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. Solar weather/climate predictions

    NASA Technical Reports Server (NTRS)

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

    1979-01-01

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

  4. 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.

  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. 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

  8. 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

  9. Seasonal and decadal predictions toward climate services.

    NASA Astrophysics Data System (ADS)

    Buontempo, C.; Hewitt, C.; Maidens, A.

    2012-04-01

    While societies have flourished or collapsed depending on their ability to adapt to changes in climate, it is only recently that science and technology have been able to provide useful insights into future climate. Seasonal to decadal (S2D) forecasts hold the potential to be of great value to a wide range of decision-making, where outcomes are heavily influenced by climate variability. Recent advances in our understanding and ability to forecast climate variability and climate change have brought us to the point where skilful predictions are beginning to be routinely made. Access to credible forecast data, supported by informed guidance from the science community, could lead to significant advances in society's ability to effectively prepare for and manage climate-related risks. This new ability will effectively represent the core of climate service offers in the coming years. A number of initiatives such as Global Framework for Climate Service or the International Conference on Climate Service have recently been launched to coordinate climate services activities internationally. The European Union acknowledged this new development in the research agenda and last summer opened a call on seasonal and decadal predictions toward climate services. While it is not yet know which projects will be funded they will all have to improve the underpinning capability of the models and at the same time develop an effective mechanisms to make the prediction relevant and usable by decision makers. We will discuss the GloSea4 seasonal forecasting system, giving a brief description of the system and some of the products we supply to end-users as part of our climate services and our seamless approach to forecasting across varying timescales. The basic approach of a recently submitted proposal to the EC to exploit these emerging prediction capabilities and, more importantly, to engage with potential users of such predictions will also be presented.

  10. 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…

  11. Climate Modeling and Prediction at NSIPP

    NASA Technical Reports Server (NTRS)

    Suarez, Max; Einaudi, Franco (Technical Monitor)

    2001-01-01

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

  12. 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

  13. Administrative Satisfaction and the Regulatory Climate at Public Universities.

    ERIC Educational Resources Information Center

    Volkwein, James Fredericks; Malik, Shaukat M.; Napierski-Prancl, Michelle

    1998-01-01

    A study measured the financial, personnel, and academic dimensions of state regulation at 122 public universities, and examined how university and state characteristics affect regulatory climate and administrative flexibility. It also analyzed the dimensions of administrator satisfaction in 12 specific administrative positions in relation to…

  14. Cognitive Illusions, Heuristics, and Climate Prediction.

    NASA Astrophysics Data System (ADS)

    Nicholls, Neville

    1999-07-01

    A number of studies in meteorological journals have documented some of the constraints to the effective use of climate forecasts. One major constraint, the considerable difficulty people have in estimating and dealing with probabilities, risk, and uncertainty, has received relatively little attention in the climate field. Some of this difficulty arises from problems known as cognitive illusions or biases. These illusions, and ways to avoid them impacting on decision making, have been studied in the fields of law, medicine, and business. The relevance of some of these illusions to climate prediction is discussed here. The optimal use of climate predictions requires providers of forecasts to understand these difficulties and to make adjustments for them in the way forecasts are prepared and disseminated.

  15. 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.

  16. Predictability of Pacific Decadal Climate Variability and Climate Impacts (Invited)

    NASA Astrophysics Data System (ADS)

    Newman, M.

    2013-12-01

    Predictability of Pacific sea surface temperature (SST) climate variations and climate impacts on time scales of 1-10 years is discussed, using a global linear inverse model (LIM) as an empirical benchmark for decadal surface temperature forecast skill. Constructed from the observed simultaneous and 1-yr lag covariability statistics of annually averaged sea surface temperature (SST) and surface (2 m) land temperature global anomalies during 1901-2009, the LIM has hindcast skill for leads of 2-5 yr and 6-9 yr comparable to and sometimes even better than skill of the phase 5 of the Coupled Model Intercomparison Project (CMIP5) model hindcasts initialized annually over the period 1960-2000 and has skill far better than damped persistence (e.g., a local univariate AR1 process). Pronounced similarity in geographical variations of skill between LIM and CMIP5 hindcasts suggests similarity in their sources of skill as well, supporting additional evaluation of LIM predictability. For forecast leads above 1-2 yr, LIM skill almost entirely results from three nonorthogonal patterns: one corresponding to the secular trend and two more, each with about 10-yr decorrelation time scales but no trend, that represent most of the predictable portions of the Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) indices, respectively. In contrast, for forecasts greater than about two years, ENSO acts as noise and degrades forecast skill. These results suggest that current coupled model decadal forecasts may not yet have much skill beyond that captured by multivariate, predictably linear dynamics. A particular focus will be on the predictability of the PDO, which represents the dominant mode of Pacific decadal SST variability. The PDO is shown to represent a few different physical processes, including wind-driven changes of SSTs that can occur either due to daily weather variability or to tropical forcing, and variations in the North Pacific western boundary

  17. Developing a Healthy Climate for Educational Change: An Administrative Approach.

    ERIC Educational Resources Information Center

    Walker, Paul D.

    1981-01-01

    Finds three areas of faculty/administrator interaction to have the greatest influence on organizational climate: goal setting and internal governance, application of resources, and organizational and personal development. Suggests strategies under each area for promoting a positive climate. Reports briefly on a panel's assessment of the analysis…

  18. An Assessment of a College of Business Administration's Ethical Climate

    ERIC Educational Resources Information Center

    Schulte, Laura; Carter, Amanda

    2004-01-01

    This study investigated graduate faculty and student perceptions of the ethical climate of a College of Business Administration within a Midwestern metropolitan university and the perceived importance of the ethical climate in the retention of students within graduate academic programs. Eighteen faculty and 90 graduate students completed the…

  19. The predictability problems in numerical weather and climate prediction

    NASA Astrophysics Data System (ADS)

    Mu, Mu; Wansuo, Duan; Jiacheng, Wang

    2002-03-01

    The uncertainties caused by the errors of the initial states and the parameters in the numerical model are investigated. Three problems of predictability in numerical weather and climate prediction are proposed, which are related to the maximum predictable time, the maximum prediction error, and the maximum admissible errors of the initial values and the parameters in the model respectively. The three problems are then formulated into nonlinear optimization problems. Effective approaches to deal with these nonlinear optimization problems are provided. The Lorenz’ model is employed to demonstrate how to use these ideas in dealing with these three problems.

  20. 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

  1. 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

  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. Administrative Climate and Novices' Intent to Remain Teaching

    ERIC Educational Resources Information Center

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

    2012-01-01

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

  4. On Prediction and Predictability of the Arctic Climate System

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  5. Permafrost, climate, and change: predictive modelling approach.

    NASA Astrophysics Data System (ADS)

    Anisimov, O.

    2003-04-01

    Predicted by GCMs enhanced warming of the Arctic will lead to discernible impacts on permafrost and northern environment. Mathematical models of different complexity forced by scenarios of climate change may be used to predict such changes. Permafrost models that are currently in use may be divided into four groups: index-based models (e.g. frost index model, N-factor model); models of intermediate complexity based on equilibrium simplified solution of the Stephan problem ("Koudriavtcev's" model and its modifications), and full-scale comprehensive dynamical models. New approach of stochastic modelling came into existence recently and has good prospects for the future. Important task is to compare the ability of the models that are different in complexity, concept, and input data requirements to capture the major impacts of changing climate on permafrost. A progressive increase in the depth of seasonal thawing (often referred to as the active-layer thickness, ALT) could be a relatively short-term reaction to climatic warming. At regional and local scales, it may produce substantial effects on vegetation, soil hydrology and runoff, as the water storage capacity of near-surface permafrost will be changed. Growing public concerns are associated with the impacts that warming of permafrost may have on engineered infrastructure built upon it. At the global scale, increase of ALT could facilitate further climatic change if more greenhouse gases are released when the upper layer of the permafrost thaws. Since dynamic permafrost models require complete set of forcing data that is not readily available on the circumpolar scale, they could be used most effectively in regional studies, while models of intermediate complexity are currently best tools for the circumpolar assessments. Set of five transient scenarios of climate change for the period 1980 - 2100 has been constructed using outputs from GFDL, NCAR, CCC, HadCM, and ECHAM-4 models. These GCMs were selected in the course

  6. 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

  7. 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., Jr.; 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.

  8. 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

  9. Using the CMIP ensemble for climate prediction

    NASA Astrophysics Data System (ADS)

    Annan, James; Hargreaves, Julia

    2015-04-01

    The collection of GCMs which contribute to CMIP are often described as an ensemble of opportunity, with no specific overall design or sampling strategy. Thus, it is challenging to generate probabilistic predictions from these simulations. A particular issue that has raised much discussion is regarding the independence (or otherwise) of evidence arising both from observational analyses, and different model simulations. Climate models broadly agree on such features as overall CO2-forced global warming, with amplification of this warming at high latitudes and over land, and an intensified hydrological cycle. Does this large (and growing) ensemble of consistent models justify increased confidence in their results, or are they all merely replicating the same errors? And how should we combine observational evidence arising from the observed period of warming, together with paleoclimate analyses and model simulations? We will show a way forward based on rigorous mathematical definitions and understanding which has been generally lacking in the literature to date.

  10. Predicting climate effects on Pacific sardine

    PubMed Central

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

    2013-01-01

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

  11. 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

  12. 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...

  13. 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...

  14. 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.

  15. Can Abrupt Seasonal Transitions be Predicted in Climate Forecasts?

    NASA Astrophysics Data System (ADS)

    Kirtman, B. P.

    2014-12-01

    There is on ongoing debate in the seasonal prediction community as to whether high frequency weather statistics in climate forecasts have any inherent predictability, and ultimately prediction skill. The North American Multi-Model Ensemble (NMME) seasonal-to-interannual prediction experiment is the ideal test-bed to evaluate the predictability and prediction of weather within climate. NMME is multi-institutional multi-agency system to improve operational monthly and seasonal forecasts based on the prediction systems developed at the major US climate modeling centers (NOAA/EMC, NOAA/GFDL, NCAR, NASA) and Canada. Although currently in an experimental stage, the NMME prediction system has been providing routine real-time monthly and seasonal forecasts since August 2011 that adhere to the CPC operational schedule. In addition to the monthly data, daily output from some of the retrospective forecasts are now being archived. Based on the NMME daily output this talk evaluates the predictability and prediction of two aspects of weather within climate: (i) monsoon onset in India and in South West North America and (ii) onset of spring severe weather in the mid-west US. The analysis estimates predictability by examining how well the individual models "predict" themselves and how well they "predict" other models. Prediction quality is assessed based on comparisons with observational estimates.

  16. 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.

  17. Ensemble-based Regional Climate Prediction: Political Impacts

    NASA Astrophysics Data System (ADS)

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

    2008-12-01

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

  18. Towards predictive understanding of regional climate change

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

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

  19. National Climate Predictions and Projections (NCPP)

    NASA Astrophysics Data System (ADS)

    Anderson, D. E.; DeLuca, C.

    2011-12-01

    The NCPP Supports state-of-the-art approaches to develop and deliver comprehensive regional climate information and facilitate its use in decision making and adaptation planning. NCPP is a community enterprise where climate information users, infrastructure developers, and scientists come together in a collaborative problem solving environment. We will describe the evolving infrastructure and tools under development through open source, open access collaboration across US government agencies, universities and international interactions.

  20. 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.

  1. 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

  2. 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.

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

    USGS Publications Warehouse

    Zimmermann, N.E.; Yoccoz, N.G.; Edwards, T.C., Jr.; 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.

  4. Using Highly Detailed Administrative Data to Predict Pneumonia Mortality

    PubMed Central

    Rothberg, Michael B.; Pekow, Penelope S.; Priya, Aruna; Zilberberg, Marya D.; Belforti, Raquel; Skiest, Daniel; Lagu, Tara; Higgins, Thomas L.; Lindenauer, Peter K.

    2014-01-01

    Background Mortality prediction models generally require clinical data or are derived from information coded at discharge, limiting adjustment for presenting severity of illness in observational studies using administrative data. Objectives To develop and validate a mortality prediction model using administrative data available in the first 2 hospital days. Research Design After dividing the dataset into derivation and validation sets, we created a hierarchical generalized linear mortality model that included patient demographics, comorbidities, medications, therapies, and diagnostic tests administered in the first 2 hospital days. We then applied the model to the validation set. Subjects Patients aged ≥18 years admitted with pneumonia between July 2007 and June 2010 to 347 hospitals in Premier, Inc.’s Perspective database. Measures In hospital mortality. Results The derivation cohort included 200,870 patients and the validation cohort had 50,037. Mortality was 7.2%. In the multivariable model, 3 demographic factors, 25 comorbidities, 41 medications, 7 diagnostic tests, and 9 treatments were associated with mortality. Factors that were most strongly associated with mortality included receipt of vasopressors, non-invasive ventilation, and bicarbonate. The model had a c-statistic of 0.85 in both cohorts. In the validation cohort, deciles of predicted risk ranged from 0.3% to 34.3% with observed risk over the same deciles from 0.1% to 33.7%. Conclusions A mortality model based on detailed administrative data available in the first 2 hospital days had good discrimination and calibration. The model compares favorably to clinically based prediction models and may be useful in observational studies when clinical data are not available. PMID:24498090

  5. 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

  6. 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...

  7. 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.

  8. The origins of computer weather prediction and climate modeling

    NASA Astrophysics Data System (ADS)

    Lynch, Peter

    2008-03-01

    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.

  9. 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.

  10. 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...

  11. Beyond predictions: biodiversity conservation in a changing climate.

    PubMed

    Dawson, Terence P; Jackson, Stephen T; House, Joanna I; Prentice, Iain Colin; Mace, Georgina M

    2011-04-01

    Climate change is predicted to become a major threat to biodiversity in the 21st century, but accurate predictions and effective solutions have proved difficult to formulate. Alarming predictions have come from a rather narrow methodological base, but a new, integrated science of climate-change biodiversity assessment is emerging, based on multiple sources and approaches. Drawing on evidence from paleoecological observations, recent phenological and microevolutionary responses, experiments, and computational models, we review the insights that different approaches bring to anticipating and managing the biodiversity consequences of climate change, including the extent of species' natural resilience. We introduce a framework that uses information from different sources to identify vulnerability and to support the design of conservation responses. Although much of the information reviewed is on species, our framework and conclusions are also applicable to ecosystems, habitats, ecological communities, and genetic diversity, whether terrestrial, marine, or fresh water. PMID:21454781

  12. Using climate data to predict grizzly bear litter size

    USGS Publications Warehouse

    Picton, Harold D.; Knight, Richard R.

    1986-01-01

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

  13. 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.

  14. 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.

  15. 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.

  16. Rain attenuation prediction during rain events in different climatic regions

    NASA Astrophysics Data System (ADS)

    Das, Dalia; Maitra, Animesh

    2015-06-01

    A rain attenuation prediction method has been applied to different climatic regions to test the validity of the model. The significant difference in rain rate and attenuation statistics for the tropical and temperate region needs to be considered in developing channel model to predict time series of rain attenuation for earth space communication links. Model parameters obtained for a tropical location has been successfully applied to predict time series of rain attenuation at other tropical locations. Separate model parameters are derived from the experimental data obtained at a temperate location and these are used to predict rain attenuation during rain events for other temperate locations showing the effectiveness of the technique.

  17. The Impact of Land Initialization and Assimilation on Climate Predictability and Prediction

    NASA Technical Reports Server (NTRS)

    Schlosser, C. Adam; Milly, P. C. D.; Dirmeyer, Paul A.; Mocko, David

    2002-01-01

    Analysis will be presented which explores the impact of land conditions on monthly to seasonal climate simulations in a variety of atmospheric general circulation models (AGCMs). In one set of experiments, the Geophysical Fluid Dynamics Laboratory (GDFL) AGCM is used to explore the nature of soil-moisture predictability and associated climate predictability as an initial value problem. For another set of experiments, the Center for Ocean Land Atmosphere (COLA) and the Goddard Earth Observing System 2 (GEOS-2) AGCMs are used to investigate the impact of realistic snow initialization and assimilation in retrospective climate forecasts for the northern hemisphere spring (March-June).

  18. Climate variability and predictability in Northwest Africa

    NASA Astrophysics Data System (ADS)

    Baddour, O.; Djellouli, Y.

    2003-04-01

    Northwest Africa defined here as the area including Morocco, Algeria and Tunisia, occupies a large territory in North Africa with an area exceeding 3.5 million km2. The geographical contrast is very important: while most of the southern part is desert, the northern and northwestern parts exhibit a contrasting geography including large flat areas in the western part of Morocco, northern Algeria and eastern part of Tunisia and the formidable Atlas mountains barrier extends from south west of Morocco toward north west of Tunisia crossing central Morocco and north Algeria. Agriculture is one of major socio-economic activities in the region with an extensive cash-crop for exporting to Europe especially from Morocco and Tunisia. The influence of the recurring droughts during the 80s and 90s was very crucial for the economic and societal aspects of the region. In Morocco, severe droughts have caused GDP fluctuation within past 20 years from 10% increase down to negative values in some particular years. Recent studies have investigated seasonal rainfall variability and prediction over MOROCCO in the framework of regional and international collaboration. Results from this work has shown that the main general circulation feature associated with the rainfall variability within Morocco is the North Atlantic Oscillation. The relationship is in fact due to the major role played by the AZORES high pressure with its role in modulating the main position of the active synoptic systems in the north Atlantic area and therefore in modulating the frequency and the intensity of the weather systems that impact the western part of the region. Mediterranean sea plays also major role in the mid of the region. In this paper we applied EOF technique on 500 hPa. The data used are monthly reanalysis NCEP/NCAR analyses for November from 1960 to 1990 climatological time series. Correlation analysis is then performed between EOF time series and global 4x4 degre SST anomalies. The results we

  19. Using climate model ensemble forecasts for seasonal hydrologic prediction

    NASA Astrophysics Data System (ADS)

    Wood, Andrew Whitaker

    Seasonal hydrologic forecasting has long played an invaluable role in the development and use of water resources. Despite notable advances in the science and practice of climate prediction, current approaches of hydrologists and water managers largely fail to incorporate seasonal climate forecast information that has become operationally available during the last decade. This study is motivated by the view that a combination of hydrologic and climate prediction methods affords a new opportunity to improve hydrologic forecast skill. A relatively direct statistical approach for achieving this combination (i.e., downscaling) was formulated that used ensemble climate model forecasts with a six month lead time produced by the NCEP/CPC Global Spectral Model (GSM) as input to the macroscale Variable Infiltration Capacity hydrologic model to produce ensemble runoff and streamflow forecasts. The approach involved the bias correction of climate model precipitation and temperature fields, and spatial and temporal disaggregation from monthly climate model scale (about 2 degrees latitude by longitude) fields to daily hydrology model scale (1/8 degrees) inputs. A qualitative evaluation of the approach in the eastern U.S. suggested that it was successful in translating climate forecast signals to local hydrologic variables and streamflow, but that the dominant influence on forecast results tended to be persistence in initial hydrologic conditions. The suitability of the statistical downscaling approach for supporting hydrologic simulation was then assessed (using a continuous retrospective 20-year climate simulation from the DOE Parallel Climate Model) relative to dynamical downscaling via a regional, meso-scale climate model. The statistical approach generally outperformed the dynamical approach, in that the dynamical approach alone required additional bias-correction to reproduce the retrospective hydrology as well as the statistical approach. Finally, using 21 years of

  20. The Impact of Ocean Observations in Seasonal Climate Prediction

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  1. 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. PMID:25268907

  2. 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

  3. Climate fails to predict wood decomposition at regional scales

    NASA Astrophysics Data System (ADS)

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

    2014-07-01

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

  4. PREDICTING INSECT DISTRIBUTIONS FROM CLIMATE AND HABITAT DATA

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Knowing the effects of climate and habitat on pest and natural enemy distribution would help target the search for natural enemies, increase establishment of intentional introductions, and improve risk assessment for accidental introductions. Existing methods used to predict insect distributions eit...

  5. Predictability of near-surface climate extreme events

    NASA Astrophysics Data System (ADS)

    Becker, E. J.; van den Dool, H. M.; Pena, M.

    2010-12-01

    This study seeks an answer to this question: How well can we currently predict climate extremes? We have examined the predictability of near-surface climate extremes in the region of the Americas, in the form of monthly anomalies in 2-meter surface temperature, precipitation rate, and sea surface temperature, using retrospective forecasts from two “state of the art” coupled ocean-land-atmosphere models. The two models, the National Center for Atmospheric Research (NCAR) Community Climate System Model, version 3.5 (CCSM3.5) and the operational NOAA Climate Forecast System (CFS), were studied individually and as a multi-model ensemble. The 17-year span between 1982 and 1998 was available for both models, with January and July initial conditions. The climatologies and PDFs of the model forecasts were compared to those of corresponding observations as an initial assessment of the models. To approximate removing the model biases, we removed the model climatology from the forecast and replaced it with the observed climatology. For the purposes of this study, we have defined a “climate extreme” as a departure from the monthly mean above/below a specified multiple of the local standard deviation of the variable. Several measures of forecast skill were employed, including the ranked probability score, root-mean-square error, and anomaly correlation. Some positive skill scores for forecast climate extremes at leads of 1 - 4 months were found for all three variables studied, with the best scores for sea-surface temperature, followed by 2-meter surface temperature and precipitation rate. Prediction skill is higher over South America than North America. Skill, by any measure, increases when the sample is restricted to extreme events. Anomaly correlations are generally higher when an extreme event occurred in the observed record (i.e., an extreme occurred, was it predicted?) than when verification of a predicted extreme was assessed (i.e. the model predicted an extreme

  6. Initializing decadal climate predictions over the North Atlantic region

    NASA Astrophysics Data System (ADS)

    Matei, Daniela Mihaela; Pohlmann, Holger; Jungclaus, Johann; Müller, Wolfgang; Haak, Helmuth; Marotzke, Jochem

    2010-05-01

    Decadal climate prediction aims to predict the internally-generated decadal climate variability in addition to externally-forced climate change signal. In order to achieve this it is necessary to start the predictions from the current climate state. In this study we investigate the forecast skill of the North Atlantic decadal climate predictions using two different ocean initialization strategies. First we apply an assimilation of ocean synthesis data provided by the GECCO project (Köhl and Stammer, 2008) as initial conditions for the coupled model ECHAM5/MPI-OM. Hindcast experiments are then performed over the period 1952-2001. An alternative approach is one in which the subsurface ocean temperature and salinity are diagnosed from an ensemble of ocean model runs forced by the NCEP-NCAR atmospheric reanalyzes for the period 1948-2007, then nudge into the coupled model to produce initial conditions for the hindcast experiments. An anomaly coupling scheme is used in both approaches to avoid the hindcast drift and the associated initial shock. Differences between the two assimilation approaches are discussed by comparing them with the observational data in key regions and processes. We asses the skill of the initialized decadal hindcast experiments against the prediction skill of the non-initialized hindcasts simulation. We obtain an overview of the regions with the highest predictability from the regional distribution of the anomaly correlation coefficients and RMSE for the SAT. For the first year the hindcast skill is increased over almost all ocean regions in the NCEP-forced approach. This increase in the hindcast skill for the 1 year lead time is somewhat reduced in the GECCO approach. At lead time 5yr and 10yr, the skill enhancement is still found over the North Atlantic and North Pacific regions. We also consider the potential predictability of the Atlantic Meridional Overturning Circulation (AMOC) and Nordic Seas Overflow by comparing the predicted values to

  7. Robust model predictive control for optimal continuous drug administration.

    PubMed

    Sopasakis, Pantelis; Patrinos, Panagiotis; Sarimveis, Haralambos

    2014-10-01

    In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise. A case study on a PBPK model with 7 compartments, constraints on 5 tissues and a variable drug concentration set-point illustrates the efficiency of the methodology in drug dosing control applications. The proposed methodology is also tested in an uncertain setting and proves successful in presence of modelling errors and inaccurate measurements. PMID:24986530

  8. 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.

  9. Scalar Prediction in Climate Forecasting Using Satellite Data

    NASA Astrophysics Data System (ADS)

    Leroy, S.; Dykema, J.; Anderson, J.

    2007-12-01

    Scalar detection in climate change research, having taken the form of optimal detection/linear multi-pattern regression, has been used in the recent past to detect multiple climate signals in the presence of natural inter- annual variability and associate those signals with specific causes. It has been applied to many climate observables to show high probabilities of human influence on climatic trends. One of the sources of uncertainty and instability in this methodology concerns the degree to which one can trust the fine details of a signal's shape in using it as a fingerprint associated with forced climate change. In a recent paper by Huntingford et al.~(2006), this problem has been largely solved using multi-model ensemble simulations of signal shapes to ascertain the degree to which details of signal shapes can be trusted. We show that this method, when generalized in the context of Bayesian inference, is a powerful tool----one that carefully incorporates the scientific method----for predicting arbitrary scalar trends in the climate system that optimally considers both observed trends and ensemble model prediction of those trends. In this method, arbitrary but informative data sets with credible trends can be used in conjunction with a large ensemble of disparate climate models to forecast anything from regional trends in temperature, humidity, cloud-cover, and precipitation to global scale trends in surface air temperature or widening of the Hadley circulation. The method weights data by inter-annual variability and connects arbitrary data sets to scalar quantities of interest according to the certainty of the physics that relates the data type to the quantities. Depending on the data set and geophysical variable of interest, forecast accuracy for that variable can be improved by large factors over simple trending of past measurements of that variable. We will present a Bayesian derivation of this methodology and give several illustrative examples for its

  10. 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.

  11. 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…

  12. 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.

  13. 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

  14. 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.

  15. 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…

  16. 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...

  17. 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

  18. 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.

  19. 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

  20. Predicting Weather Conditions and Climate for Mars Expeditions

    NASA Astrophysics Data System (ADS)

    Read, P. L.; Lewis, S. R.; Bingham, S. J.; Newman, C. E.

    Weather and climatic conditions are among the most important factors to be taken into account when planning expeditions to remote and challenging locations on Earth. This is likely to be equally the case for expedition planners on Mars, where conditions (in terms of extremes of temperature, etc.) can be at least as daunting as back on Earth. With the success of recent unmanned missions to Mars, such as NASA's Mars Pathfinder, Mars Global Surveyor and Mars Odyssey, there is now a great deal of information available on the range of environ- mental conditions on Mars, from the tropics to the CO2 ice-covered polar caps. This has been further supple- mented by the development of advanced numerical models of the Martian atmosphere, allowing detailed and accurate simulations and predictions of the weather and climate across the planet. This report discusses the main weather and climate variables which future Martian human expedition planners will need to take into account. The range of conditions likely to be encountered at a variety of typical locations on Mars is then considered, with reference to predictions from the ESA Mars Climate Database.

  1. 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

  2. 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.

  3. 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).

  4. Decadal climate predictions with an high resolution coupled model

    NASA Astrophysics Data System (ADS)

    Monerie, P. A.; Valcke, S.; Moine, M. P.; Maisonnave, E.; Coquart, L.; Cassou, C.; Terray, L.

    2014-12-01

    We analyze the decadal prediction skill of sea surface temperature variability with a high resolution coupled Ocean-Atmosphere General Circulation Model (OAGCM). The HR CERFACS was developed at the CERFACS (Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique) laboratory in the framework of the EU-FP7 SPECS (Seasonal-to-decadal climate Predictions for the improvement of European Climate Services) project in order to address the question of decadal predictability with the use of a high spatial resolution. The atmospheric model is ARPEGE/IFS with a T359 spectral truncature and the oceanic model is NEMO at 0.25° resolution including the LIM2 sea ice model. Each hindcasts consist of a 10-members ensemble integrated over a 10-years period. These hindcasts are full-field initialized every year from 1993 to 2009 and initial oceanic state is given by the GLORYS2V1 (0.25° resolution) sea-surface temperatures. Members of a given ensemble (one initialization date) are generated by perturbations of the atmospheric initial conditions. We study the predictability of the global sea-surface temperature focusing on the Atlantic Multidecadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), the North Atlantic Subpolar Gyre (SPG) and the El-Nino Southern Oscillation (ENSO). We also investigate the prediction skill of the Atlantic Meridional Overturning Circulation (AMOC).

  5. 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

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

    NASA Astrophysics Data System (ADS)

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

    2012-10-01

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

  7. Climatic impacts on winter wheat in Oklahoma and potential applications to climatic and crop yield prediction.

    PubMed

    Greene, J Scott; Maxwell, Erin

    2007-12-01

    Climatic anomalies can pose severe challenges for farmers and resource managers. This is particularly significant with respect to gradually developing anomalies such as droughts. The impact of the 1995-1996 drought on the Oklahoma wheat crop, and the possibility that predictive information might have reduced some of the losses, is examined through a combined modeling approach using climatological data and a crop growth model that takes into account an extensive range of soil, climatic, and plant variables. The results show potential outcomes and also illustrate the point at which all possible climatic outcomes were predicting a significantly low wheat yield. Based on anecdotal evidence of the 1995-1996 drought, which suggested that farmers who planted at different times experienced different yields, the model was run assuming a variety of different planting dates. Results indicate that there is indeed a noticeable difference in the modeled wheat yields given different planting dates. The information regarding effectiveness of planting date can be used in conjunction with current long-range forecasts to develop improved predictions for the current growing season. This approach produces information regarding the likelihood of extreme precipitation events and the impact on crop yield, which can provide a powerful tool to farmers and others during periods of drought or other climatic extremes. PMID:17578606

  8. 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.

  9. Biological Invasions Impact Ecosystem Properties and can Affect Climate Predictions

    NASA Astrophysics Data System (ADS)

    Gonzalez-Meler, M.; Matamala, R.; Cook, D. R.; Graham, S.; Fan, Z.; Gomez-Casanovas, N.

    2012-12-01

    Climate change models vary widely in their predictions of the effects of climate forcing, in part because of difficulties in assigning sources of uncertainties and in simulating changes in the carbon source/sink status and climate-carbon cycle feedbacks of terrestrial ecosystems. We studied the impacts of vegetation and weather variations on carbon and energy fluxes at a restored tallgrass prairie in Illinois. The prairie was a strong carbon sink, despite a prolonged drought period and vegetation changes due to the presence of a non-native biennial plant. A model considering the combined effects of air temperature, precipitation, RH, incoming solar radiation, and vegetation was also developed and used to describe net ecosystem exchange for all years. The vegetation factor was represented in the model with summer albedo and/or NDVI. Results showed that the vegetation factor was more important than abiotic factors in describing changes in C and energy fluxes in ecosystems under disturbances. Changes from natives to a non-native forbs species had the strongest effect in reducing net ecosystem production and increasing sensible heat flux and albedo, which may result in positive feedbacks on warming. Here we show that non-native species invasions can alter the ecosystem sensitivity to climatic factors often construed in models.

  10. 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.

  11. 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

  12. Do seasonal-to-decadal climate predictions underestimate the predictability of the real world?

    PubMed Central

    Eade, Rosie; Smith, Doug; Scaife, Adam; Wallace, Emily; Dunstone, Nick; Hermanson, Leon; Robinson, Niall

    2014-01-01

    Seasonal-to-decadal predictions are inevitably uncertain, depending on the size of the predictable signal relative to unpredictable chaos. Uncertainties can be accounted for using ensemble techniques, permitting quantitative probabilistic forecasts. In a perfect system, each ensemble member would represent a potential realization of the true evolution of the climate system, and the predictable components in models and reality would be equal. However, we show that the predictable component is sometimes lower in models than observations, especially for seasonal forecasts of the North Atlantic Oscillation and multiyear forecasts of North Atlantic temperature and pressure. In these cases the forecasts are underconfident, with each ensemble member containing too much noise. Consequently, most deterministic and probabilistic measures underestimate potential skill and idealized model experiments underestimate predictability. However, skilful and reliable predictions may be achieved using a large ensemble to reduce noise and adjusting the forecast variance through a postprocessing technique proposed here. PMID:25821271

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Do GCM's Predict the Climate.... Or the Low Frequency Weather?

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

    predicting this low frequency weather so as to predict the climate, they need appropriate climate forcings and/ or new internal mechanisms of variability. We examine this using wavelet analyses of forced and unforced GCM outputs, including the ECHO-G simulation used in the Millenium project. For example, we find that climate scenarios with large CO2 increases do give rise to a climate regime but that Hc>1 i.e. much larger than that of natural variability which for temperatures has Hc≈0.4. In comparison, the (largely volcanic) forcing of the ECHO-G Millenium simulation is fairly realistic (Hc≈0.4), although it is not clear that this mechanism can explain the even lower frequency variability found in the paleotemperature series, nor is it clear that this is compatible with low frequency solar or orbital forcings.

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

    NASA Astrophysics Data System (ADS)

    Patil, Sopan D.; Wigington, Parker J.; Leibowitz, Scott G.; Sproles, Eric A.; Comeleo, Randy L.

    2014-09-01

    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 (distributed) meteorological inputs instead of spatially uniform (lumped) meteorological inputs. Both lumped and distributed versions of the EXP-HYDRO model are implemented at 41 meso-scale (500-5000 km2) catchments in the Pacific Northwest region of USA. We use two complementary metrics of long-term spatial climate variability, moisture homogeneity index (IM) and temperature variability index (ITV), to analyze the performance improvement with distributed model. Results show that the distributed model performs better than the lumped model in 38 out of 41 catchments, and noticeably better (>10% improvement) in 13 catchments. Furthermore, spatial variability of moisture distribution alone is insufficient to explain the observed patterns of model performance improvement. For catchments with low moisture homogeneity (IM < 80%), IM is a better predictor of model performance improvement than ITV; whereas for catchments with high moisture homogeneity (IM > 80%), ITV is a better predictor of performance improvement than IM. Based on the results, we conclude that: (1) catchments that have low homogeneity of moisture distribution are the obvious candidates for using spatially distributed meteorological inputs, and (2) catchments with a homogeneous moisture distribution benefit from spatially distributed meteorological inputs if they also have high spatial variability of precipitation phase (rain vs. snow).

  20. 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.

  1. 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.

  2. 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.

  3. Probabilistic climate change predictions applying Bayesian model averaging.

    PubMed

    Min, Seung-Ki; Simonis, Daniel; Hense, Andreas

    2007-08-15

    This study explores the sensitivity of probabilistic predictions of the twenty-first century surface air temperature (SAT) changes to different multi-model averaging methods using available simulations from the Intergovernmental Panel on Climate Change fourth assessment report. A way of observationally constrained prediction is provided by training multi-model simulations for the second half of the twentieth century with respect to long-term components. The Bayesian model averaging (BMA) produces weighted probability density functions (PDFs) and we compare two methods of estimating weighting factors: Bayes factor and expectation-maximization algorithm. It is shown that Bayesian-weighted PDFs for the global mean SAT changes are characterized by multi-modal structures from the middle of the twenty-first century onward, which are not clearly seen in arithmetic ensemble mean (AEM). This occurs because BMA tends to select a few high-skilled models and down-weight the others. Additionally, Bayesian results exhibit larger means and broader PDFs in the global mean predictions than the unweighted AEM. Multi-modality is more pronounced in the continental analysis using 30-year mean (2070-2099) SATs while there is only a little effect of Bayesian weighting on the 5-95% range. These results indicate that this approach to observationally constrained probabilistic predictions can be highly sensitive to the method of training, particularly for the later half of the twenty-first century, and that a more comprehensive approach combining different regions and/or variables is required. PMID:17569647

  4. 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…

  5. Administrative Satisfaction and the Regulatory Climate at Public Institutions. AIR 1997 Annual Forum Paper.

    ERIC Educational Resources Information Center

    Volkwein, James Fredericks; Malik, Shaukat M.; Napierski-Prancl, Michelle

    This study examined the effects of state regulation of financial, personnel, and academic resources on the administrative flexibility granted to universities, and tested the hypothesis that state regulatory climate influences levels of managerial satisfaction. Data were gathered through two surveys. The first covered management flexibility and…

  6. 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.

  7. 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...

  8. 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

  9. 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.

  10. Prediction of future climate change for the Blue Nile, using a nested Regional Climate Model

    NASA Astrophysics Data System (ADS)

    Soliman, E.; Jeuland, M.

    2009-04-01

    Although the Nile River Basin is rich in natural resources, it faces many challenges. Rainfall is highly variable across the region, on both seasonal and inter-annual scales. This variability makes the region vulnerable to droughts and floods. Many development projects involving Nile waters are currently underway, or being studied. These projects will lead to land-use patterns changes and water distribution and availability. It is thus important to assess the effects of a) these projects and b) evolving water resource management and policies, on regional hydrological processes. This paper seeks to establish a basis for evaluation of such impacts within the Blue Nile River sub-basin, using the RegCM3 Regional Climate Model to simulate interactions between the land surface and climatic processes. We first present results from application of this RCM model nested with downscaled outputs obtained from the ECHAM5/MPI-OM1 transient simulations for the 20th Century. We then investigate changes associated with mid-21st century emissions forcing of the SRES A1B scenario. The results obtained from the climate model are then fed as inputs to the Nile Forecast System (NFS), a hydrologic distributed rainfall runoff model of the Nile Basin, The interaction between climatic and hydrological processes on the land surface has been fully coupled. Rainfall patterns and evaporation rates have been generated using RegCM3, and the resulting runoff and Blue Nile streamflow patterns have been simulated using the NFS. This paper compares the results obtained from the RegCM3 climate model with observational datasets for precipitation and temperature from the Climate Research Unit (UK) and the NASA Goddard Space Flight Center GPCP (USA) for 1985-2000. The validity of the streamflow predictions from the NFS is assessed using historical gauge records. Finally, we present results from modeling of the A1B emissions scenario of the IPCC for the years 2034-2055. Our results indicate that future

  11. Extreme Climatic Events: Understanding, Modeling and Predicting Them (Invited)

    NASA Astrophysics Data System (ADS)

    Ghil, M.

    2009-12-01

    Extreme climatic events include strong El Niños and La Niñas, heat and cold waves, droughts and floods — along with any number of associated phenomena, from wind bursts to firestorms — and cover continental down to regional scales. I will start by illustrating some of these phenomena graphically, and proceed with an in-depth discussion of one set of such events, those arising from the El Niño/Southern Oscillation (ENSO) phenomenon. The connection between the understanding as a nonlinear, complex phenomenon, the modeling across a full hierarchy — from “toy” via intermediate to highly detailed GCMs (“general circulation” or “global climate” models) — and the prediction via statistical, dynamical and combined methods will be emphasized. This talk and another invited talk, in session NG11, complément each other.

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. COMBINING CLIMATE MODEL PREDICTIONS, HYDROLOGICAL MODELING, AND ECOLOGICAL NICHE MODELING ALGORITHMS TO PREDICT THE IMPACTS OF CLIMATE CHANGE ON AQUATIC BIODIVERSITY

    EPA Science Inventory

    The results of this research will provide a broad taxonomic and regional assessment of the impacts of climate change on aquatic species in the United States by producing predictions of current and future habitat quality for aquatic taxa based on multiple climate change scen...

  17. In search of climate refuge: predicting Indian Ocean coral reef communities and climate impacts using ocean climate satellite data

    NASA Astrophysics Data System (ADS)

    McClanahan, T. R.; Maina, J. M.; Ateweberhan, M.

    2008-12-01

    Ocean climate derived variables from satellites are increasingly being used to predict ecosystem states and processes. Despite the concerted efforts to develop such models, and the urgency to incorporate their outputs into management plans and actions, few models have been tested with field data for the purpose of refinement and eventual integration into the management decisions. Here we test two existing coral bleaching vulnerability models for the Indian Ocean using field data from > 100 sites and investigate their utility in predicting changes in coral reef communities - abundance and diversity. Field data on relative abundance; of coral taxa, site-specific responses to temperature anomalies, change in coral cover associated with the 1998 temperature anomaly, and species richness distribution were tested against 1) vulnerability indices from a multivariate susceptibility model and 2) the conventional cumulative degree heating weeks calculated from satellite sea surface temperature data. Relative abundance of susceptible taxa and the whole coral community decreased with an increase environmental vulnerability. The relationship is caused by the 1998 bleaching event, which changed the coral communities towards coral taxa tolerant of climate disturbance or mortality threshold temperatures. Coral loss in 1998 was positively associated with the site's multivariate vulnerability index. We describe reef areas that have retained high diversity and relative abundance of susceptible taxa in order to prioritize conservation and management actions.

  18. 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.

  19. 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.

  20. Predicting Low Flow Conditions from Climatic Indices - Indicator-Based Modeling for Climate Change Impact Assessment

    NASA Astrophysics Data System (ADS)

    Fangmann, Anne; Haberlandt, Uwe

    2014-05-01

    In the face of climate change, the assessment of future hydrological regimes has become indispensable in the field of water resources management. Investigation of potential change is vital for proper planning, especially with regard to hydrological extremes. Commonly, projection of future streamflow is done applying process-based hydrological models, using climate model data as input, whose complex model structures generally require excessive amounts of time and effort for set-up and computation. This study aims at identifying practical alternatives to the employment of sophisticated models by considering simpler, yet sufficiently accurate methods for modeling rainfall-runoff relations with regard to hydrological extremes. The focus is thereby put on the prediction of low flow periods, which are, in contrast to flood events, characterized by extended durations and spatial dimensions. The models to be established in this study base on indicators, which characterize both meteorological and hydrological conditions within dry periods. This approach makes direct use of the coupling between atmospheric driving forces and streamflow response with the underlying presumption that low-precipitation and high-evaporation periods result in diminished flow, implying that relationships exist between the properties of both meteorological and hydrological events (duration, volume, severity etc.). Eventually, optimal combinations of meteorological indicators are sought that are suitable to predict various low flow characteristics with satisfactory accuracy. Two approaches for model specification are tested: a) multiple linear regression, and b) Fuzzy logic. The data used for this study are daily time series of mean discharge obtained from 294 gauges with variable record length situated in the federal state of Lower Saxony, Germany, as well as interpolated climate variables available for a period from 1951 to 2011. After extraction of a variety of indicators from the available

  1. 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

  2. EARLY CAREER: THE HAZARDS OF EXTREME CLIMATIC EVENTS: PREDICTING IMPACTS

    EPA Science Inventory

    One of the greatest threats to water quality is water-borne pathogens, which are more common now than they have been historically. A factor implicated in the emergence of water-borne diseases is climate change-driven increases in extreme climatic events. Although climatic e...

  3. 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.

  4. 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

  5. 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

  6. 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.

  7. Response of soybean to predicted climate change in the USA

    SciTech Connect

    Curry, R.B.; Jones, J.W.; Boote, K.J.; Peart, R.M.; Allen, L.H. Jr.; Pickering, N.B.

    1995-12-31

    Soybean [Glycine max (L.) Merr.] is a major crop in the USA in terms of production, exports, and area of land used in its production. Projected changes in climate could have major impacts on the production of this crop as well as on areas where it would be produced. A simulation study was conducted to characterize these possible effects using a soybean crop growth model, historical weather data, and three possible climate change scenarios based on global climate model (GCM) results. For each weather scenario, rainfed and irrigation management cases were studied under normal and elevated atmospheric CO{sub 2} levels. Results showed that the high annual variability of rainfed soybean yields under historical weather conditions was amplified by the GCM-based climate change scenarios. The negative effects of climate change alone on soybean yields were mostly offset by the increased atmospheric CO{sub 2} concentration for two of the scenarios for both irrigated and rainfed cases. The third scenario resulted in decreased yields in most locations. On average, the climate change scenarios resulted in a 60% increase in estimated water requirements for irrigation. These results suggest that soybean production in the USA would not be adversely affected by climate change if the climate changes according to two of the GCMs, unless water supply for irrigation is limited. Under the most severe climate change scenario, however, soybean production in the USA could be reduced considerably unless changes in production regions and practices are made. 31 refs., 11 figs., 6 tabs.

  8. 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

  9. 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

  10. Taxonomies of Higher Educational Institutions Predicted from Organizational Climate.

    ERIC Educational Resources Information Center

    Lysons, Art

    1990-01-01

    Application of the Perceived Climate Questionnaire involving senior-level staff from Australian institutions used climate factors as the basis for testing hypothesized taxonomies of the institutions. Results reinforce the relevance of contemporary management theories and demonstrate the importance of leadership styles in organizational…

  11. 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

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

    PubMed

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

    2015-01-01

    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

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. Monitoring Top-of-Atmosphere Radiative Energy Imbalance for Climate Prediction

    NASA Technical Reports Server (NTRS)

    Lin, Bing; Chambers, Lin H.; Stackhouse, Paul W., Jr.; Minnis, Patrick

    2009-01-01

    Large climate feedback uncertainties limit the prediction accuracy of the Earth s future climate with an increased CO2 atmosphere. One potential to reduce the feedback uncertainties using satellite observations of top-of-atmosphere (TOA) radiative energy imbalance is explored. Instead of solving the initial condition problem in previous energy balance analysis, current study focuses on the boundary condition problem with further considerations on climate system memory and deep ocean heat transport, which is more applicable for the climate. Along with surface temperature measurements of the present climate, the climate feedbacks are obtained based on the constraints of the TOA radiation imbalance. Comparing to the feedback factor of 3.3 W/sq m/K of the neutral climate system, the estimated feedback factor for the current climate system ranges from -1.3 to -1.0 W/sq m/K with an uncertainty of +/-0.26 W/sq m/K. That is, a positive climate feedback is found because of the measured TOA net radiative heating (0.85 W/sq m) to the climate system. The uncertainty is caused by the uncertainties in the climate memory length. The estimated time constant of the climate is large (70 to approx. 120 years), implying that the climate is not in an equilibrium state under the increasing CO2 forcing in the last century.

  20. 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

  1. Climatic Changes in Lebanon, Predicting Uncertain Precipitation Events. Do climatic cycles exist?

    NASA Astrophysics Data System (ADS)

    Arkadan, A. R. M.

    2009-04-01

    Lebanon Mountain Range. Analysis of precipitation data is important in predicting the occurrence of uncertain events with time and to determine if there is a meteorological cycle in which periods of heavy precipitation or drought events are part of a climatic cycle. Because of the extreme variability of precipitation events it is necessary to use several methods to estimate the processes and the force behind it if present. Methods such as moving average, probability of exceedence, coherent rainfall and statistics concepts are used to aid in defining theses events. Results showed that a possible cyclicity occurs at a return period of 14 years but of low coherence. This could be related to the amount of data which covers a period of 35 years, which might not be enough to determine whether a climatic cyclicity controls the prevailed climatic conditions over Lebanon.

  2. 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.

  3. Prediction of Tropical Climate on Intraseasonal Timescale using Phase Space Reconstruction

    NASA Astrophysics Data System (ADS)

    Sharma, A. S.; Krishnamurthy, V.

    2009-12-01

    Although considerable success has been achieved in weather prediction on the order of about ten days lead time, the prediction of climate variability on intraseasonal and seasonal timescales is still in developmental stage. The optimism for climate prediction comes from the realization that climate variability, especially in the tropics, is influenced mainly by slowly varying components of the climate system. Applying multichannel singular spectrum analysis (MSSA) to daily values of climate variables, such as outgoing longwave radiation (OLR) and low-level winds, the tropical climate variability is found to consist of nonlinear oscillations on intraseasonal time scales and large-scale seasonally persisting patterns. The nonlinear oscillations are found to be manifestations of the South Asian monsoon’s active-break cycles and the well-known Madden Julian Oscillation over the Indian and Pacific Oceans. Exploiting the coherent and more regularly varying nature of these nonlinear MSSA modes, this study has constructed a dynamical model for the prediction of tropical climate on intraseasonal time scale. The prediction model is constructed from the time series of the MSSA modes using time-delay embedding technique for the reconstruction of phase space. The predictions are expressed in a probabilistic manner by providing ensemble forecasts.

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

    SciTech Connect

    Jaroslav Solc

    2009-06-01

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

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

    NASA Astrophysics Data System (ADS)

    Kumar, Vijendra

    1999-11-01

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

  6. An Empirical Approach to Predicting Effects of Climate Change on Stream Water Chemistry

    NASA Astrophysics Data System (ADS)

    Olson, J. R.; Hawkins, C. P.

    2014-12-01

    Climate change may affect stream solute concentrations by three mechanisms: dilution associated with increased precipitation, evaporative concentration associated with increased temperature, and changes in solute inputs associated with changes in climate-driven weathering. We developed empirical models predicting base-flow water chemistry from watershed geology, soils, and climate for 1975 individual stream sites across the conterminous USA. We then predicted future solute concentrations (2065 and 2099) by applying down-scaled global climate model predictions to these models. The electrical conductivity model (EC, model R2 = 0.78) predicted mean increases in EC of 19 μS/cm by 2065 and 40 μS/cm by 2099. However predicted responses for individual streams ranged from a 43% decrease to a 4x increase. Streams with the greatest predicted decreases occurred in the southern Rocky Mountains and Mid-West, whereas southern California and Sierra Nevada streams showed the greatest increases. Generally, streams in dry areas underlain by non-calcareous rocks were predicted to be the most vulnerable to increases in EC associated with climate change. Predicted changes in other water chemistry parameters (e.g., Acid Neutralization Capacity (ANC), SO4, and Ca) were similar to EC, although the magnitude of ANC and SO4 change was greater. Predicted changes in ANC and SO4 are in general agreement with those changes already observed in seven locations with long term records.

  7. 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.

  8. 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...

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

    ERIC Educational Resources Information Center

    Güngör, Semra Kiranli

    2016-01-01

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

  10. 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.

  11. Abrupt climate change and thermohaline circulation: Mechanisms and predictability

    PubMed Central

    Marotzke, Jochem

    2000-01-01

    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

  12. 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

  13. 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

  14. 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

  15. 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.

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

    SciTech Connect

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

    2003-11-21

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

  17. 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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    PubMed

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

    2016-01-01

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

  20. 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

  1. Improving Weather and Climate Prediction with the AIRS on Aqua

    NASA Technical Reports Server (NTRS)

    Pagano, Thomas S.

    2009-01-01

    The Atmospheric Infrared Sounder (AIRS) on the EOS Aqua Spacecraft was launched on May 4, 2002. Early in the mission, the AIRS instrument demonstrated its value to the weather forecasting community with better than 6 hours of improvement on the 5 day forecast. Now with over six years of consistent and stable data from AIRS, scientists are able to examine processes governing weather and climate and look at seasonal and interannual trends from the AIRS data with high statistical confidence. Naturally, long-term climate trends require a longer data set, but indications are that the Aqua spacecraft and the AIRS instrument should last beyond 2016. This paper briefly describes the AIRS products, reviews past science and weather accomplishments from AIRS data product users and highlights recent findings in these areas.

  2. 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.

  3. 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

  4. 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

  5. On spatiotemporal series analysis and its application to predict the regional short term climate process

    NASA Astrophysics Data System (ADS)

    Wang, Geli; Yang, Peicai; Lü, Daren

    2004-04-01

    Based on the theory of reconstructing state space, a technique for spatiotemporal series prediction is presented. By means of this technique and NCEP/NCAR data of the monthly mean geopotential height anomaly of the 500-hPa isobaric surface in the Northern Hemisphere, a regional prediction experiment is also carried out. If using the correlation coefficient R between the observed field and the prediction field to measure the prediction accuracy, the averaged R given by 48 prediction samples reaches 21%, which corresponds to the current prediction level for the short range climate process.

  6. 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.

  7. An assessment of a multi-model ensemble of decadal climate predictions

    NASA Astrophysics Data System (ADS)

    Bellucci, A.; Haarsma, R.; Gualdi, S.; Athanasiadis, P. J.; Caian, M.; Cassou, C.; Fernandez, E.; Germe, A.; Jungclaus, J.; Kröger, J.; Matei, D.; Müller, W.; Pohlmann, H.; Salas y Melia, D.; Sanchez, E.; Smith, D.; Terray, L.; Wyser, K.; Yang, S.

    2015-05-01

    A multi-model ensemble of decadal prediction experiments, performed in the framework of the EU-funded COMBINE (Comprehensive Modelling of the Earth System for Better Climate Prediction and Projection) Project following the 5th Coupled Model Intercomparison Project protocol is examined. The ensemble combines a variety of dynamical models, initialization and perturbation strategies, as well as data assimilation products employed to constrain the initial state of the system. Taking advantage of the multi-model approach, several aspects of decadal climate predictions are assessed, including predictive skill, impact of the initialization strategy and the level of uncertainty characterizing the predicted fluctuations of key climate variables. The present analysis adds to the growing evidence that the current generation of climate models adequately initialized have significant skill in predicting years ahead not only the anthropogenic warming but also part of the internal variability of the climate system. An important finding is that the multi-model ensemble mean does generally outperform the individual forecasts, a well-documented result for seasonal forecasting, supporting the need to extend the multi-model framework to real-time decadal predictions in order to maximize the predictive capabilities of currently available decadal forecast systems. The multi-model perspective did also allow a more robust assessment of the impact of the initialization strategy on the quality of decadal predictions, providing hints of an improved forecast skill under full-value (with respect to anomaly) initialization in the near-term range, over the Indo-Pacific equatorial region. Finally, the consistency across the different model predictions was assessed. Specifically, different systems reveal a general agreement in predicting the near-term evolution of surface temperatures, displaying positive correlations between different decadal hindcasts over most of the global domain.

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

    EPA Science Inventory

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    NASA Astrophysics Data System (ADS)

    Bhend, Jonas; Mahlstein, Irina; Liniger, Mark

    2016-04-01

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

  11. Bayesian methods for discontinuity detection in climate model predictions.

    SciTech Connect

    Safta, Cosmin; Debusschere, Bert J.; Najm, Habib N.; Sargsyan, Khachik

    2010-06-01

    Discontinuity detection is an important component in many fields: Image recognition, Digital signal processing, and Climate change research. Current methods shortcomings are: Restricted to one- or two-dimensional setting, Require uniformly spaced and/or dense input data, and Give deterministic answers without quantifying the uncertainty. Spectral methods for Uncertainty Quantification with global, smooth bases are challenged by discontinuities in model simulation results. Domain decomposition reduces the impact of nonlinearities and discontinuities. However, while gaining more smoothness in each subdomain, the current domain refinement methods require prohibitively many simulations. Therefore, detecting discontinuities up front and refining accordingly provides huge improvement to the current methodologies.

  12. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  14. 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.

  15. 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

  16. 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. PMID:18451859

  17. 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.

  18. Recent advances in dynamical extra-seasonal to annual climate prediction at IAP/CAS

    NASA Astrophysics Data System (ADS)

    Lin, Zhaohui; Wang, Huijun; Zhou, Guangqing; Chen, Hong; Lang, Xianmei; Zhao, Yan; Zeng, Qingcun

    2004-06-01

    Recent advances in dynamical climate prediction at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS) during the last five years have been briefly described in this paper. Firstly, the second generation of the IAP dynamical climate prediction system (IAP DCP-II) has been described, and two sets of hindcast experiments of the summer rainfall anomalies over China for the periods of 1980 1994 with different versions of the IAP AGCM have been conducted. The comparison results show that the predictive skill of summer rainfall anomalies over China is improved with the improved IAP AGCM in which the surface albedo parameterization is modified. Furthermore, IAP DCP-II has been applied to the real-time prediction of summer rainfall anomalies over China since 1998, and the verification results show that IAP DCP-II can quite well capture the large scale patterns of the summer flood/drought situations over China during the last five years (1998 2002). Meanwhile, an investigation has demonstrated the importance of the atmospheric initial conditions on the seasonal climate prediction, along with studies on the influences from surface boundary conditions (e.g., land surface characteristics, sea surface temperature). Certain conclusions have been reached, such as, the initial atmospheric anomalies in spring may play an important role in the summer climate anomalies, and soil moisture anomalies in spring can also have a significant impact on the summer climate anomalies over East Asia. Finally, several practical techniques (e.g., ensemble technique, correction method, etc.), which lead to the increase of the prediction skill for summer rainfall anomalies over China, have also been illustrated. The paper concludes with a list of critical requirements needed for the further improvement of dynamical seasonal climate prediction.

  19. 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

  20. 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

  1. 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.

  2. 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

  3. 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

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

  5. Schoolwide Social-Behavioral Climate, Student Problem Behavior, and Related Administrative Decisions: Empirical Patterns from 1,510 Schools Nationwide

    ERIC Educational Resources Information Center

    Spaulding, Scott A.; Irvin, Larry K.; Horner, Robert H.; May, Seth L.; Emeldi, Monica; Tobin, Tary J.; Sugai, George

    2010-01-01

    Office discipline referral (ODR) data provide useful information about problem behavior and consequence patterns, social-behavioral climates, and effects of social-behavioral interventions in schools. The authors report patterns of ODRs and subsequent administrative decisions from 1,510 schools nationwide that used the School-Wide Information…

  6. 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.

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

    SciTech Connect

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

    2004-05-06

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

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

    PubMed Central

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

    2012-01-01

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

  9. Prediction of a global climate change on Jupiter.

    PubMed

    Marcus, Philip S

    2004-04-22

    Jupiter's atmosphere, as observed in the 1979 Voyager space craft images, is characterized by 12 zonal jet streams and about 80 vortices, the largest of which are the Great Red Spot and three White Ovals that had formed in the 1930s. The Great Red Spot has been observed continuously since 1665 and, given the dynamical similarities between the Great Red Spot and the White Ovals, the disappearance of two White Ovals in 1997-2000 was unexpected. Their longevity and sudden demise has been explained however, by the trapping of anticyclonic vortices in the troughs of Rossby waves, forcing them to merge. Here I propose that the disappearance of the White Ovals was not an isolated event, but part of a recurring climate cycle which will cause most of Jupiter's vortices to disappear within the next decade. In my numerical simulations, the loss of the vortices results in a global temperature change of about 10 K, which destabilizes the atmosphere and thereby leads to the formation of new vortices. After formation, the large vortices are eroded by turbulence over a time of approximately 60 years--consistent with observations of the White Ovals-until they disappear and the cycle begins again. PMID:15103369

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

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

  11. 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

  12. 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

  13. 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.

  14. 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. PMID:25607371

  15. 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.

  16. Identifying the causes of the poor decadal climate prediction skill over the North Pacific

    NASA Astrophysics Data System (ADS)

    Guemas, V.; Doblas-Reyes, F. J.; Lienert, F.; Soufflet, Y.; Du, H.

    2012-10-01

    While the North Pacific region has a strong influence on North American and Asian climate, it is also the area with the worst performance in several state-of-the-art decadal climate predictions in terms of correlation and root mean square error scores. The failure to represent two major warm sea surface temperature events occurring around 1963 and 1968 largely contributes to this poor skill. The magnitude of these events competes with the largest observed temperature anomalies in the twenty-first century that might be associated with the long-term warming. Understanding the causes of these major warm events is thus of primary concern to improve prediction of North Pacific, North American and Asian climate. The 1963 warm event stemmed from the propagation of a warm ocean heat content anomaly along the Kuroshio-Oyashio extension. The 1968 warm event originated from the upward transfer of a warm water mass centered at 200 m depth. For being associated with long-lived ocean heat content anomalies, we expect those events to be, at least partially, predictable. Biases in ocean mixing processes present in many climate prediction models seem to explain the inability to predict these two major events. Such currently unpredictable warm events, if occurring again in the next decade, would substantially enhance the effect of long-term warming in the region.

  17. 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.

  18. 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.

  19. 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…

  20. 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.

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

    PubMed Central

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

    2013-01-01

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

  2. Climate Variability and Predictability in North West Africa

    NASA Astrophysics Data System (ADS)

    Baddour, O.; Djellouli, Y.

    2003-04-01

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

  3. 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.

  4. 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.

  5. 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.

    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.

  6. 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...

  7. 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.

  8. 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...

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

    PubMed

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

    2012-01-01

    Climate change is a driving force for livestock parasite risk. This is especially true for helminths including the nematodes Haemonchus contortus, Teladorsagia circumcincta, Nematodirus battus, and the trematode Fasciola hepatica, since survival and development of free-living stages is chiefly affected by temperature and moisture. The paucity of long term predictions of helminth risk under climate change has driven us to explore optimal modelling approaches and identify current bottlenecks to generating meaningful predictions. We classify approaches as correlative or mechanistic, exploring their strengths and limitations. Climate is one aspect of a complex system and, at the farm level, husbandry has a dominant influence on helminth transmission. Continuing environmental change will necessitate the adoption of mitigation and adaptation strategies in husbandry. Long term predictive models need to have the architecture to incorporate these changes. Ultimately, an optimal modelling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modelling, incorporating changes in livestock husbandry and disease control. Irrespective of approach, the principal limitation to parasite predictions is the availability of active surveillance data and empirical data on physiological responses to climate variables. By combining improved empirical data and refined models with a broad view of the livestock system, robust projections of helminth risk can be developed. PMID:26486780

  10. 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…

  11. 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.

  12. 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

  13. 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).

  14. 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.

  15. 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

  16. Predictability of Regional Climate: A Bayesian Approach to Analysing a WRF Model Ensemble

    NASA Astrophysics Data System (ADS)

    Bruyere, C. L.; Mesquita, M. D. S.; Paimazumder, D.

    2013-12-01

    This study investigates aspects of climate predictability with a focus on climatic variables and different characteristics of extremes over nine North American climatic regions and two selected Atlantic sectors. An ensemble of state-of-the-art Weather Research and Forecasting Model (WRF) simulations is used for the analysis. The ensemble is comprised of a combination of various physics schemes, initial conditions, domain sizes, boundary conditions and breeding techniques. The main objectives of this research are: 1) to increase our understanding of the ability of WRF to capture regional climate information - both at the individual and collective ensemble members, 2) to investigate the role of different members and their synergy in reproducing regional climate 3) to estimate the associated uncertainty. In this study, we propose a Bayesian framework to study the predictability of extremes and associated uncertainties in order to provide a wealth of knowledge about WRF reliability and provide further clarity and understanding of the sensitivities and optimal combinations. The choice of the Bayesian model, as opposed to standard methods, is made because: a) this method has a mean square error that is less than standard statistics, which makes it a more robust method; b) it allows for the use of small sample sizes, which are typical in high-resolution modeling; c) it provides a probabilistic view of uncertainty, which is useful when making decisions concerning ensemble members.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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

  2. 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.

  3. 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.

  4. 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

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

    NASA Astrophysics Data System (ADS)

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

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

  6. Initialized Decadal Climate Predictions of the Observed Early-2000s Hiatus of Global Warming

    NASA Astrophysics Data System (ADS)

    Meehl, G. A.; Teng, H.; Arblaster, J.

    2014-12-01

    The slow-down in the rate of global warming in the early-2000s is not evident in the multi-model ensemble average of traditional climate change projection simulations. However, a number of individual ensemble members from that set of models successfully simulate the early-2000s hiatus when naturally-occurring climate variability involving the Interdecadal Pacific Oscillation (IPO) coincided, by chance, with the observed negative phase of the IPO that contributed to the early-2000s hiatus. If the recent methodology of initialized decadal climate prediction could have been applied in the mid-1990s using the CMIP5 multi-models, both the negative phase of the IPO in the early 2000s as well as the hiatus could have been simulated, with the multi-model average performing better than most of the individual models. The loss of predictive skill for six initial years prior to the mid-1990s points to the need for consistent hindcast skill to establish reliability of an operational decadal climate prediction system.

  7. 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.

  8. 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].

  9. 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

  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. PMID:22269559

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

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

    NASA Astrophysics Data System (ADS)

    Goetz, S. J.

    2014-12-01

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

  17. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  19. 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.

  20. Predicted climate-driven bird distribution changes and forecasted conservation conflicts in a neotropical savanna.

    PubMed

    Marini, Miguel Angelo; Barbet-Massin, Morgane; Lopes, Leonardo Esteves; Jiguet, Frédéric

    2009-12-01

    Climate-change scenarios project significant temperature changes for most of South America. We studied the potential impacts of predicted climate-driven change on the distribution and conservation of 26 broad-range birds from South America Cerrado biome (a savanna that also encompass tracts of grasslands and forests). We used 12 temperature or precipitation-related bioclimatic variables, nine niche modeling techniques, three general circulation models, and two climate scenarios (for 2030, 2065, 2099) for each species to model distribution ranges. To reach a consensus scenario, we used an ensemble-forecasting approach to obtain an average distribution for each species at each time interval. We estimated the range extent and shift of each species. Changes in range size varied across species and according to habitat dependency; future predicted range extent was negatively correlated with current predicted range extent in all scenarios. Evolution of range size under full or null dispersal scenarios varied among species from a 5% increase to an 80% decrease. The mean expected range shifts under null and full-dispersal scenarios were 175 and 200 km, respectively (range 15-399 km), and the shift was usually toward southeastern Brazil. We predicted larger range contractions and longer range shifts for forest- and grassland-dependent species than for savanna-dependent birds. A negative correlation between current range extent and predicted range loss revealed that geographically restricted species may face stronger threat and become even rarer. The predicted southeasterly direction of range changes is cause for concern because ranges are predicted to shift to the most developed and populated region of Brazil. Also, southeastern Brazil is the least likely region to contain significant dispersal corridors, to allow expansion of Cerrado vegetation types, or to accommodate creation of new reserves. PMID:19500118

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

    NASA Astrophysics Data System (ADS)

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

    2012-05-01

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

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

    PubMed

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

    2012-04-23

    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

  3. 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

  4. 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.

  5. National Scale Prediction of Soil Carbon Sequestration under Scenarios of Climate Change

    NASA Astrophysics Data System (ADS)

    Izaurralde, R. C.; Thomson, A. M.; Potter, S. R.; Atwood, J. D.; Williams, J. R.

    2006-12-01

    Carbon sequestration in agricultural soils is gaining momentum as a tool to mitigate the rate of increase of atmospheric CO2. Researchers from the Pacific Northwest National Laboratory, Texas A&M University, and USDA-NRCS used the EPIC model to develop national-scale predictions of soil carbon sequestration with adoption of no till (NT) under scenarios of climate change. In its current form, the EPIC model simulates soil C changes resulting from heterotrophic respiration and wind / water erosion. Representative modeling units were created to capture the climate, soil, and management variability at the 8-digit hydrologic unit (USGS classification) watershed scale. The soils selected represented at least 70% of the variability within each watershed. This resulted in 7,540 representative modeling units for 1,412 watersheds. Each watershed was assigned a major crop system: corn, soybean, spring wheat, winter wheat, cotton, hay, alfalfa, corn-soybean rotation or wheat-fallow rotation based on information from the National Resource Inventory. Each representative farm was simulated with conventional tillage and no tillage, and with and without irrigation. Climate change scenarios for two future periods (2015-2045 and 2045-2075) were selected from GCM model runs using the IPCC SRES scenarios of A2 and B2 from the UK Hadley Center (HadCM3) and US DOE PCM (PCM) models. Changes in mean and standard deviation of monthly temperature and precipitation were extracted from gridded files and applied to baseline climate (1960-1990) for each of the 1,412 modeled watersheds. Modeled crop yields were validated against historical USDA NASS county yields (1960-1990). The HadCM3 model predicted the most severe changes in climate parameters. Overall, there would be little difference between the A2 and B2 scenarios. Carbon offsets were calculated as the difference in soil C change between conventional and no till. Overall, C offsets during the first 30-y period (513 Tg C) are predicted to

  6. Climate drift of AMOC, North Atlantic salinity and arctic sea ice in CFSv2 decadal predictions

    NASA Astrophysics Data System (ADS)

    Huang, Bohua; Zhu, Jieshun; Marx, Lawrence; Wu, Xingren; Kumar, Arun; Hu, Zeng-Zhen; Balmaseda, Magdalena A.; Zhang, Shaoqing; Lu, Jian; Schneider, Edwin K.; Kinter, James L., III

    2015-01-01

    There are potential advantages to extending operational seasonal forecast models to predict decadal variability but major efforts are required to assess the model fidelity for this task. In this study, we examine the North Atlantic climate simulated by the NCEP Climate Forecast System, version 2 (CFSv2), using a set of ensemble decadal hindcasts and several 30-year simulations initialized from realistic ocean-atmosphere states. It is found that a substantial climate drift occurs in the first few years of the CFSv2 hindcasts, which represents a major systematic bias and may seriously affect the model's fidelity for decadal prediction. In particular, it is noted that a major reduction of the upper ocean salinity in the northern North Atlantic weakens the Atlantic meridional overturning circulation (AMOC) significantly. This freshening is likely caused by the excessive freshwater transport from the Arctic Ocean and weakened subtropical water transport by the North Atlantic Current. A potential source of the excessive freshwater is the quick melting of sea ice, which also causes unrealistically thin ice cover in the Arctic Ocean. Our sensitivity experiments with adjusted sea ice albedo parameters produce a sustainable ice cover with realistic thickness distribution. It also leads to a moderate increase of the AMOC strength. This study suggests that a realistic freshwater balance, including a proper sea ice feedback, is crucial for simulating the North Atlantic climate and its variability.

  7. 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.

  8. 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.

  9. 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.

  10. Inference of the potential predictability of seasonal land-surface climate from AMIP ensemble integrations

    SciTech Connect

    Phillips, T.J.; Santer, B.D.

    1995-12-01

    A number of recent studies of the potential predictability of seasonal climate have utilized AGCM ensemble integrations--i.e., experiments where the atmospheric model is driven by the same ocean boundary conditions and radiative forcings, but is started from different initial states. However, only a few variables of direct relevance to the climate of the land surface have been examined. In this study, the authors infer the potential predictability of 11 climate variables that are indicative of the energetics, dynamics, and hydrology of the land surface. They used a T42Ll9 ECMWF (cycle 36) AGCM having a land-surface scheme with prognostic temperature and moisture of 2 layers occupying the topmost 0.50 meters of soil, but with monthly climatological values of these fields prescribed below. Six model realizations of decadal climate (for the period 1979--1988) were considered. In each experiment, the SSTs and sea ice extents were those specified for the Atmospheric Model Intercomparison Project (AMIP), and some radiative parameters were prescribed as well. However, the initial conditions of the model atmosphere and land surface were different: the first two simulations were initialized from ECMWF analyses, while the initial states of subsequent realizations were assigned values that were the same as those at the last time step of the preceding integration.

  11. Using hydro-climatic and edaphic similarity to enhance soil moisture prediction

    NASA Astrophysics Data System (ADS)

    Coopersmith, E. J.; Minsker, B. S.; Sivapalan, M.

    2014-02-01

    Estimating soil moisture typically involves calibrating models to sparse networks of in~situ sensors, which introduces considerable error in locations where sensors are not available. We address this issue by calibrating parameters of a parsimonious soil moisture model, which requires only antecedent precipitation information, at gauged locations and then extrapolating these values to ungauged locations via a hydro-climatic classification system. Fifteen sites within the soil climate analysis network (SCAN) containing multi-year time series data for precipitation and soil moisture are used to calibrate the model. By calibrating at one of these fifteen sites and validating at another, we observe that the best results are obtained where calibration and validation occur within the same hydro-climatic class. Additionally, soil texture data are tested for their importance in improving predictions between calibration and validation sites. Results have the largest errors when calibration/validation pairs differ hydro-climatically and edaphically, improve when one of these two characteristics are aligned, and are strongest when the calibration and validation sites are hydro-climatically and edaphically similar. These findings indicate considerable promise for improving soil moisture estimation in ungauged locations by considering these similarities.

  12. 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.

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

    PubMed Central

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

    2012-01-01

    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. PMID:22072451

  14. 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

  15. 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

  16. Decadal prediction of European soil moisture from 1961 to 2010 using a regional climate model

    NASA Astrophysics Data System (ADS)

    Mieruch-Schnuelle, S.; Schädler, G.; Feldmann, H.

    2014-12-01

    The German national research program on decadal climate prediction(MiKlip) aims at the development of an operational decadal predictionsystem. To explore the potential of decadal predictions a hindcastensemble from 1961 to 2010 has been generated by the MPI-ESM, the newEarth system model of the Max Planck Institute for Meteorology. Toimprove the decadal predictions on higher spatial resolutions wedownscaled the MPI-ESM simulations by the regional model COSMO-CLM(CCLM) for Europe. In this study we will characterize and validatethe predictability of extreme states of soil moisture in Europesimulated by the MPI-ESM and the value added by the CCLM. The wateramount stored in the soil is a crucial component of the climate systemand especially important for agriculture, and has an influence onevaporation, groundwater and runoff. Thus, skillful prediction of soilmoisture in the order of years up to a decade could be used tomitigate risk and benefit society. Since soil moisture observationsare rare and validation of model output is difficult, we will ratherinvestigate the effective drought index (EDI), which can be retrievedsolely from precipitation data. Therefore we show that the EDI is agood estimator of the soil water content.

  17. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

    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.

  19. 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.

  20. 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.

  1. 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

  2. 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

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

    PubMed

    Dunstone, Nick J

    2014-09-28

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

  4. 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

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

    PubMed

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

    2016-09-01

    Statistical species distribution models (SDMs) are increasingly used to project spatial relocations of marine taxa under future climate change scenarios. However, tests of their predictive skill in the real-world are rare. Here, we use data from the Continuous Plankton Recorder program, one of the longest running and most extensive marine biological monitoring programs, to investigate the reliability of predicted plankton distributions. We apply three commonly used SDMs to 20 representative plankton species, including copepods, diatoms, and dinoflagellates, all found in the North Atlantic and adjacent seas. We fit the models to decadal subsets of the full (1958-2012) dataset, and then use them to predict both forward and backward in time, comparing the model predictions against the corresponding observations. The probability of correctly predicting presence was low, peaking at 0.5 for copepods, and model skill typically did not outperform a null model assuming distributions to be constant in time. The predicted prevalence increasingly differed from the observed prevalence for predictions with more distance in time from their training dataset. More detailed investigations based on four focal species revealed that strong spatial variations in skill exist, with the least skill at the edges of the distributions, where prevalence is lowest. Furthermore, the scores of traditional single-value model performance metrics were contrasting and some implied overoptimistic conclusions about model skill. Plankton may be particularly challenging to model, due to its short life span and the dispersive effects of constant water movements on all spatial scales, however there are few other studies against which to compare these results. We conclude that rigorous model validation, including comparison against null models, is essential to assess the robustness of projections of marine planktonic species under climate change. PMID:27040720

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. 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)

  13. 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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  15. 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

  16. 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

  17. 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

  18. Disparities between observed and predicted impacts of climate change on winter bird assemblages.

    PubMed

    La Sorte, Frank A; Lee, Tien Ming; Wilman, Hamish; Jetz, Walter

    2009-09-01

    Understanding how climate change affects the structure and function of communities is critical for gauging its full impact on biodiversity. To date, community-level changes have been poorly documented, owing, in part, to the paucity of long-term datasets. To circumvent this, the use of 'space-for-time' substitution--the forecasting of temporal trends from spatial climatic gradients--has increasingly been adopted, often with little empirical support. Here we examine changes from 1975 to 2001 in three community attributes (species richness, body mass and occupancy) for 404 assemblages of terrestrial winter avifauna in North America containing a total of 227 species. We examine the accuracy of space-for-time substitution and assess causal associations between community attributes and observed changes in annual temperature using a longitudinal study design. Annual temperature and all three community attributes increased over time. The trends for the three community attributes differed significantly from the spatially derived predictions, although richness showed broad congruence. Correlations with trends in temperature were found with richness and body mass. In the face of rapid climate change, applying space-for-time substitution as a predictive tool could be problematic with communities developing patterns not reflected by spatial ecological associations. PMID:19520804

  19. 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.

  20. 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.

  1. 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

  2. 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…

  3. The Effects of School Administration Self-Efficacy on School Climate and Student Achievement

    ERIC Educational Resources Information Center

    Davis, Brian R.

    2013-01-01

    The purpose of the study was to determine if there are significant relationships between the efficacies of the school principal, the climate of the school, and student achievement. Five schools within a small rural school district participated in this study. The principals completed the Principal Sense of Efficacy Scale, while the teachers at the…

  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. Observed and modelled summer heat predictability from soil-moisture/climate interactions in Europe

    NASA Astrophysics Data System (ADS)

    Quesada, Benjamin; Vautard, Robert; Yiou, Pascal; Hirschi, Martin; Seneviratne, Sonia

    2013-04-01

    The mega heat waves that struck Western Europe in 2003 and Russia in 2010 are believed to provide a foretaste of future European summer climate. Our ability to anticipate such events remains poor , limiting adequate society adaptation. Various studies have investigated the potential of soil moisture for climate predictability. For example in Europe, a deficit of precipitation in the preceding winter and spring seasons favours summer heatwaves, but apart from several case studies, conditions under which the identification of spring surface moisture deficits can provide useful seasonal predictions and the driving predictability mechanisms remain to be investigated. By analysing 64 years of observed temperature and precipitation (ECA&D) we show that rainy winter/spring seasons over Southern Europe inhibit hot summer days while anomalously dry months are followed by either a high or a low frequency of hot days, generalizing recent results obtained over south-eastern Europe. Further, observations indicate that summer hot day frequency is more sensitive to occurrence of specific weather regimes in initially dry cases than wet cases, inducing this asymmetry in summer heat predictability. Indeed, both the initial soil moisture conditions and specific atmospheric weather regimes are found to be critical for the occurrence of hot extremes. Then, by analysing simulations from the Coupled Model Intercomparison Project (CMIP3/CMIP5) and new regional simulations, we show that projected drier conditions over Southern Europe are likely to induce a widening in the hot summer days frequency distribution, as the initially-wet winter/spring seasons are likely to become rare. Thus even though more hot days are expected, predictability from preceding rainfall should not improve. This limitation may even be underestimated by the CMIP ensemble, since models that best reproduce the asymmetry over the historical period predict drier springs in Southern Europe and warmer summers in

  6. A New Perspective on Seasonal Predictability of Winter Climate in Middle Latitudes

    NASA Astrophysics Data System (ADS)

    Schlichtholz, P.

    2014-12-01

    While numerical weather prediction is quite accurate, prediction of regional and local climate variability remains a great challenge. One of the factors hampering skillful seasonal prediction (forecasts for future times ranging from about two weeks to a year) is a lack of understanding of the relevant feedbacks between climate subsystems, especially in the extratropics. Sources for seasonal predictability of surface atmospheric anomalies in middle latitudes have been previously sought in teleconnections to the tropical phenomenon of El-Niño-Southern Oscillation and among various extratropical drivers including sea surface temperature anomalies, Arctic sea ice cover extremes, continental snow variability and tropospheric-stratospheric interactions. However, impacts of extratropical subsurface ocean variability on atmospheric teleconnections are poorly known. Here we use a lagged regression analysis between an index of the observed summertime Atlantic water temperature (AWT) anomalies at the entrance to the Barents Sea in the period 1982-2005 and the corresponding year-round data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis to show that subsurface oceanic heat anomalies heading the Arctic Ocean are significant precursors of wintertime atmospheric anomalies over the mid-latitude Eurasia and North Pacific. We find that the remote tropospheric response arises from modification of planetary waves and interaction of mean winds with synoptic eddies leading to a reorganization of the mid-latitude storm tracks. Moreover, we show that this response may occur via a „stratospheric bridge". As about 50% of the variance of relevant wintertime meteorological fields (storm-track activity, air temperature, winds) in some regions is explained by the previous summer AWT anomalies, we recommend incorporation of subsurface ocean temperature variability in high latitudes into seasonal prediction systems.

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  8. 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.

  9. Dependence of the climate prediction skill on spatiotemporal scales: Internal versus radiatively-forced contribution

    NASA Astrophysics Data System (ADS)

    Volpi, D.; Doblas-Reyes, F. J.; GarcíA-Serrano, J.; Guemas, V.

    2013-06-01

    This article aims at quantifying the improvement in climate prediction skill as a function of temporal (from monthly to decadal) and spatial scales (from grid point to global) when initializing a perturbed parameter ensemble of the Hadley Centre Climate Model. The focus is on near-surface temperature and precipitation in the Tropical band, the Northern and Southern hemispheres. For temperature, the forecast system reproduces the dominant impact of the external forcing at global spatial scale and at decadal time scales. There are significant improvements with initialization for the first 40 forecast months in the global and tropical domains. In the Northern (Southern) hemisphere, the initialization increases the skill in the first 12 (20) months on regional but not hemispheric scales. The initialization has a stronger impact in the model variants with a weaker global-mean temperature trend. For precipitation, the initialization corrects the negative correlation found at global and tropical scales.

  10. 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

  11. Economic Impacts of Climate Variability in South Africa and Development of Resource Prediction Models.

    NASA Astrophysics Data System (ADS)

    Jury, Mark R.

    2002-01-01

    An analysis of food and water supplies and economic growth in South Africa leads to the realization that climate variability plays a major role. Summer rainfall in the period of 1980-99 is closely associated (variance = 48%) with year-to-year changes in the gross domestic product (GDP). Given the strong links between climate and resources, statistical models are formulated to predict maize yield, river flows, and GDP directly. The most influential predictor is cloud depth (outgoing longwave radiation) in the tropical Indian Ocean in the preceding spring (September-November). Reduced monsoon convection is related to enhanced rainfall over South Africa in the following summer and greater economic prosperity during the subsequent year. Methodologies are outlined and risk-reduction strategies are reviewed. It is estimated that over U.S.$1 billion could be saved annually through uptake of timely and reliable long-range forecasts.

  12. The importance of climatic factors and outliers in predicting regional monthly campylobacteriosis risk in Georgia, USA

    NASA Astrophysics Data System (ADS)

    Weisent, J.; Seaver, W.; Odoi, A.; Rohrbach, B.

    2014-01-01

    Incidence of Campylobacter infection exhibits a strong seasonal component and regional variations in temperate climate zones. Forecasting the risk of infection regionally may provide clues to identify sources of transmission affected by temperature and precipitation. The objectives of this study were to (1) assess temporal patterns and differences in campylobacteriosis risk among nine climatic divisions of Georgia, USA, (2) compare univariate forecasting models that analyze campylobacteriosis risk over time with those that incorporate temperature and/or precipitation, and (3) investigate alternatives to supposedly random walk series and non-random occurrences that could be outliers. Temporal patterns of campylobacteriosis risk in Georgia were visually and statistically assessed. Univariate and multivariable forecasting models were used to predict the risk of campylobacteriosis and the coefficient of determination (R 2) was used for evaluating training (1999-2007) and holdout (2008) samples. Statistical control charting and rolling holdout periods were investigated to better understand the effect of outliers and improve forecasts. State and division level campylobacteriosis risk exhibited seasonal patterns with peaks occurring between June and August, and there were significant associations between campylobacteriosis risk, precipitation, and temperature. State and combined division forecasts were better than divisions alone, and models that included climate variables were comparable to univariate models. While rolling holdout techniques did not improve predictive ability, control charting identified high-risk time periods that require further investigation. These findings are important in (1) determining how climatic factors affect environmental sources and reservoirs of Campylobacter spp. and (2) identifying regional spikes in the risk of human Campylobacter infection and their underlying causes.

  13. Latest decadal climate prediction research at the Met Office Hadley Centre

    NASA Astrophysics Data System (ADS)

    Dunstone, Nick; Smith, Doug; Scaife, Adam; Hermanson, Leon

    2014-05-01

    Here we present an overview of work, much of which is undertaken as contributions to the EU SPECS project, that explore mechanisms and hindcast skill using Met Office climate models and different versions of the DePreSys decadal prediction system. Here we focus on: - showing skilful predictions of the North Atlantic sub-polar gyre region in decadal hindcasts. Furthermore, we present evidence and physical mechanisms for a shift in the forecast sub-polar gyre, that would suggest a forthcoming local cooling and examine the associated likely regional climate impacts, - presenting results of experiments design to elucidate the possible impact of further anthropogenic reductions in Arctic sea-ice extent on atmospheric circulation on interannual to decadal timescales. A modest but significant negative NAO signal is seen in winter, - finally, we present details, and preliminary results, from the new high-resolution Met Office DePreSys system, which is a major step towards a seamless monthly to decadal prediction system.

  14. 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.

  15. 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

  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

    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.

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. Prediction of future climate change for the Blue Nile, using RCM nested in GCM

    NASA Astrophysics Data System (ADS)

    Sayed, E.; Jeuland, M.; Aty, M.

    2009-04-01

    Although the Nile River Basin is rich in natural resources, it faces many challenges. Rainfall is highly variable across the region, on both seasonal and inter-annual scales. This variability makes the region vulnerable to droughts and floods. Many development projects involving Nile waters are currently underway, or being studied. These projects will lead to land-use patterns changes and water distribution and availability. It is thus important to assess the effects of a) these projects and b) evolving water resource management and policies, on regional hydrological processes. This paper seeks to establish a basis for evaluation of such impacts within the Blue Nile River sub-basin, using the RegCM3 Regional Climate Model to simulate interactions between the land surface and climatic processes. We first present results from application of this RCM model nested with downscaled outputs obtained from the ECHAM5/MPI-OM1 transient simulations for the 20th Century. We then investigate changes associated with mid-21st century emissions forcing of the SRES A1B scenario. The results obtained from the climate model are then fed as inputs to the Nile Forecast System (NFS), a hydrologic distributed rainfall runoff model of the Nile Basin, The interaction between climatic and hydrological processes on the land surface has been fully coupled. Rainfall patterns and evaporation rates have been generated using RegCM3, and the resulting runoff and Blue Nile streamflow patterns have been simulated using the NFS. This paper compares the results obtained from the RegCM3 climate model with observational datasets for precipitation and temperature from the Climate Research Unit (UK) and the NASA Goddard Space Flight Center GPCP (USA) for 1985-2000. The validity of the streamflow predictions from the NFS is assessed using historical gauge records. Finally, we present results from modeling of the A1B emissions scenario of the IPCC for the years 2034-2055. Our results indicate that future

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

    ERIC Educational Resources Information Center

    Rasheed, Mohammed A.

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

  4. 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.

  5. 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

  6. 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

  7. 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

    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.

  8. 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. PMID:24356797

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. 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

  14. 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.

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

    PubMed Central

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

    2012-01-01

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

  16. 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

  17. 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

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

    NASA Astrophysics Data System (ADS)

    Suhler, G.

    2009-12-01

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

  19. 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

  20. Land surface contribution to climate predictability: the long way from early evidence to improved forecast skill

    NASA Astrophysics Data System (ADS)

    Douville, Hervé

    2013-04-01

    Seasonal forecasts performance over most land areas remains relatively weak, particularly in the mid-latitudes where the interannual ocean variability has a lesser influence than in the tropics. Yet, many observational and numerical studies suggest that there is a fraction of predictability that is still untapped over land at the monthly to seasonal time scales, due to both local and remote land surface effects. Soil moisture and snow mass anomalies may have a strong signature in the land surface energy budget and thereby influence not only surface temperature, but also precipitation through changes in surface evaporation and/or moisture convergence. Land surface anomalies may also trigger planetary waves that can have remote effects on seasonal mean climate. This talk will first illustrate some potential land surface impacts on climate predictability using both statistical and numerical evidence. Then, the limitations of such studies and the practical difficulties for taking advantage of the land surface memory will be presented, as well as on-going efforts for adressing these issues at both European (i.e., SPECS) and international (i.e., GLACE) levels.

  1. Do stratosphere-resolving models make better seasonal climate predictions in boreal winter?

    NASA Astrophysics Data System (ADS)

    Butler, Amy; Arribas, Alberto; Athanassadou, Maria; Calvo, Natalia; Charlton-Perez, Andrew; Domeisen, Daniela; Iza, Maddalen; Karpechko, Alexey; MacLachlan, Craig; O'Neill, Alan; Scaife, Adam; Sigmond, Michael

    2015-04-01

    Using an international, multi-model suite of historical forecasts from the World Climate Research Programme (WCRP) Climate-system Historical Forecast Project (CHFP), we compare the difference in seasonal prediction skill in boreal wintertime between models that resolve the stratosphere and its dynamics ("high-top") and models that do not ("low-top"). We were unable to detect more skill in the high-top ensemble mean than the low-top ensemble mean in forecasting the wintertime North Atlantic Oscillation, and skill varies widely between individual models. Increasing the ensemble size increases the skill. We then briefly examine two major processes involving stratosphere-troposphere interactions (the El Niño-Southern Oscillation/ENSO and the Quasi-biennial Oscillation/QBO) and how they relate to predictive skill on seasonal timescales, particularly over the North Atlantic and Eurasia. High-top models tend to have more realistic stratosphere-troposphere coupling related to ENSO and the QBO, which may enhance wintertime skill over high-latitudes in these models compared to low-top models during winters with ENSO or QBO forcing. However, it is not clear whether the improvement in skill in the high-top models is due entirely to better stratospheric processes.

  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. Understanding the origin of the solar cyclic activity for an improved earth climate prediction

    NASA Astrophysics Data System (ADS)

    Turck-Chièze, Sylvaine; Lambert, Pascal

    This review is dedicated to the processes which could explain the origin of the great extrema of the solar activity. We would like to reach a more suitable estimate and prediction of the temporal solar variability and its real impact on the Earth climatic models. The development of this new field is stimulated by the SoHO helioseismic measurements and by some recent solar modelling improvement which aims to describe the dynamical processes from the core to the surface. We first recall assumptions on the potential different solar variabilities. Then, we introduce stellar seismology and summarize the main SOHO results which are relevant for this field. Finally we mention the dynamical processes which are presently introduced in new solar models. We believe that the knowledge of two important elements: (1) the magnetic field interplay between the radiative zone and the convective zone and (2) the role of the gravity waves, would allow to understand the origin of the grand minima and maxima observed during the last millennium. Complementary observables like acoustic and gravity modes, radius and spectral irradiance from far UV to visible in parallel to the development of 1D-2D-3D simulations will improve this field. PICARD, SDO, DynaMICCS are key projects for a prediction of the next century variability. Some helioseismic indicators constitute the first necessary information to properly describe the Sun-Earth climatic connection.

  4. 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

  5. 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.

  6. 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

  7. 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

  8. 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

  9. 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. PMID:23405081

  10. 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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  12. Improved predictions of global climate in the decade ahead using a new version of the Met Office Hadley Centre Decadal Prediction System

    NASA Astrophysics Data System (ADS)

    Knight, Jeff; Andrews, Martin; Smith, Doug; Arribas, Alberto; Dunstone, Nick; Maclachlan, Craig; Peterson, Drew; Scaife, Adam; Williams, Andrew

    2013-04-01

    The Met Office Hadley Centre Decadal Prediction System (DePreSys) produced the first initialised short-term climate prediction in 2007. It showed, for the first time, that climate prediction up to a decade ahead was improved by including an accurate representation of the initial state of the ocean and atmosphere. Decadal predictions have subsequently been produced by a wide range of climate modelling centres, and this activity is an important new feature of IPCC AR5. Here, results from a comprehensively revised version of the Decadal Prediction System (DePreSys version 2) will be presented. The key enhancement is the use of the Met Office's latest climate model HadGEM3 within the forecast system (as opposed to the HadCM3 model used in the original system). This has approximately doubled the horizonal resolution of HadCM3, and quadrupled the number of vertical levels, in both atmospheric and oceanic components. In addition, the atmospheric component has an improved dynamical core, fully revised parameterisations, and is coupled to a different ocean model (NEMO). The initialisation methodology is essentially the same as for DePreSys version 1. Taking a global overview, indices of local predictive skill show significant improvements for key surface variables across a range of timescales relative to the previous system. In particular, there appears to be more skill in predicting multiannual to decadal variability in the Pacific Ocean and regions with which it has teleconnections. A forecast for global climate over the next few years produced by DePreSys version 2 will also be presented.

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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. PMID:25785866

  18. 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

  19. 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

  20. 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

  1. 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.

  2. Numerical Model Predictions of Intrinsically Generated Fluvial Terraces and Comparison to Climate-Change Expectations

    NASA Astrophysics Data System (ADS)

    Limaye, A. B. S.; Lamb, M. P.

    2014-12-01

    Terraces eroded into sediment (cut-fill) and bedrock (strath) preserve a geomorphic record of river activity. River terraces are often thought to form when a river switches from a period of low vertical incision rates and valley widening to high vertical incision rates and terrace abandonment. Consequently, terraces are frequently interpreted to reflect landscape response to changing external drivers, including tectonics, sea-level, and most commonly, climate. In contrast, unsteady lateral migration in meandering rivers may generate river terraces even under constant vertical incision and without changes in external forcing. To explore this latter mechanism, we use a numerical model and an automated terrace detection algorithm to simulate landscape evolution by a vertically incising, meandering river and isolate the age and geometric fingerprints of intrinsically generated river terraces. Simulations indicate that terraces form for a wide range of lateral and vertical incision rates, and the time interval between unique terrace levels is limited by a characteristic timescale for relief generation. Surprisingly, intrinsically generated terraces are commonly paired, an attribute that is thought to be diagnostic of climate change. For low ratios of vertical-to-lateral erosion rates, modeled terraces are longitudinally extensive and typically dip toward the valley center, and terrace slope is proportional to the ratio of vertical to lateral erosion. Evolving, spatial differences in bank strength between bedrock and sediment reduce terrace formation frequency and length, and can explain sub-linear terrace margins at valley boundaries. Comparison of model predictions to natural river terraces indicates that terrace length is the most reliable indicator of terrace formation by pulses of vertical incision, and may contain the imprint of past climate change on landscapes.

  3. National Oceanic and Atmospheric Administration(NOAA) Arctic Climate Change Studies: A Contribution to IPY

    NASA Astrophysics Data System (ADS)

    Calder, J.; Overland, J.; Uttal, T.; Richter-Menge, J.; Rigor, I.; Crane, K.

    2004-12-01

    NOAA has initiated four activities that respond to the Arctic Climate Impact Assessment(ACIA) recommendations and represent contributions toward the IPY: 1) Arctic cloud, radiation and aerosol observatories, 2) documentation and attribution of changes in sea-ice thickness through direct measurement and modeling, 3) deriving added value from existing multivariate and historical data, and 4) following physical and biological changes in the northern Bering and Chukchi Seas. Northeast Canada, the central Arctic coast of Russia and the continuing site at Barrow have been chosen as desirable radiation/cloud locations as they exhibit different responses to Arctic Oscillation variability. NOAA is closely collaborating with Canadian groups to establish an observatory at Eureka. NOAA has begun deployment of a network of ice-tethered ice mass balance buoys complemented by several ice profiling sonars. In combination with other sea ice investigators, the Arctic buoy program, and satellites, changes can be monitored more effectively in sea ice throughout the Arctic. Retrospective data analyses includes analysis of Arctic clouds and radiation from surface and satellite measurements, correction of systematic errors in TOVS radiance data sets for the Arctic which began in 1979, addressing the feasibility of an Arctic System Reanalysis, and an Arctic Change Detection project that incorporates historical and recent physical and biological observations and news items at a website, www.arctic.noaa.gov. NOAA has begun a long-term effort to detect change in ecosystem indicators in the northern Bering and Chukchi Seas that could provide a model for other northern marine ecosystems. The first efforts were undertaken in summer 2004 during a joint Russian-US cruise that mapped the regions physical, chemical and biological parameters to set the stage for future operations over the longer term. A line of biophysical moorings provide detection of the expected warming of this area. A

  4. Multi-Scale Predictions of the Asian Monsoons in the NCEP Climate Forecast System

    NASA Astrophysics Data System (ADS)

    Yang, S.

    2013-12-01

    A comprehensive analysis of the major features of the Asian monsoon system in the NCEP Climate Forecast System version 2 (CFSv2) and predictions of the monsoon by the model has been conducted. The intraseasonal-to-interannual variations of both summer monsoon and winter monsoon, as well as the annual cycles of monsoon climate, are focused. Features of regional monsoons including the monsoon phenomena over South Asia, East Asia, and Southeast Asia are discussed. The quasi-biweekly oscillation over tropical Asia and the Mei-yu climate over East Asia are also investigated. Several aspects of monsoon features including the relationships between monsoon and ENSO (including different types of ENSO: eastern Pacific warming and central Pacific warming), extratropical effects, dependence on time leads (initial conditions), regional monsoon features, and comparison between CFSv2 and CFS version 1 (CFSv1) are particularly emphasized. Large-scale characteristics of the Asian summer monsoon including several major dynamical monsoon indices and their associated precipitation patterns can be predicted several months in advance. The skill of predictions of the monsoon originates mostly from the impact of ENSO. It is found that large predictability errors occur in first three lead months and they only change slightly as lead time increases. The large errors in the first three lead months are associated with the large errors in surface thermal condition and atmospheric circulation in the central and eastern Pacific and the African continent. In addition, the response of the summer monsoon to ENSO becomes stronger with increase in lead time. The CFSv2 successfully simulates several major features of the East Asian winter monsoon and its relationships with the Arctic Oscillation, the East Asian subtropical jet, the East Asian trough, the Siberian high, and the lower-tropospheric winds. Surprisingly, the upper-tropospheric winds over the middle-high latitudes can be better simulated

  5. 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

  6. 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.

  7. Uncertainties of seasonal surface climate predictions induced by soil moisture biases in the La Plata Basin

    NASA Astrophysics Data System (ADS)

    Sorensson, Anna; Berbery, E. Hugo

    2015-04-01

    This work examines the evolution of soil moisture initialization biases and their effects on seasonal forecasts depending on the season and vegetation type for a regional model over the La Plata Basin in South America. WRF/Noah model simulations covering multiple cases during a two-year period are designed to emphasize the conceptual nature of the simulations at the expense of statistical significance of the results. Analysis of the surface climate shows that the seasonal predictive skill is higher when the model is initialized during the wet season and the initial soil moisture differences are small. Large soil moisture biases introduce large surface temperature biases, particularly for Savanna, Grassland and Cropland vegetation covers at any time of the year, thus introducing uncertainty in the surface climate. Regions with Evergreen Broadleaf Forest have roots that extend to the deep layer whose moisture content affects the surface temperature through changes in the partitioning of the surface fluxes. The uncertainties of monthly maximum temperature can reach several degrees during the dry season in cases when: (a) the soil is much wetter in the reanalysis than in the WRF/Noah equilibrium soil moisture, and (b) the memory of the initial value is long due to scarce rainfall and low temperatures. This study suggests that responses of the atmosphere to soil moisture initialization depend on how the initial wet and dry conditions are defined, stressing the need to take into account the characteristics of a particular region and season when defining soil moisture initialization experiments.

  8. Genetic and physiological bases for phenological responses to current and predicted climates

    PubMed Central

    Wilczek, A. M.; Burghardt, L. T.; Cobb, A. R.; Cooper, M. D.; Welch, S. M.; Schmitt, J.

    2010-01-01

    We are now reaching the stage at which specific genetic factors with known physiological effects can be tied directly and quantitatively to variation in phenology. With such a mechanistic understanding, scientists can better predict phenological responses to novel seasonal climates. Using the widespread model species Arabidopsis thaliana, we explore how variation in different genetic pathways can be linked to phenology and life-history variation across geographical regions and seasons. We show that the expression of phenological traits including flowering depends critically on the growth season, and we outline an integrated life-history approach to phenology in which the timing of later life-history events can be contingent on the environmental cues regulating earlier life stages. As flowering time in many plants is determined by the integration of multiple environmentally sensitive gene pathways, the novel combinations of important seasonal cues in projected future climates will alter how phenology responds to variation in the flowering time gene network with important consequences for plant life history. We discuss how phenology models in other systems—both natural and agricultural—could employ a similar framework to explore the potential contribution of genetic variation to the physiological integration of cues determining phenology. PMID:20819808

  9. 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.

  10. A coupled ecosystem-climate model for predicting the methane concentration in the Archean atmosphere.

    PubMed

    Kasting, J F; Pavlov, A A; Siefert, J L

    2001-06-01

    A simple coupled ecosystem-climate model is described that can predict levels of atmospheric CH4, CO2, and H2 during the Late Archean, given observed constraints on Earth's surface temperature. We find that methanogenic bacteria should have converted most of the available atmospheric H2 into CH4, and that CH4 may have been equal in importance to CO2 as a greenhouse gas. Photolysis of this CH4 may have produced a hydrocarbon smog layer that would have shielded the surface from solar UV radiation. Methanotrophic bacteria would have consumed some of the atmospheric CH4, but they would have been incapable of reducing CH4 to modern levels. The rise of O2 around 2.3 Ga would have drastically reduced the atmospheric CH4 concentration and may thereby have triggered the Huronian glaciation. PMID:11434106

  11. Sensitivity of an energy balance climate model with predicted snowfall rates

    NASA Technical Reports Server (NTRS)

    Bowman, K. P.

    1985-01-01

    A snowfall parameterization and a polar-ice-sheet model are developed and applied to the two-level zonally averaged seasonal energy-balance climate model of Held and Suarez (1979), and sensitivity experiments involving changes in insolation are performed both with and without ice sheets. The results are presented in tables and graphs, and the hydrological-cycle response to insolation changes is found to be similar to that predicted by global-circulation models employing prescribed precipitation levels, with a somewhat higher sensitivity in the snow line. The area covered by ice sheets in the ice-sheet models is shown to be greater than that covered by permanent snow in the models without ice sheets, an effect attributed to lower surface temperatures over the ice. It is inferred that an increase in the solar constant can cause increased high-latitude precipitation but not an ice age.

  12. 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

  13. Predicting University Preference and Attendance: Applied Marketing in Higher Education Administration.

    ERIC Educational Resources Information Center

    Cook, Robert W.; Zallocco, Ronald L.

    1983-01-01

    A multi-attribute attitude model was used to determine whether a multicriteria scale can be used to predict student preferences for and attendance at universities. Data were gathered from freshmen attending five state universities in Ohio. The results indicate a high level of predictability. (Author/MLW)

  14. Valuation of Mortality Risk Attributable to Climate Change: Investigating the Effect of Survey Administration Modes on a VSL

    PubMed Central

    Ščasný, Milan; Alberini, Anna

    2012-01-01

    The health impact attributable to climate change has been identified as one of the priority areas for impact assessment. The main goal of this paper is to estimate the monetary value of one key health effect, which is premature mortality. Specifically, our goal is to derive the value of a statistical life from people’s willingness to pay for avoiding the risk of dying in one post-transition country in Europe, i.e., the Czech Republic. We carried out a series of conjoint choice experiments in order to value mortality risk reductions. We found the responses to the conjoint choice questions to be reasonable and consistent with the economic paradigm. The VSL is about EUR 2.4 million, and our estimate is comparable with the value of preventing a fatality as used in one of the integrated assessment models. To investigate whether carrying out the survey through the internet may violate the welfare estimate, we administered our questionnaire to two independent samples of respondents using two different modes of survey administration. The results show that the VSLs for the two groups of respondents are €2.25 and €2.55 million, and these figures are statistically indistinguishable. However, the key parameters of indirect utility between the two modes of survey administration are statistically different when specific subgroups of population, such as older respondents, are concerned. Based on this evidence, we conclude that properly designed and administered on-line surveys are a reliable method for administering questionnaires, even when the latter are cognitively challenging. However, attention should be paid to sampling and choice regarding the mode of survey administration if the preference of specific segments of the population is elicited. PMID:23249861

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. 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

  1. Predictions of Flow Duration Curve Shifts Due to Anthropogenic and Climatic Changes

    NASA Astrophysics Data System (ADS)

    Henry, N. F.; Kroll, C. N.; Endreny, T. A.

    2014-12-01

    Methods are needed to understand and predict streamflows in systems undergoing anthropogenic and climatic alteration. This study is motivated by a need to develop methods to accurately estimate historical and future flow regimes of the Delaware River to inform management decisions for the endangered dwarf wedgemussel (Alasmidonta heterodon). Many streamflow regimes in this system have undergone substantial alteration within the past 100 years. Here, flow duration curves (FDCs), a common hydrologic tool used to assess flow regimes, are created and examined at 145 Delaware River Basin catchments. These catchments have experienced various hydrologic alterations, including land use changes, water withdrawals, and river regulation due to dams and reservoirs. Linear regression models are developed for various percentile flows across a FDC. These models use watershed characteristics that describe observed flow regimes in altered as well as unaltered systems. The characteristics that have the most significant influence on the shape of the FDCs are then identified and isolated as descriptors of the alteration. Once these models are developed to include these key variables, given a specific alteration (e.g. fresh water withdrawals, change in annual precipitation, etc.), a new flow regime can be estimated. Preliminary results indicate that certain watershed characteristics related to alteration (e.g. magnitude of land fragmentation, water withdrawals, hydrologic disturbance index) are significant in our models and influence FDC patterns. The results of this study may prove to have broader applications in regards to water resources management as the methods developed here may serve as a predictive tool as human interference and climatic changes continue to alter flow regimes.

  2. D3 receptor test in vitro predicts decreased cocaine self-administration in rats.

    PubMed

    Caine, S B; Koob, G F; Parsons, L H; Everitt, B J; Schwartz, J C; Sokoloff, P

    1997-07-01

    The three dopamine agonists with highest reported D3 receptor selectivity in vitro, pramipexole, quinelorane and PD128,907, decreased self-administration of a high dose of cocaine in rats as a result of a leftward shift in the cocaine dose-effect function. In contrast the D3 preferring antagonist nafadotride increased cocaine self-administration. Moreover the relative potencies of these and other D2-like dopamine agonists (lisuride, 7-OH-DPAT, quinpirole, apomorphine, bromocriptine) to modulate cocaine self-administration were highly correlated with their relative potencies for increasing mitogenesis in vitro in cell lines expressing D3 but not D2 receptors. These results support the hypothesis that the D3 receptor may be an important target for pharmacotherapies for cocaine abuse and dependence. PMID:9243643

  3. 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

  4. 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.

  5. 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

  6. A Comprehensive Framework for Quantitative Evaluation of Downscaled Climate Predictions and Projections

    NASA Astrophysics Data System (ADS)

    Barsugli, J. J.; Guentchev, G.

    2012-12-01

    The variety of methods used for downscaling climate predictions and projections is large and growing larger. Comparative studies of downscaling techniques to date are often initiated in relation to specific projects, are focused on limited sets of downscaling techniques, and hence do not allow for easy comparison of outcomes. In addition, existing information about the quality of downscaled datasets is not available in digital form. There is a strong need for systematic evaluation of downscaling methods using standard protocols which will allow for a fair comparison of their advantages and disadvantages with respect to specific user needs. The National Climate Predictions and Projections platform, with the contributions of NCPP's Climate Science Advisory Team, is developing community-based standards and a prototype framework for the quantitative evaluation of downscaling techniques and datasets. Certain principles guide the development of this framework. We want the evaluation procedures to be reproducible and transparent, simple to understand, and straightforward to implement. To this end we propose a set of open standards that will include the use of specific data sets, time periods of analysis, evaluation protocols, evaluation tests and metrics. Secondly, we want the framework to be flexible and extensible to downscaling techniques which may be developed in the future, to high-resolution global models, and to evaluations that are meaningful for additional applications and sectors. Collaboration among practitioners who will be using the downscaled data and climate scientists who develop downscaling methods will therefore be essential to the development of this framework. The proposed framework consists of three analysis protocols, along with two tiers of specific metrics and indices that are to be calculated. The protocols describe the following types of evaluation that can be performed: 1) comparison to observations, 2) comparison to a "perfect model" simulation

  7. Prediction of Optimal Reversal Dose of Sugammadex after Rocuronium Administration in Adult Surgical Patients

    PubMed Central

    Iwasaki, Hiroshi

    2014-01-01

    The objective of this study was to determine the point after sugammadex administration at which sufficient or insufficient dose could be determined, using first twitch height of train-of-four (T1 height) or train-of-four ratio (TOFR) as indicators. Groups A and B received 1 mg/kg and 0.5 mg/kg of sugammadex, respectively, as a first dose when the second twitch reappeared in train-of-four stimulation, and Groups C and D received 1 mg/kg and 0.5 mg/kg of sugammadex, respectively, as the first dose at posttetanic counts 1–3. Five minutes after the first dose, an additional 1 mg/kg of sugammadex was administered and changes in T1 height and TOFR were observed. Patients were divided into a recovered group and a partly recovered group, based on percentage changes in T1 height after additional dosing. T1 height and TOFR during the 5 min after first dose were then compared. In the recovered group, TOFR exceeded 90% in all patients at 3 min after sugammadex administration. In the partly recovered group, none of the patients had a TOFR above 90% at 3 min after sugammadex administration. An additional dose of sugammadex can be considered unnecessary if the train-of-four ratio is ≥90% at 3 min after sugammadex administration. This trial is registered with UMIN000007245. PMID:24672542

  8. Evaluating a physiologically based pharmacokinetic model for predicting the pharmacokinetics of midazolam in Chinese after oral administration

    PubMed Central

    Wang, Hong-yun; Chen, Xia; Jiang, Ji; Shi, Jun; Hu, Pei

    2016-01-01

    Aim: To evaluate the SimCYP simulator ethnicity-specific population model for predicting the pharmacokinetics of midazolam, a typical CYP3A4/5 substrate, in Chinese after oral administration. Methods: The physiologically based pharmacokinetic (PBPK) model for midazolam was developed using a SimCYP population-based simulator incorporating Chinese population demographic, physiological and enzyme data. A clinical trial was conducted in 40 Chinese subjects (the half was females) receiving a single oral dose of 15 mg midazolam. The subjects were separated into 4 groups based on age (20–50, 51–65, 66–75, and above 76 years), and the pharmacokinetics profiles of each age- and gender-group were determined, and the results were used to verify the PBPK model. Results: Following oral administration, the simulated profiles of midazolam plasma concentrations over time in virtual Chinese were in good agreement with the observed profiles, as were AUC and Cmax. Moreover, for subjects of varying ages (20–80 years), the ratios of predicted to observed clearances were between 0.86 and 1.12. Conclusion: The SimCYP PBPK model accurately predicted the pharmacokinetics of midazolam in Chinese from youth to old age. This study may provide novel insight into the prediction of CYP3A4/5-mediated pharmacokinetics in the Chinese population relative to Caucasians and other ethnic groups, which can support the rational design of bridging clinical trials. PMID:26592516

  9. Climatic Associations of British Species Distributions Show Good Transferability in Time but Low Predictive Accuracy for Range Change

    PubMed Central

    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

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. Impact of the assimilated sea ice data product on seasonal climate predictions with MPI-ESM

    NASA Astrophysics Data System (ADS)

    Bunzel, Felix; Notz, Dirk; Baehr, Johanna; Müller, Wolfgang; Fröhlich, Kristina

    2015-04-01

    data product used for model initialisation, and evaluate possible links to the predictability of mid- and low-latitude climate.

  18. 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.

  19. 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.

  20. Atmospheric CO2 concentrations during ancient greenhouse climates were similar to those predicted for A.D. 2100

    PubMed Central

    Breecker, D. O.; Sharp, Z. D.; McFadden, L. D.

    2010-01-01

    Quantifying atmospheric CO2 concentrations ([CO2]atm) during Earth’s ancient greenhouse episodes is essential for accurately predicting the response of future climate to elevated CO2 levels. Empirical estimates of [CO2]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 [CO2]atm have been significantly overestimated because previously assumed soil CO2 concentrations during carbonate formation are too high. More accurate [CO2]atm, resulting from better constraints on soil CO2, indicate that large (1,000s of ppmV) fluctuations in [CO2]atm did not characterize ancient climates and that past greenhouse climates were accompanied by concentrations similar to those projected for A.D. 2100. PMID:20080721

  1. 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. PMID:20080721

  2. Better Weather Prediction and Climate Diagnostics Using Rainfall Measurements from Space

    NASA Technical Reports Server (NTRS)

    Hou, Arthur; Zhang, Sara; Li, Jui-Lin; Reale, Oreste

    2002-01-01

    Progress in understanding of the role of water in global weather and climate is currently limited by our knowledge of the spatial and temporal variability of primary hydrological fields such as precipitation and evaporation. The Tropical Rainfall Measuring Mission (TRMM) has recently demonstrated that use of microwave-based rainfall observations from space in data assimilation can provide better climate data sets and improve short-range weather forecasting. At NASA, we have been exploring non-traditional approaches to assimilating TRMM Microwave Imager (TMI) and Special Sensor Microwavehager (SSM/I) surface rain rate and latent heating profile information in global systems. In this talk we show that assimilating microwave rain rates using a continuous variational assimilation scheme based on moisture tendency corrections improves quantitative precipitation estimates (QPE) and related clouds, radiation energy fluxes, and large-scale circulations in the Goddard Earth Observing System (GEOS) reanalyses. Short-range forecasts initialized with these improved analyses also yield better QPE scores and storm track predictions for Hurricanes Bonnie and Floyd. We present a status report on current efforts to assimilate convective and stratiform latent heating profile information within the general variational framework of model parameter estimation to seek further improvements. Within the next 5 years, there will be a gradual increase in microwave rain products available from operational and research satellites, culminating to a target constellation of 9 satellites to provide global rain measurements every 3 hours with the proposed Global Precipitation Measurement (GPM) mission in 2007/2008. Based on what has been learned from TRMM, there is a high degree of confidence that these observations can play a'major role in improving weather forecasts and producing better global datasets for understanding the Earth's water and energy cycle. The key to success is to adopt an

  3. 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

  4. 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

  5. Evaluation of reanalysis climate simulations for the prediction of extreme runoff characteristics

    NASA Astrophysics Data System (ADS)

    Coskun, Mehmet; Samaniego, Luis; Kumar, Rohini

    2010-05-01

    Discharge regimes of river basins are expected to be altered due to possible effects of global warming. For planning and water resources management, it is fundamental to estimate the probability of occurrence of extreme hydrological events such as magnitude and frequency of floods and droughts. So far, it is a matter of debate whether actual Global and Regional Climate Model outputs or their reanalysis products (bias corrected) are able to provide a reasonable estimate of the meteorological variables that are required to force a distributed hydrologic model. In this study, we will evaluate various climate simulations for their reliability to predict extreme runoff characteristics in three German mesoscale river basins with various sizes and hydro-meteorological conditions: Neckar (12 700 km2), Bode (3 300 km2), and Mulde (2 700 km2). Reanalysis of the global atmosphere and surface conditions were obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis (ERA-40) for the period from 1957 to 2002. These data will be used to force a grid based mesoscale hydrologic model calibrated with past meteorological and discharge observations. Several runoff characteristics will be estimated based on daily discharge simulations and then compared with their corresponding estimates derived from daily streamflow observations. Finally, nonparametric statistical test (e.g. Kolmogorov-Smirnov test) and Tukey's depth function will be employed to test two null hypotheses: 1) Meteorological observations and the reanalysis data are realisations from a common generating process, and 2) The probability of occurrence of extreme runoff characteristics obtained from both data sets is similar.

  6. 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.

  7. 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...

  8. 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...

  9. 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".

  10. 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

  11. Prediction of interdecadal variation in climate over NE China with countermeasures

    NASA Astrophysics Data System (ADS)

    Xu, Nanping; Yuan, Meiying; Pan, Huasheng; Xu, Ying; Zhang, Guihua

    2014-10-01

    The study shows that about 1.4°C rise in mean temperature occurs between the 1900-1910s and 1980-1990s, with an abrupt change around 1990 due to climate shift. We also notice that the rise is 1.6°C in winter, reaching roughly 1.3°C in spring. Nationwide, the strongest warming is found in the northern par of NE China. It is worth noting that from the 1980s to present day the climate remains to be in warming, a phenomenon that has never happened in the last century. 5-model predictions of NE China climate for the future 30-50 years indicate a higher temperature rise in the year 2030 and 2050. The yearly mean would be the 1.94°C rise in 2030, with 2.06, 1.26, 1.79 and 2.66°C increase in spring, summer, autumn and winter, respectively. These results suggest that the highest increase is in winter, in order. The temperature increase is higher in the northern than in the southern part. The increase is expected to be kept in 2050, with annual mean rise of 2.42°C, with the ascent of 2.13, 1.68, 2.56.and 3.21°C, respectively in spring, summer, autumn and winter. The winter rise is the strongest, centered on the northern part of the region. Based on the above findings, the cumulative temperature band of T>=10°C for crop growth would be shifted northward by approximately 5 latitudes. In 2050 the original first band would move to the north of the Daxinganling mountains and the other 4 bands be nearly eliminated. The dominant farming area of rice would be shifted into the Heilongjiang valley, the winter wheat zone be expanded for experiment. For this purpose 6 countermeasures are proposed for the structure of staple grain crops and the necessary adjustment of their regional distribution for the stable and high yields of crops in this region.

  12. 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

  13. 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

  14. Climate Sensitivity Runs and Regional Hydrologic Modeling for Predicting the Response of the Greater Florida Everglades Ecosystem to Climate Change

    NASA Astrophysics Data System (ADS)

    Obeysekera, Jayantha; Barnes, Jenifer; Nungesser, Martha

    2015-04-01

    It is important to understand the vulnerability of the water management system in south Florida and to determine the resilience and robustness of greater Everglades restoration plans under future climate change. The current climate models, at both global and regional scales, are not ready to deliver specific climatic datasets for water resources investigations involving future plans and therefore a scenario based approach was adopted for this first study in restoration planning. We focused on the general implications of potential changes in future temperature and associated changes in evapotranspiration, precipitation, and sea levels at the regional boundary. From these, we developed a set of six climate and sea level scenarios, used them to simulate the hydrologic response of the greater Everglades region including agricultural, urban, and natural areas, and compared the results to those from a base run of current conditions. The scenarios included a 1.5 °C increase in temperature, ±10 % change in precipitation, and a 0.46 m (1.5 feet) increase in sea level for the 50-year planning horizon. The results suggested that, depending on the rainfall and temperature scenario, there would be significant changes in water budgets, ecosystem performance, and in water supply demands met. The increased sea level scenarios also show that the ground water levels would increase significantly with associated implications for flood protection in the urbanized areas of southeastern Florida.

  15. Climate sensitivity runs and regional hydrologic modeling for predicting the response of the greater Florida Everglades ecosystem to climate change.

    PubMed

    Obeysekera, Jayantha; Barnes, Jenifer; Nungesser, Martha

    2015-04-01

    It is important to understand the vulnerability of the water management system in south Florida and to determine the resilience and robustness of greater Everglades restoration plans under future climate change. The current climate models, at both global and regional scales, are not ready to deliver specific climatic datasets for water resources investigations involving future plans and therefore a scenario based approach was adopted for this first study in restoration planning. We focused on the general implications of potential changes in future temperature and associated changes in evapotranspiration, precipitation, and sea levels at the regional boundary. From these, we developed a set of six climate and sea level scenarios, used them to simulate the hydrologic response of the greater Everglades region including agricultural, urban, and natural areas, and compared the results to those from a base run of current conditions. The scenarios included a 1.5 °C increase in temperature, ±10 % change in precipitation, and a 0.46 m (1.5 feet) increase in sea level for the 50-year planning horizon. The results suggested that, depending on the rainfall and temperature scenario, there would be significant changes in water budgets, ecosystem performance, and in water supply demands met. The increased sea level scenarios also show that the ground water levels would increase significantly with associated implications for flood protection in the urbanized areas of southeastern Florida. PMID:25011530

  16. 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…

  17. 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

  18. 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

  19. 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

  20. 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

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. Effects of the Mount Pinatubo eruption on decadal climate prediction skill of Pacific sea surface temperatures

    NASA Astrophysics Data System (ADS)

    Meehl, Gerald A.; Teng, Haiyan; Maher, Nicola; England, Matthew H.

    2015-12-01

    Multi-model simulations show a post-Pinatubo eruption sequence of Pacific sea surface temperatures (SSTs) that includes a La Niña-like pattern the third northern winter after an eruption, opposite in sign to what was observed after Pinatubo. This leads to the loss of hindcast skill for years in the 1990s affected by the Pinatubo eruption because the post-eruption internal variability of the climate system did not match the multi-model forced response. Agung (1963) and El Chichón (1982) happened to have post-eruption Pacific SST sequences more similar to the multi-model response and thus do not degrade prediction skill as measured by anomaly pattern correlation in the hindcasts. Thus, decadal hindcast skill is reduced if the post-eruption randomly occurring internal El Niño variability in the observations deviates from the multi-model forced response that, by definition, averages out internal variability in favor of the forced response.

  6. 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

  7. Lidar-measured winds from space: A key component for weather and climate prediction

    NASA Technical Reports Server (NTRS)

    Baker, Wayman E.; Emmitt, George D.; Robertson, Franklin; Atlas, Robert M.; Molinari, John E.; Bowdle, David A.; Paegle, Jan; Hardesty, R. Michael; Menzies, Robert T.; Krishnamurti, T. N.

    1995-01-01

    The deployment of a space-based Doppler lidar would provide information that is fundamental to advancing the understanding and prediction of weather and climate. This paper reviews the concepts of wind measurement by Doppler lidar, highlights the results of some observing system simulation experiments with lidar winds, and discusses the important advances in earth system science anticipated with lidar winds. Observing system simulation experiments, conducted using two different general circulation models, have shown (1) that there is a significant improvement in the forecast accuracy over the Southern Hemisphere and tropical oceans resulting from the assimilation of simulated satellite wind data, and (2) that wind data are significantly more effective than temperature or moisture data in controlling analysis error. Because accurate wind observations are currently almost entirely unavailable for the vast majority of tropical cyclones worldwide, lidar winds have the potential to substan- tially improve tropical cyclone forecasts. Similarly, to improve water vapor flux divergence calculations, a direct measure of the ageostrophic wind is needed since the present level of uncer- tainty cannot be reduced with better temperature and moisture soundings alone.

  8. Predicting equilibrium vegetation responses to global climate change using coupled biogeography and ecosystem models

    SciTech Connect

    Borchers, J.G.; Nielson, R.P.

    1995-06-01

    Much current uncertainty surrounding the sensitivity to climatic change of natural vegetation in the USA is related to widely-varying approaches taken in constructing simulation models. Our goal was to reduce this uncertainty by coupling the biogeography model MAPSS (Mapped Atmosphere-Plant-Soil System) with critical ecosystem processes as simulated by TEM (Terrestrial Ecosystem Model). MAPSS predicts changes in leaf-area index (LAI) and vegetation biome boundaries using a site water balance model in conjunction with a physiologically-conceived rule-base model. On the other hand, TEM simulates equilibrium fluxes and pools of carbon (C) and nitrogen (N) such as net primary productivity (NPP) and available N without redistributing vegetation. In the coupled version of MAPSS presented here, these hydrological and biogeochemical processes are mutually constrained. For example, N availability may limit maximum LAI, and therefore, site water balance. Alternatively, actual evapotranspiration and soil water availability may modulate NPP via photosynthesis and net N mineralization. Initial results with this TEM-coupled version of MAPSS reveal significantly different patterns of NPP and vegetation distribution for the conterminous USA compared to those from uncoupled models, particularly at thermal and hydric extremes.

  9. 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

  10. 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. PMID:24842035

  11. 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.

  12. Predicted responses of invasive mammal communities to climate-related changes in mast frequency in forest ecosystems.

    PubMed

    Tompkins, Daniel M; Byrom, Andrea E; Pech, Roger P

    2013-07-01

    Predicting the dynamics and impacts of multiple invasive species can be complex because ecological relationships, which occur among several trophic levels, are often incompletely understood. Further, the complexity of these trophic relationships exacerbates our inability to predict climate change effects on invaded ecosystems. We explore the hypothesis that interactions between two global change drivers, invasive vertebrates and climate change, will potentially make matters worse for native biodiversity. In New Zealand beech (Nothofagus spp.) forests, a highly irruptive invasive mammal community is driven by multi-annual resource pulses of beech seed (masting). Because mast frequency is predicted to increase with climate change, we use this as a model system to explore the extent to which such effects may influence invasive vertebrate communities, and the implications of such interactions for native biodiversity and its management. We build on an established model of trophic interactions in the system, combining it with a logistic probability mast function, the parameters of which were altered to simulate either contemporary conditions or conditions of more or less frequent masting. The model predicts that increased mast frequency will lead to populations of a top predator (the stoat) and a mesopredator (the ship rat) becoming less irruptive and being maintained at appreciably higher average abundances in this forest type. In addition, the ability of both current and in-development management approaches to suppress invasive mammals is predicted to be compromised. Because invasive mammals are key drivers of native fauna extinction in New Zealand, with the additional loss of associated functions such as pollination and seed dispersal, these predictions imply potentially serious adverse impacts of climate change for the conservation of biodiversity and ecosystem function. Our study also highlights the importance of long-term monitoring data for assessing and managing

  13. Statistical Climate Prediction for the Interior Southwestern U.S.: Lessons Learned From six Years of Experimental Seasonal Forecasts

    NASA Astrophysics Data System (ADS)

    Wolter, K.

    2006-05-01

    New and improved "climate divisions" can be used for the statistical prediction of climate anomalies (here: precipitation) in the interior southwestern U.S. This presentation gives a brief survey of the employed forecast technique (stepwise multiple regressions in "ensemble" mode) as well as different predictors that were found useful. Experimental climate (precipitation) forecasts were first issued in late 2001, and are updated monthly on the internet. Since the original data training period ended in September 1999, there are now six years of verification data available. Seasonal forecast skill for the interior southwestern U.S. appears to be linked not only to ENSO (and its various 'flavors'), but also to SST regions further afield (Indian Ocean) as well as closer to the U.S. (eastern subtropical Pacific and Caribbean). Other useful predictors include northern hemispheric teleconnection patterns, and antecedent regional precipitation anomalies. Cross-validated skill shows large spatial and temporal variation. It is largest in the winter season, and lowest in the spring, consistent with operational forecast skill by the Climate Prediction Center. However, independent verification for the last six years shows the opposite to be the case - perhaps attesting to rather unusual conditions during this period, such as the absence of strong ENSO events. Nevertheless, two regions of high forecast skill in winter are arguably very important for the interior Southwest: the mountains of northern Utah and Colorado, both regions NOT well predicted by CPC.

  14. 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

  15. Towards a Seamless Framework for Drought Analysis and Prediction from Seasonal to Climate Change Time Scales (Plinius Medal Lecture)

    NASA Astrophysics Data System (ADS)

    Sheffield, Justin

    2013-04-01

    Droughts arguably cause the most impacts of all natural hazards in terms of the number of people affected and the long-term economic costs and ecosystem stresses. Recent droughts worldwide have caused humanitarian and economic problems such as food insecurity across the Horn of Africa, agricultural economic losses across the central US and loss of livelihoods in rural western India. The prospect of future increases in drought severity and duration driven by projected changes in precipitation patterns and increasing temperatures is worrisome. Some evidence for climate change impacts on drought is already being seen for some regions, such as the Mediterranean and east Africa. Mitigation of the impacts of drought requires advance warning of developing conditions and enactment of drought plans to reduce vulnerability. A key element of this is a drought early warning system that at its heart is the capability to monitor evolving hydrological conditions and water resources storage, and provide reliable and robust predictions out to several months, as well as the capacity to act on this information. At longer time scales, planning and policy-making need to consider the potential impacts of climate change and its impact on drought risk, and do this within the context of natural climate variability, which is likely to dominate any climate change signal over the next few decades. There are several challenges that need to be met to advance our capability to provide both early warning at seasonal time scales and risk assessment under climate change, regionally and globally. Advancing our understanding of drought predictability and risk requires knowledge of drought at all time scales. This includes understanding of past drought occurrence, from the paleoclimate record to the recent past, and understanding of drought mechanisms, from initiation, through persistence to recovery and translation of this understanding to predictive models. Current approaches to monitoring and

  16. When What You See Isn’t What You Get: Alcohol Cues, Alcohol Administration, Prediction Error, and Human Striatal Dopamine

    PubMed Central

    Yoder, Karmen K.; Morris, Evan D.; Constantinescu, Cristian C.; Cheng, Tee-Ean; Normandin, Marc D.; O’Connor, Sean J.; Kareken, David A.

    2010-01-01

    Background The mesolimbic dopamine (DA) system is implicated in the development and maintenance of alcohol drinking; however, the exact mechanisms by which DA regulates human alcohol consumption are unclear. This study assessed the distinct effects of alcohol-related cues and alcohol administration on striatal DA release in healthy humans. Methods Subjects underwent 3 PET scans with [11C]raclopride (RAC). Subjects were informed that they would receive either an IV Ringer’s lactate infusion or an alcohol (EtOH) infusion during scanning, with naturalistic visual and olfactory cues indicating which infusion would occur. Scans were acquired in the following sequence: (1) Baseline Scan: Neutral cues predicting a Ringer’s lactate infusion, (2) CUES Scan: Alcohol-related cues predicting alcohol infusion in a Ringer’s lactate solution, but with alcohol infusion after scanning to isolate the effects of cues, and (3) EtOH Scan: Neutral cues predicting Ringer’s, but with alcohol infusion during scanning (to isolate the effects of alcohol without confounding expectation or craving). Results Relative to baseline, striatal DA concentration decreased during CUES, but increased during EtOH. Conclusion While the results appear inconsistent with some animal experiments showing dopaminergic responses to alcohol’s conditioned cues, they can be understood in the context of the hypothesized role of the striatum in reward prediction error, and of animal studies showing that midbrain dopamine neurons decrease and increase firing rates during negative and positive prediction errors, respectively. We believe that our data are the first in humans to demonstrate such changes in striatal DA during reward prediction error. PMID:18976347

  17. 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

  18. 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. PMID:25703827

  19. 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

  20. 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

  1. 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.

  2. Factors predicting team climate, and its relationship with quality of care in general practice

    PubMed Central

    2009-01-01

    Background Quality of care in general practice may be affected by the team climate perceived by its health and non-health professionals. Better team working is thought to lead to higher effectiveness and quality of care. However, there is limited evidence available on what affects team functioning and its relationship with quality of care in general practice. This study aimed to explore individual and practice factors that were associated with team climate, and to explore the relationship between team climate and quality of care. Methods Cross sectional survey of a convenience sample of 14 general practices and their staff in South Tyneside in the northeast of England. Team climate was measured using the short version of Team Climate Inventory (TCI) questionnaire. Practice characteristics were collected during a structured interview with practice managers. Quality was measured using the practice Quality and Outcome Framework (QOF) scores. Results General Practitioners (GP) had a higher team climate scores compared to other professionals. Individual's gender and tenure, and number of GPs in the practice were significantly predictors of a higher team climate. There was no significant correlation between mean practice team climate scores (or subscales) with QOF scores. Conclusion The absence of a relationship between a measure of team climate and quality of care in this exploratory study may be due to a number of methodological problems. Further research is required to explore how to best measure team functioning and its relationship with quality of care. PMID:19653911

  3. Non-MHC-linked Th2 cell development induced by soluble protein administration predicts susceptibility to Leishmania major infection.

    PubMed

    Guéry, J C; Galbiati, F; Smiroldo, S; Adorini, L

    1997-09-01

    Continuous administration of soluble protein Ag followed by immunization with the same Ag in adjuvant results in the selective development of Ag-specific CD4+ Th2 cells in both normal and beta2-microglobulin-deficient BALB/c mice. In addition to chronic administration by mini-osmotic pump, single bolus i.p., but not i.v., injection of protein Ag induces Th2 cell expansion. Strong Th2 cell priming depends on a non-MHC-linked genetic polymorphism. It is observed in all congenic strains on BALB background tested, BALB/c, BALB/b, and BALB/k, but not in MHC-matched strains on disparate genetic background, B10.D2, C57BL/6, and C3H. DBA/2 mice appear to have an intermediate phenotype, as shown by their weaker capacity to mount Th2 responses as compared with BALB/c mice after soluble Ag administered by either mini-osmotic pumps or single bolus i.p. Conversely, induction of Th1 cell unresponsiveness by soluble protein is observed in any mouse strain tested, following any mode of Ag administration. These data demonstrate that non-MHC-linked genetic polymorphism controls the priming of Th2 but not the inhibition of Th1 cells induced by administration of soluble protein. The pattern of Th2 responses in these different strains is predictive of disease outcome following Leishmania major infection and supports the hypothesis that systemic Ag presentation in the absence of strong inflammatory signals may represent an important stimulus leading to selective Th2 cell development in susceptible mouse strains. PMID:9278301

  4. Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

    PubMed Central

    Gupta, Sunil; Tran, Truyen; Luo, Wei; Phung, Dinh; Kennedy, Richard Lee; Broad, Adam; Campbell, David; Kipp, David; Singh, Madhu; Khasraw, Mustafa; Matheson, Leigh; Ashley, David M; Venkatesh, Svetha

    2014-01-01

    Objectives Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes. Setting A regional cancer centre in Australia. Participants Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data. Primary and secondary outcome measures Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC). Results The ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours. Conclusions Machine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems. PMID:24643167

  5. The PRONE score: an algorithm for predicting doctors’ risks of formal patient complaints using routinely collected administrative data

    PubMed Central

    Spittal, Matthew J; Bismark, Marie M; Studdert, David M

    2015-01-01

    Background Medicolegal agencies—such as malpractice insurers, medical boards and complaints bodies—are mostly passive regulators; they react to episodes of substandard care, rather than intervening to prevent them. At least part of the explanation for this reactive role lies in the widely recognised difficulty of making robust predictions about medicolegal risk at the individual clinician level. We aimed to develop a simple, reliable scoring system for predicting Australian doctors’ risks of becoming the subject of repeated patient complaints. Methods Using routinely collected administrative data, we constructed a national sample of 13 849 formal complaints against 8424 doctors. The complaints were lodged by patients with state health service commissions in Australia over a 12-year period. We used multivariate logistic regression analysis to identify predictors of subsequent complaints, defined as another complaint occurring within 2 years of an index complaint. Model estimates were then used to derive a simple predictive algorithm, designed for application at the doctor level. Results The PRONE (Predicted Risk Of New Event) score is a 22-point scoring system that indicates a doctor's future complaint risk based on four variables: a doctor's specialty and sex, the number of previous complaints and the time since the last complaint. The PRONE score performed well in predicting subsequent complaints, exhibiting strong validity and reliability and reasonable goodness of fit (c-statistic=0.70). Conclusions The PRONE score appears to be a valid method for assessing individual doctors’ risks of attracting recurrent complaints. Regulators could harness such information to target quality improvement interventions, and prevent substandard care and patient dissatisfaction. The approach we describe should be replicable in other agencies that handle large numbers of patient complaints or malpractice claims. PMID:25855664

  6. Northern Hemisphere climate trends in reanalysis and forecast model predictions: The 500 hPa annual means

    NASA Astrophysics Data System (ADS)

    Bordi, I.; Fraedrich, K.; Sutera, A.

    2010-06-01

    The lead time dependent climates of the ECMWF weather prediction model, initialized with ERA-40 reanalysis, are analysed using 44 years of day-1 to day-10 forecasts of the northern hemispheric 500-hPa geopotential height fields. The study addresses the question whether short-term tendencies have an impact on long-term trends. Comparing climate trends of ERA-40 with those of the forecasts, it seems that the forecast model rapidly loses the memory of initial conditions creating its own climate. All forecast trends show a high degree of consistency. Comparison results suggest that: (i) Only centers characterized by an upward trend are statistical significant when increasing the lead time. (ii) In midilatitudes an upward trend larger than the one observed in the reanalysis characterizes the forecasts, while in the tropics there is a good agreement. (iii) The downward trend in reanalysis at high latitudes characterizes also the day-1 forecast which, however, increasing lead time approaches zero.

  7. The relative influence of soil moisture and SST in climate predictability explored within ensembles of AMIP type experiments

    NASA Astrophysics Data System (ADS)

    Conil, S.; Douville, H.; Tyteca, S.

    2007-02-01

    Three ensembles of AMIP-type simulations using the Arpege-climat coupled land atmosphere model have been designed to assess the relative influence of SST and soil moisture (SM) on climate variability and predictability. The study takes advantage of the GSWP2 land surface reanalysis covering the 1986 1995 period. The GSWP2 forcings have been used to derive a global SM climatology that is fully consistent with the model used in this study. One ensemble of ten simulations has been forced by climatological SST and the simulated SM is relaxed toward the GSWP2 reanalysis. Another ensemble has been forced by observed SST and SM is evolving freely. The last ensemble combines the observed SST forcing and the relaxation toward GSWP2. Two complementary aspects of the predictability have been explored, the potential predictability (analysis of variance) and the effective predictability (skill score). An analysis of variance has revealed the effects of the SST and SM boundary forcings on the variability and potential predictability of near-surface temperature, precipitation and surface evaporation. While in the tropics SST anomalies clearly maintain a potentially predictable variability throughout the annual cycle, in the mid-latitudes the SST forced variability is only dominant in winter and SM plays a leading role in summer. In a similar fashion, the annual cycle of the hindcast skill (evaluated as the anomalous correlation coefficient of the three ensemble means with respect to the “observations”) indicates that the SST forcing is the dominant contributor over the tropical continents and in the winter mid-latitudes but that SM is supporting a significant part of the skill in the summer mid-latitudes. Focusing on boreal summer, we have then investigated different aspects of the SM and SST contribution to climate variations in terms of spatial distribution and time-evolution. Our experiments suggest that SM is potentially an additional source of climate predictability. A

  8. 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

  9. 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

  10. Predictability of Mediterranean climate variables from oceanic variability. Part I: Sea surface temperature regimes

    NASA Astrophysics Data System (ADS)

    Hertig, E.; Jacobeit, J.

    2011-03-01

    The determination of specific sea surface temperature (SST) patterns from large-scale gridded SST-fields has widely been done. Often principal component analysis (PCA) is used to condense the SST-data to major patterns of variability. In the present study SST-fields for the period 1950-2003 from the area 20°S to 60°N are analysed with respect to SST-regimes being defined as large-scale oceanic patterns with a regular and at least seasonal occurrence. This has been done in context of investigations on seasonal predictability of Mediterranean regional climate with large-scale SST-regimes as intended predictors in statistical model relationships. The SST-regimes are derived by means of a particular technique including multiple applications of s-mode PCA. Altogether 17 stationary regimes can be identified, eight for the Pacific Ocean, five for the Atlantic Ocean, two for the Indian Ocean, and two regimes which show a distinct co-variability within different ocean basins. Some regimes exist, with varying strength and spatial extent, throughout the whole year, whereas other regimes are only characteristic for a particular season. Several regimes show dominant variability modes, like the regimes associated with El Niño, with the Pacific Decadal Oscillation or with the North Atlantic Tripole, whereas other regimes describe little-known patterns of large-scale SST variability. The determined SST-regimes are subsequently used as predictors for monthly precipitation and temperature in the Mediterranean area. This subject is addressed in Part II of this paper.

  11. 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

  12. 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.

  13. 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 ...

  14. Predicting Teacher Commitment: The Impact of School Climate and Social-Emotional Learning

    ERIC Educational Resources Information Center

    Collie, Rebecca J.; Shapka, Jennifer D.; Perry, Nancy E.

    2011-01-01

    The aim of this study was to investigate whether school climate and social-emotional learning impact teacher commitment. The sample included 664 public schoolteachers from British Columbia and Ontario in Canada. Participants completed an online questionnaire about teacher commitment, school climate, and social-emotional learning. Binary logistic…

  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. Mediating effect of cooperative norm in predicting organizational citizenship behaviors from procedural justice climate.

    PubMed

    Lin, Shang-Ping; Tang, Ta-Wei; Li, Chao-Hua; Wu, Chien-Ming; Lin, Hsiu-Hsia

    2007-08-01

    Although the relationships between procedural justice climate and organizational citizenship behaviors have been examined in recent years, little research has explored the mechanism by which procedural justice climate shapes individual employee prosocial behaviors in the workplace. The purpose of this study was to examine the mediating role of a group-level cooperative norm on the relationships between the group-level procedural justice climate and individual-level organizational citizenship behaviors. The survey involved 45 work groups in four different industry fields in Taiwan, including manufacturing, technology, banking, and insurance, and each of the groups was composed of one supervisor and three subordinates. Cross-level analyses using hierarchical linear modeling (HLM) indicated that the cooperative norm fully mediated the relationship between procedural justice climate and individual helping behaviors. Procedural justice climate indirectly affects individual helping behaviors through their effects on the cooperative norm. PMID:17958109

  17. Detailed predictions of climate induced changes in the thermal and flow regimes in mountain streams of the Iberian Peninsula

    NASA Astrophysics Data System (ADS)

    Santiago, José M.; Muñoz-Mas, Rafael; García de Jalón, Diego; Solana, Joaquín; Alonso, Carlos; Martínez-Capel, Francisco; Ribalaygua, Jaime; Pórtoles, Javier; Monjo, Robert

    2016-04-01

    Streamflow and temperature regimes are well-known to influence on the availability of suitable physical habitat for instream biological communities. General Circulation Models (GCMs) have predicted significant changes in timing and geographic distribution of precipitation and atmospheric temperature for the ongoing century. However, differences in these predictions may arise when focusing on different spatial and temporal scales. Therefore, to perform substantiated mitigation and management actions detailed scales are necessary to adequately forecast the consequent thermal and flow regimes. Regional predictions are relatively abundant but detailed ones, both spatially and temporally, are still scarce. The present study aimed at predicting the effects of climate change on the thermal and flow regime in the Iberian Peninsula, refining the resolution of previous studies. For this purpose, the study encompassed 28 sites at eight different mountain rivers and streams in the central part of the Iberian Peninsula (Spain). The daily flow was modelled using different daily, monthly and quarterly lags of the historical precipitation and temperature time series. These precipitation-runoff models were developed by means of M5 model trees. On the other hand water temperature was modelled at similar time scale by means of nonlinear regression from dedicated site-specific data. The developed models were used to simulate the temperature and flow regime under two Representative Concentration Pathway (RCPs) climate change scenarios (RCP 4.5 and RCP 8.5) until the end of the present century by considering nine different GCMs, which were pertinently downscaled. The precipitation-runoff models achieved high accuracy (NSE>0.7), especially in regards of the low flows of the historical series. Results concomitantly forecasted flow reductions between 7 and 17 % (RCP4.5) and between 8 and 49% (RCP8.5) of the annual average in the most cases, being variable the magnitude and timing at each

  18. Progress and hurdles to forecasting phenology: How networked experiments and a species' traits framework can improve predictions with climate change

    NASA Astrophysics Data System (ADS)

    Wolkovich, E. M.; Cook, B. I.

    2012-12-01

    Accurate predictions of the timing of plant leafing and flowering are critical to models of global carbon budgets and future ecosystem services under climate change scenarios. Yet useful predictions have proven difficult for all but a handful of well-studied tree and crop species; further, comparisons of predictions across methods have highlighted large inconsistencies, which suggest robust forecasting of future ecosystems' plant phenology is still not within reach. Here I highlight how new approaches in ecological research could rapidly contribute to improved understanding of plant phenology across species, growing seasons and ecosystems. I first show how re-designed warming experiments--including standardized methods and analyses--would allow more useful comparisons with other methods and improved opportunities to study non-linearities and phenological cues beyond temperature. I then highlight how a species trait framework is critical for predicting shifts across years and ecosystems. I show that invasive species are generally 20-40% more sensitive to temperature than native species in temperate ecosystems--with responses in summer drought systems more complex and soil moisture dependent. Additionally, I show that annual species are 20% more temperature sensitive than perennials. Such species' responses may be especially critical for predicting longer growing seasons and their related carbon balances in future systems. Combined with a returned focus to the physiology of phenology and standardized experimental approaches ecologically-focused climate change research can allow rapid progress in understanding plant phenology across species and ecosystems.

  19. Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail

    PubMed Central

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

    2013-01-01

    Modeling the biogeographic consequences of climate change requires confidence in model predictions under novel conditions. However, models often fail when extended to new locales, and such instances have been used as evidence of a change in physiological tolerance, that is, a fundamental niche shift. We explore an alternative explanation and propose a method for predicting the likelihood of failure based on physiological performance curves and environmental variance in the original and new environments. We define the transient event margin (TEM) as the gap between energetic performance failure, defined as CTmax, and the upper lethal limit, defined as LTmax. If TEM is large relative to environmental fluctuations, models will likely fail in new locales. If TEM is small relative to environmental fluctuations, models are likely to be robust for new locales, even when mechanism is unknown. Using temperature, we predict when biogeographic models are likely to fail and illustrate this with a case study. We suggest that failure is predictable from an understanding of how climate drives nonlethal physiological responses, but for many species such data have not been collected. Successful biogeographic forecasting thus depends on understanding when the mechanisms limiting distribution of a species will differ among geographic regions, or at different times, resulting in realized niche shifts. TEM allows prediction of the likelihood of such model failure. PMID:24223272

  20. Climate change is predicted to negatively influence Moroccan endemic reptile richness. Implications for conservation in protected areas

    NASA Astrophysics Data System (ADS)

    Martínez-Freiría, Fernando; Argaz, Hamida; Fahd, Soumía; Brito, José C.

    2013-09-01

    The identification of species-rich areas and their prognosticated turnover under climate change are crucial for the conservation of endemic taxa. This study aims to identify areas of reptile endemicity richness in a global biodiversity hot spot (Morocco) under current and future climatic conditions and to investigate the role of protected areas in biodiversity conservation under climate change. Species distribution models (SDM) were performed over the distribution of 21 endemic reptiles, combined to estimate current species richness at 1 × 1 km resolution and projected to years 2050 and 2080 according to distinct story lines and ensemble global circulation models, assuming unlimited and null dispersion ability. Generalized additive models were performed between species richness and geographic characteristics of 43 protected areas. SDM found precipitation as the most important factor related to current species distributions. Important reductions in future suitable areas were predicted for 50 % of species, and four species were identified as highly vulnerable to extinction. Drastic reductions in species-rich areas were predicted for the future, with considerable variability between years and dispersal scenarios. High turnover rates of species composition were predicted for eastern Morocco, whereas low values were forecasted for the Northern Atlantic coast and mountains. Species richness for current and future conditions was significantly related to the altitude and latitude of protected areas. Protected areas located in mountains and/or in the Northern Atlantic coast were identified as refugia, where population monitoring and conservation management is needed.

  1. Predictive Understanding of Seasonal Hydrological Dynamics under Climate and Land Use-Land Cover Change

    NASA Astrophysics Data System (ADS)

    Batra, N.; Yang, Y. E.; Choi, H. I.; Kumar, P.; Cai, X.; Fraiture, C. D.

    2008-12-01

    Water has always been and will continue to be an important factor in agricultural production and any alteration in the seasonal distribution of water availability due to climate and land use-land cover change (LULCC) will significantly impact the future production. To achieve the ecologic, economic and social objectives of sustainability, physical understanding of the linkages between climatic changes, LULCC and hydrological response is required. Aided by satellite data, modeling and understanding of the interactions between physical processes of the climate system and society, more reliable regional LULCC and climate change projections are now available. However, resulting quantitative projection of changes on the regional scale hydrological components at the seasonal time scale are sparse. This study attempts to quantify the seasonal hydrological response due to projected LULCC and climate change scenario of Intergovernmental Panel on Climate Change (IPCC) in different hydro-climatic regions of the world. The Common Land Model (CLM) is used for global assessment of future hydrologic response with the development of a consistent global GIS based database for the surface boundary conditions and meteorological forcing of the model. Future climate change projections are derived from the IPCC Fourth Assessment Report: Working Group I - The Physical Science Basis. The study is performed over nine river basins selected from Asia, Africa and North America to present the broad climatic and landscape controls on the seasonal hydrological dynamics. Future changes in water availability are quite evident from our results based upon changes in the volume and seasonality of precipitation, runoff and evapotranspiration. Severe water scarcity is projected to occur in certain seasons which may not be known through annual estimates. Knowledge of the projected seasonal hydrological response can be effectively used for developing adaptive management strategies for the sustainability

  2. Elevated serum progesterone/ MII oocyte ratio on the day of human chorionic gonadotropin administration can predict impaired endometrial receptivity

    PubMed Central

    Aflatoonian, Abbas; Davar, Robab; Hojjat, Farzaneh

    2014-01-01

    Background: Increased serum progesterone on the day of human chorionic gonadotropin administration may affect in vitro fertilization (IVF) outcome. Objective: The aim of this study was to evaluate whether progesterone elevation on the day of human chorionic gonadotropin administration is associated with poor IVF outcome. Materials and Methods: To determine the relationship between serum progesterone on the day of HCG and the outcome of IVF-embryo transfer treatment, 378 infertile patients undergoing IVF-embryo transfer at Yazd Research and Clinical Center for Infertility from October 2009 to March 2011 were prospectively studied. Results: In this study, absolute p-value and P/E2 ratio were not a good predictor outcome of in-vitro fertilization but progesterone per metaphase II were predictive of implantation rate and pregnancy rate with statistically significant results but had no effect on the fertilization rate. Conclusion: We suggest avoided the increased progesterone that the cause of advanced endometrial maturation and impaired endometrial receptivity. If the progesterone is greater than 0.32 per oocyte metaphase II, the embryo transfer can be canceled and freezing all embryos for future transfer must be considered, to increase acceptance of the endometrium and thus increase the success rate. PMID:25071852

  3. Climate variation and prediction of rapid intensification in tropical cyclones in the western North Pacific

    NASA Astrophysics Data System (ADS)

    Wang, B.; Zhou, X.

    2008-02-01

    One of the greatest challenges in tropical weather forecasting is the rapid intensification (RI) of the tropical cyclone (TC), during which its one-minute maximum sustained wind speed increases at least 30 knots per 24 hours. Here we identify and elucidate the climatic conditions that are critical to the frequency and location of the RI on annual, intraseasonal, and interannual time scales. Whereas RI and formation share common environmental preferences, we found that the percentage of TCs with RI varies annually and from year to year. In August, only 30% o