developed by groups at the National Center for Atmospheric Research (NCARCCM, Washington and Meehl, 1984), the Geophysical Fluid Dynamics Laboratory of NOAA (GFDL, Manable and Wetherald, 1987), and the Goddard Institute for Space Studies of NASA (GISS, Hansen, et al., 1984) have been described by Schlesinger and Mitchell (1985) in the DOE state-of-art (SOA) report, ''Projecting the Climatic Effects of Increasing Carbon Dioxide''. The fourth model examined here is the Oregon State University GCM (OSU, Schlesinger, 1986), results for which did not become available until after publication of the SOA. We have chosen to examine only two model variables here: (1) surface air temperature, and (2) precipation. We consider these variables for both seasonally and annually averaged periods, for both the current climatic conditions and the predicted equilibrium changes after a doubling of the CO/sub 2/ concentration. The major conclusion of this study is that, although the models often agree well comparing seasonal or annual averages over the large areas, substanial disagreements become apparent as the spatial extent is reduced, particularly when detailed regional distributions are examined. At scales below continental, the correlations observed between different model predictions are often very poor, particularly for land gridpoints during the Northern Hemisphere (NH) summer, with differences of as much as 5/degree/C between models and observations and between one model and another over relatively large areas.

  16. 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

  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. 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

  19. Predicting differential vulnerabilities of stream and river temperatures to climate change

    NASA Astrophysics Data System (ADS)

    Hill, R. A.; Hawkins, C. P.

    2012-12-01

    Stream temperatures (ST) have warmed and are expected to continue warming in response to climate change (CC). It is critical to understand the expected magnitude of warming, and the factors associated with differential vulnerabilities of ST to CC to focus research and mitigation efforts. We developed Random Forest (RF) models for mean summer, winter, and annual stream temperatures (MSST, MWST, MAST) with ST data obtained from several hundred USGS ST sites. Sites were located in minimally disturbed watersheds and were distributed across the conterminous USA. We used several GIS-derived factors, such as air temperature (AT), precipitation, watershed area, stream slope, base flow index, and soils to develop the models. Model performance was generally good (MSST r2 = 0.87, RMSE = 1.9 °C; MWST r2 = 0.89, RMSE = 1.4 °C; MAST r2 = 0.95, RMSE = 1.1 °C). We assessed the potential of the models to predict the effects of CC on STs by comparing predicted and observed changes in ST between 1970 and the present. Analysis of covariance showed no difference between predicted and observed changes in MSST and MWST when regressed against observed changes in AT, i.e., the models realistically predicted the effects of climate variability on STs. The MAST model under predicted the effects of CC by ~0.35 °C and would therefore produce conservative estimates of future CC impacts. We then applied dynamically and statistically downscaled A2-CCSM air temperature projections to the models to estimate future STs (yrs. 2090-2099). We subtracted predicted future from observed current STs (ΔSTs) to quantify each stream's vulnerability to CC alteration. For the conterminous USA, the models predicted mean warming for MSST = 1.4 °C, MWST = 2.1 °C, and MAST = 1.6 °C. Geographically, MSST and MAST were most sensitive to changes in AT (ΔAT) in the Cascade, Rocky, and Appalachian Mountains, whereas MWST showed near ubiquitous sensitivity. We used RF to explore the stream and watershed features

  20. 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

  1. Integration of climate change in flood prediction: application to the Somme river (France)

    NASA Astrophysics Data System (ADS)

    Pinault, J.-L.; Amraoui, N.; Noyer, M.-L.

    2003-04-01

    Exceptional floods that have occurred for the last two years in western and central Europe were very unlikely. The concomitance of such rare events shows that they might be imputable to climate change. The statistical analysis of long rainfall series confirms that both the cumulated annual height and the temporal variability have increased for the last decade. This paper is devoted to the analysis of climate change impact on flood prediction applied to the Somme river. The exceptional pluviometry that occurred from October 2000 to April 2001, about the double of the mean value, entailed catastrophic flood between the high Somme and Abbeville. The flow reached a peak at the beginning of May 2001, involving damages in numerous habitations and communication routes, and economical activity of the region had been flood-bound for more than 2 months. The flood caught unaware the population and caused deep traumas in France since it was the first time such a sudden event was recognized as resulting from groundwater discharge. Mechanisms of flood generation were studied tightly in order to predict the behavior of the Somme catchment and other urbanized basins when the pluviometry is exceptional in winter or in spring, which occurs more and more frequently in the northern part of Europe. The contribution of groundwater in surface water flow was calculated by inverse modeling from piezometers that are representative of aquifers in valleys. They were found on the slopes and near the edge of plateaus in order to characterize the drainage processes of the watertable to the surface water network. For flood prediction, a stochastic process is used, consisting in the generation of both rainfall and PET time series. The precipitation generator uses Markov chain Monte Carlo and simulated annealing from the Hastings -- Metropolis algorithm. Coupling of rainfall and PET generators with transfer enables a new evaluation of the probability of occurrence of floods, taking into account

  2. 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

  3. 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

  4. 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.

  5. 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.

    2016-01-01

    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

  6. Predicting permafrost stability in northern peatlands with climate change and disturbance

    NASA Astrophysics Data System (ADS)

    Treat, C. C.; Wisser, D.; Marchenko, S.; Humphreys, E. R.; Frolking, S. E.; Huemmrich, K. F.

    2010-12-01

    Permafrost thaw may cause significant carbon loss from northern organic soils, a large terrestrial carbon pool. To predict permafrost stability in organic soils, we adapted an existing soil temperature model (GIPL 2.0) to peatlands by including a three-layer peat soil column and dynamic soil moisture. GIPL 2.0 numerically solves the 1-dimensional heat transfer equation. We evaluated the model at Daring Lake Fen, a sedge-dominated Arctic Fen in the Northwest Territories, Canada and College Peat, a permafrost muskeg in Fairbanks, AK. We examined the sensitivity of the model to seasonality and total soil moisture, thermal properties and organic layer thickness. We also evaluated active layer depth for future climate scenarios. Finally, we compared the relative magnitude of climate change impacts on soil temperatures to the effects of current and predicted wildfire. We simulated wildfire by removing the surface soil (5 - 15 cm) and increasing air temperatures post-fire due to changes in surface energy balance. We found that air temperature, rather than changes in soil moisture, was the most important predictor of changes in active layer depth and permafrost stability. Also, the seasonality of soil moisture was relatively unimportant, while changes in temperature seasonality were important to active layer depths. In the climate change scenarios (using IPCC scenario A1b), active layer depths and the length of the growing season (determined as soil thawed at 10 cm) increased significantly by 2100. Warmer soil temperatures at depth due to higher air temperatures resulted in an increase of liquid water in the soil and the possibility of increased biological activity. Soil temperatures and active layer depths increased following disturbance, but the increases were relatively short-lived (decades) and were strongly correlated with post-fire temperature changes. The simulated removal of a shallow layer of surface organic soil following disturbance has limited long-term effects

  7. Using Scaling to Understand, Model and Predict Global Scale Anthropogenic and Natural Climate Change

    NASA Astrophysics Data System (ADS)

    Lovejoy, S.; del Rio Amador, L.

    2014-12-01

    The atmosphere is variable over twenty orders of magnitude in time (≈10-3 to 1017 s) and almost all of the variance is in the spectral "background" which we show can be divided into five scaling regimes: weather, macroweather, climate, macroclimate and megaclimate. We illustrate this with instrumental and paleo data. Based the signs of the fluctuation exponent H, we argue that while the weather is "what you get" (H>0: fluctuations increasing with scale), that it is macroweather (H<0: fluctuations decreasing with scale) - not climate - "that you expect". The conventional framework that treats the background as close to white noise and focuses on quasi-periodic variability assumes a spectrum that is in error by a factor of a quadrillion (≈ 1015). Using this scaling framework, we can quantify the natural variability, distinguish it from anthropogenic variability, test various statistical hypotheses and make stochastic climate forecasts. For example, we estimate the probability that the warming is simply a giant century long natural fluctuation is less than 1%, most likely less than 0.1% and estimate return periods for natural warming events of different strengths and durations, including the slow down ("pause") in the warming since 1998. The return period for the pause was found to be 20-50 years i.e. not very unusual; however it immediately follows a 6 year "pre-pause" warming event of almost the same magnitude with a similar return period (30 - 40 years). To improve on these unconditional estimates, we can use scaling models to exploit the long range memory of the climate process to make accurate stochastic forecasts of the climate including the pause. We illustrate stochastic forecasts on monthly and annual scale series of global and northern hemisphere surface temperatures. We obtain forecast skill nearly as high as the theoretical (scaling) predictability limits allow: for example, using hindcasts we find that at 10 year forecast horizons we can still explain

  8. 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

  9. If the predictions of climate models have come true, then why don't people believe them? (Invited)

    NASA Astrophysics Data System (ADS)

    Oreskes, N.

    2010-12-01

    In the summer of 2010, with fires raging in Russia, flooding in China and Pakistan, and temperature and precipitation records being broken around the globe, many commentators noticed that these events could be viewed as confirmation of the predictions of climate models.. Scholarly examination confirms this impression: numerous specific predictions of climate models, dating back to the 1970s, have been essentially on target. Yet, in many quarters, there is persistent reluctance to give models credence, or to view recent weather events as demonstrating climate change. This paper considers some of the reasons for this. It also considers, given the public reluctance to believe models, whether scientists have perhaps invested too much capital in models, and may perhaps need to invoke other strategies to demonstrate to a reluctant public that our understanding of climate change is robust enough to warrant action.

  10. 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

  11. The Predictive Factors on Extended Hospital Length of Stay in Patients with AMI: Laboratory and Administrative Data.

    PubMed

    Magalhães, Teresa; Lopes, Sílvia; Gomes, João; Seixo, Filipe

    2016-01-01

    The length of hospital stay (LOS) is an important measure of efficiency in the use of hospital resources. Acute Myocardial Infarction (AMI), as one of the diseases with higher mortality and LOS variability in the OECD countries, has been studied with predominant use of administrative data, particularly on mortality risk adjustment, failing investigation in the resource planning and specifically in LOS. This paper presents results of a predictive model for extended LOS (LOSE - above 75th percentile of LOS) using both administrative and clinical data, namely laboratory data, in order to develop a decision support system. Laboratory and administrative data of a Portuguese hospital were included, using logistic regression to develop this predictive model. A model with three laboratory data and seven administrative data variables (six comorbidities and age ≥ 69 years), with excellent discriminative ability and a good calibration, was obtained. The model validation shows also good results. Comorbidities were relevant predictors, mainly diabetes with complications, showing the highest odds of LOSE (OR = 37,83; p = 0,001). AMI patients with comorbidities (diabetes with complications, cerebrovascular disease, shock, respiratory infections, pulmonary oedema), with pO2 above level, aged 69 years or older, with cardiac dysrhythmia, neutrophils above level, pO2 below level, and prothrombin time above level, showed increased risk of extended LOS. Our findings are consistent with studies that refer these variables as predictors of increased risk. PMID:26558393

  12. Global isoscapes for δ18O and δ2H in precipitation: improved prediction using regionalized climatic regression models

    NASA Astrophysics Data System (ADS)

    Terzer, S.; Wassenaar, L. I.; Araguás-Araguás, L. J.; Aggarwal, P. K.

    2013-06-01

    A Regionalized Climatic Water Isotope Prediction (RCWIP) approach, based on the Global Network for Isotopes in Precipitation (GNIP), was demonstrated for the purposes of predicting point- and large-scale spatiotemporal patterns of the stable isotope compositions of water (δ2H, δ18O) in precipitation around the world. Unlike earlier global domain and fixed regressor models, RCWIP pre-defined thirty-six climatic cluster domains, and tested all model combinations from an array of climatic and spatial regressor variables to obtain the best predictive approach to each cluster domain, as indicated by RMSE and variogram analysis. Fuzzy membership fractions were thereafter used as the weights to seamlessly amalgamate results of the optimized climatic zone prediction models into a single predictive mapping product, such as global or regional amount-weighted mean annual, mean monthly or growing-season δ18O/δ2H in precipitation. Comparative tests revealed the RCWIP approach outperformed classical global-fixed regression-interpolation based models more than 67% of the time, and significantly improved upon predictive accuracy and precision. All RCWIP isotope mapping products are available as gridded GeoTIFF files from the IAEA website (www.iaea.org/water) and are for use in hydrology, climatology, food authenticity, ecology, and forensics.

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction.

    PubMed

    Krasnopolsky, Vladimir M; Fox-Rabinovitz, Michael S

    2006-03-01

    A new practical application of neural network (NN) techniques to environmental numerical modeling has been developed. Namely, a new type of numerical model, a complex hybrid environmental model based on a synergetic combination of deterministic and machine learning model components, has been introduced. Conceptual and practical possibilities of developing hybrid models are discussed in this paper for applications to climate modeling and weather prediction. The approach presented here uses NN as a statistical or machine learning technique to develop highly accurate and fast emulations for time consuming model physics components (model physics parameterizations). The NN emulations of the most time consuming model physics components, short and long wave radiation parameterizations or full model radiation, presented in this paper are combined with the remaining deterministic components (like model dynamics) of the original complex environmental model--a general circulation model or global climate model (GCM)--to constitute a hybrid GCM (HGCM). The parallel GCM and HGCM simulations produce very similar results but HGCM is significantly faster. The speed-up of model calculations opens the opportunity for model improvement. Examples of developed HGCMs illustrate the feasibility and efficiency of the new approach for modeling complex multidimensional interdisciplinary systems.

  18. Calibration of the heat balance model for prediction of car climate

    NASA Astrophysics Data System (ADS)

    Pokorný, Jan; Fišer, Jan; Jícha, Miroslav

    2012-04-01

    In the paper, the authors refer to development a heat balance model to predict car climate and power heat load. Model is developed in Modelica language using Dymola as interpreter. It is a dynamical system, which describes a heat exchange between car cabin and ambient. Inside a car cabin, there is considered heat exchange between air zone, interior and air-conditioning system. It is considered 1D heat transfer with a heat accumulation and a relative movement Sun respect to the car cabin, whilst car is moving. Measurements of the real operating conditions of gave us data for model calibration. The model was calibrated for Škoda Felicia parking-summer scenarios.

  19. 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.

  20. Drought Prediction till 2100 Under RCP 8.5 Climate Change Scenarios for Korea

    NASA Astrophysics Data System (ADS)

    Byun, H. R.; Park, C. K.; Deo, R. C.

    2014-12-01

    An important step in mitigating the negative impacts of drought requires effective methodologies for predicting the future events. This study utilizes the daily Effective Drought Index (EDI) to precisely and quantitatively predict future drought occurrences in Korea over the period 2014-2100. The EDI is computed from precipitation data generated by the regional climate model (HadGEM3-RA) under the Representative Concentration Pathway (RCP 8.5) scenario. Using this data for 678 grid points (12.5 km interval) groups of cluster regions with similar climates, the G1 (Northwest), G2 (Middle), G3 (Northeast) and G4 (Southern) regions, are constructed. Drought forecasting period is categorised into the early phase (EP, 2014-2040), middle phase (MP, 2041-2070) and latter phase (LP, 2071-2100). Future drought events are quantified and ranked according to the duration and intensity. Moreover, the occurrences of drought (when, where, how severe) within the clustered regions are represented as a spatial map over Korea. Based on the grid-point averages, the most severe future drought throughout the 87-year period are expected to occur in Namwon around 2039-2041 with peak intensity (minimum EDI) -3.54 and projected duration of 580 days. The most severe drought by cluster analysis is expected to occur in the G3 region with a mean intensity of -2.85 in 2027. Within the spatial area of investigation, 6 years of drought periodicity and a slight decrease in the peak intensity is noted. Finally a spatial-temporal drought map is constructed for all clusters and time-periods under consideration.

  1. Drought prediction till 2100 under RCP 8.5 climate change scenarios for Korea

    NASA Astrophysics Data System (ADS)

    Park, Chang-Kyun; Byun, Hi-Ryong; Deo, Ravinesh; Lee, Bo-Ra

    2015-07-01

    An important step in mitigating the negative impacts of drought requires effective methodologies for predicting the future events. This study utilises the daily Effective Drought Index (EDI) to precisely and quantitatively predict future drought occurrences in Korea over the period 2014-2100. The EDI is computed from precipitation data generated by the regional climate model (HadGEM3-RA) under the Representative Concentration Pathway (RCP 8.5) scenario. Using this data for 678 grid points (12.5 km interval) groups of cluster regions with similar climates, the G1 (Northwest), G2 (Middle), G3 (Northeast) and G4 (Southern) regions, are constructed. Drought forecasting period is categorised into the early phase (EP, 2014-2040), middle phase (MP, 2041-2070) and latter phase (LP, 2071-2100). Future drought events are quantified and ranked according to the duration and intensity. Moreover, the occurrences of drought (when, where, how severe) within the clustered regions are represented as a spatial map over Korea. Based on the grid-point averages, the most severe future drought throughout the 87-year period are expected to occur in Namwon around 2039-2041 with peak intensity (minimum EDI) -3.54 and projected duration of 580 days. The most severe drought by cluster analysis is expected to occur in the G3 region with a mean intensity of -2.85 in 2027. Within the spatial area of investigation, 6.6 years of drought periodicity and a slight decrease in the peak intensity is noted. Finally a spatial-temporal drought map is constructed for all clusters and time-periods under consideration.

  2. 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.

  3. 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. PMID:27182711

  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 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

  6. 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.

  7. 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

  8. 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.

  9. From the single-scattering properties of ice crystals to climate prediction: A way forward

    NASA Astrophysics Data System (ADS)

    Baran, Anthony J.

    2012-08-01

    Cirrus is composed of non-spherical ice crystals, and against the blue background of the sky, they appear as tenuous wispy clouds, usually located at altitudes greater than about 6 km. Their spatial and temporal distribution about the Earth's atmosphere is significant. With such distributions, their contributions to the Earth's natural greenhouse effect and hydrological cycle are important. Therefore, it is important that climate models are able to predict the radiative effect of cirrus, as well as their contribution to the total amount of ice mass that occurs in the Earth's atmosphere. However, cirrus is composed of ice crystals that can take on a variety of geometrical shapes, from pristine habits such as hexagonal ice columns, hexagonal ice plates and bullet-rosettes, to highly randomized habits, which may have roughened surfaces and/or air cavities. These habits also aggregate together, to form chains of aggregates and compact aggregates. The sizes of these habits may also vary, from about less than 10 μm, to several cm, with the smaller ice crystals usually existing toward cloud-top and the larger ice crystals existing toward the cloud-bottom. Due to this variability of geometrical complexity, size, and ice mass, predicting the magnitude of the cirrus greenhouse effect has proven problematic. To try to constrain these radiative and hydrological uncertainties, since about 2006 there is now available the A-train constellation of satellites, which attempt to quantify the radiative and hydrological contributions of cirrus to the Earth's atmosphere. The A-train obtains nearly simultaneous measurements of cirrus from across the electromagnetic spectrum. Such simultaneous measurements pose challenges for theoretical scattering models of cirrus, as these models must conserve ice mass and be physically consistent across the electromagnetic spectrum. In this review paper, the microphysical properties of cirrus are summarized. The current idealized habit mixture models

  10. 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

  11. Predicting climate change effects on surface soil organic carbon of Louisiana, USA.

    PubMed

    Zhong, Biao; Xu, Yi Jun

    2014-10-01

    This study aimed to assess the degree of potential temperature and precipitation change as predicted by the HadCM3 (Hadley Centre Coupled Model, version 3) climate model for Louisiana, and to investigate the effects of potential climate change on surface soil organic carbon (SOC) across Louisiana using the Rothamsted Carbon Model (RothC) and GIS techniques at the watershed scale. Climate data sets at a grid cell of 0.5° × 0.5° for the entire state of Louisiana were collected from the HadCM3 model output for three climate change scenarios: B2, A2, and A1F1, that represent low, higher, and even higher greenhouse gas emissions, respectively. Geo-referenced datasets including USDA-NRCS Soil Geographic Database (STATSGO), USGS Land Cover Dataset (NLCD), and the Louisiana watershed boundary data were gathered for SOC calculation at the watershed scale. A soil carbon turnover model, RothC, was used to simulate monthly changes in SOC from 2001 to 2100 under the projected temperature and precipitation changes. The simulated SOC changes in 253 watersheds from three time periods, 2001-2010, 2041-2050, and 2091-2100, were tested for the influence of the land covers and emissions scenarios using SAS PROC GLIMMIX and PDMIX800 macro to separate Tukey-Kramer (p < 0.01) adjusted means into letter comparisons. The study found that for most of the next 100 years in Louisiana, monthly mean temperature under all three emissions projections will increase; and monthly precipitation will, however, decrease. Under three emission scenarios, A1FI, A2, and B2, the mean SOC in the upper 30-cm depth of Louisiana forest soils will decrease from 33.0 t/ha in 2001 to 26.9, 28.4, and 29.2 t/ha in 2100, respectively; the mean SOC of Louisiana cropland soils will decrease from 44.4 t/ha in 2001 to 36.3, 38.4, and 39.6 t/ha in 2100, respectively; the mean SOC of Louisiana grassland soils will change from 30.7 t/ha in 2001 to 25.4, 26.6, and 27.0 t/ha in 2100, respectively. Annual SOC

  12. Predicting climate change effects on surface soil organic carbon of Louisiana, USA.

    PubMed

    Zhong, Biao; Xu, Yi Jun

    2014-10-01

    This study aimed to assess the degree of potential temperature and precipitation change as predicted by the HadCM3 (Hadley Centre Coupled Model, version 3) climate model for Louisiana, and to investigate the effects of potential climate change on surface soil organic carbon (SOC) across Louisiana using the Rothamsted Carbon Model (RothC) and GIS techniques at the watershed scale. Climate data sets at a grid cell of 0.5° × 0.5° for the entire state of Louisiana were collected from the HadCM3 model output for three climate change scenarios: B2, A2, and A1F1, that represent low, higher, and even higher greenhouse gas emissions, respectively. Geo-referenced datasets including USDA-NRCS Soil Geographic Database (STATSGO), USGS Land Cover Dataset (NLCD), and the Louisiana watershed boundary data were gathered for SOC calculation at the watershed scale. A soil carbon turnover model, RothC, was used to simulate monthly changes in SOC from 2001 to 2100 under the projected temperature and precipitation changes. The simulated SOC changes in 253 watersheds from three time periods, 2001-2010, 2041-2050, and 2091-2100, were tested for the influence of the land covers and emissions scenarios using SAS PROC GLIMMIX and PDMIX800 macro to separate Tukey-Kramer (p < 0.01) adjusted means into letter comparisons. The study found that for most of the next 100 years in Louisiana, monthly mean temperature under all three emissions projections will increase; and monthly precipitation will, however, decrease. Under three emission scenarios, A1FI, A2, and B2, the mean SOC in the upper 30-cm depth of Louisiana forest soils will decrease from 33.0 t/ha in 2001 to 26.9, 28.4, and 29.2 t/ha in 2100, respectively; the mean SOC of Louisiana cropland soils will decrease from 44.4 t/ha in 2001 to 36.3, 38.4, and 39.6 t/ha in 2100, respectively; the mean SOC of Louisiana grassland soils will change from 30.7 t/ha in 2001 to 25.4, 26.6, and 27.0 t/ha in 2100, respectively. Annual SOC

  13. Climatic associations of British species distributions show good transferability in time but low predictive accuracy for range change.

    PubMed

    Rapacciuolo, Giovanni; Roy, David B; Gillings, Simon; Fox, Richard; Walker, Kevin; Purvis, Andy

    2012-01-01

    Conservation planners often wish to predict how species distributions will change in response to environmental changes. Species distribution models (SDMs) are the primary tool for making such predictions. Many methods are widely used; however, they all make simplifying assumptions, and predictions can therefore be subject to high uncertainty. With global change well underway, field records of observed range shifts are increasingly being used for testing SDM transferability. We used an unprecedented distribution dataset documenting recent range changes of British vascular plants, birds, and butterflies to test whether correlative SDMs based on climate change provide useful approximations of potential distribution shifts. We modelled past species distributions from climate using nine single techniques and a consensus approach, and projected the geographical extent of these models to a more recent time period based on climate change; we then compared model predictions with recent observed distributions in order to estimate the temporal transferability and prediction accuracy of our models. We also evaluated the relative effect of methodological and taxonomic variation on the performance of SDMs. Models showed good transferability in time when assessed using widespread metrics of accuracy. However, models had low accuracy to predict where occupancy status changed between time periods, especially for declining species. Model performance varied greatly among species within major taxa, but there was also considerable variation among modelling frameworks. Past climatic associations of British species distributions retain a high explanatory power when transferred to recent time--due to their accuracy to predict large areas retained by species--but fail to capture relevant predictors of change. We strongly emphasize the need for caution when using SDMs to predict shifts in species distributions: high explanatory power on temporally-independent records--as assessed using

  14. 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

  15. Dynamical Downscaling NCEP Global Climate Forecast System (CFS) Seasonal Predictions Using Regional Atmospheric Modeling System (RAMS)

    NASA Astrophysics Data System (ADS)

    Lu, L.; Zheng, Y.; Pielke, R. A.

    2009-12-01

    As part of the NOAA CPPA-sponsored MRED project, the state-of-the-art Regional Atmospheric Modeling System (RAMS) version 6.0 is used to dynamically and progressively downscale NCEP global Climate Forecast System (CFS, at 100s-km grid increment) seasonal predictions to a regional domain that covers the conterminous United States at 30-km grid increment. The first set of RCM prediction experiment focuses on the winter seasons, during which the precipitation is largely dependent on synoptic-scale mid-latitude storms and orographic dominant mesoscale processes. Our first suite of numerical experiment includes one ensemble member for each year from 1982 through 2008, with all the simulations starting on December 1 and ending on April 30. Driven by the same atmospheric and SST forcings, RAMS will be compared with other RCMs, and evaluated against observations and reanalysis (NARR) to see if the simulations capture the climatology and interannual variability of temperature and precipitation distributions. The overall strengths and weaknesses of the modeling systems will be identified, as well as the consistent model biases. In addition, we will analyze the changes in kinetic energy spectra before and after the spectral nudging algorithm is implemented. The results show that with the spectral nudging scheme, RAMS can better preserve large-scale kinetic energy than standard boundary forcing method, and allow more large-scale energy to cascade to smaller scales.

  16. Effects of predicted climatic changes on distribution of organic contaminants in brackish water mesocosms.

    PubMed

    Ripszam, M; Gallampois, C M J; Berglund, Å; Larsson, H; Andersson, A; Tysklind, M; Haglund, P

    2015-06-01

    Predicted consequences of future climate change in the northern Baltic Sea include increases in sea surface temperatures and terrestrial dissolved organic carbon (DOC) runoff. These changes are expected to alter environmental distribution of anthropogenic organic contaminants (OCs). To assess likely shifts in their distributions, outdoor mesocosms were employed to mimic pelagic ecosystems at two temperatures and two DOC concentrations, current: 15°C and 4 mg DOCL(-1) and, within ranges of predicted increases, 18°C and 6 mg DOCL(-1), respectively. Selected organic contaminants were added to the mesocosms to monitor changes in their distribution induced by the treatments. OC partitioning to particulate matter and sedimentation were enhanced at the higher DOC concentration, at both temperatures, while higher losses and lower partitioning of OCs to DOC were observed at the higher temperature. No combined effects of higher temperature and DOC on partitioning were observed, possibly because of the balancing nature of these processes. Therefore, changes in OCs' fates may largely depend on whether they are most sensitive to temperature or DOC concentration rises. Bromoanilines, phenanthrene, biphenyl and naphthalene were sensitive to the rise in DOC concentration, whereas organophosphates, chlorobenzenes (PCBz) and polychlorinated biphenyls (PCBs) were more sensitive to temperature. Mitotane and diflufenican were sensitive to both temperature and DOC concentration rises individually, but not in combination. PMID:25710621

  17. 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

  18. Climatic, Edaphic Factors and Cropping History Help Predict Click Beetle (Coleoptera: Elateridae) (Agriotes spp.) Abundance

    PubMed Central

    Kozina, A.; Lemic, D.; Bazok, R.; Mikac, K. M.; Mclean, C. M.; Ivezić, M.; Igrc Barčić, J.

    2015-01-01

    It is assumed that the abundance of Agriotes wireworms (Coleoptera: Elateridae) is affected by agro-ecological factors such as climatic and edaphic factors and the crop/previous crop grown at the sites investigated. The aim of this study, conducted in three different geographic counties in Croatia from 2007 to 2009, was to determine the factors that influence the abundance of adult click beetle of the species Agriotes brevis Cand., Agriotes lineatus (L.), Agriotes obscurus (L.), Agriotes sputator (L.), and Agriotes ustulatus Schall. The mean annual air temperature, total rainfall, percentage of coarse and fine sand, coarse and fine silt and clay, the soil pH, and humus were investigated as potential factors that may influence abundance. Adult click beetle emergence was monitored using sex pheromone traps (YATLORf and VARb3). Exploratory data analysis was preformed via regression tree models and regional differences in Agriotes species’ abundance were predicted based on the agro-ecological factors measured. It was found that the best overall predictor of A. brevis abundance was the previous crop grown. Conversely, the best predictor of A. lineatus abundance was the current crop being grown and the percentage of humus. The best predictor of A. obscurus abundance was soil pH in KCl. The best predictor of A. sputator abundance was rainfall. Finally, the best predictors of A. ustulatus abundance were soil pH in KCl and humus. These results may be useful in regional pest control programs or for predicting future outbreaks of these species. PMID:26175463

  19. 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.

  20. 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, D.B.

    1993-10-01

    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 C0{sub 2} climates was estimated.

  1. Including the dynamic relationship between climatic variables and leaf area index in a hydrological model to improve streamflow prediction under a changing climate

    NASA Astrophysics Data System (ADS)

    Tesemma, Z. K.; Wei, Y.; Peel, M. C.; Western, A. W.

    2015-06-01

    Anthropogenic climate change is projected to enrich the atmosphere with carbon dioxide, change vegetation dynamics and influence the availability of water at the catchment scale. This study combines a nonlinear model for estimating changes in leaf area index (LAI) due to climatic fluctuations with the variable infiltration capacity (VIC) hydrological model to improve catchment streamflow prediction under a changing climate. The combined model was applied to 13 gauged sub-catchments with different land cover types (crop, pasture and tree) in the Goulburn-Broken catchment, Australia, for the "Millennium Drought" (1997-2009) relative to the period 1983-1995, and for two future periods (2021-2050 and 2071-2100) and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5) which were compared with the baseline historical period of 1981-2010. This region was projected to be warmer and mostly drier in the future as predicted by 38 Coupled Model Intercomparison Project Phase 5 (CMIP5) runs from 15 global climate models (GCMs) and for two emission scenarios. The results showed that during the Millennium Drought there was about a 29.7-66.3 % reduction in mean annual runoff due to reduced precipitation and increased temperature. When drought-induced changes in LAI were included, smaller reductions in mean annual runoff of between 29.3 and 61.4 % were predicted. The proportional increase in runoff due to modeling LAI was 1.3-10.2 % relative to not including LAI. For projected climate change under the RCP4.5 emission scenario, ignoring the LAI response to changing climate could lead to a further reduction in mean annual runoff of between 2.3 and 27.7 % in the near-term (2021-2050) and 2.3 to 23.1 % later in the century (2071-2100) relative to modeling the dynamic response of LAI to precipitation and temperature changes. Similar results (near-term 2.5-25.9 % and end of century 2.6-24.2 %) were found for climate change under the RCP8.5 emission scenario

  2. Distinguishing the effects of model structural error and parameter uncertainty on predictions of pesticide leaching under climate change

    NASA Astrophysics Data System (ADS)

    Steffens, K.; Larsbo, M.; Moeys, J.; Jarvis, N.; Lewan, E.

    2012-04-01

    Studying climate change impacts on pesticide leaching is laced with various sources of uncertainty, which must be assessed in as detailed way as possible in order to understand the reliability of predictions of pesticide leaching under current and future climate conditions. One dilemma in this respect is the difficulty in separating the effects of model structural error from parameter uncertainty. An example of the former is that most of the commonly-used pesticide transport models only consider temperature-dependent degradation, whereas temperature also influences transport in soils through its effect on sorption and diffusion. Especially for climate impact assessments of pesticide leaching, the processes and parameters that depend on soil temperature and moisture should be carefully considered. Two functions, one describing temperature-dependent sorption and one for temperature-dependent diffusion, were therefore introduced as options into the process-oriented 1D pesticide fate and transport model MACRO5.2, which resulted in four structurally different versions of the MACRO-model. The aims of the study were to assess (i) the uncertainty related to model structure in relation to parameter uncertainty and (ii) the importance of these sources of uncertainty in long-term predictions of leaching in the perspective of climate change. A case study for leaching of the mobile herbicide Bentazone was performed in a two-step procedure. First, acceptable parameter sets were identified by evaluating model performance using the Nash-Sutcliff criteria against comprehensive data from a one-year field experiment on a clay soil in Lanna (Southern Sweden). Eight sensitive and uncertain parameters were sampled from uniform distributions in a Monte-Carlo approach, separately for each of the four model versions. In a second step, each model-version with its particular ensemble of different acceptable parameter combinations was used to predict leaching for a present (1970-1999) and a

  3. 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

  4. Global isoscapes for δ18O and δ2H in precipitation: improved prediction using regionalized climatic regression models

    NASA Astrophysics Data System (ADS)

    Terzer, S.; Wassenaar, L. I.; Araguás-Araguás, L. J.; Aggarwal, P. K.

    2013-11-01

    A regionalized cluster-based water isotope prediction (RCWIP) approach, based on the Global Network of Isotopes in Precipitation (GNIP), was demonstrated for the purposes of predicting point- and large-scale spatio-temporal patterns of the stable isotope composition (δ2H, δ18O) of precipitation around the world. Unlike earlier global domain and fixed regressor models, RCWIP predefined 36 climatic cluster domains and tested all model combinations from an array of climatic and spatial regressor variables to obtain the best predictive approach to each cluster domain, as indicated by root-mean-squared error (RMSE) and variogram analysis. Fuzzy membership fractions were thereafter used as the weights to seamlessly amalgamate results of the optimized climatic zone prediction models into a single predictive mapping product, such as global or regional amount-weighted mean annual, mean monthly, or growing-season δ18O/δ2H in precipitation. Comparative tests revealed the RCWIP approach outperformed classical global-fixed regression-interpolation-based models more than 67% of the time, and clearly improved upon predictive accuracy and precision. All RCWIP isotope mapping products are available as gridded GeoTIFF files from the IAEA website (www.iaea.org/water) and are for use in hydrology, climatology, food authenticity, ecology, and forensics.

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. Global atmospheric sulfur budget under volcanically quiescent conditions: Aerosol-chemistry-climate model predictions and validation

    NASA Astrophysics Data System (ADS)

    Sheng, Jian-Xiong; Weisenstein, Debra K.; Luo, Bei-Ping; Rozanov, Eugene; Stenke, Andrea; Anet, Julien; Bingemer, Heinz; Peter, Thomas

    2015-01-01

    The global atmospheric sulfur budget and its emission dependence have been investigated using the coupled aerosol-chemistry-climate model SOCOL-AER. The aerosol module comprises gaseous and aqueous sulfur chemistry and comprehensive microphysics. The particle distribution is resolved by 40 size bins spanning radii from 0.39 nm to 3.2 μm, including size-dependent particle composition. Aerosol radiative properties required by the climate model are calculated online from the aerosol module. The model successfully reproduces main features of stratospheric aerosols under nonvolcanic conditions, including aerosol extinctions compared to Stratospheric Aerosol and Gas Experiment II (SAGE II) and Halogen Occultation Experiment, and size distributions compared to in situ measurements. The calculated stratospheric aerosol burden is 109 Gg of sulfur, matching the SAGE II-based estimate (112 Gg). In terms of fluxes through the tropopause, the stratospheric aerosol layer is due to about 43% primary tropospheric aerosol, 28% SO2, 23% carbonyl sulfide (OCS), 4% H2S, and 2% dimethyl sulfide (DMS). Turning off emissions of the short-lived species SO2, H2S, and DMS shows that OCS alone still establishes about 56% of the original stratospheric aerosol burden. Further sensitivity simulations reveal that anticipated increases in anthropogenic SO2 emissions in China and India have a larger influence on stratospheric aerosols than the same increase in Western Europe or the U.S., due to deep convection in the western Pacific region. However, even a doubling of Chinese and Indian emissions is predicted to increase the stratospheric background aerosol burden only by 9%. In contrast, small to moderate volcanic eruptions, such as that of Nabro in 2011, may easily double the stratospheric aerosol loading.

  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. 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".

  12. 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...

  13. 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.

  14. 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

  15. 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

  16. The impact of climate change on indigenous Arabica coffee (Coffea arabica): predicting future trends and identifying priorities.

    PubMed

    Davis, Aaron P; Gole, Tadesse Woldemariam; Baena, Susana; Moat, Justin

    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 plantations

  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. 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.

  19. 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…

  20. Climate extremes in the Pacific: improving seasonal prediction of tropical cyclones and extreme ocean temperatures to improve resilience

    NASA Astrophysics Data System (ADS)

    Kuleshov, Y.; Jones, D.; Spillman, C. M.

    2012-04-01

    Climate change and climate extremes have a major impact on Australia and Pacific Island countries. Of particular concern are tropical cyclones and extreme ocean temperatures, the first being the most destructive events for terrestrial systems, while the latter has the potential to devastate ocean ecosystems through coral bleaching. As a practical response to climate change, under the Pacific-Australia Climate Change Science and Adaptation Planning program (PACCSAP), we are developing enhanced web-based information tools for providing seasonal forecasts for climatic extremes in the Western Pacific. Tropical cyclones are the most destructive weather systems that impact on coastal areas. Interannual variability in the intensity and distribution of tropical cyclones is large, and presently greater than any trends that are ascribable to climate change. In the warming environment, predicting tropical cyclone occurrence based on historical relationships, with predictors such as sea surface temperatures (SSTs) now frequently lying outside of the range of past variability meaning that it is not possible to find historical analogues for the seasonal conditions often faced by Pacific countries. Elevated SSTs are the primary trigger for mass coral bleaching events, which can lead to widespread damage and mortality on reef systems. Degraded coral reefs present many problems, including long-term loss of tourism and potential loss or degradation of fisheries. The monitoring and prediction of thermal stress events enables the support of a range of adaptive and management activities that could improve reef resilience to extreme conditions. Using the climate model POAMA (Predictive Ocean-Atmosphere Model for Australia), we aim to improve accuracy of seasonal forecasts of tropical cyclone activity and extreme SSTs for the regions of Western Pacific. Improved knowledge of extreme climatic events, with the assistance of tailored forecast tools, will help enhance the resilience and

  1. 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

  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. 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

  4. Application of a model to predict cyanobacterial growth patterns in response to climatic change at Farmoor reservoir, Oxfordshire, UK.

    PubMed

    Howard, Alan; Easthope, Mark P

    2002-01-23

    The cyanobacterial growth for the next 90 years at Farmoor Reservoir, Oxfordshire is predicted using the cyanobacterial growth model, CLAMM, with data obtained from HADCM2 climate change model. It is predicted that solar radiation at the water-body surface will decrease slightly due to increased cloud cover. Predictions of cyanobacterial growth indicate little change in total production although the main summer growing season may be extended. It is also suggested that increased wind velocities may affect the frequency of 'blooming incidents'. PMID:11846084

  5. 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.

  6. 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.

  7. Predicting Impacts of Future Climate Change on the Distribution of the Widespread Conifer Platycladus orientalis.

    PubMed

    Hu, Xian-Ge; Jin, Yuqing; Wang, Xiao-Ru; Mao, Jian-Feng; Li, Yue

    2015-01-01

    Chinese thuja (Platycladus orientalis) has a wide but fragmented distribution in China. It is an important conifer tree in reforestation and plays important roles in ecological restoration in the arid mountains of northern China. Based on high-resolution environmental data for current and future scenarios, we modeled the present and future suitable habitat for P. orientalis, evaluated the importance of environmental factors in shaping the species' distribution, and identified regions of high risk under climate change scenarios. The niche models showed that P. orientalis has suitable habitat of ca. 4.2×106 km2 across most of eastern China and identified annual temperature, monthly minimum and maximum ultraviolet-B radiation and wet-day frequency as the critical factors shaping habitat availability for P. orientalis. Under the low concentration greenhouse gas emissions scenario, the range of the species may increase as global warming intensifies; however, under the higher concentrations of emissions scenario, we predicted a slight expansion followed by contraction in distribution. Overall, the range shift to higher latitudes and elevations would become gradually more significant. The information gained from this study should be an useful reference for implementing long-term conservation and management strategies for the species. PMID:26132163

  8. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation.

    PubMed

    Huey, Raymond B; Kearney, Michael R; Krockenberger, Andrew; Holtum, Joseph A M; Jess, Mellissa; Williams, Stephen E

    2012-06-19

    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.

  9. Increasing horizontal resolution in numerical weather prediction and climate simulations: illusion or panacea?

    PubMed

    Wedi, Nils P

    2014-06-28

    The steady path of doubling the global horizontal resolution approximately every 8 years in numerical weather prediction (NWP) at the European Centre for Medium Range Weather Forecasts may be substantially altered with emerging novel computing architectures. It coincides with the need to appropriately address and determine forecast uncertainty with increasing resolution, in particular, when convective-scale motions start to be resolved. Blunt increases in the model resolution will quickly become unaffordable and may not lead to improved NWP forecasts. Consequently, there is a need to accordingly adjust proven numerical techniques. An informed decision on the modelling strategy for harnessing exascale, massively parallel computing power thus also requires a deeper understanding of the sensitivity to uncertainty--for each part of the model--and ultimately a deeper understanding of multi-scale interactions in the atmosphere and their numerical realization in ultra-high-resolution NWP and climate simulations. This paper explores opportunities for substantial increases in the forecast efficiency by judicious adjustment of the formal accuracy or relative resolution in the spectral and physical space. One path is to reduce the formal accuracy by which the spectral transforms are computed. The other pathway explores the importance of the ratio used for the horizontal resolution in gridpoint space versus wavenumbers in spectral space. This is relevant for both high-resolution simulations as well as ensemble-based uncertainty estimation.

  10. 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

  11. 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.

  12. 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. PMID:26236840

  13. Predicting Impacts of Future Climate Change on the Distribution of the Widespread Conifer Platycladus orientalis

    PubMed Central

    Hu, Xian-Ge; Jin, Yuqing; Wang, Xiao-Ru; Mao, Jian-Feng; Li, Yue

    2015-01-01

    Chinese thuja (Platycladus orientalis) has a wide but fragmented distribution in China. It is an important conifer tree in reforestation and plays important roles in ecological restoration in the arid mountains of northern China. Based on high-resolution environmental data for current and future scenarios, we modeled the present and future suitable habitat for P. orientalis, evaluated the importance of environmental factors in shaping the species´ distribution, and identified regions of high risk under climate change scenarios. The niche models showed that P. orientalis has suitable habitat of ca. 4.2×106 km2 across most of eastern China and identified annual temperature, monthly minimum and maximum ultraviolet-B radiation and wet-day frequency as the critical factors shaping habitat availability for P. orientalis. Under the low concentration greenhouse gas emissions scenario, the range of the species may increase as global warming intensifies; however, under the higher concentrations of emissions scenario, we predicted a slight expansion followed by contraction in distribution. Overall, the range shift to higher latitudes and elevations would become gradually more significant. The information gained from this study should be an useful reference for implementing long-term conservation and management strategies for the species. PMID:26132163

  14. Skills of yearly prediction of the early-season rainfall over southern China by the NCEP climate forecast system

    NASA Astrophysics Data System (ADS)

    Zhao, Siyu; Yang, Song; Deng, Yi; Li, Qiaoping

    2015-11-01

    The prominent rainfall over southern China from April to June, usually characterized by a rain belt aligned in a southwest-northeast direction, is referred to here as the early-season rainfall (ESR). The predictability of the ESR is studied by analyzing the 45-day hindcast made with the US National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) and several observational data sets. Skills of predicting the ESR and the associated atmospheric circulation and surface air temperature (SAT) patterns in each year are assessed with multiple methods. Results show that the ESR can be well predicted by the CFSv2 in advance by 20 days in some years including 2005 and 2006, while the lead time of skillful prediction is limited to around 1 week in some other years such as 2001 and 2010. More accurate predictions of the ESR seem to be related to the higher skills of CFSv2 in predicting the dominant modes of the rainfall variability. Moreover, atmospheric circulation patterns associated with the ESR and the SAT over the western Pacific warm pool in the preceding winter can be important signals for ESR prediction since in the years with good skills of ESR prediction, the CFSv2 also predicted these signals successfully. An overestimate (underestimate) of the SAT may lead to large biases in the predicted atmospheric circulation and subsequently result in an underestimate (overestimate) southern China ESR.

  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. PMID:24763409

  18. 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.

  19. 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.

  20. Using Tree-Ring Width Data From 1000 Sites to Predict how American Forests Will Respond to Climate Change

    NASA Astrophysics Data System (ADS)

    Williams, P.; Still, C. J.; Leavitt, S. W.; Fischer, D. T.

    2007-12-01

    Beginning in the early 1900s, tree-ring scientists began analyzing the relative widths of annual growth rings preserved in the cross-sections of trees. Over the years, many ring-width index chronologies, each representing a specific site and species, have been developed and analyzed to infer details regarding past climate, growth response to environmental fluctuation, fire activity, logging practices by past societies, and more. Of the many ring-width chronologies constructed, 1035 represent sites within the continental United States and have been published online within The International Tree-Ring Data Bank as of September 2007 (ITRDB, http://www.ncdc.noaa.gov/paleo/treering.html). Approximately 85% of these sites are located west of the Mississippi River. Here we present results from a three-step study, using this large reserve of tree-growth data to determine how various tree species in various regions have responded to climate fluctuations in the past and how they can be expected to respond to future change. In the first step, we used linear regression to compare each time series of ring-width index values to a suite of local monthly climate variables that may influence tree growth, such as rainfall, temperature, and drought severity (PDSI). We identified the range of months (of a 24- month period) during which each climate parameter most strongly affects growth by comparing Pearson correlation coefficients. In the second step, we identified all sites where at least one climate parameter, during some rage of months, correlates significantly (95% confidence) with ring-width index values. For each of these sites, we constructed a growth model that uses each significantly correlating climate parameter as a growth predictor. In the third step, we applied the growth model to predict the next 100 years of growth response to a monthly climate forecast created by the Hadley Centre for Climate Prediction and Research. This forecast (HadCM3 IS92a) assumes a business as

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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

  6. 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.

  7. 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.

  8. Predicting climate change impacts on the amount and duration of autumn colors in a New England forest.

    PubMed

    Archetti, Marco; Richardson, Andrew D; O'Keefe, John; Delpierre, Nicolas

    2013-01-01

    Climate change affects the phenology of many species. As temperature and precipitation are thought to control autumn color change in temperate deciduous trees, it is possible that climate change might also affect the phenology of autumn colors. Using long-term data for eight tree species in a New England hardwood forest, we show that the timing and cumulative amount of autumn color are correlated with variation in temperature and precipitation at specific times of the year. A phenological model driven by accumulated cold degree-days and photoperiod reproduces most of the interspecific and interannual variability in the timing of autumn colors. We use this process-oriented model to predict changes in the phenology of autumn colors to 2099, showing that, while responses vary among species, climate change under standard IPCC projections will lead to an overall increase in the amount of autumn colors for most species.

  9. 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.

  10. Predicting Unprecedented Dengue Outbreak Using Imported Cases and Climatic Factors in Guangzhou, 2014

    PubMed Central

    Bi, Peng; Yang, Weizhong; Yang, Zhicong; Xu, Lei; Yang, Jun; Liu, Xiaobo; Jiang, Tong; Wu, Haixia; Chu, Cordia; Liu, Qiyong

    2015-01-01

    Introduction Dengue is endemic in more than 100 countries, mainly in tropical and subtropical regions, and the incidence has increased 30-fold in the past 50 years. The situation of dengue in China has become more and more severe, with an unprecedented dengue outbreak hitting south China in 2014. Building a dengue early warning system is therefore urgent and necessary for timely and effective response. Methodology and Principal Findings In the study we developed a time series Poisson multivariate regression model using imported dengue cases, local minimum temperature and accumulative precipitation to predict the dengue occurrence in four districts of Guangzhou, China. The time series data were decomposed into seasonal, trend and remainder components using a seasonal-trend decomposition procedure based on loess (STL). The time lag of climatic factors included in the model was chosen based on Spearman correlation analysis. Autocorrelation, seasonality and long-term trend were controlled in the model. A best model was selected and validated using Generalized Cross Validation (GCV) score and residual test. The data from March 2006 to December 2012 were used to develop the model while the data from January 2013 to September 2014 were employed to validate the model. Time series Poisson model showed that imported cases in the previous month, minimum temperature in the previous month and accumulative precipitation with three month lags could project the dengue outbreaks occurred in 2013 and 2014 after controlling the autocorrelation, seasonality and long-term trend. Conclusions Together with the sole transmission vector Aedes albopictus, imported cases, monthly minimum temperature and monthly accumulative precipitation may be used to develop a low-cost effective early warning system. PMID:26020627

  11. Geospatial Predictive Modelling for Climate Mapping of Selected Severe Weather Phenomena Over Poland: A Methodological Approach

    NASA Astrophysics Data System (ADS)

    Walawender, Ewelina; Walawender, Jakub P.; Ustrnul, Zbigniew

    2016-02-01

    The main purpose of the study is to introduce methods for mapping the spatial distribution of the occurrence of selected atmospheric phenomena (thunderstorms, fog, glaze and rime) over Poland from 1966 to 2010 (45 years). Limited in situ observations as well the discontinuous and location-dependent nature of these phenomena make traditional interpolation inappropriate. Spatially continuous maps were created with the use of geospatial predictive modelling techniques. For each given phenomenon, an algorithm identifying its favourable meteorological and environmental conditions was created on the basis of observations recorded at 61 weather stations in Poland. Annual frequency maps presenting the probability of a day with a thunderstorm, fog, glaze or rime were created with the use of a modelled, gridded dataset by implementing predefined algorithms. Relevant explanatory variables were derived from NCEP/NCAR reanalysis and downscaled with the use of a Regional Climate Model. The resulting maps of favourable meteorological conditions were found to be valuable and representative on the country scale but at different correlation (r) strength against in situ data (from r = 0.84 for thunderstorms to r = 0.15 for fog). A weak correlation between gridded estimates of fog occurrence and observations data indicated the very local nature of this phenomenon. For this reason, additional environmental predictors of fog occurrence were also examined. Topographic parameters derived from the SRTM elevation model and reclassified CORINE Land Cover data were used as the external, explanatory variables for the multiple linear regression kriging used to obtain the final map. The regression model explained 89 % of annual frequency of fog variability in the study area. Regression residuals were interpolated via simple kriging.

  12. Virus disease in wheat predicted to increase with a changing climate.

    PubMed

    Trębicki, Piotr; Nancarrow, Narelle; Cole, Ellen; Bosque-Pérez, Nilsa A; Constable, Fiona E; Freeman, Angela J; Rodoni, Brendan; Yen, Alan L; Luck, Jo E; Fitzgerald, Glenn J

    2015-09-01

    Current atmospheric CO2 levels are about 400 μmol mol(-1) and are predicted to rise to 650 μmol mol(-1) later this century. Although the positive and negative impacts of CO2 on plants are well documented, little is known about interactions with pests and diseases. If disease severity increases under future environmental conditions, then it becomes imperative to understand the impacts of pathogens on crop production in order to minimize crop losses and maximize food production. Barley yellow dwarf virus (BYDV) adversely affects the yield and quality of economically important crops including wheat, barley and oats. It is transmitted by numerous aphid species and causes a serious disease of cereal crops worldwide. This study examined the effects of ambient (aCO2 ; 400 μmol mol(-1) ) and elevated CO2 (eCO2 ; 650 μmol mol(-1) ) on noninfected and BYDV-infected wheat. Using a RT-qPCR technique, we measured virus titre from aCO2 and eCO2 treatments. BYDV titre increased significantly by 36.8% in leaves of wheat grown under eCO2 conditions compared to aCO2 . Plant growth parameters including height, tiller number, leaf area and biomass were generally higher in plants exposed to higher CO2 levels but increased growth did not explain the increase in BYDV titre in these plants. High virus titre in plants has been shown to have a significant negative effect on plant yield and causes earlier and more pronounced symptom expression increasing the probability of virus spread by insects. The combination of these factors could negatively impact food production in Australia and worldwide under future climate conditions. This is the first quantitative evidence that BYDV titre increases in plants grown under elevated CO2 levels.

  13. Virus disease in wheat predicted to increase with a changing climate.

    PubMed

    Trębicki, Piotr; Nancarrow, Narelle; Cole, Ellen; Bosque-Pérez, Nilsa A; Constable, Fiona E; Freeman, Angela J; Rodoni, Brendan; Yen, Alan L; Luck, Jo E; Fitzgerald, Glenn J

    2015-09-01

    Current atmospheric CO2 levels are about 400 μmol mol(-1) and are predicted to rise to 650 μmol mol(-1) later this century. Although the positive and negative impacts of CO2 on plants are well documented, little is known about interactions with pests and diseases. If disease severity increases under future environmental conditions, then it becomes imperative to understand the impacts of pathogens on crop production in order to minimize crop losses and maximize food production. Barley yellow dwarf virus (BYDV) adversely affects the yield and quality of economically important crops including wheat, barley and oats. It is transmitted by numerous aphid species and causes a serious disease of cereal crops worldwide. This study examined the effects of ambient (aCO2 ; 400 μmol mol(-1) ) and elevated CO2 (eCO2 ; 650 μmol mol(-1) ) on noninfected and BYDV-infected wheat. Using a RT-qPCR technique, we measured virus titre from aCO2 and eCO2 treatments. BYDV titre increased significantly by 36.8% in leaves of wheat grown under eCO2 conditions compared to aCO2 . Plant growth parameters including height, tiller number, leaf area and biomass were generally higher in plants exposed to higher CO2 levels but increased growth did not explain the increase in BYDV titre in these plants. High virus titre in plants has been shown to have a significant negative effect on plant yield and causes earlier and more pronounced symptom expression increasing the probability of virus spread by insects. The combination of these factors could negatively impact food production in Australia and worldwide under future climate conditions. This is the first quantitative evidence that BYDV titre increases in plants grown under elevated CO2 levels. PMID:25846559

  14. Spatial analysis of plague in California: niche modeling predictions of the current distribution and potential response to climate change

    PubMed Central

    Holt, Ashley C; Salkeld, Daniel J; Fritz, Curtis L; Tucker, James R; Gong, Peng

    2009-01-01

    Background Plague, caused by the bacterium Yersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plague foci. Maxent models were constructed using geocoded seroprevalence data from surveillance of California ground squirrels (Spermophilus beecheyi) as case points and Worldclim bioclimatic data as predictor variables, and compared and validated using area under the receiver operating curve (AUC) statistics. Additionally, model results were compared to locations of positive and negative coyote (Canis latrans) samples, in order to determine the correlation between Maxent model predictions and areas of plague risk as determined via wild carnivore surveillance. Results Models of plague activity in California ground squirrels, based on recent climate conditions, accurately identified case locations (AUC of 0.913 to 0.948) and were significantly correlated with coyote samples. The final models were used to identify potential plague risk areas based on an ensemble of six future climate scenarios. These models suggest that by 2050, climate conditions may reduce plague risk in the southern parts of California and increase risk along the northern coast and Sierras. Conclusion Because different modeling approaches can yield substantially different results, care should be taken when interpreting future model predictions. Nonetheless, niche modeling can be a useful tool for exploring and mapping the potential response of plague activity to climate change. The final models in this study were used to identify potential plague risk areas based on an ensemble of six future climate scenarios, which can help public managers decide where to allocate surveillance resources. In addition, Maxent

  15. Uncertainty partition challenges the predictability of vital details of climate change

    NASA Astrophysics Data System (ADS)

    Fatichi, Simone; Ivanov, Valeriy Y.; Paschalis, Athanasios; Peleg, Nadav; Molnar, Peter; Rimkus, Stefan; Kim, Jongho; Burlando, Paolo; Caporali, Enrica

    2016-05-01

    Decision makers and consultants are particularly interested in "detailed" information on future climate to prepare adaptation strategies and adjust design criteria. Projections of future climate at local spatial scales and fine temporal resolutions are subject to the same uncertainties as those at the global scale but the partition among uncertainty sources (emission scenarios, climate models, and internal climate variability) remains largely unquantified. At the local scale, the uncertainty of the mean and extremes of precipitation is shown to be irreducible for mid and end-of-century projections because it is almost entirely caused by internal climate variability (stochasticity). Conversely, projected changes in mean air temperature and other meteorological variables can be largely constrained, even at local scales, if more accurate emission scenarios can be developed. The results were obtained by applying a comprehensive stochastic downscaling technique to climate model outputs for three exemplary locations. In contrast with earlier studies, the three sources of uncertainty are considered as dependent and, therefore, non-additive. The evidence of the predominant role of internal climate variability leaves little room for uncertainty reduction in precipitation projections; however, the inference is not necessarily negative, because the uncertainty of historic observations is almost as large as that for future projections with direct implications for climate change adaptation measures.

  16. Residence Hall Climate: Predicting First-Year Students' Adjustments to College

    ERIC Educational Resources Information Center

    Kaya, Naz

    2004-01-01

    This study investigated the relationship between residence hall climate and students' adjustment to their collegiate environment. The questionnaire was administered via the web to first-year students living in coeducational residence halls at a public university in the southeastern United States. The residence hall climate was examined in two…

  17. Predictions for snow cover, glaciers and runoff in a changing climate

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The problem of evaluating the hydrological effects of climate change has opened a new field of applications for snowmelt runoff models. The Snowmelt Runoff Model (SRM) has been used to evaluate climate change effects on basins in North America, the Swiss Alps, and the Himalayas. Snow covered area ...