A mixed model for the relationship between climate and human cranial form.

PubMed

Katz, David C; Grote, Mark N; Weaver, Timothy D

2016-08-01

We expand upon a multivariate mixed model from quantitative genetics in order to estimate the magnitude of climate effects in a global sample of recent human crania. In humans, genetic distances are correlated with distances based on cranial form, suggesting that population structure influences both genetic and quantitative trait variation. Studies controlling for this structure have demonstrated significant underlying associations of cranial distances with ecological distances derived from climate variables. However, to assess the biological importance of an ecological predictor, estimates of effect size and uncertainty in the original units of measurement are clearly preferable to significance claims based on units of distance. Unfortunately, the magnitudes of ecological effects are difficult to obtain with distance-based methods, while models that produce estimates of effect size generally do not scale to high-dimensional data like cranial shape and form. Using recent innovations that extend quantitative genetics mixed models to highly multivariate observations, we estimate morphological effects associated with a climate predictor for a subset of the Howells craniometric dataset. Several measurements, particularly those associated with cranial vault breadth, show a substantial linear association with climate, and the multivariate model incorporating a climate predictor is preferred in model comparison. Previous studies demonstrated the existence of a relationship between climate and cranial form. The mixed model quantifies this relationship concretely. Evolutionary questions that require population structure and phylogeny to be disentangled from potential drivers of selection may be particularly well addressed by mixed models. Am J Phys Anthropol 160:593-603, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Extreme waves from tropical cyclones and climate change in the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Appendini, Christian M.; Pedrozo-Acuña, Adrian; Meza-Padilla, Rafael; Torres-Freyermuth, Alec; Cerezo-Mota, Ruth; López-González, José

2017-04-01

Tropical cyclones generate extreme waves that represent a risk to infrastructure and maritime activities. The projection of the tropical cyclones derived wave climate are challenged by the short historical record of tropical cyclones, their low occurrence, and the poor wind field resolution in General Circulation Models. In this study we use synthetic tropical cyclones to overcome such limitations and be able to characterize present and future wave climate associated with tropical cyclones in the Gulf of Mexico. Synthetic events derived from the NCEP/NCAR atmospheric reanalysis and the Coupled Model Intercomparison Project Phase 5 models NOAA/GFDL CM3 and UK Met Office HADGEM2-ES, were used to force a third generation wave model to characterize the present and future wave climate under RCP 4.5 and 8.5 escenarios. An increase in wave activity is projected for the future climate, particularly for the GFDL model that shows less bias in the present climate, although some areas are expected to decrease the wave energy. The practical implications of determining the future wave climate is exemplified by means of the 100-year design wave, where the use of the present climate may result in under/over design of structures, since the lifespan of a structure includes the future wave climate period.

Nurse characteristics, leadership, safety climate, emotional labour and intention to stay for nurses: a structural equation modelling approach.

PubMed

Liang, Hui-Yu; Tang, Fu-In; Wang, Tze-Fang; Lin, Kai-Ching; Yu, Shu

2016-12-01

The aim of this study was to propose a theoretical model and apply it to examine the structural relationships among nurse characteristics, leadership characteristics, safety climate, emotional labour and intention to stay for hospital nurses. Global nursing shortages negatively affect the quality of care. The shortages can be reduced by retaining nurses. Few studies have independently examined the relationships among leadership, safety climate, emotional labour and nurses' intention to stay; more comprehensive theoretical foundations for examining nurses' intention to stay and its related factors are lacking. Cross-sectional. A purposive sample of 414 full-time nurses was recruited from two regional hospitals in Taiwan. A structured questionnaire was used to collect data from November 2013-June 2014. Structural equation modelling was employed to test the theoretical models of the relationships among the constructs. Our data supported the theoretical model. Intention to stay was positively correlated with age and the safety climate, whereas working hours per week and emotional labour were negatively correlated. The nursing position and transformational leadership indirectly affected intention to stay; this effect was mediated separately by emotional labour and the safety climate. Our data supported the model fit. Our findings provide practical implications for healthcare organizations and administrators to increase nurses' intent to stay. Strategies including a safer climate, appropriate working hours and lower emotional labour can directly increase nurses' intent to stay. Transformational leadership did not directly influence nurses' intention to stay; however, it reduced emotional labour, thereby increasing intention to stay. © 2016 John Wiley & Sons Ltd.

Ensemble catchment hydrological modelling for climate change impact analysis

NASA Astrophysics Data System (ADS)

Vansteenkiste, Thomas; Ntegeka, Victor; Willems, Patrick

2014-05-01

It is vital to investigate how the hydrological model structure affects the climate change impact given that future changes not in the range for which the models were calibrated or validated are likely. Thus an ensemble modelling approach which involves a diversity of models with different structures such as spatial resolutions and process descriptions is crucial. The ensemble modelling approach was applied to a set of models: from the lumped conceptual models NAM, PDM and VHM, an intermediate detailed and distributed model WetSpa, to the highly detailed and fully distributed model MIKE-SHE. Explicit focus was given to the high and low flow extremes. All models were calibrated for sub flows and quick flows derived from rainfall and potential evapotranspiration (ETo) time series. In general, all models were able to produce reliable estimates of the flow regimes under the current climate for extreme peak and low flows. An intercomparison of the low and high flow changes under changed climatic conditions was made using climate scenarios tailored for extremes. Tailoring was important for two reasons. First, since the use of many scenarios was not feasible it was necessary to construct few scenarios that would reasonably represent the range of extreme impacts. Second, scenarios would be more informative as changes in high and low flows would be easily traced to changes of ETo and rainfall; the tailored scenarios are constructed using seasonal changes that are defined using different levels of magnitude (high, mean and low) for rainfall and ETo. After simulation of these climate scenarios in the five hydrological models, close agreement was found among the models. The different models predicted similar range of peak flow changes. For the low flows, however, the differences in the projected impact range by different hydrological models was larger, particularly for the drier scenarios. This suggests that the hydrological model structure is critical in low flow predictions, more than in high flow conditions. Hence, the mechanism of the slow flow component simulation requires further attention. It is concluded that a multi-model ensemble approach where different plausible model structures are applied, is extremely useful. It improves the reliability of climate change impact results and allows decision making to be based on uncertainty assessment that includes model structure related uncertainties. References: Ntegeka, V., Baguis, P., Roulin, E., Willems, P., 2014. Developing tailored climate change scenarios for hydrological impact assessments. Journal of Hydrology, 508C, 307-321 Vansteenkiste, Th., Tavakoli, M., Ntegeka, V., Willems, P., De Smedt, F., Batelaan, O., 2013. Climate change impact on river flows and catchment hydrology: a comparison of two spatially distributed models. Hydrological Processes, 27(25), 3649-3662. Vansteenkiste, Th., Tavakoli, M., Ntegeka, V., Van Steenbergen, N., De Smedt, F., Batelaan, O., Pereira, F., Willems, P., 2014. Intercomparison of five lumped and distributed models for catchment runoff and extreme flow simulation. Journal of Hydrology, in press. Vansteenkiste, Th., Tavakoli, M., Ntegeka, V., De Smedt, F., Batelaan, O., Pereira, F., Willems, P., 2014. Intercomparison of climate scenario impact predictions by a lumped and distributed model ensemble. Journal of Hydrology, in revision.

Variable effects of climate on forest growth in relation to climate extremes, disturbance, and forest dynamics.

PubMed

Itter, Malcolm S; Finley, Andrew O; D'Amato, Anthony W; Foster, Jane R; Bradford, John B

2017-06-01

Changes in the frequency, duration, and severity of climate extremes are forecast to occur under global climate change. The impacts of climate extremes on forest productivity and health remain difficult to predict due to potential interactions with disturbance events and forest dynamics-changes in forest stand composition, density, size and age structure over time. Such interactions may lead to non-linear forest growth responses to climate involving thresholds and lag effects. Understanding how forest dynamics influence growth responses to climate is particularly important given stand structure and composition can be modified through management to increase forest resistance and resilience to climate change. To inform such adaptive management, we develop a hierarchical Bayesian state space model in which climate effects on tree growth are allowed to vary over time and in relation to past climate extremes, disturbance events, and forest dynamics. The model is an important step toward integrating disturbance and forest dynamics into predictions of forest growth responses to climate extremes. We apply the model to a dendrochronology data set from forest stands of varying composition, structure, and development stage in northeastern Minnesota that have experienced extreme climate years and forest tent caterpillar defoliation events. Mean forest growth was most sensitive to water balance variables representing climatic water deficit. Forest growth responses to water deficit were partitioned into responses driven by climatic threshold exceedances and interactions with insect defoliation. Forest growth was both resistant and resilient to climate extremes with the majority of forest growth responses occurring after multiple climatic threshold exceedances across seasons and years. Interactions between climate and disturbance were observed in a subset of years with insect defoliation increasing forest growth sensitivity to water availability. Forest growth was particularly sensitive to climate extremes during periods of high stem density following major regeneration events when average inter-tree competition was high. Results suggest the resistance and resilience of forest growth to climate extremes can be increased through management steps such as thinning to reduce competition during early stages of stand development and small-group selection harvests to maintain forest structures characteristic of older, mature stands. © 2017 by the Ecological Society of America.

Variable effects of climate on forest growth in relation to climate extremes, disturbance, and forest dynamics

USGS Publications Warehouse

Itter, Malcolm S.; Finley, Andrew O.; D'Amato, Anthony W.; Foster, Jane R.; Bradford, John B.

2017-01-01

Changes in the frequency, duration, and severity of climate extremes are forecast to occur under global climate change. The impacts of climate extremes on forest productivity and health remain difficult to predict due to potential interactions with disturbance events and forest dynamics—changes in forest stand composition, density, size and age structure over time. Such interactions may lead to non-linear forest growth responses to climate involving thresholds and lag effects. Understanding how forest dynamics influence growth responses to climate is particularly important given stand structure and composition can be modified through management to increase forest resistance and resilience to climate change. To inform such adaptive management, we develop a hierarchical Bayesian state space model in which climate effects on tree growth are allowed to vary over time and in relation to past climate extremes, disturbance events, and forest dynamics. The model is an important step toward integrating disturbance and forest dynamics into predictions of forest growth responses to climate extremes. We apply the model to a dendrochronology data set from forest stands of varying composition, structure, and development stage in northeastern Minnesota that have experienced extreme climate years and forest tent caterpillar defoliation events. Mean forest growth was most sensitive to water balance variables representing climatic water deficit. Forest growth responses to water deficit were partitioned into responses driven by climatic threshold exceedances and interactions with insect defoliation. Forest growth was both resistant and resilient to climate extremes with the majority of forest growth responses occurring after multiple climatic threshold exceedances across seasons and years. Interactions between climate and disturbance were observed in a subset of years with insect defoliation increasing forest growth sensitivity to water availability. Forest growth was particularly sensitive to climate extremes during periods of high stem density following major regeneration events when average inter-tree competition was high. Results suggest the resistance and resilience of forest growth to climate extremes can be increased through management steps such as thinning to reduce competition during early stages of stand development and small-group selection harvests to maintain forest structures characteristic of older, mature stands.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

NASA Astrophysics Data System (ADS)

Weitzel, Nils; Hense, Andreas; Ohlwein, Christian

2017-04-01

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

The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations

NASA Astrophysics Data System (ADS)

Alexander, K.; Easterbrook, S. M.

2015-04-01

We analyze the source code of eight coupled climate models, selected from those that participated in the CMIP5 (Taylor et al., 2012) or EMICAR5 (Eby et al., 2013; Zickfeld et al., 2013) intercomparison projects. For each model, we sort the preprocessed code into components and subcomponents based on dependency structure. We then create software architecture diagrams that show the relative sizes of these components/subcomponents and the flow of data between them. The diagrams also illustrate several major classes of climate model design; the distribution of complexity between components, which depends on historical development paths as well as the conscious goals of each institution; and the sharing of components between different modeling groups. These diagrams offer insights into the similarities and differences in structure between climate models, and have the potential to be useful tools for communication between scientists, scientific institutions, and the public.

On the generation of climate model ensembles

NASA Astrophysics Data System (ADS)

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

2014-10-01

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

Probabilistic projections of 21st century climate change over Northern Eurasia

NASA Astrophysics Data System (ADS)

Monier, E.; Sokolov, A. P.; Schlosser, C. A.; Scott, J. R.; Gao, X.

2013-12-01

We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an earth system model of intermediate complexity, with a two-dimensional zonal-mean atmosphere, to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three dimensional atmospheric model; and a statistical downscaling, where a pattern scaling algorithm uses climate-change patterns from 17 climate models. This framework allows for key sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections; climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate); natural variability; and structural uncertainty. Results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also nd that dierent initial conditions lead to dierences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider all sources of uncertainty when modeling climate impacts over Northern Eurasia.

Probabilistic projections of 21st century climate change over Northern Eurasia

NASA Astrophysics Data System (ADS)

Monier, Erwan; Sokolov, Andrei; Schlosser, Adam; Scott, Jeffery; Gao, Xiang

2013-12-01

We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity with a two-dimensional zonal-mean atmosphere to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three-dimensional atmospheric model, and a statistical downscaling, where a pattern scaling algorithm uses climate change patterns from 17 climate models. This framework allows for four major sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections, climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate), natural variability, and structural uncertainty. The results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also find that different initial conditions lead to differences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider these sources of uncertainty when modeling climate impacts over Northern Eurasia.

Multilevel multi-informant structure of the authoritative school climate survey.

PubMed

Konold, Timothy; Cornell, Dewey; Huang, Francis; Meyer, Patrick; Lacey, Anna; Nekvasil, Erin; Heilbrun, Anna; Shukla, Kathan

2014-09-01

The Authoritative School Climate Survey was designed to provide schools with a brief assessment of 2 key characteristics of school climate--disciplinary structure and student support--that are hypothesized to influence 2 important school climate outcomes--student engagement and prevalence of teasing and bullying in school. The factor structure of these 4 constructs was examined with exploratory and confirmatory factor analyses in a statewide sample of 39,364 students (Grades 7 and 8) attending 423 schools. Notably, the analyses used a multilevel structural approach to model the nesting of students in schools for purposes of evaluating factor structure, demonstrating convergent and concurrent validity and gauging the structural invariance of concurrent validity coefficients across gender. These findings provide schools with a core group of school climate measures guided by authoritative discipline theory. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Climate change impacts on net primary production (NPP) and export production (EP) regulated by increasing stratification and phytoplankton community structure in the CMIP5 models

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

Fu, Weiwei; Randerson, James T.; Moore, J. Keith

We examine climate change impacts on net primary production (NPP) and export production (sinking particulate flux; EP) with simulations from nine Earth system models (ESMs) performed in the framework of the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Global NPP and EP are reduced by the end of the century for the intense warming scenario of Representative Concentration Pathway (RCP) 8.5. Relative to the 1990s, NPP in the 2090s is reduced by 2–16% and EP by 7–18%. The models with the largest increases in stratification (and largest relative declines in NPP and EP) also show the largest positivemore » biases in stratification for the contemporary period, suggesting overestimation of climate change impacts on NPP and EP. All of the CMIP5 models show an increase in stratification in response to surface–ocean warming and freshening, which is accompanied by decreases in surface nutrients, NPP and EP. There is considerable variability across the models in the magnitudes of NPP, EP, surface nutrient concentrations and their perturbations by climate change. The negative response of NPP and EP to increasing stratification reflects primarily a bottom-up control, as upward nutrient flux declines at the global scale. Models with dynamic phytoplankton community structure show larger declines in EP than in NPP. This pattern is driven by phytoplankton community composition shifts, with reductions in productivity by large phytoplankton as smaller phytoplankton (which export less efficiently) are favored under the increasing nutrient stress. Thus, the projections of the NPP response to climate change are critically dependent on the simulated phytoplankton community structure, the efficiency of the biological pump and the resulting levels of regenerated production, which vary widely across the models. In conclusion, community structure is represented simply in the CMIP5 models, and should be expanded to better capture the spatial patterns and climate-driven changes in export efficiency.« less

Climate change impacts on net primary production (NPP) and export production (EP) regulated by increasing stratification and phytoplankton community structure in the CMIP5 models

DOE PAGES

Fu, Weiwei; Randerson, James T.; Moore, J. Keith

2016-09-16

We examine climate change impacts on net primary production (NPP) and export production (sinking particulate flux; EP) with simulations from nine Earth system models (ESMs) performed in the framework of the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Global NPP and EP are reduced by the end of the century for the intense warming scenario of Representative Concentration Pathway (RCP) 8.5. Relative to the 1990s, NPP in the 2090s is reduced by 2–16% and EP by 7–18%. The models with the largest increases in stratification (and largest relative declines in NPP and EP) also show the largest positivemore » biases in stratification for the contemporary period, suggesting overestimation of climate change impacts on NPP and EP. All of the CMIP5 models show an increase in stratification in response to surface–ocean warming and freshening, which is accompanied by decreases in surface nutrients, NPP and EP. There is considerable variability across the models in the magnitudes of NPP, EP, surface nutrient concentrations and their perturbations by climate change. The negative response of NPP and EP to increasing stratification reflects primarily a bottom-up control, as upward nutrient flux declines at the global scale. Models with dynamic phytoplankton community structure show larger declines in EP than in NPP. This pattern is driven by phytoplankton community composition shifts, with reductions in productivity by large phytoplankton as smaller phytoplankton (which export less efficiently) are favored under the increasing nutrient stress. Thus, the projections of the NPP response to climate change are critically dependent on the simulated phytoplankton community structure, the efficiency of the biological pump and the resulting levels of regenerated production, which vary widely across the models. In conclusion, community structure is represented simply in the CMIP5 models, and should be expanded to better capture the spatial patterns and climate-driven changes in export efficiency.« less

Validation of a model with climatic and flow scenario analysis: case of Lake Burrumbeet in southeastern Australia.

PubMed

Yihdego, Yohannes; Webb, John

2016-05-01

Forecast evaluation is an important topic that addresses the development of reliable hydrological probabilistic forecasts, mainly through the use of climate uncertainties. Often, validation has no place in hydrology for most of the times, despite the parameters of a model are uncertain. Similarly, the structure of the model can be incorrectly chosen. A calibrated and verified dynamic hydrologic water balance spreadsheet model has been used to assess the effect of climate variability on Lake Burrumbeet, southeastern Australia. The lake level has been verified to lake level, lake volume, lake surface area, surface outflow and lake salinity. The current study aims to increase lake level confidence model prediction through historical validation for the year 2008-2013, under different climatic scenario. Based on the observed climatic condition (2008-2013), it fairly matches with a hybridization of scenarios, being the period interval (2008-2013), corresponds to both dry and wet climatic condition. Besides to the hydrologic stresses uncertainty, uncertainty in the calibrated model is among the major drawbacks involved in making scenario simulations. In line with this, the uncertainty in the calibrated model was tested using sensitivity analysis and showed that errors in the model can largely be attributed to erroneous estimates of evaporation and rainfall, and surface inflow to a lesser. The study demonstrates that several climatic scenarios should be analysed, with a combination of extreme climate, stream flow and climate change instead of one assumed climatic sequence, to improve climate variability prediction in the future. Performing such scenario analysis is a valid exercise to comprehend the uncertainty with the model structure and hydrology, in a meaningful way, without missing those, even considered as less probable, ultimately turned to be crucial for decision making and will definitely increase the confidence of model prediction for management of the water resources.

Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

PubMed

Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

2015-04-01

Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for epidemiological surveillance.

Temperate Mountain Forest Biodiversity under Climate Change: Compensating Negative Effects by Increasing Structural Complexity

PubMed Central

Braunisch, Veronika; Coppes, Joy; Arlettaz, Raphaël; Suchant, Rudi; Zellweger, Florian; Bollmann, Kurt

2014-01-01

Species adapted to cold-climatic mountain environments are expected to face a high risk of range contractions, if not local extinctions under climate change. Yet, the populations of many endothermic species may not be primarily affected by physiological constraints, but indirectly by climate-induced changes of habitat characteristics. In mountain forests, where vertebrate species largely depend on vegetation composition and structure, deteriorating habitat suitability may thus be mitigated or even compensated by habitat management aiming at compositional and structural enhancement. We tested this possibility using four cold-adapted bird species with complementary habitat requirements as model organisms. Based on species data and environmental information collected in 300 1-km2 grid cells distributed across four mountain ranges in central Europe, we investigated (1) how species’ occurrence is explained by climate, landscape, and vegetation, (2) to what extent climate change and climate-induced vegetation changes will affect habitat suitability, and (3) whether these changes could be compensated by adaptive habitat management. Species presence was modelled as a function of climate, landscape and vegetation variables under current climate; moreover, vegetation-climate relationships were assessed. The models were extrapolated to the climatic conditions of 2050, assuming the moderate IPCC-scenario A1B, and changes in species’ occurrence probability were quantified. Finally, we assessed the maximum increase in occurrence probability that could be achieved by modifying one or multiple vegetation variables under altered climate conditions. Climate variables contributed significantly to explaining species occurrence, and expected climatic changes, as well as climate-induced vegetation trends, decreased the occurrence probability of all four species, particularly at the low-altitudinal margins of their distribution. These effects could be partly compensated by modifying single vegetation factors, but full compensation would only be achieved if several factors were changed in concert. The results illustrate the possibilities and limitations of adaptive species conservation management under climate change. PMID:24823495

Temperate mountain forest biodiversity under climate change: compensating negative effects by increasing structural complexity.

PubMed

Braunisch, Veronika; Coppes, Joy; Arlettaz, Raphaël; Suchant, Rudi; Zellweger, Florian; Bollmann, Kurt

2014-01-01

Species adapted to cold-climatic mountain environments are expected to face a high risk of range contractions, if not local extinctions under climate change. Yet, the populations of many endothermic species may not be primarily affected by physiological constraints, but indirectly by climate-induced changes of habitat characteristics. In mountain forests, where vertebrate species largely depend on vegetation composition and structure, deteriorating habitat suitability may thus be mitigated or even compensated by habitat management aiming at compositional and structural enhancement. We tested this possibility using four cold-adapted bird species with complementary habitat requirements as model organisms. Based on species data and environmental information collected in 300 1-km2 grid cells distributed across four mountain ranges in central Europe, we investigated (1) how species' occurrence is explained by climate, landscape, and vegetation, (2) to what extent climate change and climate-induced vegetation changes will affect habitat suitability, and (3) whether these changes could be compensated by adaptive habitat management. Species presence was modelled as a function of climate, landscape and vegetation variables under current climate; moreover, vegetation-climate relationships were assessed. The models were extrapolated to the climatic conditions of 2050, assuming the moderate IPCC-scenario A1B, and changes in species' occurrence probability were quantified. Finally, we assessed the maximum increase in occurrence probability that could be achieved by modifying one or multiple vegetation variables under altered climate conditions. Climate variables contributed significantly to explaining species occurrence, and expected climatic changes, as well as climate-induced vegetation trends, decreased the occurrence probability of all four species, particularly at the low-altitudinal margins of their distribution. These effects could be partly compensated by modifying single vegetation factors, but full compensation would only be achieved if several factors were changed in concert. The results illustrate the possibilities and limitations of adaptive species conservation management under climate change.

Uncertainty in simulating wheat yields under climate change

NASA Astrophysics Data System (ADS)

Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.; Ruane, A. C.; Boote, K. J.; Thorburn, P. J.; Rötter, R. P.; Cammarano, D.; Brisson, N.; Basso, B.; Martre, P.; Aggarwal, P. K.; Angulo, C.; Bertuzzi, P.; Biernath, C.; Challinor, A. J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.; Heng, L.; Hooker, J.; Hunt, L. A.; Ingwersen, J.; Izaurralde, R. C.; Kersebaum, K. C.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O'Leary, G.; Olesen, J. E.; Osborne, T. M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Semenov, M. A.; Shcherbak, I.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J. W.; Williams, J. R.; Wolf, J.

2013-09-01

Projections of climate change impacts on crop yields are inherently uncertain. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking.

A network-base analysis of CMIP5 "historical" experiments

NASA Astrophysics Data System (ADS)

Bracco, A.; Foudalis, I.; Dovrolis, C.

2012-12-01

In computer science, "complex network analysis" refers to a set of metrics, modeling tools and algorithms commonly used in the study of complex nonlinear dynamical systems. Its main premise is that the underlying topology or network structure of a system has a strong impact on its dynamics and evolution. By allowing to investigate local and non-local statistical interaction, network analysis provides a powerful, but only marginally explored, framework to validate climate models and investigate teleconnections, assessing their strength, range, and impacts on the climate system. In this work we propose a new, fast, robust and scalable methodology to examine, quantify, and visualize climate sensitivity, while constraining general circulation models (GCMs) outputs with observations. The goal of our novel approach is to uncover relations in the climate system that are not (or not fully) captured by more traditional methodologies used in climate science and often adopted from nonlinear dynamical systems analysis, and to explain known climate phenomena in terms of the network structure or its metrics. Our methodology is based on a solid theoretical framework and employs mathematical and statistical tools, exploited only tentatively in climate research so far. Suitably adapted to the climate problem, these tools can assist in visualizing the trade-offs in representing global links and teleconnections among different data sets. Here we present the methodology, and compare network properties for different reanalysis data sets and a suite of CMIP5 coupled GCM outputs. With an extensive model intercomparison in terms of the climate network that each model leads to, we quantify how each model reproduces major teleconnections, rank model performances, and identify common or specific errors in comparing model outputs and observations.

Measuring School Climate in High Schools: A Focus on Safety, Engagement, and the Environment

ERIC Educational Resources Information Center

Bradshaw, Catherine P.; Waasdorp, Tracy E.; Debnam, Katrina J.; Johnson, Sarah Lindstrom

2014-01-01

Background: School climate has been linked to multiple student behavioral, academic, health, and social-emotional outcomes. The US Department of Education (USDOE) developed a 3-factor model of school climate comprised of safety, engagement, and environment. This article examines the factor structure and measurement invariance of the USDOE model.…

Comparison of the Motivational Climates Created during Multi-Activity Instruction and Sport Education

ERIC Educational Resources Information Center

Parker, Mitchum B.; Curtner-Smith, Matthew D.

2014-01-01

Previous research has suggested that sport education (SE) may be a superior curriculum model to multi-activity (MA) teaching because its pedagogies and structures create a task-involving motivational climate. The purpose of this study was to describe and compare the objective motivational climates teachers create within the MA and SE models.…

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Hydrological Modeling in the Bull Run Watershed in Support of a Piloting Utility Modeling Applications (PUMA) Project

NASA Astrophysics Data System (ADS)

Nijssen, B.; Chiao, T. H.; Lettenmaier, D. P.; Vano, J. A.

2016-12-01

Hydrologic models with varying complexities and structures are commonly used to evaluate the impact of climate change on future hydrology. While the uncertainties in future climate projections are well documented, uncertainties in streamflow projections associated with hydrologic model structure and parameter estimation have received less attention. In this study, we implemented and calibrated three hydrologic models (the Distributed Hydrology Soil Vegetation Model (DHSVM), the Precipitation-Runoff Modeling System (PRMS), and the Variable Infiltration Capacity model (VIC)) for the Bull Run watershed in northern Oregon using consistent data sources and best practice calibration protocols. The project was part of a Piloting Utility Modeling Applications (PUMA) project with the Portland Water Bureau (PWB) under the umbrella of the Water Utility Climate Alliance (WUCA). Ultimately PWB would use the model evaluation to select a model to perform in-house climate change analysis for Bull Run Watershed. This presentation focuses on the experimental design of the comparison project, project findings and the collaboration between the team at the University of Washington and at PWB. After calibration, the three models showed similar capability to reproduce seasonal and inter-annual variations in streamflow, but differed in their ability to capture extreme events. Furthermore, the annual and seasonal hydrologic sensitivities to changes in climate forcings differed among models, potentially attributable to different model representations of snow and vegetation processes.

Climatic water deficit, tree species ranges, and climate change in Yosemite National Park

Treesearch

James A. Lutz; Jan W. van Wagtendonk; Jerry F. Franklin

2010-01-01

Modelled changes in climate water deficit between past, present and future climate scenarios suggest that recent past changes in forest structure and composition may accelerate in the future, with species responding individualistically to further declines in water availability. Declining water availability may disproportionately affect Pinus monticola...

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

PubMed

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

2015-07-15

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

Clouds at Barbados are representative of clouds across the trade wind regions in observations and climate models.

PubMed

Medeiros, Brian; Nuijens, Louise

2016-05-31

Trade wind regions cover most of the tropical oceans, and the prevailing cloud type is shallow cumulus. These small clouds are parameterized by climate models, and changes in their radiative effects strongly and directly contribute to the spread in estimates of climate sensitivity. This study investigates the structure and variability of these clouds in observations and climate models. The study builds upon recent detailed model evaluations using observations from the island of Barbados. Using a dynamical regimes framework, satellite and reanalysis products are used to compare the Barbados region and the broader tropics. It is shown that clouds in the Barbados region are similar to those across the trade wind regions, implying that observational findings from the Barbados Cloud Observatory are relevant to clouds across the tropics. The same methods are applied to climate models to evaluate the simulated clouds. The models generally capture the cloud radiative effect, but underestimate cloud cover and show an array of cloud vertical structures. Some models show strong biases in the environment of the Barbados region in summer, weakening the connection between the regional biases and those across the tropics. Even bearing that limitation in mind, it is shown that covariations of cloud and environmental properties in the models are inconsistent with observations. The models tend to misrepresent sensitivity to moisture variations and inversion characteristics. These model errors are likely connected to cloud feedback in climate projections, and highlight the importance of the representation of shallow cumulus convection.

Clouds at Barbados are representative of clouds across the trade wind regions in observations and climate models

PubMed Central

Nuijens, Louise

2016-01-01

Trade wind regions cover most of the tropical oceans, and the prevailing cloud type is shallow cumulus. These small clouds are parameterized by climate models, and changes in their radiative effects strongly and directly contribute to the spread in estimates of climate sensitivity. This study investigates the structure and variability of these clouds in observations and climate models. The study builds upon recent detailed model evaluations using observations from the island of Barbados. Using a dynamical regimes framework, satellite and reanalysis products are used to compare the Barbados region and the broader tropics. It is shown that clouds in the Barbados region are similar to those across the trade wind regions, implying that observational findings from the Barbados Cloud Observatory are relevant to clouds across the tropics. The same methods are applied to climate models to evaluate the simulated clouds. The models generally capture the cloud radiative effect, but underestimate cloud cover and show an array of cloud vertical structures. Some models show strong biases in the environment of the Barbados region in summer, weakening the connection between the regional biases and those across the tropics. Even bearing that limitation in mind, it is shown that covariations of cloud and environmental properties in the models are inconsistent with observations. The models tend to misrepresent sensitivity to moisture variations and inversion characteristics. These model errors are likely connected to cloud feedback in climate projections, and highlight the importance of the representation of shallow cumulus convection. PMID:27185925

Multilevel model of safety climate for furniture industries.

PubMed

Rodrigues, Matilde A; Arezes, Pedro M; Leão, Celina P

2015-01-01

Furniture companies can analyze their safety status using quantitative measures. However, the data needed are not always available and the number of accidents is under-reported. Safety climate scales may be an alternative. However, there are no validated Portuguese scales that account for the specific attributes of the furniture sector. The current study aims to develop and validate an instrument that uses a multilevel structure to measure the safety climate of the Portuguese furniture industry. The Safety Climate in Wood Industries (SCWI) model was developed and applied to the safety climate analysis using three different scales: organizational, group and individual. A multilevel exploratory factor analysis was performed to analyze the factorial structure. The studied companies' safety conditions were also analyzed. Different factorial structures were found between and within levels. In general, the results show the presence of a group-level safety climate. The scores of safety climates are directly and positively related to companies' safety conditions; the organizational scale is the one that best reflects the actual safety conditions. The SCWI instrument allows for the identification of different safety climates in groups that comprise the same furniture company and it seems to reflect those groups' safety conditions. The study also demonstrates the need for a multilevel analysis of the studied instrument.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Jump-Diffusion models and structural changes for asset forecasting in hydrology

NASA Astrophysics Data System (ADS)

Tranquille Temgoua, André Guy; Martel, Richard; Chang, Philippe J. J.; Rivera, Alfonso

2017-04-01

Impacts of climate change on surface water and groundwater are of concern in many regions of the world since water is an essential natural resource. Jump-Diffusion models are generally used in economics and other related fields but not in hydrology. The potential application could be made for hydrologic data series analysis and forecast. The present study uses Jump-Diffusion models by adding structural changes to detect fluctuations in hydrologic processes in relationship with climate change. The model implicitly assumes that modifications in rivers' flowrates can be divided into three categories: (a) normal changes due to irregular precipitation events especially in tropical regions causing major disturbance in hydrologic processes (this component is modelled by a discrete Brownian motion); (b) abnormal, sudden and non-persistent modifications in hydrologic proceedings are handled by Poisson processes; (c) the persistence of hydrologic fluctuations characterized by structural changes in hydrological data related to climate variability. The objective of this paper is to add structural changes in diffusion models with jumps, in order to capture the persistence of hydrologic fluctuations. Indirectly, the idea is to observe if there are structural changes of discharge/recharge over the study area, and to find an efficient and flexible model able of capturing a wide variety of hydrologic processes. Structural changes in hydrological data are estimated using the method of nonlinear discrete filters via Method of Simulated Moments (MSM). An application is given using sensitive parameters such as baseflow index and recession coefficient to capture discharge/recharge. Historical dataset are examined by the Volume Spread Analysis (VSA) to detect real time and random perturbations in hydrologic processes. The application of the method allows establishing more accurate hydrologic parameters. The impact of this study is perceptible in forecasting floods and groundwater recession. Keywords: hydrologic processes, Jump-Diffusion models, structural changes, forecast, climate change

Uncertainty in Simulating Wheat Yields Under Climate Change

NASA Technical Reports Server (NTRS)

Asseng, S.; Ewert, F.; Rosenzweig, Cynthia; Jones, J. W.; Hatfield, J. W.; Ruane, A. C.; Boote, K. J.; Thornburn, P. J.; Rotter, R. P.; Cammarano, D.;

Exploring tropical forest vegetation dynamics using the FATES model

NASA Astrophysics Data System (ADS)

Koven, C. D.; Fisher, R.; Knox, R. G.; Chambers, J.; Kueppers, L. M.; Christoffersen, B. O.; Davies, S. J.; Dietze, M.; Holm, J.; Massoud, E. C.; Muller-Landau, H. C.; Powell, T.; Serbin, S.; Shuman, J. K.; Walker, A. P.; Wright, S. J.; Xu, C.

2017-12-01

Tropical forest vegetation dynamics represent a critical climate feedback in the Earth system, which is poorly represented in current global modeling approaches. We discuss recent progress on exploring these dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a demographic vegetation model for the CESM and ACME ESMs. We will discuss benchmarks of FATES predictions for forest structure against inventory sites, sensitivity of FATES predictions of size and age structure to model parameter uncertainty, and experiments using the FATES model to explore PFT competitive dynamics and the dynamics of size and age distributions in responses to changing climate and CO2.

A Simple Climate Model Program for High School Education

NASA Astrophysics Data System (ADS)

Dommenget, D.

2012-04-01

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

Influence of perceived motivational climate on achievement goals in physical education: a structural equation mixture modeling analysis.

PubMed

Wang, J C; Liu, W C; Chatzisarantis, N L; Lim, C B

2010-06-01

The purpose of the current study was to examine the influence of perceived motivational climate on achievement goals in physical education using a structural equation mixture modeling (SEMM) analysis. Within one analysis, we identified groups of students with homogenous profiles in perceptions of motivational climate and examined the relationships between motivational climate, 2 x 2 achievement goals, and affect, concurrently. The findings of the current study showed that there were at least two distinct groups of students with differing perceptions of motivational climate: one group of students had much higher perceptions in both climates compared with the other group. Regardless of their grouping, the relationships between motivational climate, achievement goals, and enjoyment seemed to be invariant. Mastery climate predicted the adoption of mastery-approach and mastery-avoidance goals; performance climate was related to performance-approach and performance-avoidance goals. Mastery-approach goal had a strong positive effect while performance-avoidance had a small negative effect on enjoyment. Overall, it was concluded that only perception of a mastery motivational climate in physical education may foster intrinsic interest in physical education through adoption of mastery-approach goals.

Development of a Climate Resilience Screening Index (CRSI): An Assessment of Resilience to Acute Meteorological Events and Selected Natural Hazards

EPA Science Inventory

We developed a conceptual model of climate resilience (CRSI – Climate Resilience Screening Index ) designed to be sensitive to changes in the natural environment, built environment, governance, and social structure and vulnerability or risk to climate events. CRSI has been used ...

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

Chen, Gang

Mid-latitude extreme weather events are responsible for a large part of climate-related damage. Yet large uncertainties remain in climate model projections of heat waves, droughts, and heavy rain/snow events on regional scales, limiting our ability to effectively use these projections for climate adaptation and mitigation. These uncertainties can be attributed to both the lack of spatial resolution in the models, and to the lack of a dynamical understanding of these extremes. The approach of this project is to relate the fine-scale features to the large scales in current climate simulations, seasonal re-forecasts, and climate change projections in a very widemore » range of models, including the atmospheric and coupled models of ECMWF over a range of horizontal resolutions (125 to 10 km), aqua-planet configuration of the Model for Prediction Across Scales and High Order Method Modeling Environments (resolutions ranging from 240 km – 7.5 km) with various physics suites, and selected CMIP5 model simulations. The large scale circulation will be quantified both on the basis of the well tested preferred circulation regime approach, and very recently developed measures, the finite amplitude Wave Activity (FAWA) and its spectrum. The fine scale structures related to extremes will be diagnosed following the latest approaches in the literature. The goal is to use the large scale measures as indicators of the probability of occurrence of the finer scale structures, and hence extreme events. These indicators will then be applied to the CMIP5 models and time-slice projections of a future climate.« less

Development of probabilistic regional climate scenario in East Asia

NASA Astrophysics Data System (ADS)

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

2015-12-01

Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. In order to develop probabilistic regional climate information that represents the uncertainty in climate scenario experiments in East Asia (CORDEX-EA and Japan), the probability distribution of 2m air temperature was estimated by using developed regression model. The method can be easily applicable to other regions and other physical quantities, and also to downscale to finer-scale dependent on availability of observation dataset. Probabilistic climate information in present (1969-1998) and future (2069-2098) climate was developed using CMIP3 SRES A1b scenarios 21 models and the observation data (CRU_TS3.22 & University of Delaware in CORDEX-EA, NIAES AMeDAS mesh data in Japan). The prototype of probabilistic information in CORDEX-EA and Japan represent the quantified structural uncertainties of multi-model ensemble experiments of climate change scenarios. Appropriate combination of statistical methods and optimization of climate ensemble experiments using multi-General Circulation Models (GCMs) and multi-regional climate models (RCMs) ensemble downscaling experiments are investigated.

Assessment of hi-resolution multi-ensemble statistical downscaling regional climate scenarios over Japan

NASA Astrophysics Data System (ADS)

Dairaku, K.

2017-12-01

The Asia-Pacific regions are increasingly threatened by large scale natural disasters. Growing concerns that loss and damages of natural disasters are projected to further exacerbate by climate change and socio-economic change. Climate information and services for risk assessments are of great concern. Fundamental regional climate information is indispensable for understanding changing climate and making decisions on when and how to act. To meet with the needs of stakeholders such as National/local governments, spatio-temporal comprehensive and consistent information is necessary and useful for decision making. Multi-model ensemble regional climate scenarios with 1km horizontal grid-spacing over Japan are developed by using CMIP5 37 GCMs (RCP8.5) and a statistical downscaling (Bias Corrected Spatial Disaggregation (BCSD)) to investigate uncertainty of projected change associated with structural differences of the GCMs for the periods of historical climate (1950-2005) and near future climate (2026-2050). Statistical downscaling regional climate scenarios show good performance for annual and seasonal averages for precipitation and temperature. The regional climate scenarios show systematic underestimate of extreme events such as hot days of over 35 Celsius and annual maximum daily precipitation because of the interpolation processes in the BCSD method. Each model projected different responses in near future climate because of structural differences. The most of CMIP5 37 models show qualitatively consistent increase of average and extreme temperature and precipitation. The added values of statistical/dynamical downscaling methods are also investigated for locally forced nonlinear phenomena, extreme events.

Composition and structure of Pinus koraiensis mixed forest respond to spatial climatic changes.

PubMed

Zhang, Jingli; Zhou, Yong; Zhou, Guangsheng; Xiao, Chunwang

2014-01-01

Although some studies have indicated that climate changes can affect Pinus koraiensis mixed forest, the responses of composition and structure of Pinus koraiensis mixed forests to climatic changes are unknown and the key climatic factors controlling the composition and structure of Pinus koraiensis mixed forest are uncertain. Field survey was conducted in the natural Pinus koraiensis mixed forests along a latitudinal gradient and an elevational gradient in Northeast China. In order to build the mathematical models for simulating the relationships of compositional and structural attributes of the Pinus koraiensis mixed forest with climatic and non-climatic factors, stepwise linear regression analyses were performed, incorporating 14 dependent variables and the linear and quadratic components of 9 factors. All the selected new models were computed under the +2°C and +10% precipitation and +4°C and +10% precipitation scenarios. The Max Temperature of Warmest Month, Mean Temperature of Warmest Quarter and Precipitation of Wettest Month were observed to be key climatic factors controlling the stand densities and total basal areas of Pinus koraiensis mixed forest. Increased summer temperatures and precipitations strongly enhanced the stand densities and total basal areas of broadleaf trees but had little effect on Pinus koraiensis under the +2°C and +10% precipitation scenario and +4°C and +10% precipitation scenario. These results show that the Max Temperature of Warmest Month, Mean Temperature of Warmest Quarter and Precipitation of Wettest Month are key climatic factors which shape the composition and structure of Pinus koraiensis mixed forest. Although the Pinus koraiensis would persist, the current forests dominated by Pinus koraiensis in the region would all shift and become broadleaf-dominated forests due to the dramatic increase of broadleaf trees under the future global warming and increased precipitation.

Modeling the factors affecting unsafe behavior in the construction industry from safety supervisors' perspective.

PubMed

Khosravi, Yahya; Asilian-Mahabadi, Hassan; Hajizadeh, Ebrahim; Hassanzadeh-Rangi, Narmin; Bastani, Hamid; Khavanin, Ali; Mortazavi, Seyed Bagher

2014-01-01

There can be little doubt that the construction is the most hazardous industry in the worldwide. This study was designed to modeling the factors affecting unsafe behavior from the perspective of safety supervisors. The qualitative research was conducted to extract a conceptual model. A structural model was then developed based on a questionnaire survey (n=266) by two stage Structural Equation Model (SEM) approach. An excellent confirmed 12-factors structure explained about 62% of variances unsafe behavior in the construction industry. A good fit structural model indicated that safety climate factors were positively correlated with safety individual factors (P<0.001) and workplace safety condition (P<0.001). The workplace safety condition was found to play a strong mediating role in linking the safety climate and construction workers' engagement in safe or unsafe behavior. In order to improve construction safety performance, more focus on the workplace condition is required.

Assessing a Norwegian translation of the Organizational Climate Measure.

PubMed

Bernstrøm, Vilde Hoff; Lone, Jon Anders; Bjørkli, Cato A; Ulleberg, Pål; Hoff, Thomas

2013-04-01

This study investigated the Norwegian translation of the Organizational Climate Measure developed by Patterson and colleagues. The Organizational Climate Measure is a global measure of organizational climate based on Quinn and Rohrbaugh's competing values model. The survey was administered to a Norwegian branch of an international service sector company (N = 555). The results revealed satisfactory internal reliability and interrater agreement for the 17 scales, and confirmatory factor analysis supported the original factor structure. The findings gave preliminary support for the Organizational Climate Measure as a reliable measure with a stable factor structure, and indicated that it is potentially useful in the Norwegian context.

Improving Climate Projections Using "Intelligent" Ensembles

NASA Technical Reports Server (NTRS)

Baker, Noel C.; Taylor, Patrick C.

2015-01-01

Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Evaluating the responses of forest ecosystems to climate change and CO2 using dynamic global vegetation models.

PubMed

Song, Xiang; Zeng, Xiaodong

2017-02-01

The climate has important influences on the distribution and structure of forest ecosystems, which may lead to vital feedback to climate change. However, much of the existing work focuses on the changes in carbon fluxes or water cycles due to climate change and/or atmospheric CO 2 , and few studies have considered how and to what extent climate change and CO 2 influence the ecosystem structure (e.g., fractional coverage change) and the changes in the responses of ecosystems with different characteristics. In this work, two dynamic global vegetation models (DGVMs): IAP-DGVM coupled with CLM3 and CLM4-CNDV, were used to investigate the response of the forest ecosystem structure to changes in climate (temperature and precipitation) and CO 2 concentration. In the temperature sensitivity tests, warming reduced the global area-averaged ecosystem gross primary production in the two models, which decreased global forest area. Furthermore, the changes in tree fractional coverage (Δ F tree ; %) from the two models were sensitive to the regional temperature and ecosystem structure, i.e., the mean annual temperature (MAT; °C) largely determined whether Δ F tree was positive or negative, while the tree fractional coverage ( F tree ; %) played a decisive role in the amplitude of Δ F tree around the globe, and the dependence was more remarkable in IAP-DGVM. In cases with precipitation change, F tree had a uniformly positive relationship with precipitation, especially in the transition zones of forests (30% <  F tree  < 60%) for IAP-DGVM and in semiarid and arid regions for CLM4-CNDV. Moreover, Δ F tree had a stronger dependence on F tree than on the mean annual precipitation (MAP; mm/year). It was also demonstrated that both models captured the fertilization effects of the CO 2 concentration.

Wasp waist or beer belly? Modeling food web structure and energetic control in Alaskan marine ecosystems, with implications for fishing and environmental forcing

NASA Astrophysics Data System (ADS)

Gaichas, Sarah; Aydin, Kerim; Francis, Robert C.

2015-11-01

The Eastern Bering Sea (EBS) and Gulf of Alaska (GOA) continental shelf ecosystems show some similar and some distinctive groundfish biomass dynamics. Given that similar species occupy these regions and fisheries management is also comparable, similarities might be expected, but to what can we attribute the differences? Different types of ecosystem structure and control (e.g. top-down, bottom-up, mixed) can imply different ecosystem dynamics and climate interactions. Further, the structural type identified for a given ecosystem may suggest optimal management for sustainable fishing. Here, we use information on the current system state derived from food web models of both the EBS and the GOA combined with dynamic ecosystem models incorporating uncertainty to classify each ecosystem by its structural type. We then suggest how this structure might be generally related to dynamics and predictability. We find that the EBS and GOA have fundamentally different food web structures both overall, and when viewed from the perspective of the same commercially and ecologically important species in each system, walleye pollock (Gadus chalcogrammus). EBS food web structure centers on a large mass of pollock, which appears to contribute to relative system stability and predictability. In contrast, GOA food web structure features high predator biomass, which contributes to a more dynamic, less predictable ecosystem. Mechanisms for climate influence on pollock production in the EBS are increasingly understood, while climate forcing mechanisms contributing to the potentially destabilizing high predator biomass in the GOA remain enigmatic. We present results of identical pollock fishing and climate-driven pollock recruitment simulations in the EBS and GOA which show different system responses, again with less predictable response in the GOA. Overall, our results suggest that identifying structural properties of fished food webs is as important for sustainable fisheries management as attempting to predict climate and fisheries effects within each ecosystem.

Climate Change Impacts on the Cryosphere of Mountain Regions: Validation of a Novel Model Using the Alaska Range

NASA Astrophysics Data System (ADS)

Mosier, T. M.; Hill, D. F.; Sharp, K. V.

2015-12-01

Mountain regions are natural water towers, storing water seasonally as snowpack and for much longer as glaciers. Understanding the response of these systems to climate change is necessary in order to make informed decisions about prevention or mitigation measures. Yet, mountain regions are often data sparse, leading many researchers to implement simple or enhanced temperature index (ETI) models to simulate cryosphere processes. These model structures do not account for the thermal inertia of snowpack and glaciers and do not robustly capture differences in system response to climate regimes that differ from those the model was calibrated for. For instance, a temperature index calibration parameter will differ substantially in cold-dry conditions versus warm-wet ones. To overcome these issues, we have developed a cryosphere hydrology model, called the Significantly Enhanced Temperature Index (SETI), which uses an energy balance structure but parameterizes energy balance components in terms of minimum, maximum and mean temperature, precipitation, and geometric inputs using established relationships. Additionally, the SETI model includes a glacier sliding model and can therefore be used to estimate long-term glacier response to climate change. Sensitivity of the SETI model to changing climate is compared with an ETI and a simple temperature index model for several partially-glaciated watersheds within Alaska, including Wolverine glacier where multi-decadal glacier stake measurements are available, to highlight the additional fidelity attributed to the increased complexity of the SETI structure. The SETI model is then applied to the entire Alaska Range region for an ensemble of global climate models (GCMs), using representative concentration pathways 4.5 and 8.5. Comparing model runs based on ensembles of GCM projections to historic conditions, total annual snowfall within the Alaska region is not expected to change appreciably, but the spatial distribution of snow shifts towards higher elevations and for a large portion of the region the duration of snow cover decreases. The changes in temperature and snow distribution also lead to spatially heterogeneous responses by glaciers within the region. The SETI model is designed to be easy to apply for any mountain region where cryospheric processes dominate.

Fossils tell of mild winters in an ancient hothouse

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

Kerr, R.A.

Fossil evidence from the Eocene points to a warmer winter climate in the continental interior (e.g. North Dakota) than that predicted by computer models. Paleobotanists have been able to quantify approximate winter mean temperatures by using leaf characteristics. As one example, leaves from colder climates have toothed edges. Leaf structure was correlated with modern climate regimes, and these relations were then applied to Eocene fossils. They found cold-month mean temperatures of 1-8[degrees]C in Wyoming and Montana, well above model predictions. Climate models can be manipulated to reproduce these temperatures, but not without overheating the entire globe. The problem could bemore » that the Eocene atmospheric circulation was different from today, something not accounted for well by climate models.« less

Moisture status during a strong El Niño explains a tropical montane cloud forest's upper limit.

PubMed

Crausbay, Shelley D; Frazier, Abby G; Giambelluca, Thomas W; Longman, Ryan J; Hotchkiss, Sara C

2014-05-01

Growing evidence suggests short-duration climate events may drive community structure and composition more directly than long-term climate means, particularly at ecotones where taxa are close to their physiological limits. Here we use an empirical habitat model to evaluate the role of microclimate during a strong El Niño in structuring a tropical montane cloud forest's upper limit and composition in Hawai'i. We interpolate climate surfaces, derived from a high-density network of climate stations, to permanent vegetation plots. Climatic predictor variables include (1) total rainfall, (2) mean relative humidity, and (3) mean temperature representing non-El Niño periods and a strong El Niño drought. Habitat models explained species composition within the cloud forest with non-El Niño rainfall; however, the ecotone at the cloud forest's upper limit was modeled with relative humidity during a strong El Niño drought and secondarily with non-El Niño rainfall. This forest ecotone may be particularly responsive to strong, short-duration climate variability because taxa here, particularly the isohydric dominant Metrosideros polymorpha, are near their physiological limits. Overall, this study demonstrates moisture's overarching influence on a tropical montane ecosystem, and suggests that short-term climate events affecting moisture status are particularly relevant at tropical ecotones. This study further suggests that predicting the consequences of climate change here, and perhaps in other tropical montane settings, will rely on the skill and certainty around future climate models of regional rainfall, relative humidity, and El Niño.

Ecological opportunity and the evolution of habitat preferences in an arid-zone bird: implications for speciation in a climate-modified landscape

PubMed Central

Norman, Janette A.; Christidis, Les

2016-01-01

Bioclimatic models are widely used to investigate the impacts of climate change on species distributions. Range shifts are expected to occur as species track their current climate niche yet the potential for exploitation of new ecological opportunities that may arise as ecosystems and communities remodel is rarely considered. Here we show that grasswrens of the Amytornis textilis-modestus complex responded to new ecological opportunities in Australia’s arid biome through shifts in habitat preference following the development of chenopod shrublands during the late Plio-Pleistocene. We find evidence of spatially explicit responses to climatically driven landscape changes including changes in niche width and patterns of population growth. Conservation of structural and functional aspects of the ancestral niche appear to have facilitated recent habitat shifts, while demographic responses to late Pleistocene climate change provide evidence for the greater resilience of populations inhabiting the recently evolved chenopod shrubland communities. Similar responses could occur under future climate change in species exposed to novel ecological conditions, or those already occupying spatially heterogeneous landscapes. Mechanistic models that consider structural and functional aspects of the niche along with regional hydro-dynamics may be better predictors of future climate responses in Australia’s arid biome than bioclimatic models alone. PMID:26787111

Scale Development for Perceived School Climate for Girls' Physical Activity

ERIC Educational Resources Information Center

Birnbaum, Amanda S.; Evenson, Kelly R.; Motl, Robert W.; Dishman, Rod K.; Voorhees, Carolyn C.; Sallis, James F.; Elder, John P.; Dowda, Marsha

2005-01-01

Objectives: To test an original scale assessing perceived school climate for girls' physical activity in middle school girls. Methods: Confirmatory factor analysis (CFA) and structural equation modeling (SEM). Results: CFA retained 5 of 14 original items. A model with 2 correlated factors, perceptions about teachers' and boys' behaviors,…

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Climate-induced changes in lake ecosystem structure inferred from coupled neo- and paleoecological approaches

USGS Publications Warehouse

Saros, Jasmine E.; Stone, Jeffery R.; Pederson, Gregory T.; Slemmons, Krista; Spanbauer, Trisha; Schliep, Anna; Cahl, Douglas; Williamson, Craig E.; Engstrom, Daniel R.

2015-01-01

Over the 20th century, surface water temperatures have increased in many lake ecosystems around the world, but long-term trends in the vertical thermal structure of lakes remain unclear, despite the strong control that thermal stratification exerts on the biological response of lakes to climate change. Here we used both neo- and paleoecological approaches to develop a fossil-based inference model for lake mixing depths and thereby refine understanding of lake thermal structure change. We focused on three common planktonic diatom taxa, the distributions of which previous research suggests might be affected by mixing depth. Comparative lake surveys and growth rate experiments revealed that these species respond to lake thermal structure when nitrogen is sufficient, with species optima ranging from shallower to deeper mixing depths. The diatom-based mixing depth model was applied to sedimentary diatom profiles extending back to 1750 AD in two lakes with moderate nitrate concentrations but differing climate settings. Thermal reconstructions were consistent with expected changes, with shallower mixing depths inferred for an alpine lake where treeline has advanced, and deeper mixing depths inferred for a boreal lake where wind strength has increased. The inference model developed here provides a new tool to expand and refine understanding of climate-induced changes in lake ecosystems.

Student Background, School Climate, School Disorder, and Student Achievement: An Empirical Study of New York City's Middle Schools

ERIC Educational Resources Information Center

Chen, Greg; Weikart, Lynne A.

2008-01-01

This study develops and tests a school disorder and student achievement model based upon the school climate framework. The model was fitted to 212 New York City middle schools using the Structural Equations Modeling Analysis method. The analysis shows that the model fits the data well based upon test statistics and goodness of fit indices. The…

Agriculture and Climate Change in Global Scenarios: Why Don't the Models Agree

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

Nelson, Gerald; van der Mensbrugghe, Dominique; Ahammad, Helal

Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs makes direct use of weather inputs. Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes of key variables such as prices, production, and trade. These divergent outcomes arise from differences in model inputs and model specification. The goal of this paper is to review climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. Bymore » providing common productivity drivers that include climate change effects, differences in model outcomes are reduced. All models show higher prices in 2050 because of negative productivity shocks from climate change. The magnitude of the price increases, and the adaptation responses, differ significantly across the various models. Substantial differences exist in the structural parameters affecting demand, area, and yield, and should be a topic for future research.« less

Strategies for Teaching Regional Climate Modeling: Online Professional Development for Scientists and Decision Makers

NASA Astrophysics Data System (ADS)

Walton, P.; Yarker, M. B.; Mesquita, M. D. S.; Otto, F. E. L.

2014-12-01

There is a clear role for climate science in supporting decision making at a range of scales and in a range of contexts: from Global to local, from Policy to Industry. However, clear a role climate science can play, there is also a clear discrepancy in the understanding of how to use the science and associated tools (such as climate models). Despite there being a large body of literature on the science there is clearly a need to provide greater support in how to apply appropriately. However, access to high quality professional development courses can be problematic, due to geographic, financial and time constraints. In attempt to address this gap we independently developed two online professional courses that focused on helping participants use and apply two regional climate models, WRF and PRECIS. Both courses were designed to support participants' learning through tutor led programs that covered the basic climate scientific principles of regional climate modeling and how to apply model outputs. The fundamental differences between the two courses are: 1) the WRF modeling course expected participants to design their own research question that was then run on a version of the model, whereas 2) the PRECIS course concentrated on the principles of regional modeling and how the climate science informed the modeling process. The two courses were developed to utilise the cost and time management benefits associated with eLearning, with the recognition that this mode of teaching can also be accessed internationally, providing professional development courses in countries that may not be able to provide their own. The development teams saw it as critical that the courses reflected sound educational theory, to ensure that participants had the maximum opportunity to learn successfully. In particular, the role of reflection is central to both course structures to help participants make sense of the science in relation to their own situation. This paper details the different structures of both courses, evaluating the advantages and disadvantages of each, along with the educational approaches used. We conclude by proposing a framework for the develop of educationally robust online professional development programs that actively supports decision makers in understanding, developing and applying regional climate models.

Spatial models reveal the microclimatic buffering capacity of old-growth forests

PubMed Central

Frey, Sarah J. K.; Hadley, Adam S.; Johnson, Sherri L.; Schulze, Mark; Jones, Julia A.; Betts, Matthew G.

2016-01-01

Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed independent test data well (r = 0.87). As expected, elevation strongly predicted temperatures, but vegetation and microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decreased by 2.5°C across the observed gradient in old-growth structure. These cooling effects across a gradient in forest structure are of similar magnitude to 50-year forecasts of the Intergovernmental Panel on Climate Change and therefore have the potential to mitigate climate warming at local scales. Management strategies to conserve old-growth characteristics and to curb current rates of primary forest loss could maintain microrefugia, enhancing biodiversity persistence in mountainous systems under climate warming. PMID:27152339

Spatial models reveal the microclimatic buffering capacity of old-growth forests.

PubMed

Frey, Sarah J K; Hadley, Adam S; Johnson, Sherri L; Schulze, Mark; Jones, Julia A; Betts, Matthew G

2016-04-01

Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed independent test data well (r = 0.87). As expected, elevation strongly predicted temperatures, but vegetation and microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decreased by 2.5°C across the observed gradient in old-growth structure. These cooling effects across a gradient in forest structure are of similar magnitude to 50-year forecasts of the Intergovernmental Panel on Climate Change and therefore have the potential to mitigate climate warming at local scales. Management strategies to conserve old-growth characteristics and to curb current rates of primary forest loss could maintain microrefugia, enhancing biodiversity persistence in mountainous systems under climate warming.

Degrees of Isolation: The Impact of Climate Change on the Dispersal and Population Genetic Structure of Two Antarctic Fish Species

NASA Astrophysics Data System (ADS)

Young, E. F.; Belchier, M.; Meredith, M. P.; Tysklind, N.; Carvalho, G. R.

2016-02-01

Understanding the key drivers of larval dispersal and population connectivity in the marine environment is essential for estimating the potential impacts of climate change on the genetic structure and resilience of populations. Small, isolated and fragmented communities will differ fundamentally in their response and resilience to environmental stress, compared with species that are broadly distributed, abundant, and with a frequent exchange of members. Using a `seascape genetics' approach, combining oceanographic modelling and genetic analyses, we have elucidated the fundamental roles of oceanographic transport and planktonic duration on the connectivity and population genetic structure of two Antarctic fish species with contrasting early life histories, Champsocephalus gunnari and Notothenia rossii. Here, we extend these analyses to consider the impact of rising sea temperatures due to climate change on planktonic dispersal and population connectivity. Using a theoretical approach, the effect of increased water temperatures on mortality rates and species-specific egg and larval phase durations has been incorporated into the models, and the relative impact of these climate-related influences on connectivity and population genetic structure has been investigated. Here we present the key findings of our research and consider the roles of early life history and oceanography in the response of fragmented fish populations to climate change.

Invasive species: an increasing threat to marine ecosystems under climate change?

NASA Astrophysics Data System (ADS)

Artioli, Yuri; Galienne, Chris; Holt, Jason; Wakelin, Sarah; Butenschön, Momme; Schrum, Corinna; Daewel, Ute; Pushpadas, Dhania; Cannaby, Heather; Salihoglu, Baris; Zavatarelli, Marco; Clementi, Emanuela; Olenin, Sergej; Allen, Icarus

2013-04-01

Planktonic Non-Indigenous Species (NIS) are a potential threat to marine ecosystems: a successful invasion of such organisms can alter significantly the ecosystem structure with shift in species composition that can affect different levels of the trophic network and also with local extinction of native species in the more extreme cases. Such changes will also impact some ecosystem functions like primary and secondary production or nutrient cycling, and services, like fishery, aquaculture or carbon sequestration. Understanding how climate change influences the susceptibility of a marine ecosystem to invasion is challenging as the success and the impact of an invasion depend on many different factors all tightly interconnected (e.g. time of the invasion, location, state of the ecosystem…). Here we present DivERSEM, a new version of the biogeochemical model ERSEM modified in order to account for phytoplankton diversity. With such a model, we are able to simulate invasion from phytoplankton NIS, to assess the likelihood of success of such an invasion and to estimate the potential impact on ecosystem structure, using indicator like the Biopollution index. In the MEECE project (www.meece.eu), the model has been coupled to a 1D water column model (GOTM) in two different climate scenarios (present day and the IPCC SRES A1B scenario for 2100) in 4 different European shelf seas (North Sea, Baltic Sea, Black Sea and Adriatic Sea). The model has been forced with atmospheric data coming from the IPSL climate model, and nutrient concentration extracted from a set of 3D biogeochemical models running under the same climate scenario. The response of the ecosystem susceptibility to invasion to climate change has been analysed comparing the successfulness of invasions in the two time slices and its impact on community structure and ecosystem functions. At the same time, the comparison among the different basins allowed to highlight some of the characteristics that make the ecosystems more vulnerable to NIS.

Climate Change Effects on Agriculture: Economic Responses to Biophysical Shocks

NASA Technical Reports Server (NTRS)

Nelson, Gerald C.; Valin, Hugo; Sands, Ronald D.; Havlik, Petr; Ahammad, Helal; Deryng, Delphine; Elliott, Joshua; Fujimori, Shinichiro; Hasegawa, Tomoko; Heyhoe, Edwina

2014-01-01

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(sup 2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Climate change effects on agriculture: Economic responses to biophysical shocks

PubMed Central

Nelson, Gerald C.; Valin, Hugo; Sands, Ronald D.; Havlík, Petr; Ahammad, Helal; Deryng, Delphine; Elliott, Joshua; Fujimori, Shinichiro; Hasegawa, Tomoko; Heyhoe, Edwina; Kyle, Page; Von Lampe, Martin; Lotze-Campen, Hermann; Mason d’Croz, Daniel; van Meijl, Hans; van der Mensbrugghe, Dominique; Müller, Christoph; Popp, Alexander; Robertson, Richard; Robinson, Sherman; Schmid, Erwin; Schmitz, Christoph; Tabeau, Andrzej; Willenbockel, Dirk

2014-01-01

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change’s representative concentration pathway with end-of-century radiative forcing of 8.5 W/m2. The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change. PMID:24344285

Climate change effects on agriculture: economic responses to biophysical shocks.

PubMed

Nelson, Gerald C; Valin, Hugo; Sands, Ronald D; Havlík, Petr; Ahammad, Helal; Deryng, Delphine; Elliott, Joshua; Fujimori, Shinichiro; Hasegawa, Tomoko; Heyhoe, Edwina; Kyle, Page; Von Lampe, Martin; Lotze-Campen, Hermann; Mason d'Croz, Daniel; van Meijl, Hans; van der Mensbrugghe, Dominique; Müller, Christoph; Popp, Alexander; Robertson, Richard; Robinson, Sherman; Schmid, Erwin; Schmitz, Christoph; Tabeau, Andrzej; Willenbockel, Dirk

2014-03-04

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Effects of model structural uncertainty on carbon cycle projections: biological nitrogen fixation as a case study

NASA Astrophysics Data System (ADS)

Wieder, William R.; Cleveland, Cory C.; Lawrence, David M.; Bonan, Gordon B.

2015-04-01

Uncertainties in terrestrial carbon (C) cycle projections increase uncertainty of potential climate feedbacks. Efforts to improve model performance often include increased representation of biogeochemical processes, such as coupled carbon-nitrogen (N) cycles. In doing so, models are becoming more complex, generating structural uncertainties in model form that reflect incomplete knowledge of how to represent underlying processes. Here, we explore structural uncertainties associated with biological nitrogen fixation (BNF) and quantify their effects on C cycle projections. We find that alternative plausible structures to represent BNF result in nearly equivalent terrestrial C fluxes and pools through the twentieth century, but the strength of the terrestrial C sink varies by nearly a third (50 Pg C) by the end of the twenty-first century under a business-as-usual climate change scenario representative concentration pathway 8.5. These results indicate that actual uncertainty in future C cycle projections may be larger than previously estimated, and this uncertainty will limit C cycle projections until model structures can be evaluated and refined.

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

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

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

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

Comparing effects of climate warming, fire, and timber harvesting on a boreal forest landscape in northeastern China.

PubMed

Li, Xiaona; He, Hong S; Wu, Zhiwei; Liang, Yu; Schneiderman, Jeffrey E

2013-01-01

Forest management under a changing climate requires assessing the effects of climate warming and disturbance on the composition, age structure, and spatial patterns of tree species. We investigated these effects on a boreal forest in northeastern China using a factorial experimental design and simulation modeling. We used a spatially explicit forest landscape model (LANDIS) to evaluate the effects of three independent variables: climate (current and expected future), fire regime (current and increased fire), and timber harvesting (no harvest and legal harvest). Simulations indicate that this forested landscape would be significantly impacted under a changing climate. Climate warming would significantly increase the abundance of most trees, especially broadleaf species (aspen, poplar, and willow). However, climate warming would have less impact on the abundance of conifers, diversity of forest age structure, and variation in spatial landscape structure than burning and harvesting. Burning was the predominant influence in the abundance of conifers except larch and the abundance of trees in mid-stage. Harvesting impacts were greatest for the abundance of larch and birch, and the abundance of trees during establishment stage (1-40 years), early stage (41-80 years) and old- growth stage (>180 years). Disturbance by timber harvesting and burning may significantly alter forest ecosystem dynamics by increasing forest fragmentation and decreasing forest diversity. Results from the simulations provide insight into the long term management of this boreal forest.

Assessing the impact of model and climate uncertainty in malaria simulations for the Kenyan Highlands.

NASA Astrophysics Data System (ADS)

Tompkins, A. M.; Thomson, M. C.

2017-12-01

Simulations of the impact of climate variations on a vector-bornedisease such as malaria are subject to a number of sources ofuncertainty. These include the model structure and parameter settingsin addition to errors in the climate data and the neglect of theirspatial heterogeneity, especially over complex terrain. We use aconstrained genetic algorithm to confront these two sources ofuncertainty for malaria transmission in the highlands of Kenya. Thetechnique calibrates the parameter settings of a process-based,mathematical model of malaria transmission to vary within theirassessed level of uncertainty and also allows the calibration of thedriving climate data. The simulations show that in highland settingsclose to the threshold for sustained transmission, the uncertainty inclimate is more important to address than the malaria modeluncertainty. Applications of the coupled climate-malaria modelling system are briefly presented.

Tests of species-specific models reveal the importance of drought in postglacial range shifts of a Mediterranean-climate tree: insights from integrative distributional, demographic and coalescent modelling and ABC model selection.

PubMed

Bemmels, Jordan B; Title, Pascal O; Ortego, Joaquín; Knowles, L Lacey

2016-10-01

Past climate change has caused shifts in species distributions and undoubtedly impacted patterns of genetic variation, but the biological processes mediating responses to climate change, and their genetic signatures, are often poorly understood. We test six species-specific biologically informed hypotheses about such processes in canyon live oak (Quercus chrysolepis) from the California Floristic Province. These hypotheses encompass the potential roles of climatic niche, niche multidimensionality, physiological trade-offs in functional traits, and local-scale factors (microsites and local adaptation within ecoregions) in structuring genetic variation. Specifically, we use ecological niche models (ENMs) to construct temporally dynamic landscapes where the processes invoked by each hypothesis are reflected by differences in local habitat suitabilities. These landscapes are used to simulate expected patterns of genetic variation under each model and evaluate the fit of empirical data from 13 microsatellite loci genotyped in 226 individuals from across the species range. Using approximate Bayesian computation (ABC), we obtain very strong support for two statistically indistinguishable models: a trade-off model in which growth rate and drought tolerance drive habitat suitability and genetic structure, and a model based on the climatic niche estimated from a generic ENM, in which the variables found to make the most important contribution to the ENM have strong conceptual links to drought stress. The two most probable models for explaining the patterns of genetic variation thus share a common component, highlighting the potential importance of seasonal drought in driving historical range shifts in a temperate tree from a Mediterranean climate where summer drought is common. © 2016 John Wiley & Sons Ltd.

A likelihood-based time series modeling approach for application in dendrochronology to examine the growth-climate relations and forest disturbance history

EPA Science Inventory

A time series intervention analysis (TSIA) of dendrochronological data to infer the tree growth-climate-disturbance relations and forest disturbance history is described. Maximum likelihood is used to estimate the parameters of a structural time series model with components for ...

Influence of Climate Change on Flood Hazard using Climate Informed Bayesian Hierarchical Model in Johnson Creek River

NASA Astrophysics Data System (ADS)

Zarekarizi, M.; Moradkhani, H.

2015-12-01

Extreme events are proven to be affected by climate change, influencing hydrologic simulations for which stationarity is usually a main assumption. Studies have discussed that this assumption would lead to large bias in model estimations and higher flood hazard consequently. Getting inspired by the importance of non-stationarity, we determined how the exceedance probabilities have changed over time in Johnson Creek River, Oregon. This could help estimate the probability of failure of a structure that was primarily designed to resist less likely floods according to common practice. Therefore, we built a climate informed Bayesian hierarchical model and non-stationarity was considered in modeling framework. Principle component analysis shows that North Atlantic Oscillation (NAO), Western Pacific Index (WPI) and Eastern Asia (EA) are mostly affecting stream flow in this river. We modeled flood extremes using peaks over threshold (POT) method rather than conventional annual maximum flood (AMF) mainly because it is possible to base the model on more information. We used available threshold selection methods to select a suitable threshold for the study area. Accounting for non-stationarity, model parameters vary through time with climate indices. We developed a couple of model scenarios and chose one which could best explain the variation in data based on performance measures. We also estimated return periods under non-stationarity condition. Results show that ignoring stationarity could increase the flood hazard up to four times which could increase the probability of an in-stream structure being overtopped.

Assessing the skill of hydrology models at simulating the water cycle in the HJ Andrews LTER: Assumptions, strengths and weaknesses

EPA Science Inventory

Simulated impacts of climate on hydrology can vary greatly as a function of the scale of the input data, model assumptions, and model structure. Four models are commonly used to simulate streamflow in model assumptions, and model structure. Four models are commonly used to simu...

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Carbon fluxes in tropical forest ecosystems: the value of Eddy-covariance data for individual-based dynamic forest gap models

NASA Astrophysics Data System (ADS)

Roedig, Edna; Cuntz, Matthias; Huth, Andreas

2015-04-01

The effects of climatic inter-annual fluctuations and human activities on the global carbon cycle are uncertain and currently a major issue in global vegetation models. Individual-based forest gap models, on the other hand, model vegetation structure and dynamics on a small spatial (<100 ha) and large temporal scale (>1000 years). They are well-established tools to reproduce successions of highly-diverse forest ecosystems and investigate disturbances as logging or fire events. However, the parameterizations of the relationships between short-term climate variability and forest model processes are often uncertain in these models (e.g. daily variable temperature and gross primary production (GPP)) and cannot be constrained from forest inventories. We addressed this uncertainty and linked high-resolution Eddy-covariance (EC) data with an individual-based forest gap model. The forest model FORMIND was applied to three diverse tropical forest sites in the Amazonian rainforest. Species diversity was categorized into three plant functional types. The parametrizations for the steady-state of biomass and forest structure were calibrated and validated with different forest inventories. The parameterizations of relationships between short-term climate variability and forest model processes were evaluated with EC-data on a daily time step. The validations of the steady-state showed that the forest model could reproduce biomass and forest structures from forest inventories. The daily estimations of carbon fluxes showed that the forest model reproduces GPP as observed by the EC-method. Daily fluctuations of GPP were clearly reflected as a response to daily climate variability. Ecosystem respiration remains a challenge on a daily time step due to a simplified soil respiration approach. In the long-term, however, the dynamic forest model is expected to estimate carbon budgets for highly-diverse tropical forests where EC-measurements are rare.

The use of EuroCordex in marine climate projections

NASA Astrophysics Data System (ADS)

Tinker, Jonathan; Palmer, Matthew; Lowe, Jason; Howard, Tom

2017-04-01

The Northwest European Shelf seas (NWS, including the North Sea, Irish Sea and Celtic Sea) are of economic, environmental and cultural importance to a number of European countries. However, their representation by global climate models (GCMs) is very crude, due to their inability to represent the complex geometry and the absence of tides. Therefore, there is a need to employ dynamical downscaling methods when considering the potential impacts of climate change on the European (and other) shelf seas. Using a shelf seas model to dynamically downscale of the ocean component of the GCM is a well established method. While taking open ocean lateral boundary conditions from the GCM ocean is acceptable, using surface flux forcings from the GCM atmosphere is often problematic. The CORDEX project provides an important dataset of high spatial and temporal resolution atmospheric forcings, derived from 'parent' CMIP5 GCM simulations. We drive the NEMO shelf seas model with data from CMIP5 models and EURO-CORDEX Regional Climate Model (RCM) data to produce a set of NWS climate projections. We require relatively high temporal resolution output, and run-off (for the river forcings), and so are limited to a subset of the available EURO-CORDEX RCMs. From these we select two CMIP5 GCMs with the same RCM with two emissions scenarios to give a minimum estimate of GCM model structural and emission scenario uncertainty. Other experiments allow an initial estimate of the uncertainty associated with the model structure of both the shelf seas and the RCM. Our analysis is focused on the uncertainty associated with the mean change in a number of physical marine impacts and the drivers of coastal variability and change, including sea level and the propagation of open ocean signals onto the shelf. Our work is part of the UK Climate Projections (UKCP18) and will inform the following UK Climate Change Risk Assessments, required as part of the Climate Change Act.

Multilevel Factor Structure and Concurrent Validity of the Teacher Version of the Authoritative School Climate Survey.

PubMed

Huang, Francis L; Cornell, Dewey G; Konold, Timothy; Meyer, Joseph P; Lacey, Anna; Nekvasil, Erin K; Heilbrun, Anna; Shukla, Kathan D

2015-12-01

School climate is well recognized as an important influence on student behavior and adjustment to school, but there is a need for theory-guided measures that make use of teacher perspectives. Authoritative school climate theory hypothesizes that a positive school climate is characterized by high levels of disciplinary structure and student support. A teacher version of the Authoritative School Climate Survey (ASCS) was administered to a statewide sample of 9099 7th- and 8th-grade teachers from 366 schools. The study used exploratory and multilevel confirmatory factor analyses (MCFA) that accounted for the nested data structure and allowed for the modeling of the factor structures at 2 levels. Multilevel confirmatory factor analyses conducted on both an exploratory (N = 4422) and a confirmatory sample (N = 4677) showed good support for the factor structures investigated. Factor correlations at 2 levels indicated that schools with greater levels of disciplinary structure and student support had higher student engagement, less teasing and bullying, and lower student aggression toward teachers. The teacher version of the ASCS can be used to assess 2 key domains of school climate and associated measures of student engagement and aggression toward peers and teachers. © 2015, American School Health Association.

Landscape structure and climate influences on hydrologic response

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

Resilience landscapes for Congo basin rainforests vs. climate and management impacts

NASA Astrophysics Data System (ADS)

Pietsch, Stephan Alexander; Gautam, Sishir; Elias Bednar, Johannes; Stanzl, Patrick; Mosnier, Aline; Obersteiner, Michael

2015-04-01

Past climate change caused severe disturbances of the Central African rainforest belt, with forest fragmentation and re-expansion due to drier and wetter climate conditions. Besides climate, human induced forest degradation affected biodiversity, structure and carbon storage of Congo basin rainforests. Information on climatically stable, mature rainforest, unaffected by human induced disturbances, provides means of assessing the impact of forest degradation and may serve as benchmarks of carbon carrying capacity over regions with similar site and climate conditions. BioGeoChemical (BGC) ecosystem models explicitly consider the impacts of site and climate conditions and may assess benchmark levels over regions devoid of undisturbed conditions. We will present a BGC-model validation for the Western Congolian Lowland Rainforest (WCLRF) using field data from a recently confirmed forest refuge, show model - data comparisons for disturbed und undisturbed forests under different site and climate conditions as well as for sites with repeated assessment of biodiversity and standing biomass during recovery from intensive exploitation. We will present climatic thresholds for WCLRF stability, and construct resilience landscapes for current day conditions vs. climate and management impacts.

Climate effects and feedback structure determining weed population dynamics in a long-term experiment.

PubMed

Lima, Mauricio; Navarrete, Luis; González-Andujar, José Luis

2012-01-01

Pest control is one of the areas in which population dynamic theory has been successfully applied to solve practical problems. However, the links between population dynamic theory and model construction have been less emphasized in the management and control of weed populations. Most management models of weed population dynamics have emphasized the role of the endogenous process, but the role of exogenous variables such as climate have been ignored in the study of weed populations and their management. Here, we use long-term data (22 years) on two annual weed species from a locality in Central Spain to determine the importance of endogenous and exogenous processes (local and large-scale climate factors). Our modeling study determined two different feedback structures and climate effects in the two weed species analyzed. While Descurainia sophia exhibited a second-order feedback and low climate influence, Veronica hederifolia was characterized by a first-order feedback structure and important effects from temperature and rainfall. Our results strongly suggest the importance of theoretical population dynamics in understanding plant population systems. Moreover, the use of this approach, discerning between the effect of exogenous and endogenous factors, can be fundamental to applying weed management practices in agricultural systems and to controlling invasive weedy species. This is a radical change from most approaches currently used to guide weed and invasive weedy species managements.

Climate Effects and Feedback Structure Determining Weed Population Dynamics in a Long-Term Experiment

PubMed Central

Lima, Mauricio; Navarrete, Luis; González-Andujar, José Luis

2012-01-01

Pest control is one of the areas in which population dynamic theory has been successfully applied to solve practical problems. However, the links between population dynamic theory and model construction have been less emphasized in the management and control of weed populations. Most management models of weed population dynamics have emphasized the role of the endogenous process, but the role of exogenous variables such as climate have been ignored in the study of weed populations and their management. Here, we use long-term data (22 years) on two annual weed species from a locality in Central Spain to determine the importance of endogenous and exogenous processes (local and large-scale climate factors). Our modeling study determined two different feedback structures and climate effects in the two weed species analyzed. While Descurainia sophia exhibited a second-order feedback and low climate influence, Veronica hederifolia was characterized by a first-order feedback structure and important effects from temperature and rainfall. Our results strongly suggest the importance of theoretical population dynamics in understanding plant population systems. Moreover, the use of this approach, discerning between the effect of exogenous and endogenous factors, can be fundamental to applying weed management practices in agricultural systems and to controlling invasive weedy species. This is a radical change from most approaches currently used to guide weed and invasive weedy species managements. PMID:22272362

On the Value of Climate Elasticity Indices to Assess the Impact of Climate Change on Streamflow Projection using an ensemble of bias corrected CMIP5 dataset

NASA Astrophysics Data System (ADS)

Demirel, Mehmet; Moradkhani, Hamid

2015-04-01

Changes in two climate elasticity indices, i.e. temperature and precipitation elasticity of streamflow, were investigated using an ensemble of bias corrected CMIP5 dataset as forcing to two hydrologic models. The Variable Infiltration Capacity (VIC) and the Sacramento Soil Moisture Accounting (SAC-SMA) hydrologic models, were calibrated at 1/16 degree resolution and the simulated streamflow was routed to the basin outlet of interest. We estimated precipitation and temperature elasticity of streamflow from: (1) observed streamflow; (2) simulated streamflow by VIC and SAC-SMA models using observed climate for the current climate (1963-2003); (3) simulated streamflow using simulated climate from 10 GCM - CMIP5 dataset for the future climate (2010-2099) including two concentration pathways (RCP4.5 and RCP8.5) and two downscaled climate products (BCSD and MACA). The streamflow sensitivity to long-term (e.g., 30-year) average annual changes in temperature and precipitation is estimated for three periods i.e. 2010-40, 2040-70 and 2070-99. We compared the results of the three cases to reflect on the value of precipitation and temperature indices to assess the climate change impacts on Columbia River streamflow. Moreover, these three cases for two models are used to assess the effects of different uncertainty sources (model forcing, model structure and different pathways) on the two climate elasticity indices.

An anatomy of the projected North Atlantic warming hole in CMIP5 models

NASA Astrophysics Data System (ADS)

Menary, Matthew B.; Wood, Richard A.

2018-04-01

Global mean surface air temperature has increased over the past century and climate models project this trend to continue. However, the pattern of change is not homogeneous. Of particular interest is the subpolar North Atlantic, which has cooled in recent years and is projected to continue to warm less rapidly than the global mean. This is often termed the North Atlantic warming hole (WH). In climate model projections, the development of the WH is concomitant with a weakening of the Atlantic meridional overturning circulation (AMOC). Here, we further investigate the possible link between the AMOC and WH and the competing drivers of vertical mixing and surface heat fluxes. Across a large ensemble of 41 climate models we find that the spatial structure of the WH varies considerably from model to model but is generally upstream of the simulated deep water formation regions. A heat budget analysis suggests the formation of the WH is related to changes in ocean heat transport. Although the models display a plethora of AMOC mean states, they generally predict a weakening and shallowing of the AMOC also consistent with the evolving depth structure of the WH. A lagged regression analysis during the WH onset phase suggests that reductions in wintertime mixing lead a weakening of the AMOC by 5 years in turn leading initiation of the WH by 5 years. Inter-model differences in the evolution and structure of the WH are likely to lead to somewhat different projected climate impacts in nearby Europe and North America.

Quantifying climatic controls on river network topology across scales

NASA Astrophysics Data System (ADS)

Ranjbar Moshfeghi, S.; Hooshyar, M.; Wang, D.; Singh, A.

2017-12-01

Branching structure of river networks is an important topologic and geomorphologic feature that depends on several factors (e.g. climate, tectonic). However, mechanisms that cause these drainage patterns in river networks are poorly understood. In this study, we investigate the effects of varying climatic forcing on river network topology and geomorphology. For this, we select 20 catchments across the United States with different long-term climatic conditions quantified by climate aridity index (AI), defined here as the ratio of mean annual potential evaporation (Ep) to precipitation (P), capturing variation in runoff and vegetation cover. The river networks of these catchments are extracted, using a curvature-based method, from high-resolution (1 m) digital elevation models and several metrics such as drainage density, branching angle, and width functions are computed. We also use a multiscale-entropy-based approach to quantify the topologic irregularity and structural richness of these river networks. Our results reveal systematic impacts of climate forcing on the structure of river networks.

Untangling climatic and autogenic signals in peat records

NASA Astrophysics Data System (ADS)

Morris, Paul J.; Baird, Andrew J.; Young, Dylan M.; Swindles, Graeme T.

2016-04-01

Raised bogs contain potentially valuable information about Holocene climate change. However, autogenic processes may disconnect peatland hydrological behaviour from climate, and overwrite and degrade climatic signals in peat records. How can genuine climate signals be separated from autogenic changes? What level of detail of climatic information should we expect to be able to recover from peat-based reconstructions? We used an updated version of the DigiBog model to simulate peatland development and response to reconstructed Holocene rainfall and temperature reconstructions. The model represents key processes that are influential in peatland development and climate signal preservation, and includes a network of feedbacks between peat accumulation, decomposition, hydraulic structure and hydrological processes. It also incorporates the effects of temperature upon evapotranspiration, plant (litter) productivity and peat decomposition. Negative feedbacks in the model cause simulated water-table depths and peat humification records to exhibit homeostatic recovery from prescribed changes in rainfall, chiefly through changes in drainage. However, the simulated bogs show less resilience to changes in temperature, which cause lasting alterations to peatland structure and function and may therefore be more readily detectable in peat records. The network of feedbacks represented in DigiBog also provide both high- and low-pass filters for climatic information, meaning that the fidelity with which climate signals are preserved in simulated peatlands is determined by both the magnitude and the rate of climate change. Large-magnitude climatic events of an intermediate frequency (i.e., multi-decadal to centennial) are best preserved in the simulated bogs. We found that simulated humification records are further degraded by a phenomenon known as secondary decomposition. Decomposition signals are consistently offset from the climatic events that generate them, and decomposition records of dry-wet-dry climate sequences appear to be particularly vulnerable to overwriting. Our findings have direct implications not only for the interpretation of peat-based records of past climates, but also for understanding the likely vulnerability of peatland ecosystems and carbon stocks to future climate change.

Uncertainty in Model Predictions of Vibrio Vulnificus Response to Climate Variability and Change: A Chesapeake Bay Case Study

NASA Technical Reports Server (NTRS)

Urquhart, Erin A.; Zaitchik, Benjamin F.; Waugh, Darryn W.; Guikema, Seth D.; Del Castillo, Carlos E.

2014-01-01

The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3-0.4 C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist.

Gap formation following climatic events in spatially structured plant communities

PubMed Central

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

2015-01-01

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

Modeling drivers of phosphorus loads in Chesapeake Bay tributaries and inferences about long-term change

USGS Publications Warehouse

Ryberg, Karen R.; Blomquist, Joel; Sprague, Lori A.; Sekellick, Andrew J.; Keisman, Jennifer

2018-01-01

Causal attribution of changes in water quality often consists of correlation, qualitative reasoning, listing references to the work of others, or speculation. To better support statements of attribution for water-quality trends, structural equation modeling was used to model the causal factors of total phosphorus loads in the Chesapeake Bay watershed. By transforming, scaling, and standardizing variables, grouping similar sites, grouping some causal factors into latent variable models, and using methods that correct for assumption violations, we developed a structural equation model to show how causal factors interact to produce total phosphorus loads. Climate (in the form of annual total precipitation and the Palmer Hydrologic Drought Index) and anthropogenic inputs are the major drivers of total phosphorus load in the Chesapeake Bay watershed. Increasing runoff due to natural climate variability is offsetting purposeful management actions that are otherwise decreasing phosphorus loading; consequently, management actions may need to be reexamined to achieve target reductions in the face of climate variability.

Recent Naval Postgraduate School Publications.

DTIC Science & Technology

1982-04-01

477 p. Haney, R L; et al.; eds. Ocean models for climate research: A workshop Sponsored by the U.S. Committee for the Global Atmos. Hes. Program. Nat... climate variability Oceanus, vol. 21, no. 4, p. 33-39, (1978). Williams, R T A review of theoretical models of atmospheric frontogenesis Chapman Conf...structure in large-scale optimization models Symp. 9 n Computer-Assisted Analysis and Model Simpification, Boulder, Colo., Mar. 24, 1980. Brown, G G

Probabilistic Climate Scenario Information for Risk Assessment

NASA Astrophysics Data System (ADS)

Dairaku, K.; Ueno, G.; Takayabu, I.

2014-12-01

Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. In order to develop probabilistic regional climate information that represents the uncertainty in climate scenario experiments in Japan, we compared the physics ensemble experiments using the 60km global atmospheric model of the Meteorological Research Institute (MRI-AGCM) with multi-model ensemble experiments with global atmospheric-ocean coupled models (CMIP3) of SRES A1b scenario experiments. The MRI-AGCM shows relatively good skills particularly in tropics for temperature and geopotential height. Variability in surface air temperature of physical ensemble experiments with MRI-AGCM was within the range of one standard deviation of the CMIP3 model in the Asia region. On the other hand, the variability of precipitation was relatively well represented compared with the variation of the CMIP3 models. Models which show the similar reproducibility in the present climate shows different future climate change. We couldn't find clear relationships between present climate and future climate change in temperature and precipitation. We develop a new method to produce probabilistic information of climate change scenarios by weighting model ensemble experiments based on a regression model (Krishnamurti et al., Science, 1999). The method can be easily applicable to other regions and other physical quantities, and also to downscale to finer-scale dependent on availability of observation dataset. The prototype of probabilistic information in Japan represents the quantified structural uncertainties of multi-model ensemble experiments of climate change scenarios. Acknowledgments: This study was supported by the SOUSEI Program, funded by Ministry of Education, Culture, Sports, Science and Technology, Government of Japan.

Understanding National Models for Climate Assessments

NASA Astrophysics Data System (ADS)

Dave, A.; Weingartner, K.

2017-12-01

National-level climate assessments have been produced or are underway in a number of countries. These efforts showcase a variety of approaches to mapping climate impacts onto human and natural systems, and involve a variety of development processes, organizational structures, and intended purposes. This presentation will provide a comparative overview of national `models' for climate assessments worldwide, drawing from a geographically diverse group of nations with varying capacities to conduct such assessments. Using an illustrative sampling of assessment models, the presentation will highlight the range of assessment mandates and requirements that drive this work, methodologies employed, focal areas, and the degree to which international dimensions are included for each nation's assessment. This not only allows the U.S. National Climate Assessment to be better understood within an international context, but provides the user with an entry point into other national climate assessments around the world, enabling a better understanding of the risks and vulnerabilities societies face.

Variance analysis of forecasted streamflow maxima in a wet temperate climate

NASA Astrophysics Data System (ADS)

Al Aamery, Nabil; Fox, James F.; Snyder, Mark; Chandramouli, Chandra V.

2018-05-01

Coupling global climate models, hydrologic models and extreme value analysis provides a method to forecast streamflow maxima, however the elusive variance structure of the results hinders confidence in application. Directly correcting the bias of forecasts using the relative change between forecast and control simulations has been shown to marginalize hydrologic uncertainty, reduce model bias, and remove systematic variance when predicting mean monthly and mean annual streamflow, prompting our investigation for maxima streamflow. We assess the variance structure of streamflow maxima using realizations of emission scenario, global climate model type and project phase, downscaling methods, bias correction, extreme value methods, and hydrologic model inputs and parameterization. Results show that the relative change of streamflow maxima was not dependent on systematic variance from the annual maxima versus peak over threshold method applied, albeit we stress that researchers strictly adhere to rules from extreme value theory when applying the peak over threshold method. Regardless of which method is applied, extreme value model fitting does add variance to the projection, and the variance is an increasing function of the return period. Unlike the relative change of mean streamflow, results show that the variance of the maxima's relative change was dependent on all climate model factors tested as well as hydrologic model inputs and calibration. Ensemble projections forecast an increase of streamflow maxima for 2050 with pronounced forecast standard error, including an increase of +30(±21), +38(±34) and +51(±85)% for 2, 20 and 100 year streamflow events for the wet temperate region studied. The variance of maxima projections was dominated by climate model factors and extreme value analyses.

Flood Control Structures Research Program. Annotated Bibliography on Grade Control Structures

DTIC Science & Technology

1991-09-01

evaluating the effects of geology, geomorphology, soils, land use, and climate on runoff and sediment production from major source areas; (4...Otto, and ,t:iji, Ahmed. 1987. "Theoret- ical Flow Model for Drop Structures," -’. aulic ’ngineering, Proceed- ings of the 1987 National Confere’,ce on...Facilities for Unique Flood Problems," Journal of the-Waterways and Harbors Division, ASCE, Vol 97, No. WWI, pp 185-203. The unusual climatic

Organizational Climate Partially Mediates the Effect of Culture on Work Attitudes and Staff Turnover in Mental Health Services

PubMed Central

Aarons, Gregory A.; Sawitzky, Angelina C.

2006-01-01

Staff turnover in mental health service organizations is an ongoing problem with implications for staff morale, productivity, organizational effectiveness, and implementation of innovation. Recent studies in public sector services have examined the impact of organizational culture and climate on work attitudes (i.e., job satisfaction and organizational commitment) and, ultimately, staff turnover. However, mediational models of the impact of culture and climate on work attitudes have not been examined. The present study examined full and partial mediation models of the effects of culture and climate on work attitudes and the subsequent impact of work attitudes on staff turnover. Multilevel structural equation models supported a partial mediation model in which organizational culture had both direct influence on work attitudes and indirect influence through organizational climate. Work attitudes significantly predicted one-year staff turnover rates. These findings support the contention that both culture and climate impact work attitudes and subsequent staff turnover. PMID:16544205

Organizational climate partially mediates the effect of culture on work attitudes and staff turnover in mental health services.

PubMed

Aarons, Gregory A; Sawitzky, Angelina C

2006-05-01

Staff turnover in mental health service organizations is an ongoing problem with implications for staff morale, productivity, organizational effectiveness, and implementation of innovation. Recent studies in public sector services have examined the impact of organizational culture and climate on work attitudes (i.e., job satisfaction and organizational commitment) and, ultimately, staff turnover. However, mediational models of the impact of culture and climate on work attitudes have not been examined. The present study examined full and partial mediation models of the effects of culture and climate on work attitudes and the subsequent impact of work attitudes on staff turnover. Multilevel structural equation models supported a partial mediation model in which organizational culture had both direct influence on work attitudes and indirect influence through organizational climate. Work attitudes significantly predicted one-year staff turnover rates. These findings support the contention that both culture and climate impact work attitudes and subsequent staff turnover.

Hysteresis in the Central African Rainforest

NASA Astrophysics Data System (ADS)

Pietsch, Stephan Alexander; Elias Bednar, Johannes; Gautam, Sishir; Petritsch, Richard; Schier, Franziska; Stanzl, Patrick

2014-05-01

Past climate change caused severe disturbances of the Central African rainforest belt, with forest fragmentation and re-expansion due to drier and wetter climate conditions. Besides climate, human induced forest degradation affected biodiversity, structure and carbon storage of Congo basin rainforests. Information on climatically stable, mature rainforest, unaffected by human induced disturbances, provides means of assessing the impact of forest degradation and may serve as benchmarks of carbon carrying capacity over regions with similar site and climate conditions. BioGeoChemical (BGC) ecosystem models explicitly consider the impacts of site and climate conditions and may assess benchmark levels over regions devoid of undisturbed conditions. We will present a BGC-model validation for the Western Congolian Lowland Rainforest (WCLRF) using field data from a recently confirmed forest refuge, show model - data comparisons for disturbed und undisturbed forests under different site and climate conditions as well as for sites with repeated assessment of biodiversity and standing biomass during recovery from intensive exploitation. We will present climatic thresholds for WCLRF stability, analyse the relationship between resilience, standing C-stocks and change in climate and finally provide evidence of hysteresis.

Dynamics of combined forest damage risks for 21st century (SRES A1B, B1)

NASA Astrophysics Data System (ADS)

Panferov, Oleg; Merklein, Johannes; Sogachev, Andrey; Junghans, Udo; Ahrends, Bernd

2010-05-01

The ongoing climate change can result in increasing frequency of weather extremes (Leckebusch et al., 2008) which in turn can produce wide area forest damage (windthrows, droughts, insect attacks) within forest ecosystems in Europe. The probability and extent of damage, depend not only on a strength of a driving force itself but especially on combinations of effecting agents and their interactions with forest ecosystem structure and soil properties. The combined effect of several factors which are not the extremes themselves can lead to the biotic and/or abiotic damage so that the combination becomes an extreme event. As soon as a damage event occurs, the forest structure is changed. The changes in forest structure in their turn strengthen or inhibits the influence of different climatic factors thus increase or decrease the probability of the next damage event creating positive or negative feedbacks. To assess the roles of separate meteorological factors and their combinations in forest damage under present and future climatic conditions the coupled model was created in University of Goettingen, as a part of a Decision Support System (Jansen et al, 2008, Panferov et al., 2009). The model combines the 3D ABL Model SCADIS (Panferov and Sogachev, 2008) with modified soil hydrology model BROOK 90 (Federer, 2003, Ahrends et al. 2009) and the model of climate dependent biotic damage. The projected future developments of forest damage events in 21st Century were carried out under conditions of SRES scenarios A1B and B1; the present conditions were evaluated using the measured data of German Weather Service. Climate scenario data of coupled ECHAM5-MPIOM were downscaled by the regional climate model Climate Local Model (CLM) to the spatial resolution of 0.2° x 0.2° and temporal resolution of 24 hours. Using these data as input the small-scale coupled process based modeling was then carried out for example region of Solling, Germany calculating the water and energy balance of forest ecosystems, wind loading on trees and biotic damage for several tree species and typical soil types. The damage risks a certain forest stand at a given soil results from daily combinations of air and soil temperatures, soil water characteristics, static and gust wind loads on trees with dynamic LAI and of soil texture. Some damaged stands show higher vulnerability and thus - positive feedbacks to climate forcing (Vygodskaya et al., 2007). Therefore, changes of microclimate in remaining stands after changes in forest structure are taken into account. Model output is aggregated to 30-years periods and compared to "present conditions" of 1981-2010. The results show considerable increment of both biotic and abiotic risks towards 2100 relatively to "present" caused by weak changes in precipitation and wind patterns and strong increase of mean air temperature and soil temperatures. It is shown, e.g. that the wind- damage-induced changes of structure and microclimate provide a positive feedback i.e. - increase the probability of the next damage event. The study was financed by BMBF within the frames of joint project "Decision Support System - Forest and Climate Change" (DSS-WuK) and by Grant of Ministry for Science and Culture of Lower Saxony "KLIFF". We gratefully acknowledge this support.

Coupling a distributed hydrological model with detailed forest structural information for large-scale global change impact assessment

NASA Astrophysics Data System (ADS)

Eisner, Stephanie; Huang, Shaochun; Majasalmi, Titta; Bright, Ryan; Astrup, Rasmus; Beldring, Stein

2017-04-01

Forests are recognized for their decisive effect on landscape water balance with structural forest characteristics as stand density or species composition determining energy partitioning and dominant flow paths. However, spatial and temporal variability in forest structure is often poorly represented in hydrological modeling frameworks, in particular in regional to large scale hydrological modeling and impact analysis. As a common practice, prescribed land cover classes (including different generic forest types) are linked to parameter values derived from literature, or parameters are determined by calibration. While national forest inventory (NFI) data provide comprehensive, detailed information on hydrologically relevant forest characteristics, their potential to inform hydrological simulation over larger spatial domains is rarely exploited. In this study we present a modeling framework that couples the distributed hydrological model HBV with forest structural information derived from the Norwegian NFI and multi-source remote sensing data. The modeling framework, set up for the entire of continental Norway at 1 km spatial resolution, is explicitly designed to study the combined and isolated impacts of climate change, forest management and land use change on hydrological fluxes. We use a forest classification system based on forest structure rather than biomes which allows to implicitly account for impacts of forest management on forest structural attributes. In the hydrological model, different forest classes are represented by three parameters: leaf area index (LAI), mean tree height and surface albedo. Seasonal cycles of LAI and surface albedo are dynamically simulated to make the framework applicable under climate change conditions. Based on a hindcast for the pilot regions Nord-Trøndelag and Sør-Trøndelag, we show how forest management has affected regional hydrological fluxes during the second half of the 20th century as contrasted to climate variability.

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

Climate, trees, pests, and weeds: Change, uncertainty, and biotic stressors in eastern US national park forests

Treesearch

Nicholas A. Fisichelli; Scott R. Abella; Matthew Peters; Frank J. Krist

2014-01-01

The US National Park Service (NPS) manages over 8900 km2 of forest area in the eastern United States where climate change and nonnative species are altering forest structure, composition, and processes. Understanding potential forest change in response to climate, differences in habitat projections among models (uncertainty), and nonnative biotic...

Comparing Effects of Climate Warming, Fire, and Timber Harvesting on a Boreal Forest Landscape in Northeastern China

PubMed Central

Li, Xiaona; He, Hong S.; Wu, Zhiwei; Liang, Yu; Schneiderman, Jeffrey E.

2013-01-01

Forest management under a changing climate requires assessing the effects of climate warming and disturbance on the composition, age structure, and spatial patterns of tree species. We investigated these effects on a boreal forest in northeastern China using a factorial experimental design and simulation modeling. We used a spatially explicit forest landscape model (LANDIS) to evaluate the effects of three independent variables: climate (current and expected future), fire regime (current and increased fire), and timber harvesting (no harvest and legal harvest). Simulations indicate that this forested landscape would be significantly impacted under a changing climate. Climate warming would significantly increase the abundance of most trees, especially broadleaf species (aspen, poplar, and willow). However, climate warming would have less impact on the abundance of conifers, diversity of forest age structure, and variation in spatial landscape structure than burning and harvesting. Burning was the predominant influence in the abundance of conifers except larch and the abundance of trees in mid-stage. Harvesting impacts were greatest for the abundance of larch and birch, and the abundance of trees during establishment stage (1–40 years), early stage (41–80 years) and old- growth stage (>180 years). Disturbance by timber harvesting and burning may significantly alter forest ecosystem dynamics by increasing forest fragmentation and decreasing forest diversity. Results from the simulations provide insight into the long term management of this boreal forest. PMID:23573209

Can integrative catchment management mitigate future water quality issues caused by climate change and socio-economic development?

NASA Astrophysics Data System (ADS)

Honti, Mark; Schuwirth, Nele; Rieckermann, Jörg; Stamm, Christian

2017-03-01

The design and evaluation of solutions for integrated surface water quality management requires an integrated modelling approach. Integrated models have to be comprehensive enough to cover the aspects relevant for management decisions, allowing for mapping of larger-scale processes such as climate change to the regional and local contexts. Besides this, models have to be sufficiently simple and fast to apply proper methods of uncertainty analysis, covering model structure deficits and error propagation through the chain of sub-models. Here, we present a new integrated catchment model satisfying both conditions. The conceptual iWaQa model was developed to support the integrated management of small streams. It can be used to predict traditional water quality parameters, such as nutrients and a wide set of organic micropollutants (plant and material protection products), by considering all major pollutant pathways in urban and agricultural environments. Due to its simplicity, the model allows for a full, propagative analysis of predictive uncertainty, including certain structural and input errors. The usefulness of the model is demonstrated by predicting future surface water quality in a small catchment with mixed land use in the Swiss Plateau. We consider climate change, population growth or decline, socio-economic development, and the implementation of management strategies to tackle urban and agricultural point and non-point sources of pollution. Our results indicate that input and model structure uncertainties are the most influential factors for certain water quality parameters. In these cases model uncertainty is already high for present conditions. Nevertheless, accounting for today's uncertainty makes management fairly robust to the foreseen range of potential changes in the next decades. The assessment of total predictive uncertainty allows for selecting management strategies that show small sensitivity to poorly known boundary conditions. The identification of important sources of uncertainty helps to guide future monitoring efforts and pinpoints key indicators, whose evolution should be closely followed to adapt management. The possible impact of climate change is clearly demonstrated by water quality substantially changing depending on single climate model chains. However, when all climate trajectories are combined, the human land use and management decisions have a larger influence on water quality against a time horizon of 2050 in the study.

Climate change effects on agriculture: Economic responses to biophysical shocks

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

Nelson, Gerald; Valin, Hugo; Sands, Ronald

Agricultural production is sensitive to weather and will thus be directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments inmore » yields, area, consumption, and international trade. We apply biophysical shocks derived from the IPCC’s Representative Concentration Pathway that result in end-of-century radiative forcing of 8.5 watts per square meter. The mean biophysical impact on crop yield with no incremental CO2 fertilization is a 17 percent reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11 percent, increase area of major crops by 12 percent, and reduce consumption by 2 percent. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences includes model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.« less

Genetically informed ecological niche models improve climate change predictions.

PubMed

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

2017-01-01

We examined the hypothesis that ecological niche models (ENMs) more accurately predict species distributions when they incorporate information on population genetic structure, and concomitantly, local adaptation. Local adaptation is common in species that span a range of environmental gradients (e.g., soils and climate). Moreover, common garden studies have demonstrated a covariance between neutral markers and functional traits associated with a species' ability to adapt to environmental change. We therefore predicted that genetically distinct populations would respond differently to climate change, resulting in predicted distributions with little overlap. To test whether genetic information improves our ability to predict a species' niche space, we created genetically informed ecological niche models (gENMs) using Populus fremontii (Salicaceae), a widespread tree species in which prior common garden experiments demonstrate strong evidence for local adaptation. Four major findings emerged: (i) gENMs predicted population occurrences with up to 12-fold greater accuracy than models without genetic information; (ii) tests of niche similarity revealed that three ecotypes, identified on the basis of neutral genetic markers and locally adapted populations, are associated with differences in climate; (iii) our forecasts indicate that ongoing climate change will likely shift these ecotypes further apart in geographic space, resulting in greater niche divergence; (iv) ecotypes that currently exhibit the largest geographic distribution and niche breadth appear to be buffered the most from climate change. As diverse agents of selection shape genetic variability and structure within species, we argue that gENMs will lead to more accurate predictions of species distributions under climate change. © 2016 John Wiley & Sons Ltd.

Creating a Climate for Innovation in Education: Reframing Structure, Culture, and Leadership Practices

ERIC Educational Resources Information Center

Wong-Kam, JoAnn C. W. N.

2012-01-01

The focus of this study was on creating a climate for innovation in schools to lead to improvements in student achievement. Bolman and Deal's (2008) four frame model of organizational thinking was used as a framework for the study. The study examined the influence of leadership practices, structure, and school culture in the context of a K-12…

The Contribution of Vegetation and Landscape Configuration for Predicting Environmental Change Impacts on Iberian Birds

PubMed Central

Triviño, Maria; Thuiller, Wilfried; Cabeza, Mar; Hickler, Thomas; Araújo, Miguel B.

2011-01-01

Although climate is known to be one of the key factors determining animal species distributions amongst others, projections of global change impacts on their distributions often rely on bioclimatic envelope models. Vegetation structure and landscape configuration are also key determinants of distributions, but they are rarely considered in such assessments. We explore the consequences of using simulated vegetation structure and composition as well as its associated landscape configuration in models projecting global change effects on Iberian bird species distributions. Both present-day and future distributions were modelled for 168 bird species using two ensemble forecasting methods: Random Forests (RF) and Boosted Regression Trees (BRT). For each species, several models were created, differing in the predictor variables used (climate, vegetation, and landscape configuration). Discrimination ability of each model in the present-day was then tested with four commonly used evaluation methods (AUC, TSS, specificity and sensitivity). The different sets of predictor variables yielded similar spatial patterns for well-modelled species, but the future projections diverged for poorly-modelled species. Models using all predictor variables were not significantly better than models fitted with climate variables alone for ca. 50% of the cases. Moreover, models fitted with climate data were always better than models fitted with landscape configuration variables, and vegetation variables were found to correlate with bird species distributions in 26–40% of the cases with BRT, and in 1–18% of the cases with RF. We conclude that improvements from including vegetation and its landscape configuration variables in comparison with climate only variables might not always be as great as expected for future projections of Iberian bird species. PMID:22216263

GFDL's ESM2 global coupled climate-carbon Earth System Models. Part I: physical formulation and baseline simulation characteristics

USGS Publications Warehouse

Dunne, John P.; John, Jasmin G.; Adcroft, Alistair J.; Griffies, Stephen M.; Hallberg, Robert W.; Shevalikova, Elena; Stouffer, Ronald J.; Cooke, William; Dunne, Krista A.; Harrison, Matthew J.; Krasting, John P.; Malyshev, Sergey L.; Milly, P.C.D.; Phillipps, Peter J.; Sentman, Lori A.; Samuels, Bonita L.; Spelman, Michael J.; Winton, Michael; Wittenberg, Andrew T.; Zadeh, Niki

2012-01-01

We describe the physical climate formulation and simulation characteristics of two new global coupled carbon-climate Earth System Models, ESM2M and ESM2G. These models demonstrate similar climate fidelity as the Geophysical Fluid Dynamics Laboratory's previous CM2.1 climate model while incorporating explicit and consistent carbon dynamics. The two models differ exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4.1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. Differences in the ocean mean state include the thermocline depth being relatively deep in ESM2M and relatively shallow in ESM2G compared to observations. The crucial role of ocean dynamics on climate variability is highlighted in the El Niño-Southern Oscillation being overly strong in ESM2M and overly weak ESM2G relative to observations. Thus, while ESM2G might better represent climate changes relating to: total heat content variability given its lack of long term drift, gyre circulation and ventilation in the North Pacific, tropical Atlantic and Indian Oceans, and depth structure in the overturning and abyssal flows, ESM2M might better represent climate changes relating to: surface circulation given its superior surface temperature, salinity and height patterns, tropical Pacific circulation and variability, and Southern Ocean dynamics. Our overall assessment is that neither model is fundamentally superior to the other, and that both models achieve sufficient fidelity to allow meaningful climate and earth system modeling applications. This affords us the ability to assess the role of ocean configuration on earth system interactions in the context of two state-of-the-art coupled carbon-climate models.

Structural Model Error and Decision Relevancy

NASA Astrophysics Data System (ADS)

Goldsby, M.; Lusk, G.

2017-12-01

The extent to which climate models can underwrite specific climate policies has long been a contentious issue. Skeptics frequently deny that climate models are trustworthy in an attempt to undermine climate action, whereas policy makers often desire information that exceeds the capabilities of extant models. While not skeptics, a group of mathematicians and philosophers [Frigg et al. (2014)] recently argued that even tiny differences between the structure of a complex dynamical model and its target system can lead to dramatic predictive errors, possibly resulting in disastrous consequences when policy decisions are based upon those predictions. They call this result the Hawkmoth effect (HME), and seemingly use it to rebuke rightwing proposals to forgo mitigation in favor of adaptation. However, a vigorous debate has emerged between Frigg et al. on one side and another philosopher-mathematician pair [Winsberg and Goodwin (2016)] on the other. On one hand, Frigg et al. argue that their result shifts the burden to climate scientists to demonstrate that their models do not fall prey to the HME. On the other hand, Winsberg and Goodwin suggest that arguments like those asserted by Frigg et al. can be, if taken seriously, "dangerous": they fail to consider the variety of purposes for which models can be used, and thus too hastily undermine large swaths of climate science. They put the burden back on Frigg et al. to show their result has any effect on climate science. This paper seeks to attenuate this debate by establishing an irenic middle position; we find that there is more agreement between sides than it first seems. We distinguish a `decision standard' from a `burden of proof', which helps clarify the contributions to the debate from both sides. In making this distinction, we argue that scientists bear the burden of assessing the consequences of HME, but that the standard Frigg et al. adopt for decision relevancy is too strict.

Archaeobotanical evidence for climate as a driver of ecological community change across the anthropocene boundary.

PubMed

Ellis, Christopher J; Yahr, Rebecca; Belinchón, Rocío; Coppins, Brian J

2014-07-01

The biodiversity response to climate change is a major focus in conservation research and policy. Predictive models that are used to project the impact of climate change scenarios - such as bioclimatic envelope models - are widely applied and have come under severe scrutiny. Criticisms of such models have focussed on at least two problems. First, there is an assumption that climate is the primary driver of observed species distributions ('climatic equilibrium'), when other biogeographical controls are often reliably established. Second, a species' sensitivity to macroclimate may become less relevant when impacts are down-scaled to a local level, incorporating a modifying effect of species interactions structuring communities. This article examines the role of different drivers (climate, pollution and landscape habitat structure) in explaining spatial community variation for a widely applied bioindicator group: lichen epiphytes. To provide an analysis free of 'legacy effects' (e.g. formerly higher pollution loads), the study focused on hazel stems as a relatively short-lived and recently colonized substratum. For communities during the present day, climate is shown to interact with stem size/age as the most likely explanation of community composition, thus coupling a macroclimatic and community-scale effect. The position of present-day communities was projected into ordination space for eight sites in England and compared to the position of historical epiphyte communities from the same sites, reconstructed using preserved hazel wattles dating mainly to the 16th Century. This comparison of community structure for the late- to post-Mediaeval period, with the post-Industrial period, demonstrated a consistent shift among independent sites towards warmer and drier conditions, concurrent with the end of the Little Ice Age. Long-term temporal sensitivity of epiphyte communities to climate variation thus complements spatial community patterns. If more widely applied, preserved lichen epiphytes have potential to generate new baseline conditions of environment and biodiversity for preindustrial lowland Europe. © 2014 John Wiley & Sons Ltd.

Therapist turnover and new program sustainability in mental health clinics as a function of organizational culture, climate, and service structure.

PubMed

Glisson, Charles; Schoenwald, Sonja K; Kelleher, Kelly; Landsverk, John; Hoagwood, Kimberly Eaton; Mayberg, Stephen; Green, Philip

2008-03-01

The present study incorporates organizational theory and organizational characteristics in examining issues related to the successful implementation of mental health services. Following the theoretical foundations of socio-technical and cultural models of organizational effectiveness, organizational climate, culture, legal and service structures, and workforce characteristics are examined as correlates of therapist turnover and new program sustainability in a nationwide sample of mental health clinics. Results of General Linear Modeling (GLM) with the organization as the unit of analysis revealed that organizations with the best climates as measured by the Organizational Social Context (OSC) profiling system, had annual turnover rates (10%) that were less than half the rates found in organizations with the worst climates (22%). In addition, organizations with the best culture profiles sustained new treatment or service programs over twice as long (50 vs. 24 months) as organizations with the worst cultures. Finally, clinics with separate children's services units had higher turnover rates than clinics that served adults and children within the same unit. The findings suggest that strategies to support the implementation of new mental health treatments and services should attend to organizational culture and climate, and to the compatibility of organizational service structures with the demand characteristics of treatments.

High Resolution Global Climate Modeling with GEOS-5: Intense Precipitation, Convection and Tropical Cyclones on Seasonal Time-Scales.

NASA Technical Reports Server (NTRS)

Putnam, WilliamM.

2011-01-01

In 2008 the World Modeling Summit for Climate Prediction concluded that "climate modeling will need-and is ready-to move to fundamentally new high-resolution approaches to capitalize on the seamlessness of the weather-climate continuum." Following from this, experimentation with very high-resolution global climate modeling has gained enhanced priority within many modeling groups and agencies. The NASA Goddard Earth Observing System model (GEOS-5) has been enhanced to provide a capability for the execution at the finest horizontal resolutions POS,SIOle with a global climate model today. Using this high-resolution, non-hydrostatic version of GEOS-5, we have developed a unique capability to explore the intersection of weather and climate within a seamless prediction system. Week-long weather experiments, to mUltiyear climate simulations at global resolutions ranging from 3.5- to 14-km have demonstrated the predictability of extreme events including severe storms along frontal systems, extra-tropical storms, and tropical cyclones. The primary benefits of high resolution global models will likely be in the tropics, with better predictions of the genesis stages of tropical cyclones and of the internal structure of their mature stages. Using satellite data we assess the accuracy of GEOS-5 in representing extreme weather phenomena, and their interaction within the global climate on seasonal time-scales. The impacts of convective parameterization and the frequency of coupling between the moist physics and dynamics are explored in terms of precipitation intensity and the representation of deep convection. We will also describe the seasonal variability of global tropical cyclone activity within a global climate model capable of representing the most intense category 5 hurricanes.

Effects of different regional climate model resolution and forcing scales on projected hydrologic changes

NASA Astrophysics Data System (ADS)

Mendoza, Pablo A.; Mizukami, Naoki; Ikeda, Kyoko; Clark, Martyn P.; Gutmann, Ethan D.; Arnold, Jeffrey R.; Brekke, Levi D.; Rajagopalan, Balaji

2016-10-01

We examine the effects of regional climate model (RCM) horizontal resolution and forcing scaling (i.e., spatial aggregation of meteorological datasets) on the portrayal of climate change impacts. Specifically, we assess how the above decisions affect: (i) historical simulation of signature measures of hydrologic behavior, and (ii) projected changes in terms of annual water balance and hydrologic signature measures. To this end, we conduct our study in three catchments located in the headwaters of the Colorado River basin. Meteorological forcings for current and a future climate projection are obtained at three spatial resolutions (4-, 12- and 36-km) from dynamical downscaling with the Weather Research and Forecasting (WRF) regional climate model, and hydrologic changes are computed using four different hydrologic model structures. These projected changes are compared to those obtained from running hydrologic simulations with current and future 4-km WRF climate outputs re-scaled to 12- and 36-km. The results show that the horizontal resolution of WRF simulations heavily affects basin-averaged precipitation amounts, propagating into large differences in simulated signature measures across model structures. The implications of re-scaled forcing datasets on historical performance were primarily observed on simulated runoff seasonality. We also found that the effects of WRF grid resolution on projected changes in mean annual runoff and evapotranspiration may be larger than the effects of hydrologic model choice, which surpasses the effects from re-scaled forcings. Scaling effects on projected variations in hydrologic signature measures were found to be generally smaller than those coming from WRF resolution; however, forcing aggregation in many cases reversed the direction of projected changes in hydrologic behavior.

Simulating changes in ecosystem structure and composition in response to climate change: a case study focused on tropical nitrogen-fixing trees (Invited)

NASA Astrophysics Data System (ADS)

Medvigy, D.; Levy, J.; Xu, X.; Batterman, S. A.; Hedin, L.

2013-12-01

Ecosystems, by definition, involve a community of organisms. These communities generally exhibit heterogeneity in their structure and composition as a result of local variations in climate, soil, topography, disturbance history, and other factors. Climate-driven shifts in ecosystems will likely include an internal re-organization of community structure and composition and as well as the introduction of novel species. In terms of vegetation, this ecosystem heterogeneity can occur at relatively small scales, sometimes of the order of tens of meters or even less. Because this heterogeneous landscape generally has a variable and nonlinear response to environmental perturbations, it is necessary to carefully aggregate the local competitive dynamics between individual plants to the large scales of tens or hundreds of kilometers represented in climate models. Accomplishing this aggregation in a computationally efficient way has proven to be an extremely challenging task. To meet this challenge, the Ecosystem Demography 2 (ED2) model statistically characterizes a distribution of local resource environments, and then simulates the competition between individuals of different sizes and species (or functional groupings). Within this framework, it is possible to explicitly simulate the impacts of climate change on ecosystem structure and composition, including both internal re-organization and the introduction of novel species or functional groups. This presentation will include several illustrative applications of the evolution of ecosystem structure and composition under climate change. One application pertains to the role of nitrogen-fixing species in tropical forests. Will increasing CO2 concentrations increase the demand for nutrients and perhaps give a competitive edge to nitrogen-fixing species? Will potentially warmer and drier conditions make some tropical forests more water-limited, reducing the demand for nitrogen, thereby giving a competitive advantage to non-nitrogen-fixing species? Will the response of nitrogen-fixing species to climate change be sensitive to local disturbance histories?

Dynamic modeling of potentially conflicting energy reduction strategies for residential structures in semi-arid climates.

PubMed

Hester, Nathan; Li, Ke; Schramski, John R; Crittenden, John

2012-04-30

Globally, residential energy consumption continues to rise due to a variety of trends such as increasing access to modern appliances, overall population growth, and the overall increase of electricity distribution. Currently, residential energy consumption accounts for approximately one-fifth of total U.S. energy consumption. This research analyzes the effectiveness of a range of energy-saving measures for residential houses in semi-arid climates. These energy-saving measures include: structural insulated panels (SIP) for exterior wall construction, daylight control, increased window area, efficient window glass suitable for the local weather, and several combinations of these. Our model determined that energy consumption is reduced by up to 6.1% when multiple energy savings technologies are combined. In addition, pre-construction technologies (structural insulated panels (SIPs), daylight control, and increased window area) provide roughly 4 times the energy savings when compared to post-construction technologies (window blinds and efficient window glass). The model also illuminated the importance variations in local climate and building configuration; highlighting the site-specific nature of this type of energy consumption quantification for policy and building code considerations. Published by Elsevier Ltd.

Creating "Intelligent" Ensemble Averages Using a Process-Based Framework

NASA Astrophysics Data System (ADS)

Baker, Noel; Taylor, Patrick

2014-05-01

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is used to add value to individual model projections and construct a consensus projection. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, individual models reproduce certain climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. The intention is to produce improved ("intelligent") unequal-weight ensemble averages. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Several climate process metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument in combination with surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing the equal-weighted ensemble average and an ensemble weighted using the process-based metric. Additionally, this study investigates the dependence of the metric weighting scheme on the climate state using a combination of model simulations including a non-forced preindustrial control experiment, historical simulations, and several radiative forcing Representative Concentration Pathway (RCP) scenarios. Ultimately, the goal of the framework is to advise better methods for ensemble averaging models and create better climate predictions.

Teachers' Views of Their School Climate and Its Relationship with Teacher Self-Efficacy and Job Satisfaction

ERIC Educational Resources Information Center

Aldridge, Jill M.; Fraser, Barry J.

2016-01-01

The purpose of this study, in part, was to confirm the factor structure of the School-Level Environment Questionnaire, which assesses six school climate factors that can be considered important for improving schools. The study also tested a research model of the relationships between the school climate, teachers' self-efficacy and job…

Motivational climate, staff and members' behaviors, and members' psychological well-being at a national fitness franchise.

PubMed

Brown, Theresa C; Fry, Mary D

2014-06-01

The purpose of this study was to examine the association between members' perceptions of staffs behaviors, motivational climate, their own behaviors, commitment to future exercise, and life satisfaction in a group-fitness setting. The theory-driven hypothesized mediating role of perceptions of the climate was also tested. Members (N = 5,541) of a national group-fitness studio franchise completed a survey regarding their class experiences. The survey included questions that measured participants' perceptions of the motivational climate (caring, task-involving, ego-involving), perceptions of staff's behaviors, their own behaviors, commitment to exercise, and life satisfaction. Structural equation modeling was used to assess both the association between variables and the theoretically driven predictive relationships. The participants perceived the environment as highly caring and task-involving and low ego-involving. They reported high exercise commitment and moderately high life satisfaction and perceived that the staffs and their own behaviors reflected caring, task-involving characteristics. Structural equation modeling demonstrated that those who perceived a higher caring, task-involving climate and lower ego-involving climate were more likely to report more task-involving, caring behaviors among the staff and themselves as well as greater commitment to exercise. In addition, a theory-driven mediational model suggested that staff behaviors may be an antecedent to members' exercise experiences by impacting their perceptions of the climate. The results of this study give direction to specific behaviors in which staff of group-fitness programs might engage to positively influence members' exercise experiences.

Influence of cirrus clouds on weather and climate processes A global perspective

NASA Technical Reports Server (NTRS)

Liou, K.-N.

1986-01-01

Current understanding and knowledge of the composition and structure of cirrus clouds are reviewed and documented in this paper. In addition, the radiative properties of cirrus clouds as they relate to weather and climate processes are described in detail. To place the relevance and importance of cirrus composition, structure and radiative properties into a global perspective, pertinent results derived from simulation experiments utilizing models with varying degrees of complexity are presented; these have been carried out for the investigation of the influence of cirrus clouds on the thermodynamics and dynamics of the atmosphere. In light of these reviews, suggestions are outlined for cirrus-radiation research activities aimed toward the development and improvement of weather and climate models for a physical understanding of cause and effect relationships and for prediction purposes.

Measuring school climate in high schools: a focus on safety, engagement, and the environment.

PubMed

Bradshaw, Catherine P; Waasdorp, Tracy E; Debnam, Katrina J; Johnson, Sarah Lindstrom

2014-09-01

School climate has been linked to multiple student behavioral, academic, health, and social-emotional outcomes. The US Department of Education (USDOE) developed a 3-factor model of school climate comprised of safety, engagement, and environment. This article examines the factor structure and measurement invariance of the USDOE model. Drawing upon 2 consecutive waves of data from over 25,000 high school students (46% minority), a series of exploratory and confirmatory factor analyses examined the fit of the Maryland Safe and Supportive Schools Climate Survey with the USDOE model. The results indicated adequate model fit with the theorized 3-factor model of school climate, which included 13 subdomains: safety (perceived safety, bullying and aggression, and drug use); engagement (connection to teachers, student connectedness, academic engagement, school connectedness, equity, and parent engagement); environment (rules and consequences, physical comfort, and support, disorder). We also found consistent measurement invariance with regard to student sex, grade level, and ethnicity. School-level interclass correlation coefficients ranged from 0.04 to .10 for the scales. Findings supported the USDOE 3-factor model of school climate and suggest measurement invariance and high internal consistency of the 3 scales and 13 subdomains. These results suggest the 56-item measure may be a potentially efficient, yet comprehensive measure of school climate. © 2014, American School Health Association.

Assessing Regional Scale Variability in Extreme Value Statistics Under Altered Climate Scenarios

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

Brunsell, Nathaniel; Mechem, David; Ma, Chunsheng

Recent studies have suggested that low-frequency modes of climate variability can significantly influence regional climate. The climatology associated with extreme events has been shown to be particularly sensitive. This has profound implications for droughts, heat waves, and food production. We propose to examine regional climate simulations conducted over the continental United States by applying a recently developed technique which combines wavelet multi–resolution analysis with information theory metrics. This research is motivated by two fundamental questions concerning the spatial and temporal structure of extreme events. These questions are 1) what temporal scales of the extreme value distributions are most sensitive tomore » alteration by low-frequency climate forcings and 2) what is the nature of the spatial structure of variation in these timescales? The primary objective is to assess to what extent information theory metrics can be useful in characterizing the nature of extreme weather phenomena. Specifically, we hypothesize that (1) changes in the nature of extreme events will impact the temporal probability density functions and that information theory metrics will be sensitive these changes and (2) via a wavelet multi–resolution analysis, we will be able to characterize the relative contribution of different timescales on the stochastic nature of extreme events. In order to address these hypotheses, we propose a unique combination of an established regional climate modeling approach and advanced statistical techniques to assess the effects of low-frequency modes on climate extremes over North America. The behavior of climate extremes in RCM simulations for the 20th century will be compared with statistics calculated from the United States Historical Climatology Network (USHCN) and simulations from the North American Regional Climate Change Assessment Program (NARCCAP). This effort will serve to establish the baseline behavior of climate extremes, the validity of an innovative multi–resolution information theory approach, and the ability of the RCM modeling framework to represent the low-frequency modulation of extreme climate events. Once the skill of the modeling and analysis methodology has been established, we will apply the same approach for the AR5 (IPCC Fifth Assessment Report) climate change scenarios in order to assess how climate extremes and the the influence of lowfrequency variability on climate extremes might vary under changing climate. The research specifically addresses the DOE focus area 2. Simulation of climate extremes under a changing climate. Specific results will include (1) a better understanding of the spatial and temporal structure of extreme events, (2) a thorough quantification of how extreme values are impacted by low-frequency climate teleconnections, (3) increased knowledge of current regional climate models ability to ascertain these influences, and (4) a detailed examination of the how the distribution of extreme events are likely to change under different climate change scenarios. In addition, this research will assess the ability of the innovative wavelet information theory approach to characterize extreme events. Any and all of these results will greatly enhance society’s ability to understand and mitigate the regional ramifications of future global climate change.« less

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

NASA Technical Reports Server (NTRS)

Glotter, Michael J.; Ruane, Alex C.; Moyer, Elisabeth J.; Elliott, Joshua W.

2015-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

Evaluating the sensitivity of agricultural model performance to different climate inputs

PubMed Central

Glotter, Michael J.; Moyer, Elisabeth J.; Ruane, Alex C.; Elliott, Joshua W.

2017-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled to observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections, but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely-used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources – reanalysis, reanalysis bias-corrected with observed climate, and a control dataset – and compared to observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by un-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. However, some issues persist for all choices of climate inputs: crop yields appear oversensitive to precipitation fluctuations but undersensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves. PMID:29097985

A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

DOE PAGES

Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

2016-07-20

A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less

A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

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

Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less

Making work safer: testing a model of social exchange and safety management.

PubMed

DeJoy, David M; Della, Lindsay J; Vandenberg, Robert J; Wilson, Mark G

2010-04-01

This study tests a conceptual model that focuses on social exchange in the context of safety management. The model hypothesizes that supportive safety policies and programs should impact both safety climate and organizational commitment. Further, perceived organizational support is predicted to partially mediate both of these relationships. Study outcomes included traditional outcomes for both organizational commitment (e.g., withdrawal behaviors) as well as safety climate (e.g., self-reported work accidents). Questionnaire responses were obtained from 1,723 employees of a large national retailer. Using structural equation modeling (SEM) techniques, all of the model's hypothesized relationships were statistically significant and in the expected directions. The results are discussed in terms of social exchange in organizations and research on safety climate. Maximizing safety is a social-technical enterprise. Expectations related to social exchange and reciprocity figure prominently in creating a positive climate for safety within the organization. Copyright 2010 Elsevier Ltd. All rights reserved.

SPARC's Stratospheric Sulfur and its Role in Climate Activity (SSiRC)

NASA Technical Reports Server (NTRS)

Thomason, Larry

2015-01-01

The stratospheric aerosol layer is a key component in the climate system. It affects the radiative balance of the atmosphere directly through interactions with solar and terrestrial radiation, and indirectly through its effect on stratospheric ozone. Because the stratospheric aerosol layer is prescribed in many climate models and Chemistry-Climate Models (CCMs), model simulations of future atmospheric conditions and climate generally do not account for the interaction between the aerosol-sulfur cycle and changes in the climate system. The present understanding of how the stratospheric aerosol layer may be affected by future climate change and how the stratospheric aerosol layer may drive climate change is, therefore, very limited. The purposes of SSiRC (Stratospheric Sulfur and its Role in Climate) include: (i) providing a coordinating structure for the various individual activities already underway in different research centers; (ii) encouraging and supporting new instrumentation and measurements of sulfur containing compounds, such as COS, DMS, and non-volcanic SO2 in the UT/LS globally; and (iii) initiating new model/data inter-comparisons. SSiRC is developing collaborations with a number of other SPARC activities including CCMI and ACAM. This presentation will highlight the scientific goals of this project and on-going activities and propose potential interactions between SSiRC and ACAM.

On the uncertainty of phenological responses to climate change and its implication for terrestrial biosphere models

NASA Astrophysics Data System (ADS)

Migliavacca, M.; Sonnentag, O.; Keenan, T. F.; Cescatti, A.; O'Keefe, J.; Richardson, A. D.

2012-01-01

Phenology, the timing of recurring life cycle events, controls numerous land surface feedbacks to the climate systems through the regulation of exchanges of carbon, water and energy between the biosphere and atmosphere. Land surface models, however, are known to have systematic errors in the simulation of spring phenology, which potentially could propagate to uncertainty in modeled responses to future climate change. Here, we analyzed the Harvard Forest phenology record to investigate and characterize the sources of uncertainty in phenological forecasts and the subsequent impacts on model forecasts of carbon and water cycling in the future. Using a model-data fusion approach, we combined information from 20 yr of phenological observations of 11 North American woody species with 12 phenological models of different complexity to predict leaf bud-burst. The evaluation of different phenological models indicated support for spring warming models with photoperiod limitations and, though to a lesser extent, to chilling models based on the alternating model structure. We assessed three different sources of uncertainty in phenological forecasts: parameter uncertainty, model uncertainty, and driver uncertainty. The latter was characterized running the models to 2099 using 2 different IPCC climate scenarios (A1fi vs. B1, i.e. high CO2 emissions vs. low CO2 emissions scenario). Parameter uncertainty was the smallest (average 95% CI: 2.4 day century-1 for scenario B1 and 4.5 day century-1 for A1fi), whereas driver uncertainty was the largest (up to 8.4 day century-1 in the simulated trends). The uncertainty related to model structure is also large and the predicted bud-burst trends as well as the shape of the smoothed projections varied somewhat among models (±7.7 day century-1 for A1fi, ±3.6 day century-1 for B1). The forecast sensitivity of bud-burst to temperature (i.e. days bud-burst advanced per degree of warming) varied between 2.2 day °C-1 and 5.2 day °C-1 depending on model structure. We quantified the impact of uncertainties in bud-burst forecasts on simulated carbon and water fluxes using a process-based terrestrial biosphere model. Uncertainty in phenology model structure led to uncertainty in the description of the seasonality of processes, which accumulated to uncertainty in annual model estimates of gross primary productivity (GPP) and evapotranspiration (ET) of 9.6% and 2.9% respectively. A sensitivity analysis shows that a variation of ±10 days in bud-burst dates led to a variation of ±5.0% for annual GPP and about ±2.0% for ET. For phenology models, differences among future climate scenarios represent the largest source of uncertainty, followed by uncertainties related to model structure, and finally, uncertainties related to model parameterization. The uncertainties we have quantified will affect the description of the seasonality of processes and in particular the simulation of carbon uptake by forest ecosystems, with a larger impact of uncertainties related to phenology model structure, followed by uncertainties related to phenological model parameterization.

Continuous and discrete extreme climatic events affecting the dynamics of a high-arctic reindeer population.

PubMed

Chan, Kung-Sik; Mysterud, Atle; Øritsland, Nils Are; Severinsen, Torbjørn; Stenseth, Nils Chr

2005-10-01

Climate at northern latitudes are currently changing both with regard to the mean and the temporal variability at any given site, increasing the frequency of extreme events such as cold and warm spells. Here we use a conceptually new modelling approach with two different dynamic terms of the climatic effects on a Svalbard reindeer population (the Brøggerhalvøya population) which underwent an extreme icing event ("locked pastures") with 80% reduction in population size during one winter (1993/94). One term captures the continuous and linear effect depending upon the Arctic Oscillation and another the discrete (rare) "event" process. The introduction of an "event" parameter describing the discrete extreme winter resulted in a more parsimonious model. Such an approach may be useful in strongly age-structured ungulate populations, with young and very old individuals being particularly prone to mortality factors during adverse conditions (resulting in a population structure that differs before and after extreme climatic events). A simulation study demonstrates that our approach is able to properly detect the ecological effects of such extreme climate events.

Development of a Cloud Resolving Model for Heterogeneous Supercomputers

NASA Astrophysics Data System (ADS)

Sreepathi, S.; Norman, M. R.; Pal, A.; Hannah, W.; Ponder, C.

2017-12-01

A cloud resolving climate model is needed to reduce major systematic errors in climate simulations due to structural uncertainty in numerical treatments of convection - such as convective storm systems. This research describes the porting effort to enable SAM (System for Atmosphere Modeling) cloud resolving model on heterogeneous supercomputers using GPUs (Graphical Processing Units). We have isolated a standalone configuration of SAM that is targeted to be integrated into the DOE ACME (Accelerated Climate Modeling for Energy) Earth System model. We have identified key computational kernels from the model and offloaded them to a GPU using the OpenACC programming model. Furthermore, we are investigating various optimization strategies intended to enhance GPU utilization including loop fusion/fission, coalesced data access and loop refactoring to a higher abstraction level. We will present early performance results, lessons learned as well as optimization strategies. The computational platform used in this study is the Summitdev system, an early testbed that is one generation removed from Summit, the next leadership class supercomputer at Oak Ridge National Laboratory. The system contains 54 nodes wherein each node has 2 IBM POWER8 CPUs and 4 NVIDIA Tesla P100 GPUs. This work is part of a larger project, ACME-MMF component of the U.S. Department of Energy(DOE) Exascale Computing Project. The ACME-MMF approach addresses structural uncertainty in cloud processes by replacing traditional parameterizations with cloud resolving "superparameterization" within each grid cell of global climate model. Super-parameterization dramatically increases arithmetic intensity, making the MMF approach an ideal strategy to achieve good performance on emerging exascale computing architectures. The goal of the project is to integrate superparameterization into ACME, and explore its full potential to scientifically and computationally advance climate simulation and prediction.

Diagnosing and Assessing Uncertainties of the Carbon Cycle in Terrestrial Ecosystem Models from a Multi-Model Ensemble Experiment

NASA Astrophysics Data System (ADS)

Wang, W.; Dungan, J. L.; Hashimoto, H.; Michaelis, A.; Milesi, C.; Ichii, K.; Nemani, R. R.

2009-12-01

We are conducting an ensemble modeling exercise using the Terrestrial Observation and Prediction System (TOPS) to characterize structural uncertainty in carbon fluxes and stocks estimates from different ecosystem models. The experiment uses public-domain versions of Biome-BGC, LPJ, TOPS-BGC, and CASA, driven by a consistent set of climate fields for North America at 8km resolution and daily/monthly time steps over the period of 1982-2006. A set of diagnostics is developed to characterize the behavior of the models in the climate (temperature-precipitation) space, and to evaluate the simulated carbon cycle in an integrated way. The key findings of this study include that: (relative) optimal primary production is generally found in climate regions where the relationship between annual temperature (T, oC) and precipitation (P, mm) is defined by P = 50*T+500; the ratios between NPP and GPP are close to 50% on average, yet can vary between models and in different climate regions; the allocation of carbon to leaf growth represents a positive feedback to the primary production, and different approaches to constrain this process have significant impacts on the simulated carbon cycle; substantial differences in biomass stocks may be induced by small differences in the tissue turnover rate and the plant mortality; the mean residence time of soil carbon pools is strongly influenced by schemes of temperature regulations; non-respiratory disturbances (e.g., fires) are the main driver for NEP, yet its magnitudes vary between models. Overall, these findings indicate that although the structures of the models are similar, the uncertainties among them can be large, highlighting the problem inherent in relying on only one modeling approach to map surface carbon fluxes or to assess vegetation-climate interactions.

Structural uncertainty of downscaled climate model output in a difficult-to-resolve environment: data sparseness and parameterization error contribution to statistical and dynamical downscaling output in the U.S. Caribbean region

NASA Astrophysics Data System (ADS)

Terando, A. J.; Grade, S.; Bowden, J.; Henareh Khalyani, A.; Wootten, A.; Misra, V.; Collazo, J.; Gould, W. A.; Boyles, R.

2016-12-01

Sub-tropical island nations may be particularly vulnerable to anthropogenic climate change because of predicted changes in the hydrologic cycle that would lead to significant drying in the future. However, decision makers in these regions have seen their adaptation planning efforts frustrated by the lack of island-resolving climate model information. Recently, two investigations have used statistical and dynamical downscaling techniques to develop climate change projections for the U.S. Caribbean region (Puerto Rico and U.S. Virgin Islands). We compare the results from these two studies with respect to three commonly downscaled CMIP5 global climate models (GCMs). The GCMs were dynamically downscaled at a convective-permitting scale using two different regional climate models. The statistical downscaling approach was conducted at locations with long-term climate observations and then further post-processed using climatologically aided interpolation (yielding two sets of projections). Overall, both approaches face unique challenges. The statistical approach suffers from a lack of observations necessary to constrain the model, particularly at the land-ocean boundary and in complex terrain. The dynamically downscaled model output has a systematic dry bias over the island despite ample availability of moisture in the atmospheric column. Notwithstanding these differences, both approaches are consistent in projecting a drier climate that is driven by the strong global-scale anthropogenic forcing.

The Relationship between Goal Orientations, Motivational Climate and Selfreported Discipline in Physical Education

PubMed Central

Moreno-Murcia, Juan A.; Sicilia, Alvaro; Cervelló, Eduardo; Huéscar, Elisa; Dumitru, Delia C.

2011-01-01

The purpose of this study was to test a motivational model on the links between situational and dispositional motivation and self-reported indiscipline/discipline based on the achievement goals theory. The model postulates that a task-involving motivational climate facilitates self-reported discipline, either directly or mediated by task orientation. In contrast, an ego-involving motivational climate favors self-reported indiscipline, either directly or by means of ego orientation. An additional purpose was to examine gender differences according to the motivational model proposed. Children (n = 565) from a large Spanish metropolitan school district were participants in this study and completed questionnaires assessing goal orientations, motivational climates and self-reported discipline. The results from the analysis of structural equation model showed the direct effect of motivational climates on self-reported discipline and provided support to the model. Furthermore, the gender differences found in self-reported discipline were associated with the differences found in the students’ dispositional and situational motivation pursuant to the model tested. The implications of these results with regard to teaching instructional actions in physical education classes are discussed. Key points A task-involving motivational climate predicts self-reported discipline behaviors, either directly or mediated by task orientation. An ego-involving motivational climate favors self-reported undisciplined, either directly or mediated by ego orientation. A significant gender difference was found in the motivational disposition perceived climate and self-reported discipline. PMID:24149304

A Multi-Model Framework to Achieve Consistent Evaluation of Climate Change Impacts in the United States

NASA Astrophysics Data System (ADS)

Sarofim, M. C.; Martinich, J.; Waldhoff, S.; DeAngelo, B. J.; McFarland, J.; Jantarasami, L.; Shouse, K.; Crimmins, A.; Li, J.

2014-12-01

The Climate Change Impacts and Risk Analysis (CIRA) project establishes a new multi-model framework to systematically assess the physical impacts, economic damages, and risks from climate change. The primary goal of this framework is to estimate the degree to which climate change impacts and damages in the United States are avoided or reduced in the 21st century under multiple greenhouse gas (GHG) emissions mitigation scenarios. The first phase of the CIRA project is a modeling exercise that included two integrated assessment models and 15 sectoral models encompassing five broad impacts sectors: water resources, electric power, infrastructure, human health, and ecosystems. Three consistent socioeconomic and climate scenarios are used to analyze the benefits of global GHG mitigation targets: a reference scenario and two policy scenarios with total radiative forcing targets in 2100 of 4.5 W/m2 and 3.7 W/m2. In this exercise, the implications of key uncertainties are explored, including climate sensitivity, climate model, natural variability, and model structures and parameters. This presentation describes the motivations and goals of the CIRA project; the design and academic contribution of the first CIRA modeling exercise; and briefly summarizes several papers published in a special issue of Climatic Change. The results across impact sectors show that GHG mitigation provides benefits to the United States that increase over time, the effects of climate change can be strongly influenced by near-term policy choices, adaptation can reduce net damages, and impacts exhibit spatial and temporal patterns that may inform mitigation and adaptation policy discussions.

The 1997/98 El Nino: A Test for Climate Models

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

Lu, R; Dong, B; Cess, R D

Version 3 of the Hadley Centre Atmospheric Model (HadAM3) has been used to demonstrate one means of comparing a general circulation model with observations for a specific climate perturbation, namely the strong 1997/98 El Nino. This event was characterized by the collapse of the tropical Pacific's Walker circulation, caused by the lack of a zonal sea surface temperature gradient during the El Nino. Relative to normal years, cloud altitudes were lower in the western portion of the Pacific and higher in the eastern portion. HadAM3 likewise produced the observed collapse of the Walker circulation, and it did a reasonable jobmore » of reproducing the west/east cloud structure changes. This illustrates that the 1997/98 El Nino serves as a useful means of testing cloud-climate interactions in climate models.« less

Effect of soil property uncertainties on permafrost thaw projections: a calibration-constrained analysis

NASA Astrophysics Data System (ADS)

Harp, D. R.; Atchley, A. L.; Painter, S. L.; Coon, E. T.; Wilson, C. J.; Romanovsky, V. E.; Rowland, J. C.

2016-02-01

The effects of soil property uncertainties on permafrost thaw projections are studied using a three-phase subsurface thermal hydrology model and calibration-constrained uncertainty analysis. The null-space Monte Carlo method is used to identify soil hydrothermal parameter combinations that are consistent with borehole temperature measurements at the study site, the Barrow Environmental Observatory. Each parameter combination is then used in a forward projection of permafrost conditions for the 21st century (from calendar year 2006 to 2100) using atmospheric forcings from the Community Earth System Model (CESM) in the Representative Concentration Pathway (RCP) 8.5 greenhouse gas concentration trajectory. A 100-year projection allows for the evaluation of predictive uncertainty (due to soil property (parametric) uncertainty) and the inter-annual climate variability due to year to year differences in CESM climate forcings. After calibrating to measured borehole temperature data at this well-characterized site, soil property uncertainties are still significant and result in significant predictive uncertainties in projected active layer thickness and annual thaw depth-duration even with a specified future climate. Inter-annual climate variability in projected soil moisture content and Stefan number are small. A volume- and time-integrated Stefan number decreases significantly, indicating a shift in subsurface energy utilization in the future climate (latent heat of phase change becomes more important than heat conduction). Out of 10 soil parameters, ALT, annual thaw depth-duration, and Stefan number are highly dependent on mineral soil porosity, while annual mean liquid saturation of the active layer is highly dependent on the mineral soil residual saturation and moderately dependent on peat residual saturation. By comparing the ensemble statistics to the spread of projected permafrost metrics using different climate models, we quantify the relative magnitude of soil property uncertainty to another source of permafrost uncertainty, structural climate model uncertainty. We show that the effect of calibration-constrained uncertainty in soil properties, although significant, is less than that produced by structural climate model uncertainty for this location.

ELICITED EXPERT PERCEPTIONS FOR CLIMATE CHANGE RISKS AND ADAPTATION IN AGRICULTURE AND FOOD PRODUCTION THROUGH MENTAL MODELS APPROACH

NASA Astrophysics Data System (ADS)

Suda, Eiko; Kubota, Hiromi; Baba, Kenshi; Hijioka, Yasuaki; Takahashi, Kiyoshi; Hanasaki, Naota

Impacts of climate change have become obvious in agriculture and food production in Japan these days, and researches to adapt to their risks have been conducted as a key effort to cope with the climate change. Numerous scientific findings on climate change impacts have been presented so far; however, prospective risks to be adapted to and their management in the context of individual on-site situations have not been investigated in detail. The structure of climate change risks and their management vary depending on geographical and social features in the regions where the adaptation options should be applied; therefore, a practical adaptation strategy should consider actual on-site situations. This study intended to clarify climate change risks to be adapted to in the Japanese agricultural sector, and factors to be considered in adaptation options, for encouragement of decision-making on adaptation implementation in the field. Semi-structured individual interviews have been conducted with 9 multidisciplinary experts engaging in climate change impacts research in agricultural production, economics, engineering, policy, and so on. Based on the results of the interviews, and the latest literatures available for risk assessment and adaptation, an expert mental model including their perceptions which cover the process from climate change impacts assessment to adaptation has been developed. The prospective risks, adaptation options, and issues to be examined to progress the development of practical and effective adaptation options and to support individual or social decision-making, have been shown on the developed expert mental model. It is the basic information for developing social communication and stakeholders cooperations in climate change adaptation strategies in agriculture and food production in Japan.

Major modes of short-term climate variability in the newly developed NUIST Earth System Model (NESM)

NASA Astrophysics Data System (ADS)

Cao, Jian; Wang, Bin; Xiang, Baoqiang; Li, Juan; Wu, Tianjie; Fu, Xiouhua; Wu, Liguang; Min, Jinzhong

2015-05-01

A coupled earth system model (ESM) has been developed at the Nanjing University of Information Science and Technology (NUIST) by using version 5.3 of the European Centre Hamburg Model (ECHAM), version 3.4 of the Nucleus for European Modelling of the Ocean (NEMO), and version 4.1 of the Los Alamos sea ice model (CICE). The model is referred to as NUIST ESM1 (NESM1). Comprehensive and quantitative metrics are used to assess the model's major modes of climate variability most relevant to subseasonal-to-interannual climate prediction. The model's assessment is placed in a multi-model framework. The model yields a realistic annual mean and annual cycle of equatorial SST, and a reasonably realistic precipitation climatology, but has difficulty in capturing the spring-fall asymmetry and monsoon precipitation domains. The ENSO mode is reproduced well with respect to its spatial structure, power spectrum, phase locking to the annual cycle, and spatial structures of the central Pacific (CP)-ENSO and eastern Pacific (EP)-ENSO; however, the equatorial SST variability, biennial component of ENSO, and the amplitude of CP-ENSO are overestimated. The model captures realistic intraseasonal variability patterns, the vertical-zonal structures of the first two leading predictable modes of Madden-Julian Oscillation (MJO), and its eastward propagation; but the simulated MJO speed is significantly slower than observed. Compared with the T42 version, the high resolution version (T159) demonstrates improved simulation with respect to the climatology, interannual variance, monsoon-ENSO lead-lag correlation, spatial structures of the leading mode of the Asian-Australian monsoon rainfall variability, and the eastward propagation of the MJO.

Impacts of future climate change on river discharge based on hydrological inference: A case study of the Grand River Watershed in Ontario, Canada.

PubMed

Li, Zhong; Huang, Guohe; Wang, Xiuquan; Han, Jingcheng; Fan, Yurui

2016-04-01

Over the recent years, climate change impacts have been increasingly studied at the watershed scale. However, the impact assessment is strongly dependent upon the performance of the climatic and hydrological models. This study developed a two-step method to assess climate change impacts on water resources based on the Providing Regional Climates for Impacts Studies (PRECIS) modeling system and a Hydrological Inference Model (HIM). PRECIS runs provided future temperature and precipitation projections for the watershed under the Intergovernmental Panel on Climate Change SRES A2 and B2 emission scenarios. The HIM based on stepwise cluster analysis is developed to imitate the complex nonlinear relationships between climate input variables and targeted hydrological variables. Its robust mathematical structure and flexibility in predictor selection makes it a desirable tool for fully utilizing various climate modeling outputs. Although PRECIS and HIM cannot fully cover the uncertainties in hydro-climate modeling, they could provide efficient decision support for investigating the impacts of climate change on water resources. The proposed method is applied to the Grand River Watershed in Ontario, Canada. The model performance is demonstrated with comparison to observation data from the watershed during the period 1972-2006. Future river discharge intervals that accommodate uncertainties in hydro-climatic modeling are presented and future river discharge variations are analyzed. The results indicate that even though the total annual precipitation would not change significantly in the future, the inter-annual distribution is very likely to be altered. The water availability is expected to increase in Winter while it is very likely to decrease in Summer over the Grand River Watershed, and adaptation strategies would be necessary. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

The Pliocene Model Intercomparison Project - Phase 2

NASA Astrophysics Data System (ADS)

Haywood, Alan; Dowsett, Harry; Dolan, Aisling; Rowley, David; Abe-Ouchi, Ayako; Otto-Bliesner, Bette; Chandler, Mark; Hunter, Stephen; Lunt, Daniel; Pound, Matthew; Salzmann, Ulrich

2016-04-01

The Pliocene Model Intercomparison Project (PlioMIP) is a co-ordinated international climate modelling initiative to study and understand climate and environments of the Late Pliocene, and their potential relevance in the context of future climate change. PlioMIP examines the consistency of model predictions in simulating Pliocene climate, and their ability to reproduce climate signals preserved by geological climate archives. Here we provide a description of the aim and objectives of the next phase of the model intercomparison project (PlioMIP Phase 2), and we present the experimental design and boundary conditions that will be utilised for climate model experiments in Phase 2. Following on from PlioMIP Phase 1, Phase 2 will continue to be a mechanism for sampling structural uncertainty within climate models. However, Phase 1 demonstrated the requirement to better understand boundary condition uncertainties as well as uncertainty in the methodologies used for data-model comparison. Therefore, our strategy for Phase 2 is to utilise state-of-the-art boundary conditions that have emerged over the last 5 years. These include a new palaeogeographic reconstruction, detailing ocean bathymetry and land/ice surface topography. The ice surface topography is built upon the lessons learned from offline ice sheet modelling studies. Land surface cover has been enhanced by recent additions of Pliocene soils and lakes. Atmospheric reconstructions of palaeo-CO2 are emerging on orbital timescales and these are also incorporated into PlioMIP Phase 2. New records of surface and sea surface temperature change are being produced that will be more temporally consistent with the boundary conditions and forcings used within models. Finally we have designed a suite of prioritized experiments that tackle issues surrounding the basic understanding of the Pliocene and its relevance in the context of future climate change in a discrete way.

The foundation for climate services in Belgium: CORDEX.be

NASA Astrophysics Data System (ADS)

Van Schaeybroeck, Bert; Termonia, Piet; De Ridder, Koen; Fettweis, Xavier; Gobin, Anne; Luyten, Patrick; Marbaix, Philippe; Pottiaux, Eric; Stavrakou, Trissevgeni; Van Lipzig, Nicole; van Ypersele, Jean-Pascal; Willems, Patrick

2017-04-01

According to the Global Framework for Climate Services (GFCS) there are four pillars required to build climate services. As the first step towards the realization of a climate center in Belgium, the national project CORDEX.be focused on one pillar: research modelling and projection. By bringing together the Belgian climate and impact modeling research of nine groups a data-driven capacity development and community building in Belgium based on interactions with users. The project is based on the international CORDEX ("COordinated Regional Climate Downscaling Experiment") project where ".be" indicates it will go beyond for Belgium. Our national effort links to the regional climate initiatives through the contribution of multiple high-resolution climate simulations over Europe following the EURO-CORDEX guidelines. Additionally the same climate simulations were repeated at convection-permitting resolutions over Belgium (3 to 5 km). These were used to drive different local impact models to investigate the impact of climate change on urban effects, storm surges and waves, crop production and changes in emissions from vegetation. Akin to international frameworks such as CMIP and CORDEX a multi-model approach is adopted allowing for uncertainty estimation, a crucial aspect of climate projections for policy-making purposes. However, due to the lack of a large set of high resolution model runs, a combination of all available climate information is supplemented with the statistical downscaling approach. The organization of the project, together with its main results will be outlined. The proposed coordination framework could serve as a demonstration case for regions or countries where the climate-research capacity is present but a structure is required to assemble it coherently. Based on interactions and feedback with stakeholders different applications are planned, demonstrating the use of the climate data.

Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed

NASA Astrophysics Data System (ADS)

Chen, Jie; Brissette, FrançOis P.; Poulin, Annie; Leconte, Robert

2011-12-01

General circulation models (GCMs) and greenhouse gas emissions scenarios (GGES) are generally considered to be the two major sources of uncertainty in quantifying the climate change impacts on hydrology. Other sources of uncertainty have been given less attention. This study considers overall uncertainty by combining results from an ensemble of two GGES, six GCMs, five GCM initial conditions, four downscaling techniques, three hydrological model structures, and 10 sets of hydrological model parameters. Each climate projection is equally weighted to predict the hydrology on a Canadian watershed for the 2081-2100 horizon. The results show that the choice of GCM is consistently a major contributor to uncertainty. However, other sources of uncertainty, such as the choice of a downscaling method and the GCM initial conditions, also have a comparable or even larger uncertainty for some hydrological variables. Uncertainties linked to GGES and the hydrological model structure are somewhat less than those related to GCMs and downscaling techniques. Uncertainty due to the hydrological model parameter selection has the least important contribution among all the variables considered. Overall, this research underlines the importance of adequately covering all sources of uncertainty. A failure to do so may result in moderately to severely biased climate change impact studies. Results further indicate that the major contributors to uncertainty vary depending on the hydrological variables selected, and that the methodology presented in this paper is successful at identifying the key sources of uncertainty to consider for a climate change impact study.

Disentangling the effects of feedback structure and climate on Poaceae annual airborne pollen fluctuations and the possible consequences of climate change.

PubMed

García de León, David; García-Mozo, Herminia; Galán, Carmen; Alcázar, Purificación; Lima, Mauricio; González-Andújar, José L

2015-10-15

Pollen allergies are the most common form of respiratory allergic disease in Europe. Most studies have emphasized the role of environmental processes, as the drivers of airborne pollen fluctuations, implicitly considering pollen production as a random walk. This work shows that internal self-regulating processes of the plants (negative feedback) should be included in pollen dynamic systems in order to give a better explanation of the observed pollen temporal patterns. This article proposes a novel methodological approach based on dynamic systems to investigate the interaction between feedback structure of plant populations and climate in shaping long-term airborne Poaceae pollen fluctuations and to quantify the effects of climate change on future airborne pollen concentrations. Long-term historical airborne Poaceae pollen data (30 years) from Cordoba city (Southern Spain) were analyzed. A set of models, combining feedback structure, temperature and actual evapotranspiration effects on airborne Poaceae pollen were built and compared, using a model selection approach. Our results highlight the importance of first-order negative feedback and mean annual maximum temperature in driving airborne Poaceae pollen dynamics. The best model was used to predict the effects of climate change under two standardized scenarios representing contrasting temporal patterns of economic development and CO2 emissions. Our results predict an increase in pollen levels in southern Spain by 2070 ranging from 28.5% to 44.3%. The findings from this study provide a greater understanding of airborne pollen dynamics and how climate change might impact the future evolution of airborne Poaceae pollen concentrations and thus the future evolution of related pollen allergies. Copyright © 2015 Elsevier B.V. All rights reserved.

The Coupled Mars Dust and Water Cycles: Understanding How Clouds Affect the Vertical Distribution and Meridional Transport of Dust and Water.

NASA Technical Reports Server (NTRS)

Kahre, M. A.

2015-01-01

The dust and water cycles are crucial to the current Martian climate, and they are coupled through cloud formation. Dust strongly impacts the thermal structure of the atmosphere and thus greatly affects atmospheric circulation, while clouds provide radiative forcing and control the hemispheric exchange of water through the modification of the vertical distributions of water and dust. Recent improvements in the quality and sophistication of both observations and climate models allow for a more comprehensive understanding of how the interaction between the dust and water cycles (through cloud formation) affects the dust and water cycles individually. We focus here on the effects of clouds on the vertical distribution of dust and water, and how those vertical distributions control the net meridional transport of water. For this study, we utilize observations of temperature, dust and water ice from the Mars Climate Sounder (MCS) on the Mars Reconnaissance Orbiter (MRO) combined with the NASA ARC Mars Global Climate Model (MGCM). We demonstrate that the magnitude and nature of the net meridional transport of water between the northern and southern hemispheres during NH summer is sensitive to the vertical structure of the simulated aphelion cloud belt. We further examine how clouds influence the atmospheric thermal structure and thus the vertical structure of the cloud belt. Our goal is to identify and understand the importance of radiative/dynamic feedbacks due to the physical processes involved with cloud formation and evolution on the current climate of Mars.

Teleconnections in the Presence of Climate Change: A Case Study of the Annular Modes

NASA Astrophysics Data System (ADS)

Gerber, Edwin; Baldwin, Mark

2010-05-01

Long model integrations of future and past climates present a problem for defining teleconnection patterns through Empirical Orthogonal Function (EOF) or correlation analysis when trends in the underlying climate begin to dominate the covariance structure. Similar issues may soon appear in observations as the record becomes longer, especially if climate trends accelerate. The Northern and Southern Annular Modes provide a prime example, because the poleward shift of the jet streams strongly projects onto these patterns, particularly in the Southern Hemisphere. Climate forecasts of the 21st century by chemistry climate models provide a case study. Computation of the annular modes in these long data sets with secular trends requires refinement of the standard definition of the annular mode, and a more robust procedure that allows for slowly varying trends is established and verified. The new procedure involves two key changes. First, the global mean geopotential height is removed at each time step before computing anomalies. This is particularly important high in the atmosphere, where seasonal variations in geopotential height become significant, and filters out trends due to changes in the temperature structure of the atmosphere. Pattern definition can be very sensitive near the tropopause, as regions of the atmosphere that used to be more of stratospheric character begin to take on tropospheric characteristics as the tropopause rises. The second change is to define anomalies relative to a slowly evolving seasonal climatology, so that the covariance structure reflects internal variability. Once these changes are accounted for, it is found that the zonal mean variability of the atmosphere stays remarkably constant, despite significant changes in the baseline climate forecast for the rest of the century. This stability of the internal variability makes it possible to relate trends in climate to teleconnections.

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

USGS Publications Warehouse

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

2016-01-01

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

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

PubMed

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

2016-06-01

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

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

NASA Astrophysics Data System (ADS)

Blażejeczyk, Krzysztof; Krawczyk, Barbara

1991-06-01

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

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling.

PubMed

Feng, Xiaohui; Uriarte, María; González, Grizelle; Reed, Sasha; Thompson, Jill; Zimmerman, Jess K; Murphy, Lora

2018-01-01

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here, we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured interannual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including aboveground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate. © 2017 John Wiley & Sons Ltd.

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

USGS Publications Warehouse

Feng, Xiaohui; Uriarte, María; González, Grizelle; Reed, Sasha C.; Thompson, Jill; Zimmerman, Jess K.; Murphy, Lora

2018-01-01

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured inter-annual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including above-ground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.

Effects of climate change and shifts in forest composition on forest net primary production

Treesearch

Jyh-Min Chiang; Louts [Louis] R. Iverson; Anantha Prasad; Kim J. Brown

2008-01-01

Forests are dynamic in both structure and species composition, and these dynamics are strongly influenced by climate. However, the net effects of future tree species composition on net primary production (NPP) are not well understood. The objective of this work was to model the potential range shifts of tree species (DISTRIB Model) and predict their impacts on NPP (...

Global Three-Dimensional Atmospheric Structure of the Atlantic Multidecadal Oscillation as Revealed by Two Reanalyses

NASA Astrophysics Data System (ADS)

Stuckman, Scott Seele

This study is a first documentation of the structure of the entire AMO life cycle, including extreme and transition phases, throughout the global troposphere. The extreme phase climate signature is constructed based on the strongest and most robust patterns identified by two methods (linear correlation and composite analyses), two reanalysis datasets (the National Centers for Environmental Prediction/National Center for Atmospheric Research and Twentieth Century Reanalysis, supplemented with precipitation data from the University of Delaware dataset) and data from two consecutive AMO cycles. The first characterization of the AMO transition phases uses a transition index based on the time derivative of AMO index. When trying to compare the zonal mean structure of AMO with the El Niño-Southern Oscillation (ENSO), a literature search showed the zonal mean structure of ENSO remained unpublished, despite the otherwise generally well-characterized horizontal structures. Therefore this study includes a seasonal analysis of the ENSO zonal mean structure during boreal winter (DJF) and summer (JJA). The AMO extreme phase is characterized by a blend of low and middle latitude centers of action, with the associated tilt of geopotential height anomaly patterns consistent with off-equatorial heating patterns generated by the Held idealized model. The surface climate signature is connected to the upper air with baroclinic vertical structure over the North Atlantic but barotropic structures elsewhere. The associated zonal mean circulation features three circulation cells globally with strong inter-hemispheric mixing that suggests the traditional view of the AMO involving a Northern-Southern Hemisphere asymmetry is accurate only near the surface. The AMO transition phase features a more equatorial-based climate signature and associated geopotential height anomaly patterns consistent with the Matsuno-Gill idealized model. The zonal mean circulation of the transition phases features six, rather than three, circulation cells globally. The only baroclinic structure, over North America, and several barotropic structures are positioned west of corresponding similar structures during the AMO extreme phase, suggesting an eastward evolution of climate anomalies as the AMO progresses from a cool-to-warm transition phase to warm phase. The Pacific-based climate signature resembles the IPO warm phase and it is proposed the AMO and IPO are different basin-wide expressions of a single multidecadal oscillation. The identification of an AMO transition phase climate signature distinct from the extreme phase suggests transition phases are not neutral and may provide an additional source of information for characterizing climate cycles.

Impacts of Seed Dispersal on Future Vegetation Structure under Changing Climates

NASA Astrophysics Data System (ADS)

Lee, E.; Schlosser, C. A.; Gao, X.; Prinn, R. G.

2011-12-01

As the impacts between land cover change, future climates and ecosystems are expected to be substantial, there are growing needs for improving the capability of simulating the global vegetation structure and landscape as realistically as possible. Current DGVMs assume ubiquitous availability of seeds and do not consider any seed dispersal mechanisms in plant migration process, which may influence the assessment of impacts to the ecosystem that rely on the vegetation structure changes (i.e., change in albedo, runoff, and terrestrial carbon sequestration capacity). This study incorporates time-varying wind-driven seed dispersal (i.e., the SEED configuration) as a dynamic constraint to the migration process of natural vegetation in the Community Land Model (CLM)-DGVM. The SEED configuration is validated using a satellite-derived tree cover dataset. Then the configuration is applied to project future vegetation structures and their implications for carbon fluxes, albedo, and hydrology under two climate mitigation scenarios (No-policy vs. 450ppm CO2 stabilization) for the 21st century. Our results show that regional changes of vegetation structure under changing climates are expected to be significant. For example, Alaska and Siberia are expected to experience substantial shifts of forestry structure, characterized by expansion of needle-leaf boreal forest and shrinkage of C3 grass Arctic. A suggested vulnerability assessment shows that vegetation structures in Alaska, Greenland, Central America, southern South America, East Africa and East Asia are susceptible to changing climates, regardless of the two climate mitigation scenarios. Regions such as Greenland, Tibet, South Asia and Northern Australia, however, may substantially alleviate their risks of rapid change in vegetation structure, given a robust greenhouse gas stabilization target. Proliferation of boreal forests in the high latitudes is expected to amplify the warming trend (i.e., a positive feedback to climate), if no mitigation policy is implemented. In contrast, under the 450ppm scenario, vegetation structure may buffer the warming trend, which is a negative feedback to climate. Moreover, runoff changes due to vegetation shifts may offset or complement runoff changes under anthropogenic climate warming.

Representative Agricultural Pathways and Climate Impact Assessment for Pacific Northwest Agricultural Systems

NASA Astrophysics Data System (ADS)

MU, J.; Antle, J. M.; Zhang, H.; Capalbo, S. M.; Eigenbrode, S.; Kruger, C.; Stockle, C.; Wolfhorst, J. D.

2013-12-01

Representative Agricultural Pathways (RAPs) are projections of plausible future biophysical and socio-economic conditions used to carry out climate impact assessments for agriculture. The development of RAPs iss motivated by the fact that the various global and regional models used for agricultural climate change impact assessment have been implemented with individualized scenarios using various data and model structures, often without transparent documentation or public availability. These practices have hampered attempts at model inter-comparison, improvement, and synthesis of model results across studies. This paper aims to (1) present RAPs developed for the principal wheat-producing region of the Pacific Northwest, and to (2) combine these RAPs with downscaled climate data, crop model simulations and economic model simulations to assess climate change impacts on winter wheat production and farm income. This research was carried out as part of a project funded by the USDA known as the Regional Approaches to Climate Change in the Pacific Northwest (REACCH). The REACCH study region encompasses the major winter wheat production area in Pacific Northwest and preliminary research shows that farmers producing winter wheat could benefit from future climate change. However, the future world is uncertain in many dimensions, including commodity and input prices, production technology, and policies, as well as increased probability of disturbances (pests and diseases) associated with a changing climate. Many of these factors cannot be modeled, so they are represented in the regional RAPS. The regional RAPS are linked to global agricultural and shared social-economic pathways, and used along with climate change projections to simulate future outcomes for the wheat-based farms in the REACCH region.

Rethinking the Default Construction of Multimodel Climate Ensembles

DOE PAGES

Rauser, Florian; Gleckler, Peter; Marotzke, Jochem

2015-07-21

Here, we discuss the current code of practice in the climate sciences to routinely create climate model ensembles as ensembles of opportunity from the newest phase of the Coupled Model Intercomparison Project (CMIP). We give a two-step argument to rethink this process. First, the differences between generations of ensembles corresponding to different CMIP phases in key climate quantities are not large enough to warrant an automatic separation into generational ensembles for CMIP3 and CMIP5. Second, we suggest that climate model ensembles cannot continue to be mere ensembles of opportunity but should always be based on a transparent scientific decision process.more » If ensembles can be constrained by observation, then they should be constructed as target ensembles that are specifically tailored to a physical question. If model ensembles cannot be constrained by observation, then they should be constructed as cross-generational ensembles, including all available model data to enhance structural model diversity and to better sample the underlying uncertainties. To facilitate this, CMIP should guide the necessarily ongoing process of updating experimental protocols for the evaluation and documentation of coupled models. Finally, with an emphasis on easy access to model data and facilitating the filtering of climate model data across all CMIP generations and experiments, our community could return to the underlying idea of using model data ensembles to improve uncertainty quantification, evaluation, and cross-institutional exchange.« less

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

NASA Astrophysics Data System (ADS)

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

2012-06-01

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

A plant’s perspective of extremes: Terrestrial plant responses to changing climatic variability

PubMed Central

Reyer, C.; Leuzinger, S.; Rammig, A.; Wolf, A.; Bartholomeus, R. P.; Bonfante, A.; de Lorenzi, F.; Dury, M.; Gloning, P.; Abou Jaoudé, R.; Klein, T.; Kuster, T. M.; Martins, M.; Niedrist, G.; Riccardi, M.; Wohlfahrt, G.; de Angelis, P.; de Dato, G.; François, L.; Menzel, A.; Pereira, M.

2013-01-01

We review observational, experimental and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied but potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heatwaves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational and /or modeling studies have the potential to overcome important caveats of the respective individual approaches. PMID:23504722

Is There Any Evidence for Rapid, Genetically-Based, Climatic Niche Expansion in the Invasive Common Ragweed?

PubMed

Gallien, Laure; Thuiller, Wilfried; Fort, Noémie; Boleda, Marti; Alberto, Florian J; Rioux, Delphine; Lainé, Juliette; Lavergne, Sébastien

2016-01-01

Climatic niche shifts have been documented in a number of invasive species by comparing the native and adventive climatic ranges in which they occur. However, these shifts likely represent changes in the realized climatic niches of invasive species, and may not necessarily be driven by genetic changes in climatic affinities. Until now the role of rapid niche evolution in the spread of invasive species remains a challenging issue with conflicting results. Here, we document a likely genetically-based climatic niche expansion of an annual plant invader, the common ragweed (Ambrosia artemisiifolia L.), a highly allergenic invasive species causing substantial public health issues. To do so, we looked for recent evolutionary change at the upward migration front of its adventive range in the French Alps. Based on species climatic niche models estimated at both global and regional scales we stratified our sampling design to adequately capture the species niche, and localized populations suspected of niche expansion. Using a combination of species niche modeling, landscape genetics models and common garden measurements, we then related the species genetic structure and its phenotypic architecture across the climatic niche. Our results strongly suggest that the common ragweed is rapidly adapting to local climatic conditions at its invasion front and that it currently expands its niche toward colder and formerly unsuitable climates in the French Alps (i.e. in sites where niche models would not predict its occurrence). Such results, showing that species climatic niches can evolve on very short time scales, have important implications for predictive models of biological invasions that do not account for evolutionary processes.

VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

USGS Publications Warehouse

Kittel, T.G.F.; Rosenbloom, N.A.; Royle, J. Andrew; Daly, Christopher; Gibson, W.P.; Fisher, H.H.; Thornton, P.; Yates, D.N.; Aulenbach, S.; Kaufman, C.; McKeown, R.; Bachelet, D.; Schimel, D.S.; Neilson, R.; Lenihan, J.; Drapek, R.; Ojima, D.S.; Parton, W.J.; Melillo, J.M.; Kicklighter, D.W.; Tian, H.; McGuire, A.D.; Sykes, M.T.; Smith, B.; Cowling, S.; Hickler, T.; Prentice, I.C.; Running, S.; Hibbard, K.A.; Post, W.M.; King, A.W.; Smith, T.; Rizzo, B.; Woodward, F.I.

2004-01-01

Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895-1993 on a 0.5?? latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables are: precipitation, mininimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed techniques that rely on geostatistical and physical relationships to create the temporally and spatially complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to grid the infilled monthly station data. We implemented a stochastic weather generator (modified WGEN) to disaggregate the gridded monthly series to dailies. Radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100 yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. The VEMAP dataset exhibits statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion datasets are available online at data archive centers. ?? Inter-Research 2004.

Application of the NASA A-Train to Evaluate Clouds Simulated by the Weather Research and Forecast Model

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Jedlovec, Gary J.; Lapenta, William M.

2008-01-01

The CloudSat Mission, part of the NASA A-Train, is providing the first global survey of cloud profiles and cloud physical properties, observing seasonal and geographical variations that are pertinent to evaluating the way clouds are parameterized in weather and climate forecast models. CloudSat measures the vertical structure of clouds and precipitation from space through the Cloud Profiling Radar (CPR), a 94 GHz nadir-looking radar measuring the power backscattered by clouds as a function of distance from the radar. One of the goals of the CloudSat mission is to evaluate the representation of clouds in forecast models, thereby contributing to improved predictions of weather, climate and the cloud-climate feedback problem. This paper highlights potential limitations in cloud microphysical schemes currently employed in the Weather Research and Forecast (WRF) modeling system. The horizontal and vertical structure of explicitly simulated cloud fields produced by the WRF model at 4-km resolution are being evaluated using CloudSat observations in concert with products derived from MODIS and AIRS. A radiative transfer model is used to produce simulated profiles of radar reflectivity given WRF input profiles of hydrometeor mixing ratios and ambient atmospheric conditions. The preliminary results presented in the paper will compare simulated and observed reflectivity fields corresponding to horizontal and vertical cloud structures associated with midlatitude cyclone events.

Chasing Perfection: Should We Reduce Model Uncertainty in Carbon Cycle-Climate Feedbacks

NASA Astrophysics Data System (ADS)

Bonan, G. B.; Lombardozzi, D.; Wieder, W. R.; Lindsay, K. T.; Thomas, R. Q.

2015-12-01

Earth system model simulations of the terrestrial carbon (C) cycle show large multi-model spread in the carbon-concentration and carbon-climate feedback parameters. Large differences among models are also seen in their simulation of global vegetation and soil C stocks and other aspects of the C cycle, prompting concern about model uncertainty and our ability to faithfully represent fundamental aspects of the terrestrial C cycle in Earth system models. Benchmarking analyses that compare model simulations with common datasets have been proposed as a means to assess model fidelity with observations, and various model-data fusion techniques have been used to reduce model biases. While such efforts will reduce multi-model spread, they may not help reduce uncertainty (and increase confidence) in projections of the C cycle over the twenty-first century. Many ecological and biogeochemical processes represented in Earth system models are poorly understood at both the site scale and across large regions, where biotic and edaphic heterogeneity are important. Our experience with the Community Land Model (CLM) suggests that large uncertainty in the terrestrial C cycle and its feedback with climate change is an inherent property of biological systems. The challenge of representing life in Earth system models, with the rich diversity of lifeforms and complexity of biological systems, may necessitate a multitude of modeling approaches to capture the range of possible outcomes. Such models should encompass a range of plausible model structures. We distinguish between model parameter uncertainty and model structural uncertainty. Focusing on improved parameter estimates may, in fact, limit progress in assessing model structural uncertainty associated with realistically representing biological processes. Moreover, higher confidence may be achieved through better process representation, but this does not necessarily reduce uncertainty.

Integrating bioclimate with population models to improve forecasts of species extinctions under climate change.

PubMed

Brook, Barry W; Akçakaya, H Resit; Keith, David A; Mace, Georgina M; Pearson, Richard G; Araújo, Miguel B

2009-12-23

Climate change is already affecting species worldwide, yet existing methods of risk assessment have not considered interactions between demography and climate and their simultaneous effect on habitat distribution and population viability. To address this issue, an international workshop was held at the University of Adelaide in Australia, 25-29 May 2009, bringing leading species distribution and population modellers together with plant ecologists. Building on two previous workshops in the UK and Spain, the participants aimed to develop methodological standards and case studies for integrating bioclimatic and metapopulation models, to provide more realistic forecasts of population change, habitat fragmentation and extinction risk under climate change. The discussions and case studies focused on several challenges, including spatial and temporal scale contingencies, choice of predictive climate, land use, soil type and topographic variables, procedures for ensemble forecasting of both global climate and bioclimate models and developing demographic structures that are realistic and species-specific and yet allow generalizations of traits that make species vulnerable to climate change. The goal is to provide general guidelines for assessing the Red-List status of large numbers of species potentially at risk, owing to the interactions of climate change with other threats such as habitat destruction, overexploitation and invasive species.

Convergence in France facing Big Data era and Exascale challenges for Climate Sciences

NASA Astrophysics Data System (ADS)

Denvil, Sébastien; Dufresne, Jean-Louis; Salas, David; Meurdesoif, Yann; Valcke, Sophie; Caubel, Arnaud; Foujols, Marie-Alice; Servonnat, Jérôme; Sénési, Stéphane; Derouillat, Julien; Voury, Pascal

2014-05-01

The presentation will introduce a french national project : CONVERGENCE that has been funded for four years. This project will tackle big data and computational challenges faced by climate modeling community in HPC context. Model simulations are central to the study of complex mechanisms and feedbacks in the climate system and to provide estimates of future and past climate changes. Recent trends in climate modelling are to add more physical components in the modelled system, increasing the resolution of each individual component and the more systematic use of large suites of simulations to address many scientific questions. Climate simulations may therefore differ in their initial state, parameter values, representation of physical processes, spatial resolution, model complexity, and degree of realism or degree of idealisation. In addition, there is a strong need for evaluating, improving and monitoring the performance of climate models using a large ensemble of diagnostics and better integration of model outputs and observational data. High performance computing is currently reaching the exascale and has the potential to produce this exponential increase of size and numbers of simulations. However, post-processing, analysis, and exploration of the generated data have stalled and there is a strong need for new tools to cope with the growing size and complexity of the underlying simulations and datasets. Exascale simulations require new scalable software tools to generate, manage and mine those simulations ,and data to extract the relevant information and to take the correct decision. The primary purpose of this project is to develop a platform capable of running large ensembles of simulations with a suite of models, to handle the complex and voluminous datasets generated, to facilitate the evaluation and validation of the models and the use of higher resolution models. We propose to gather interdisciplinary skills to design, using a component-based approach, a specific programming environment for scalable scientific simulations and analytics, integrating new and efficient ways of deploying and analysing the applications on High Performance Computing (HPC) system. CONVERGENCE, gathering HPC and informatics expertise that cuts across the individual partners and the broader HPC community, will allow the national climate community to leverage information technology (IT) innovations to address its specific needs. Our methodology consists in developing an ensemble of generic elements needed to run the French climate models with different grids and different resolution, ensuring efficient and reliable execution of these models, managing large volume and number of data and allowing analysis of the results and precise evaluation of the models. These elements include data structure definition and input-output (IO), code coupling and interpolation, as well as runtime and pre/post-processing environments. A common data and metadata structure will allow transferring consistent information between the various elements. All these generic elements will be open source and publicly available. The IPSL-CM and CNRM-CM climate models will make use of these elements that will constitute a national platform for climate modelling. This platform will be used, in its entirety, to optimise and tune the next version of the IPSL-CM model and to develop a global coupled climate model with a regional grid refinement. It will also be used, at least partially, to run ensembles of the CNRM-CM model at relatively high resolution and to run a very-high resolution prototype of this model. The climate models we developed are already involved in many international projects. For instance we participate to the CMIP (Coupled Model Intercomparison Project) project that is very demanding but has a high visibility: its results are widely used and are in particular synthesised in the IPCC (Intergovernmental Panel on Climate Change) assessment reports. The CONVERGENCE project will constitute an invaluable step for the French climate community to prepare and better contribute to the next phase of the CMIP project.

Sensitivity of Latent Heating Profiles to Environmental Conditions: Implications for TRMM and Climate Research

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Einaudi, Franco (Technical Monitor)

2000-01-01

The Tropical Rainfall Measuring Mission (TRMM) as a part of NASA's Earth System Enterprise is the first mission dedicated to measuring tropical rainfall through microwave and visible sensors, and includes the first spaceborne rain radar. Tropical rainfall comprises two-thirds of global rainfall. It is also the primary distributor of heat through the atmosphere's circulation. It is this circulation that defines Earth's weather and climate. Understanding rainfall and its variability is crucial to understanding and predicting global climate change. Weather and climate models need an accurate assessment of the latent heating released as tropical rainfall occurs. Currently, cloud model-based algorithms are used to derive latent heating based on rainfall structure. Ultimately, these algorithms can be applied to actual data from TRMM. This study investigates key underlying assumptions used in developing the latent heating algorithms. For example, the standard algorithm is highly dependent on a system's rainfall amount and structure. It also depends on an a priori database of model-derived latent heating profiles based on the aforementioned rainfall characteristics. Unanswered questions remain concerning the sensitivity of latent heating profiles to environmental conditions (both thermodynamic and kinematic), regionality, and seasonality. This study investigates and quantifies such sensitivities and seeks to determine the optimal latent heating profile database based on the results. Ultimately, the study seeks to produce an optimized latent heating algorithm based not only on rainfall structure but also hydrometeor profiles.

Linking organizational resources and work engagement to employee performance and customer loyalty: the mediation of service climate.

PubMed

Salanova, Marisa; Agut, Sonia; Peiró, José María

2005-11-01

This study examined the mediating role of service climate in the prediction of employee performance and customer loyalty. Contact employees (N=342) from 114 service units (58 hotel front desks and 56 restaurants) provided information about organizational resources, engagement, and service climate. Furthermore, customers (N=1,140) from these units provided information on employee performance and customer loyalty. Structural equation modeling analyses were consistent with a full mediation model in which organizational resources and work engagement predict service climate, which in turn predicts employee performance and then customer loyalty. Further analyses revealed a potential reciprocal effect between service climate and customer loyalty. Implications of the study are discussed, together with limitations and suggestions for future research. ((c) 2005 APA, all rights reserved).

climwin: An R Toolbox for Climate Window Analysis.

PubMed

Bailey, Liam D; van de Pol, Martijn

2016-01-01

When studying the impacts of climate change, there is a tendency to select climate data from a small set of arbitrary time periods or climate windows (e.g., spring temperature). However, these arbitrary windows may not encompass the strongest periods of climatic sensitivity and may lead to erroneous biological interpretations. Therefore, there is a need to consider a wider range of climate windows to better predict the impacts of future climate change. We introduce the R package climwin that provides a number of methods to test the effect of different climate windows on a chosen response variable and compare these windows to identify potential climate signals. climwin extracts the relevant data for each possible climate window and uses this data to fit a statistical model, the structure of which is chosen by the user. Models are then compared using an information criteria approach. This allows users to determine how well each window explains variation in the response variable and compare model support between windows. climwin also contains methods to detect type I and II errors, which are often a problem with this type of exploratory analysis. This article presents the statistical framework and technical details behind the climwin package and demonstrates the applicability of the method with a number of worked examples.

Close packing effects on clean and dirty snow albedo and associated climatic implications

NASA Astrophysics Data System (ADS)

He, C.; Liou, K. N.; Takano, Y.

2017-12-01

Previous modeling of snow albedo, a key climate feedback parameter, follows the independent scattering approximation (ISA) such that snow grains are considered as a number of separate units with distances longer than wavelengths. Here we develop a new snow albedo model for widely observed close-packed snow grains internally mixed with black carbon (BC) and demonstrate that albedo simulations match closer to observations. Close packing results in a stronger light absorption for clean and BC-contaminated snow. Compared with ISA, close packing reduces pure snow albedos by up to 0.05, whereas it enhances BC-induced snow albedo reduction and associated surface radiative forcing by up to 15% (20%) for fresh (old) snow, with larger enhancements for stronger structure packing. Finally, our results suggest that BC-snow albedo forcing and snow albedo feedback (climate sensitivity) are underestimated in previous modeling studies, making snow close packing consideration a necessity in climate modeling and analysis.

Close packing effects on clean and dirty snow albedo and associated climatic implications

NASA Astrophysics Data System (ADS)

He, Cenlin; Takano, Yoshi; Liou, Kuo-Nan

2017-04-01

Previous modeling of snow albedo, a key climate feedback parameter, follows the independent scattering approximation (ISA) such that snow grains are considered as a number of separate units with distances longer than wavelengths. Here we develop a new snow albedo model for widely observed close-packed snow grains internally mixed with black carbon (BC) and demonstrate that albedo simulations match closer to observations. Close packing results in a stronger light absorption for clean and BC-contaminated snow. Compared with ISA, close packing reduces pure snow albedos by up to 0.05, whereas it enhances BC-induced snow albedo reduction and associated surface radiative forcing by up to 15% (20%) for fresh (old) snow, with larger enhancements for stronger structure packing. Finally, our results suggest that BC-snow albedo forcing and snow albedo feedback (climate sensitivity) are underestimated in previous modeling studies, making snow close packing consideration a necessity in climate modeling and analysis.

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

2016-01-01

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

Potential Effects of Drought on Tree Dieback in Great Britain and Implications for Forest Management in Adaptation to Climate Change

NASA Astrophysics Data System (ADS)

Yu, Jianjun; Berry, Pam

2017-04-01

The drought and heat stress has alerted the composition, structure and biogeography of forests globally, whilst the projected severe and widespread droughts are potentially increasing. This challenges the sustainable forest management to better cope with future climate and maintain the forest ecosystem functions and services. Many studies have investigated the climate change impacts on forest ecosystem but less considered the climate extremes like drought. In this study, we implement a dynamic ecosystem model based on a version of LPJ-GUESS parameterized with European tree species and apply to Great Britain at a finer spatial resolution of 5*5 km. The model runs for the baseline from 1961 to 2011 and projects to the latter 21st century using 100 climate scenarios generated from MaRIUS project to tackle the climate model uncertainty. We will show the potential impacts of climate change on forest ecosystem and vegetation transition in Great Britain by comparing the modelled conditions in the 2030s and the 2080s relative to the baseline. In particular, by analyzing the modelled tree mortality, we will show the tree dieback patterns in response to drought for various species, and assess their drought vulnerability across Great Britain. We also use species distribution modelling to project the suitable climate space for selected tree species using the same climate scenarios. Aided by these two modelling approaches and based on the corresponding modelling results, we will discuss the implications for adaptation strategy for forest management, especially in extreme drought conditions. The gained knowledge and lessons for Great Britain are considered to be transferable in many other regions.

Short-Term Effects of Climatic Variables on Hand, Foot, and Mouth Disease in Mainland China, 2008–2013: A Multilevel Spatial Poisson Regression Model Accounting for Overdispersion

PubMed Central

Yang, Fang; Yang, Min; Hu, Yuehua; Zhang, Juying

2016-01-01

Background Hand, Foot, and Mouth Disease (HFMD) is a worldwide infectious disease. In China, many provinces have reported HFMD cases, especially the south and southwest provinces. Many studies have found a strong association between the incidence of HFMD and climatic factors such as temperature, rainfall, and relative humidity. However, few studies have analyzed cluster effects between various geographical units. Methods The nonlinear relationships and lag effects between weekly HFMD cases and climatic variables were estimated for the period of 2008–2013 using a polynomial distributed lag model. The extra-Poisson multilevel spatial polynomial model was used to model the exact relationship between weekly HFMD incidence and climatic variables after considering cluster effects, provincial correlated structure of HFMD incidence and overdispersion. The smoothing spline methods were used to detect threshold effects between climatic factors and HFMD incidence. Results The HFMD incidence spatial heterogeneity distributed among provinces, and the scale measurement of overdispersion was 548.077. After controlling for long-term trends, spatial heterogeneity and overdispersion, temperature was highly associated with HFMD incidence. Weekly average temperature and weekly temperature difference approximate inverse “V” shape and “V” shape relationships associated with HFMD incidence. The lag effects for weekly average temperature and weekly temperature difference were 3 weeks and 2 weeks. High spatial correlated HFMD incidence were detected in northern, central and southern province. Temperature can be used to explain most of variation of HFMD incidence in southern and northeastern provinces. After adjustment for temperature, eastern and Northern provinces still had high variation HFMD incidence. Conclusion We found a relatively strong association between weekly HFMD incidence and weekly average temperature. The association between the HFMD incidence and climatic variables spatial heterogeneity distributed across provinces. Future research should explore the risk factors that cause spatial correlated structure or high variation of HFMD incidence which can be explained by temperature. When analyzing association between HFMD incidence and climatic variables, spatial heterogeneity among provinces should be evaluated. Moreover, the extra-Poisson multilevel model was capable of modeling the association between overdispersion of HFMD incidence and climatic variables. PMID:26808311

Short-Term Effects of Climatic Variables on Hand, Foot, and Mouth Disease in Mainland China, 2008-2013: A Multilevel Spatial Poisson Regression Model Accounting for Overdispersion.

PubMed

Liao, Jiaqiang; Yu, Shicheng; Yang, Fang; Yang, Min; Hu, Yuehua; Zhang, Juying

2016-01-01

Hand, Foot, and Mouth Disease (HFMD) is a worldwide infectious disease. In China, many provinces have reported HFMD cases, especially the south and southwest provinces. Many studies have found a strong association between the incidence of HFMD and climatic factors such as temperature, rainfall, and relative humidity. However, few studies have analyzed cluster effects between various geographical units. The nonlinear relationships and lag effects between weekly HFMD cases and climatic variables were estimated for the period of 2008-2013 using a polynomial distributed lag model. The extra-Poisson multilevel spatial polynomial model was used to model the exact relationship between weekly HFMD incidence and climatic variables after considering cluster effects, provincial correlated structure of HFMD incidence and overdispersion. The smoothing spline methods were used to detect threshold effects between climatic factors and HFMD incidence. The HFMD incidence spatial heterogeneity distributed among provinces, and the scale measurement of overdispersion was 548.077. After controlling for long-term trends, spatial heterogeneity and overdispersion, temperature was highly associated with HFMD incidence. Weekly average temperature and weekly temperature difference approximate inverse "V" shape and "V" shape relationships associated with HFMD incidence. The lag effects for weekly average temperature and weekly temperature difference were 3 weeks and 2 weeks. High spatial correlated HFMD incidence were detected in northern, central and southern province. Temperature can be used to explain most of variation of HFMD incidence in southern and northeastern provinces. After adjustment for temperature, eastern and Northern provinces still had high variation HFMD incidence. We found a relatively strong association between weekly HFMD incidence and weekly average temperature. The association between the HFMD incidence and climatic variables spatial heterogeneity distributed across provinces. Future research should explore the risk factors that cause spatial correlated structure or high variation of HFMD incidence which can be explained by temperature. When analyzing association between HFMD incidence and climatic variables, spatial heterogeneity among provinces should be evaluated. Moreover, the extra-Poisson multilevel model was capable of modeling the association between overdispersion of HFMD incidence and climatic variables.

Attributing runoff changes to climate variability and human activities: uncertainty analysis using four monthly water balance models

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

Li, Shuai; Xiong, Lihua; Li, Hong-Yi

2015-05-26

Hydrological simulations to delineate the impacts of climate variability and human activities are subjected to uncertainties related to both parameter and structure of the hydrological models. To analyze the impact of these uncertainties on the model performance and to yield more reliable simulation results, a global calibration and multimodel combination method that integrates the Shuffled Complex Evolution Metropolis (SCEM) and Bayesian Model Averaging (BMA) of four monthly water balance models was proposed. The method was applied to the Weihe River Basin (WRB), the largest tributary of the Yellow River, to determine the contribution of climate variability and human activities tomore » runoff changes. The change point, which was used to determine the baseline period (1956-1990) and human-impacted period (1991-2009), was derived using both cumulative curve and Pettitt’s test. Results show that the combination method from SCEM provides more skillful deterministic predictions than the best calibrated individual model, resulting in the smallest uncertainty interval of runoff changes attributed to climate variability and human activities. This combination methodology provides a practical and flexible tool for attribution of runoff changes to climate variability and human activities by hydrological models.« less

Diagnosis and Quantification of Climatic Sensitivity of Carbon Fluxes in Ensemble Global Ecosystem Models

NASA Astrophysics Data System (ADS)

Wang, W.; Hashimoto, H.; Milesi, C.; Nemani, R. R.; Myneni, R.

2011-12-01

Terrestrial ecosystem models are primary scientific tools to extrapolate our understanding of ecosystem functioning from point observations to global scales as well as from the past climatic conditions into the future. However, no model is nearly perfect and there are often considerable structural uncertainties existing between different models. Ensemble model experiments thus become a mainstream approach in evaluating the current status of global carbon cycle and predicting its future changes. A key task in such applications is to quantify the sensitivity of the simulated carbon fluxes to climate variations and changes. Here we develop a systematic framework to address this question solely by analyzing the inputs and the outputs from the models. The principle of our approach is to assume the long-term (~30 years) average of the inputs/outputs as a quasi-equlibrium of the climate-vegetation system while treat the anomalies of carbon fluxes as responses to climatic disturbances. In this way, the corresponding relationships can be largely linearized and analyzed using conventional time-series techniques. This method is used to characterize three major aspects of the vegetation models that are mostly important to global carbon cycle, namely the primary production, the biomass dynamics, and the ecosystem respiration. We apply this analytical framework to quantify the climatic sensitivity of an ensemble of models including CASA, Biome-BGC, LPJ as well as several other DGVMs from previous studies, all driven by the CRU-NCEP climate dataset. The detailed analysis results are reported in this study.

How will climate change affect watershed mercury export in a representative Coastal Plain watershed?

NASA Astrophysics Data System (ADS)

Golden, H. E.; Knightes, C. D.; Conrads, P. A.; Feaster, T.; Davis, G. M.; Benedict, S. T.; Bradley, P. M.

2012-12-01

Future climate change is expected to drive variations in watershed hydrological processes and water quality across a wide range of physiographic provinces, ecosystems, and spatial scales. How such shifts in climatic conditions will impact watershed mercury (Hg) dynamics and hydrologically-driven Hg transport is a significant concern. We simulate the responses of watershed hydrological and total Hg (HgT) fluxes and concentrations to a unified set of past and future climate change projections in a Coastal Plain basin using multiple watershed models. We use two statistically downscaled global precipitation and temperature models, ECHO, a hybrid of the ECHAM4 and HOPE-G models, and the Community Climate System Model (CCSM3) across two thirty-year simulations (1980 to 2010 and 2040 to 2070). We apply three watershed models to quantify and bracket potential changes in hydrologic and HgT fluxes, including the Visualizing Ecosystems for Land Management Assessment Model for Hg (VELMA-Hg), the Grid Based Mercury Model (GBMM), and TOPLOAD, a water quality constituent model linked to TOPMODEL hydrological simulations. We estimate a decrease in average annual HgT fluxes in response to climate change using the ECHO projections and an increase with the CCSM3 projections in the study watershed. Average monthly HgT fluxes increase using both climate change projections between in the late spring (March through May), when HgT concentrations and flow are high. Results suggest that hydrological transport associated with changes in precipitation and temperature is the primary mechanism driving HgT flux response to climate change. Our multiple model/multiple projection approach allows us to bracket the relative response of HgT fluxes to climate change, thereby illustrating the uncertainty associated with the projections. In addition, our approach allows us to examine potential variations in climate change-driven water and HgT export based on different conceptualizations of watershed HgT dynamics and the representative mathematical structures underpinning existing watershed Hg models.

Studying Climate Response to Forcing by the Nonlinear Dynamical Mode Decomposition

NASA Astrophysics Data System (ADS)

Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander

2017-04-01

An analysis of global climate response to external forcing, both anthropogenic (mainly, CO2 and aerosol) and natural (solar and volcanic), is needed for adequate predictions of global climate change. Being complex dynamical system, the climate reacts to external perturbations exciting feedbacks (both positive and negative) making the response non-trivial and poorly predictable. Thus an extraction of internal modes of climate system, investigation of their interaction with external forcings and further modeling and forecast of their dynamics, are all the problems providing the success of climate modeling. In the report the new method for principal mode extraction from climate data is presented. The method is based on the Nonlinear Dynamical Mode (NDM) expansion [1,2], but takes into account a number of external forcings applied to the system. Each NDM is represented by hidden time series governing the observed variability, which, together with external forcing time series, are mapped onto data space. While forcing time series are considered to be known, the hidden unknown signals underlying the internal climate dynamics are extracted from observed data by the suggested method. In particular, it gives us an opportunity to study the evolution of principal system's mode structure in changing external conditions and separate the internal climate variability from trends forced by external perturbations. Furthermore, the modes so obtained can be extrapolated beyond the observational time series, and long-term prognosis of modes' structure including characteristics of interconnections and responses to external perturbations, can be carried out. In this work the method is used for reconstructing and studying the principal modes of climate variability on inter-annual and decadal time scales accounting the external forcings such as anthropogenic emissions, variations of the solar activity and volcanic activity. The structure of the obtained modes as well as their response to external factors, e.g. forecast their change in 21 century under different CO2 emission scenarios, are discussed. [1] Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. http://doi.org/10.1038/srep15510 [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J. (2016). Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101. http://doi.org/10.1063/1.4968852

Modelling the potential role of forest thinning in maintaining water supplies under a changing climate across the conterminous United States

Treesearch

Ge Sun; Peter V. Caldwell; Steven G. McNulty

2015-01-01

The goal of this study was to test the sensitivity of water yield to forest thinning and other forest management/disturbances and climate across the conterminous United States (CONUS). Leaf area index (LAI) was selected as a key parameter linking changes in forest ecosystem structure and functions. We used the Water Supply Stress Index model to examine water yield...

Objective tropical cyclone extratropical transition detection in high-resolution reanalysis and climate model data

DOE PAGES

Zarzycki, Colin M.; Thatcher, Diana R.; Jablonowski, Christiane

2017-01-22

This paper describes an objective technique for detecting the extratropical transition (ET) of tropical cyclones (TCs) in high-resolution gridded climate data. The algorithm is based on previous observational studies using phase spaces to define the symmetry and vertical thermal structure of cyclones. Storm tracking is automated, allowing for direct analysis of climate data. Tracker performance in the North Atlantic is assessed using 23 years of data from the variable-resolution Community Atmosphere Model (CAM) at two different resolutions (DX 55 km and 28 km), the Climate Forecast System Reanalysis (CFSR, DX 38 km), and the ERA-Interim Reanalysis (ERA-I, DX 80 km).more » The mean spatiotemporal climatologies and seasonal cycles of objectively detected ET in the observationally constrained CFSR and ERA-I are well matched to previous observational studies, demonstrating the capability of the scheme to adequately find events. High resolution CAM reproduces TC and ET statistics that are in general agreement with reanalyses. One notable model bias, however, is significantly longer time between ET onset and ET completion in CAM, particularly for TCs that lose symmetry prior to developing a cold-core structure and becoming extratropical cyclones, demonstrating the capability of this method to expose model biases in simulated cyclones beyond the tropical phase.« less

Observationally-based Metrics of Ocean Carbon and Biogeochemical Variables are Essential for Evaluating Earth System Model Projections

NASA Astrophysics Data System (ADS)

Russell, J. L.; Sarmiento, J. L.

2017-12-01

The Southern Ocean is central to the climate's response to increasing levels of atmospheric greenhouse gases as it ventilates a large fraction of the global ocean volume. Global coupled climate models and earth system models, however, vary widely in their simulations of the Southern Ocean and its role in, and response to, the ongoing anthropogenic forcing. Due to its complex water-mass structure and dynamics, Southern Ocean carbon and heat uptake depend on a combination of winds, eddies, mixing, buoyancy fluxes and topography. Understanding how the ocean carries heat and carbon into its interior and how the observed wind changes are affecting this uptake is essential to accurately projecting transient climate sensitivity. Observationally-based metrics are critical for discerning processes and mechanisms, and for validating and comparing climate models. As the community shifts toward Earth system models with explicit carbon simulations, more direct observations of important biogeochemical parameters, like those obtained from the biogeochemically-sensored floats that are part of the Southern Ocean Carbon and Climate Observations and Modeling project, are essential. One goal of future observing systems should be to create observationally-based benchmarks that will lead to reducing uncertainties in climate projections, and especially uncertainties related to oceanic heat and carbon uptake.

On the key role of droughts in the dynamics of summer fires in Mediterranean Europe.

PubMed

Turco, Marco; von Hardenberg, Jost; AghaKouchak, Amir; Llasat, Maria Carmen; Provenzale, Antonello; Trigo, Ricardo M

2017-03-06

Summer fires frequently rage across Mediterranean Europe, often intensified by high temperatures and droughts. According to the state-of-the-art regional fire risk projections, in forthcoming decades climate effects are expected to become stronger and possibly overcome fire prevention efforts. However, significant uncertainties exist and the direct effect of climate change in regulating fuel moisture (e.g. warmer conditions increasing fuel dryness) could be counterbalanced by the indirect effects on fuel structure (e.g. warmer conditions limiting fuel amount), affecting the transition between climate-driven and fuel-limited fire regimes as temperatures increase. Here we analyse and model the impact of coincident drought and antecedent wet conditions (proxy for the climatic factor influencing total fuel and fine fuel structure) on the summer Burned Area (BA) across all eco-regions in Mediterranean Europe. This approach allows BA to be linked to the key drivers of fire in the region. We show a statistically significant relationship between fire and same-summer droughts in most regions, while antecedent climate conditions play a relatively minor role, except in few specific eco-regions. The presented models for individual eco-regions provide insights on the impacts of climate variability on BA, and appear to be promising for developing a seasonal forecast system supporting fire management strategies.

Climate impacts on agricultural biomass production in the CORDEX.be project context

NASA Astrophysics Data System (ADS)

Gobin, Anne; Van Schaeybroeck, Bert; Termonia, Piet; Willems, Patrick; Van Lipzig, Nicole; Marbaix, Philippe; van Ypersele, Jean-Pascal; Fettweis, Xavier; De Ridder, Koen; Stavrakou, Trissevgeni; Luyten, Patrick; Pottiaux, Eric

2016-04-01

The most important coordinated international effort to translate the IPCC-AR5 outcomes to regional climate modelling is the so-called "COordinated Regional climate Downscaling EXperiment" (CORDEX, http://wcrp-cordex.ipsl.jussieu.fr/). CORDEX.be is a national initiative that aims at combining the Belgian climate and impact modelling research into a single network. The climate network structure is naturally imposed by the top-down data flow, from the four participating upper-air Regional Climate Modelling groups towards seven Local Impact Models (LIMs). In addition to the production of regional climate projections following the CORDEX guidelines, very high-resolution results are provided at convection-permitting resolutions of about 4 km across Belgium. These results are coupled to seven local-impact models with severity indices as output. A multi-model approach is taken that allows uncertainty estimation, a crucial aspect of climate projections for policy-making purposes. The down-scaled scenarios at 4 km resolution allow for impact assessment in different Belgian agro-ecological zones. Climate impacts on arable agriculture are quantified using REGCROP which is a regional dynamic agri-meteorological model geared towards modelling climate impact on biomass production of arable crops (Gobin, 2010, 2012). Results from previous work show that heat stress and water shortages lead to reduced crop growth, whereas increased CO2-concentrations and a prolonged growing season have a positive effect on crop yields. The interaction between these effects depend on the crop type and the field conditions. Root crops such as potato will experience increased drought stress particularly when the probability rises that sensitive crop stages coincide with dry spells. This may be aggravated when wet springs cause water logging in the field and delay planting dates. Despite lower summer precipitation projections for future climate in Belgium, winter cereal yield reductions due to drought stress will be smaller due to earlier maturity. Preliminary results will be presented using the new scenario runs for Belgium.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Understanding Climate Uncertainty with an Ocean Focus

NASA Astrophysics Data System (ADS)

Tokmakian, R. T.

2009-12-01

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

A weather-driven model of malaria transmission.

PubMed

Hoshen, Moshe B; Morse, Andrew P

2004-09-06

Climate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission. This paper describes a mathematical-biological model of the parasite dynamics, comprising both the weather-dependent within-vector stages and the weather-independent within-host stages. Numerical evaluations of the model in both time and space show that it qualitatively reconstructs the prevalence of infection. A process-based modelling structure has been developed that may be suitable for the simulation of malaria forecasts based on seasonal weather forecasts.

Scaling Forest Management Practices in Earth System Models: Case Study of Southeast and Pacific Northwest Forests

NASA Astrophysics Data System (ADS)

Pourmokhtarian, A.; Becknell, J. M.; Hall, J.; Desai, A. R.; Boring, L. R.; Duffy, P.; Staudhammer, C. L.; Starr, G.; Dietze, M.

2014-12-01

A wide array of human-induced disturbances can alter the structure and function of forests, including climate change, disturbance and management. While there have been numerous studies on climate change impacts on forests, interactions of management with changing climate and natural disturbance are poorly studied. Forecasts of the range of plausible responses of forests to climate change and management are need for informed decision making on new management approaches under changing climate, as well as adaptation strategies for coming decades. Terrestrial biosphere models (TBMs) provide an excellent opportunity to investigate and assess simultaneous responses of terrestrial ecosystems to climatic perturbations and management across multiple spatio-temporal scales, but currently do not represent a wide array of management activities known to impact carbon, water, surface energy fluxes, and biodiversity. The Ecosystem Demography model 2 (ED2) incorporates non-linear impacts of fine-scale (~10-1 km) heterogeneity in ecosystem structure both horizontally and vertically at a plant level. Therefore it is an ideal candidate to incorporate different forest management practices and test various hypotheses under changing climate and across various spatial scales. The management practices that we implemented were: clear-cut, conversion, planting, partial harvest, low intensity fire, restoration, salvage, and herbicide. The results were validated against observed data across 8 different sites in the U.S. Southeast (Duke Forest, Joseph Jones Ecological Research Center, North Carolina Loblolly Pine, and Ordway-Swisher Biological Station) and Pacific Northwest (Metolius Research Natural Area, H.J. Andrews Experimental Forest, Wind River Field Station, and Mount Rainier National Park). These sites differ in regards to climate, vegetation, soil, and historical land disturbance as well as management approaches. Results showed that different management practices could successfully and realistically be implemented in the ED2 model at a site level. Moreover, sensitivity analyses determined the most important processes at different spatial scales, and also those which could be ignored while minimizing overall error.

Computational data sciences for assessment and prediction of climate extremes

NASA Astrophysics Data System (ADS)

Ganguly, A. R.

2011-12-01

Climate extremes may be defined inclusively as severe weather events or large shifts in global or regional weather patterns which may be caused or exacerbated by natural climate variability or climate change. This area of research arguably represents one of the largest knowledge-gaps in climate science which is relevant for informing resource managers and policy makers. While physics-based climate models are essential in view of non-stationary and nonlinear dynamical processes, their current pace of uncertainty reduction may not be adequate for urgent stakeholder needs. The structure of the models may in some cases preclude reduction of uncertainty for critical processes at scales or for the extremes of interest. On the other hand, methods based on complex networks, extreme value statistics, machine learning, and space-time data mining, have demonstrated significant promise to improve scientific understanding and generate enhanced predictions. When combined with conceptual process understanding at multiple spatiotemporal scales and designed to handle massive data, interdisciplinary data science methods and algorithms may complement or supplement physics-based models. Specific examples from the prior literature and our ongoing work suggests how data-guided improvements may be possible, for example, in the context of ocean meteorology, climate oscillators, teleconnections, and atmospheric process understanding, which in turn can improve projections of regional climate, precipitation extremes and tropical cyclones in an useful and interpretable fashion. A community-wide effort is motivated to develop and adapt computational data science tools for translating climate model simulations to information relevant for adaptation and policy, as well as for improving our scientific understanding of climate extremes from both observed and model-simulated data.

Spatial analysis of future East Asian seasonal temperature using two regional climate model simulations

NASA Astrophysics Data System (ADS)

Kim, Yura; Jun, Mikyoung; Min, Seung-Ki; Suh, Myoung-Seok; Kang, Hyun-Suk

2016-05-01

CORDEX-East Asia, a branch of the coordinated regional climate downscaling experiment (CORDEX) initiative, provides high-resolution climate simulations for the domain covering East Asia. This study analyzes temperature data from regional climate models (RCMs) participating in the CORDEX - East Asia region, accounting for the spatial dependence structure of the data. In particular, we assess similarities and dissimilarities of the outputs from two RCMs, HadGEM3-RA and RegCM4, over the region and over time. A Bayesian functional analysis of variance (ANOVA) approach is used to simultaneously model the temperature patterns from the two RCMs for the current and future climate. We exploit nonstationary spatial models to handle the spatial dependence structure of the temperature variable, which depends heavily on latitude and altitude. For a seasonal comparison, we examine changes in the winter temperature in addition to the summer temperature data. We find that the temperature increase projected by RegCM4 tends to be smaller than the projection of HadGEM3-RA for summers, and that the future warming projected by HadGEM3-RA tends to be weaker for winters. Also, the results show that there will be a warming of 1-3°C over the region in 45 years. More specifically, the warming pattern clearly depends on the latitude, with greater temperature increases in higher latitude areas, which implies that warming may be more severe in the northern part of the domain.

A compound reconstructed prediction model for nonstationary climate processes

NASA Astrophysics Data System (ADS)

Wang, Geli; Yang, Peicai

2005-07-01

Based on the idea of climate hierarchy and the theory of state space reconstruction, a local approximation prediction model with the compound structure is built for predicting some nonstationary climate process. By means of this model and the data sets consisting of north Indian Ocean sea-surface temperature, Asian zonal circulation index and monthly mean precipitation anomaly from 37 observation stations in the Inner Mongolia area of China (IMC), a regional prediction experiment for the winter precipitation of IMC is also carried out. When using the same sign ratio R between the prediction field and the actual field to measure the prediction accuracy, an averaged R of 63% given by 10 predictions samples is reached.

Assessment of bias correction under transient climate change

NASA Astrophysics Data System (ADS)

Van Schaeybroeck, Bert; Vannitsem, Stéphane

2015-04-01

Calibration of climate simulations is necessary since large systematic discrepancies are generally found between the model climate and the observed climate. Recent studies have cast doubt upon the common assumption of the bias being stationary when the climate changes. This led to the development of new methods, mostly based on linear sensitivity of the biases as a function of time or forcing (Kharin et al. 2012). However, recent studies uncovered more fundamental problems using both low-order systems (Vannitsem 2011) and climate models, showing that the biases may display complicated non-linear variations under climate change. This last analysis focused on biases derived from the equilibrium climate sensitivity, thereby ignoring the effect of the transient climate sensitivity. Based on the linear response theory, a general method of bias correction is therefore proposed that can be applied on any climate forcing scenario. The validity of the method is addressed using twin experiments with a climate model of intermediate complexity LOVECLIM (Goosse et al., 2010). We evaluate to what extent the bias change is sensitive to the structure (frequency) of the applied forcing (here greenhouse gases) and whether the linear response theory is valid for global and/or local variables. To answer these question we perform large-ensemble simulations using different 300-year scenarios of forced carbon-dioxide concentrations. Reality and simulations are assumed to differ by a model error emulated as a parametric error in the wind drag or in the radiative scheme. References [1] H. Goosse et al., 2010: Description of the Earth system model of intermediate complexity LOVECLIM version 1.2, Geosci. Model Dev., 3, 603-633. [2] S. Vannitsem, 2011: Bias correction and post-processing under climate change, Nonlin. Processes Geophys., 18, 911-924. [3] V.V. Kharin, G. J. Boer, W. J. Merryfield, J. F. Scinocca, and W.-S. Lee, 2012: Statistical adjustment of decadal predictions in a changing climate, Geophys. Res. Lett., 39, L19705.

A climate-associated multispecies cryptic cline in the northwest Atlantic

PubMed Central

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

2018-01-01

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

Adaptive Governance, Uncertainty, and Risk: Policy Framing and Responses to Climate Change, Drought, and Flood.

PubMed

Hurlbert, Margot; Gupta, Joyeeta

2016-02-01

As climate change impacts result in more extreme events (such as droughts and floods), the need to understand which policies facilitate effective climate change adaptation becomes crucial. Hence, this article answers the question: How do governments and policymakers frame policy in relation to climate change, droughts, and floods and what governance structures facilitate adaptation? This research interrogates and analyzes through content analysis, supplemented by semi-structured qualitative interviews, the policy response to climate change, drought, and flood in relation to agricultural producers in four case studies in river basins in Chile, Argentina, and Canada. First, an epistemological explanation of risk and uncertainty underscores a brief literature review of adaptive governance, followed by policy framing in relation to risk and uncertainty, and an analytical model is developed. Pertinent findings of the four cases are recounted, followed by a comparative analysis. In conclusion, recommendations are made to improve policies and expand adaptive governance to better account for uncertainty and risk. This article is innovative in that it proposes an expanded model of adaptive governance in relation to "risk" that can help bridge the barrier of uncertainty in science and policy. © 2015 Society for Risk Analysis.

Flexibility in Flood Management Design: Proactive Planning Under Climate Change Uncertainty

NASA Astrophysics Data System (ADS)

Smet, K.; de Neufville, R.; van der Vlist, M.

2015-12-01

This paper presents an innovative, value-enhancing procedure for effective planning and design of long-lived flood management infrastructure given uncertain future flooding threats due to climate change. Designing infrastructure that can be adapted over time is a method to safeguard the efficacy of current design decisions given uncertainty about rates and future impacts of climate change. This paper explores the value of embedding "options" in a physical structure, where an option is the right but not the obligation to do something at a later date (e.g. over-dimensioning a floodwall foundation now facilitates a future height addition in response to observed increases in sea level; building of extra pump bays in a pumping station now enables the addition of pumping capacity whenever increased precipitation warrants an expansion.) The proposed procedure couples a simulation model that captures future climate induced changes to the hydrologic operating environment of a structure, with an economic model that estimates the lifetime economic performance of alternative investments. The economic model uses Real "In" Options analysis, a type of cash flow analysis that quantifies the implicit value of options and the flexibility they provide. This procedure is demonstrated using replacement planning for the multi-functional pumping station IJmuiden on the North Sea Canal in the Netherlands. Flexibility in design decisions is modelled, varying the size and specific options included in the new structure. Results indicate that the incorporation of options within the structural design has the potential to improve its economic performance, as compared to more traditional, "build it once and build it big" designs where flexibility is not an explicit design criterion. The added value resulting from the incorporation of flexibility varies with the range of future conditions considered, as well as the options examined. This procedure could be applied more broadly to explore investment strategies for the design of other flood management structures.

Trends and uncertainties in budburst projections of Norway spruce in Northern Europe.

PubMed

Olsson, Cecilia; Olin, Stefan; Lindström, Johan; Jönsson, Anna Maria

2017-12-01

Budburst is regulated by temperature conditions, and a warming climate is associated with earlier budburst. A range of phenology models has been developed to assess climate change effects, and they tend to produce different results. This is mainly caused by different model representations of tree physiology processes, selection of observational data for model parameterization, and selection of climate model data to generate future projections. In this study, we applied (i) Bayesian inference to estimate model parameter values to address uncertainties associated with selection of observational data, (ii) selection of climate model data representative of a larger dataset, and (iii) ensembles modeling over multiple initial conditions, model classes, model parameterizations, and boundary conditions to generate future projections and uncertainty estimates. The ensemble projection indicated that the budburst of Norway spruce in northern Europe will on average take place 10.2 ± 3.7 days earlier in 2051-2080 than in 1971-2000, given climate conditions corresponding to RCP 8.5. Three provenances were assessed separately (one early and two late), and the projections indicated that the relationship among provenance will remain also in a warmer climate. Structurally complex models were more likely to fail predicting budburst for some combinations of site and year than simple models. However, they contributed to the overall picture of current understanding of climate impacts on tree phenology by capturing additional aspects of temperature response, for example, chilling. Model parameterizations based on single sites were more likely to result in model failure than parameterizations based on multiple sites, highlighting that the model parameterization is sensitive to initial conditions and may not perform well under other climate conditions, whether the change is due to a shift in space or over time. By addressing a range of uncertainties, this study showed that ensemble modeling provides a more robust impact assessment than would a single phenology model run.

Creating "Intelligent" Climate Model Ensemble Averages Using a Process-Based Framework

NASA Astrophysics Data System (ADS)

Baker, N. C.; Taylor, P. C.

2014-12-01

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is often used to add value to model projections: consensus projections have been shown to consistently outperform individual models. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, certain models reproduce climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument and surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing weighted and unweighted model ensembles. For example, one tested metric weights the ensemble by how well models reproduce the time-series probability distribution of the cloud forcing component of reflected shortwave radiation. The weighted ensemble for this metric indicates lower simulated precipitation (up to .7 mm/day) in tropical regions than the unweighted ensemble: since CMIP5 models have been shown to overproduce precipitation, this result could indicate that the metric is effective in identifying models which simulate more realistic precipitation. Ultimately, the goal of the framework is to identify performance metrics for advising better methods for ensemble averaging models and create better climate predictions.

Overview of the Special Issue: A Multi-Model Framework to Achieve Consistent Evaluation of Climate Change Impacts in the United States

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

Waldhoff, Stephanie T.; Martinich, Jeremy; Sarofim, Marcus

2015-07-01

The Climate Change Impacts and Risk Analysis (CIRA) modeling exercise is a unique contribution to the scientific literature on climate change impacts, economic damages, and risk analysis that brings together multiple, national-scale models of impacts and damages in an integrated and consistent fashion to estimate climate change impacts, damages, and the benefits of greenhouse gas (GHG) mitigation actions in the United States. The CIRA project uses three consistent socioeconomic, emissions, and climate scenarios across all models to estimate the benefits of GHG mitigation policies: a Business As Usual (BAU) and two policy scenarios with radiative forcing (RF) stabilization targets ofmore » 4.5 W/m2 and 3.7 W/m2 in 2100. CIRA was also designed to specifically examine the sensitivity of results to uncertainties around climate sensitivity and differences in model structure. The goals of CIRA project are to 1) build a multi-model framework to produce estimates of multiple risks and impacts in the U.S., 2) determine to what degree risks and damages across sectors may be lowered from a BAU to policy scenarios, 3) evaluate key sources of uncertainty along the causal chain, and 4) provide information for multiple audiences and clearly communicate the risks and damages of climate change and the potential benefits of mitigation. This paper describes the motivations, goals, and design of the CIRA modeling exercise and introduces the subsequent papers in this special issue.« less

Geodynamic contributions to global climatic change

NASA Technical Reports Server (NTRS)

Bills, Bruce G.

1992-01-01

Orbital and rotational variations perturb the latitudinal and seasonal pattern of incident solar radiation, producing major climatic change on time scales of 10(exp 4)-10(exp 6) years. The orbital variations are oblivious to internal structure and processes, but the rotational variations are not. A program of investigation whose objective would be to explore and quantify three aspects of orbital, rotational, and climatic interactions is described. An important premise of this investigation is the synergism between geodynamics and paleoclimate. Better geophysical models of precessional dynamics are needed in order to accurately reconstruct the radiative input to climate models. Some of the paleoclimate proxy records contain information relevant to solid Earth processes, on time scales which are difficult to constrain otherwise. Specific mechanisms which will be addressed include: (1) climatic consequences of deglacial polar motion; and (2) precessional and climatic consequences of glacially induced perturbations in the gravitational oblateness and partial decoupling of the mantle and core. The approach entails constructing theoretical models of the rotational, deformational, radiative, and climatic response of the Earth to known orbital perturbations, and comparing these with extensive records of paleoclimate proxy data. Several of the mechanisms of interest may participate in previously unrecognized feed-back loops in the climate dynamics system. A new algorithm for estimating climatically diagnostic locations and seasons from the paleoclimate time series is proposed.

Structural and psychological empowerment climates, performance, and the moderating role of shared felt accountability: a managerial perspective.

PubMed

Wallace, J Craig; Johnson, Paul D; Mathe, Kimberly; Paul, Jeff

2011-07-01

The authors proposed and tested a model in which data were collected from managers (n = 539) at 116 corporate-owned quick service restaurants to assess the structural and psychological empowerment process as moderated by shared-felt accountability on indices of performance from a managerial perspective. The authors found that empowering leadership climate positively relates to psychological empowerment climate. In turn, psychological empowerment climate relates to performance only under conditions of high-felt accountability; it does not relate to performance under conditions of low-felt accountability. Overall, the present results indicate that the quick-service restaurant managers, who feel more empowered, operate restaurants that perform better than managers who feel less empowered, but only when those empowered managers also feel a high sense of accountability.

The predictive validity of safety climate.

PubMed

Johnson, Stephen E

2007-01-01

Safety professionals have increasingly turned their attention to social science for insight into the causation of industrial accidents. One social construct, safety climate, has been examined by several researchers [Cooper, M. D., & Phillips, R. A. (2004). Exploratory analysis of the safety climate and safety behavior relationship. Journal of Safety Research, 35(5), 497-512; Gillen, M., Baltz, D., Gassel, M., Kirsch, L., & Vacarro, D. (2002). Perceived safety climate, job Demands, and coworker support among union and nonunion injured construction workers. Journal of Safety Research, 33(1), 33-51; Neal, A., & Griffin, M. A. (2002). Safety climate and safety behaviour. Australian Journal of Management, 27, 66-76; Zohar, D. (2000). A group-level model of safety climate: Testing the effect of group climate on microaccidents in manufacturing jobs. Journal of Applied Psychology, 85(4), 587-596; Zohar, D., & Luria, G. (2005). A multilevel model of safety climate: Cross-level relationships between organization and group-level climates. Journal of Applied Psychology, 90(4), 616-628] who have documented its importance as a factor explaining the variation of safety-related outcomes (e.g., behavior, accidents). Researchers have developed instruments for measuring safety climate and have established some degree of psychometric reliability and validity. The problem, however, is that predictive validity has not been firmly established, which reduces the credibility of safety climate as a meaningful social construct. The research described in this article addresses this problem and provides additional support for safety climate as a viable construct and as a predictive indicator of safety-related outcomes. This study used 292 employees at three locations of a heavy manufacturing organization to complete the 16 item Zohar Safety Climate Questionnaire (ZSCQ) [Zohar, D., & Luria, G. (2005). A multilevel model of safety climate: Cross-level relationships between organization and group-level climates. Journal of Applied Psychology, 90(4), 616-628]. In addition, safety behavior and accident experience data were collected for 5 months following the survey and were statistically analyzed (structural equation modeling, confirmatory factor analysis, exploratory factor analysis, etc.) to identify correlations, associations, internal consistency, and factorial structures. Results revealed that the ZSCQ: (a) was psychometrically reliable and valid, (b) served as an effective predictor of safety-related outcomes (behavior and accident experience), and (c) could be trimmed to an 11 item survey with little loss of explanatory power. Practitioners and researchers can use the ZSCQ with reasonable certainty of the questionnaire's reliability and validity. This provides a solid foundation for the development of meaningful organizational interventions and/or continued research into social factors affecting industrial accident experience.

Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization

DOE PAGES

Eyring, Veronika; Bony, Sandrine; Meehl, Gerald A.; ...

2016-05-26

By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) andmore » CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.« less

On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model

NASA Astrophysics Data System (ADS)

Migliavacca, M.; Sonnentag, O.; Keenan, T. F.; Cescatti, A.; O'Keefe, J.; Richardson, A. D.

2012-06-01

Phenology, the timing of recurring life cycle events, controls numerous land surface feedbacks to the climate system through the regulation of exchanges of carbon, water and energy between the biosphere and atmosphere. Terrestrial biosphere models, however, are known to have systematic errors in the simulation of spring phenology, which potentially could propagate to uncertainty in modeled responses to future climate change. Here, we used the Harvard Forest phenology record to investigate and characterize sources of uncertainty in predicting phenology, and the subsequent impacts on model forecasts of carbon and water cycling. Using a model-data fusion approach, we combined information from 20 yr of phenological observations of 11 North American woody species, with 12 leaf bud-burst models that varied in complexity. Akaike's Information Criterion indicated support for spring warming models with photoperiod limitations and, to a lesser extent, models that included chilling requirements. We assessed three different sources of uncertainty in phenological forecasts: parameter uncertainty, model uncertainty, and driver uncertainty. The latter was characterized running the models to 2099 using 2 different IPCC climate scenarios (A1fi vs. B1, i.e. high CO2 emissions vs. low CO2 emissions scenario). Parameter uncertainty was the smallest (average 95% Confidence Interval - CI: 2.4 days century-1 for scenario B1 and 4.5 days century-1 for A1fi), whereas driver uncertainty was the largest (up to 8.4 days century-1 in the simulated trends). The uncertainty related to model structure is also large and the predicted bud-burst trends as well as the shape of the smoothed projections varied among models (±7.7 days century-1 for A1fi, ±3.6 days century-1 for B1). The forecast sensitivity of bud-burst to temperature (i.e. days bud-burst advanced per degree of warming) varied between 2.2 days °C-1 and 5.2 days °C-1 depending on model structure. We quantified the impact of uncertainties in bud-burst forecasts on simulated photosynthetic CO2 uptake and evapotranspiration (ET) using a process-based terrestrial biosphere model. Uncertainty in phenology model structure led to uncertainty in the description of forest seasonality, which accumulated to uncertainty in annual model estimates of gross primary productivity (GPP) and ET of 9.6% and 2.9%, respectively. A sensitivity analysis shows that a variation of ±10 days in bud-burst dates led to a variation of ±5.0% for annual GPP and about ±2.0% for ET. For phenology models, differences among future climate scenarios (i.e. driver) represent the largest source of uncertainty, followed by uncertainties related to model structure, and finally, related to model parameterization. The uncertainties we have quantified will affect the description of the seasonality of ecosystem processes and in particular the simulation of carbon uptake by forest ecosystems, with a larger impact of uncertainties related to phenology model structure, followed by uncertainties related to phenological model parameterization.

Morphological Inheritance in Sandy Coastline Morphologies Subject to Long-Term Changes in Wave Climate: Surprising Insights from a Coastline Evolution Model

NASA Astrophysics Data System (ADS)

Murray, A. B.; Thomas, C.; Hurst, M. D.; Barkwith, A.; Ashton, A. D.; Ellis, M. A.

2014-12-01

Recent numerical modelling demonstrates that when sandy coastlines are affected predominantly by waves approaching from "high" angles (> ~45° between the coastline and wave crests at the offshore limit of shore-parallel contours), large-scale (kms to 100 kms) morphodynamic instabilities and finite-amplitude interactions can lead to the emergence of striking coastline features, including sand waves, capes and spits. The type of feature that emerges depends on the wave climate, defined as the angular distribution of wave influences on alongshore sediment transport. Under a constant wave climate, coastline morphology reaches a dynamical steady state; the cross-shore/alongshore aspect ratio and the general appearance of the features remains constant. In previous modelling involving wave-climate change, as well as comparisons between observed coastline morphologies and wave climates, it has been implicitly assumed that the morphology adjusts in a quasi-equilibrium fashion, so that at any time the coastline shape reflects the current forcing. However, here we present new model results showing pronounced path dependence in coastline morphodynamics. In experiments with a period of constant wave climate followed by a period of transition to a new wave climate and then a run-on phase, the features that exist during the run-on phase can be qualitatively and quantitatively different from those that would develop initially under the final wave climate. Although the features inherited from the past wave-climate history may in some case be true alternate stable states, in other cases the inherited features gradually decay toward the morphology that would be expected given the final wave climate. A suite of such experiments allows us to characterize how the e-folding timescale of this decay depends on 1) the initial wave climate, 2) the path through wave-climate space, and 3) the rate of transition. When the initial features are flying spits with cross-shore amplitudes of 6 - 8 km, e-folding times can be on the order of millennia or longer. These results could provide a new perspective when interpreting current and past coastline features. In addition, the complex paleo-coastline structure that develops in the coastal hinterlands in these experiments could be relevant to the structures observed in some coastal environments.

Quantifying early-seral forest composition with remote sensing

Treesearch

Rayma A. Cooley; Peter T. Wolter; Brian R. Sturtevant

2016-01-01

Spatially explicit modeling of recovering forest structure within two years following wildfire disturbance has not been attempted, yet such knowledge is critical for determining successional pathways. We used remote sensing and field data, along with digital climate and terrain data, to model and map early-seral aspen structure and vegetation species richness following...

Statistical structure of intrinsic climate variability under global warming

NASA Astrophysics Data System (ADS)

Zhu, Xiuhua; Bye, John; Fraedrich, Klaus

2017-04-01

Climate variability is often studied in terms of fluctuations with respect to the mean state, whereas the dependence between the mean and variability is rarely discussed. We propose a new climate metric to measure the relationship between means and standard deviations of annual surface temperature computed over non-overlapping 100-year segments. This metric is analyzed based on equilibrium simulations of the Max Planck Institute-Earth System Model (MPI-ESM): the last millennium climate (800-1799), the future climate projection following the A1B scenario (2100-2199), and the 3100-year unforced control simulation. A linear relationship is globally observed in the control simulation and thus termed intrinsic climate variability, which is most pronounced in the tropical region with negative regression slopes over the Pacific warm pool and positive slopes in the eastern tropical Pacific. It relates to asymmetric changes in temperature extremes and associates fluctuating climate means with increase or decrease in intensity and occurrence of both El Niño and La Niña events. In the future scenario period, the linear regression slopes largely retain their spatial structure with appreciable changes in intensity and geographical locations. Since intrinsic climate variability describes the internal rhythm of the climate system, it may serve as guidance for interpreting climate variability and climate change signals in the past and the future.

Positive organizational behavior and safety in the offshore oil industry: Exploring the determinants of positive safety climate.

PubMed

Hystad, Sigurd W; Bartone, Paul T; Eid, Jarle

2014-01-01

Much research has now documented the substantial influence of safety climate on a range of important outcomes in safety critical organizations, but there has been scant attention to the question of what factors might be responsible for positive or negative safety climate. The present paper draws from positive organizational behavior theory to test workplace and individual factors that may affect safety climate. Specifically, we explore the potential influence of authentic leadership style and psychological capital on safety climate and risk outcomes. Across two samples of offshore oil-workers and seafarers working on oil platform supply ships, structural equation modeling yielded results that support a model in which authentic leadership exerts a direct effect on safety climate, as well as an indirect effect via psychological capital. This study shows the importance of leadership qualities as well as psychological factors in shaping a positive work safety climate and lowering the risk of accidents.

Comparing Mid-Century Climate Change Projections at Convective Resolving Scales (2-km) for Life Zones Within Puerto Rico

NASA Astrophysics Data System (ADS)

Bowden, J.; Wootten, A.; Terando, A. J.; Boyles, R.; Misra, V.; Bhardwaj, A.

2016-12-01

Puerto Rico is home to over 3.5 million people and numerous endemic plant and animal species that may be at risk as a result of anthropogenic climate change. This study downscales three CMIP5 Global Circulation Models (GCMs) to a 2-km horizontal resolution using different regional climate models (RCMs) to resolve the island's climate. Here we compare projected climate change from a single GCM, CCSM4, from two RCMs centered on the mid-century, 2041-2060, for a high greenhouse gas emission scenario, RCP8.5. We will discuss similarities and differences in ecologically relevant climate variables, which were selected based on dialogue with experts who have knowledge about potential biological impacts of climate change for current life zones within Puerto Rico. Notable differences appear between the RCMs and include regions with critical ecosystems, such as the El Yunque National Forest in northeast Puerto Rico. This study helps to highlight RCMs structural uncertainty at convective resolving scales.

Positive organizational behavior and safety in the offshore oil industry: Exploring the determinants of positive safety climate

PubMed Central

Hystad, Sigurd W.; Bartone, Paul T.; Eid, Jarle

2013-01-01

Much research has now documented the substantial influence of safety climate on a range of important outcomes in safety critical organizations, but there has been scant attention to the question of what factors might be responsible for positive or negative safety climate. The present paper draws from positive organizational behavior theory to test workplace and individual factors that may affect safety climate. Specifically, we explore the potential influence of authentic leadership style and psychological capital on safety climate and risk outcomes. Across two samples of offshore oil-workers and seafarers working on oil platform supply ships, structural equation modeling yielded results that support a model in which authentic leadership exerts a direct effect on safety climate, as well as an indirect effect via psychological capital. This study shows the importance of leadership qualities as well as psychological factors in shaping a positive work safety climate and lowering the risk of accidents. PMID:24454524

Structural and climatic determinants of demographic rates of Scots pine forests across the Iberian Peninsula.

PubMed

Vilà-Cabrera, Albert; Martínez-Vilalta, Jordi; Vayreda, Jordi; Retana, Javier

2011-06-01

The demographic rates of tree species typically show large spatial variation across their range. Understanding the environmental factors underlying this variation is a key topic in forest ecology, with far-reaching management implications. Scots pine (Pinus sylvestris L.) covers large areas of the Northern Hemisphere, the Iberian Peninsula being its southwestern distribution limit. In recent decades, an increase in severe droughts and a densification of forests as a result of changes in forest uses have occurred in this region. Our aim was to use climate and stand structure data to explain mortality and growth patterns of Scots pine forests across the Iberian Peninsula. We used data from 2392 plots dominated by Scots pine, sampled for the National Forest Inventory of Spain. Plots were sampled from 1986 to 1996 (IFN2) and were resampled from 1997 to 2007 (IFN3), allowing for the calculation of growth and mortality rates. We fitted linear models to assess the response of growth and mortality rates to the spatial variability of climate, climatic anomalies, and forest structure. Over the period of approximately 10 years between the IFN2 and IFN3, the amount of standing dead trees increased 11-fold. Higher mortality rates were related to dryness, and growth was reduced with increasing dryness and temperature, but results also suggested that effects of climatic stressors were not restricted to dry sites only. Forest structure was strongly related to demographic rates, suggesting that stand development and competition are the main factors associated with demography. In the case of mortality, forest structure interacted with climate, suggesting that competition for water resources induces tree mortality in dry sites. A slight negative relationship was found between mortality and growth, indicating that both rates are likely to be affected by the same stress factors. Additionally, regeneration tended to be lower in plots with higher mortality. Taken together, our results suggest a large-scale self-thinning related to the recent densification of Scots pine forests. This process appears to be enhanced by dry conditions and may lead to a mismatch in forest turnover. Forest management may be an essential adaptive tool under the drier conditions predicted by most climate models.

Genetic divergence in the common bush-tanager Chlorospingus ophthalmicus (Aves: Emberizidae) throughout Mexican cloud forests: The role of geography, ecology and Pleistocene climatic fluctuations.

PubMed

Maldonado-Sánchez, Denisse; Gutiérrez-Rodríguez, Carla; Ornelas, Juan Francisco

2016-06-01

By integrating mitochondrial DNA (mtDNA), microsatellites and ecological niche modelling (ENM), we investigated the phylogeography of Mexican populations of the common bush-tanager Chlorospingus ophthalmicus to examine the relative role of geographical and ecological features, as well as Pleistocene climatic oscillations in driving the diversification. We sequenced mtDNA of individuals collected throughout the species range in Mexico and genotyped them at seven microsatellite loci. Phylogeographic, population genetics and coalescent methods were used to assess patterns of genetic structure, gene flow and demographic history. ENM was used to infer contractions and expansions at different time periods as well as differences in climatic conditions among lineages. The retrieved mitochondrial and microsatellite groups correspond with the fragmented cloud forest distribution in mountain ranges and morphotectonic provinces. Differing climatic conditions between mountain ranges were detected, and palaeodistribution modelling as well as demographic history analyses, indicated recent population expansions throughout the Sierra Madre Oriental (SMO). The marked genetic structure of C. ophthalmicus was promoted by the presence of ecological and geographical barriers that restricted the movement of individuals among mountain ranges. The SMO was mainly affected by Pleistocene climatic oscillations, with the moist forests model best fitting the displayed genetic patterns of populations in this mountain range. Copyright © 2016 Elsevier Inc. All rights reserved.

Energy Structure and Energy Security under Climate Mitigation Scenarios in China

PubMed Central

Matsumoto, Ken’ichi

2015-01-01

This study investigates how energy structure and energy security in China will change in the future under climate mitigation policy scenarios using Representative Concentration Pathways in a computable general equilibrium model. The findings suggest that to reduce greenhouse gas emissions, China needs to shift its energy structure from fossil fuel dominance to renewables and nuclear. The lower the allowable emissions, the larger the shifts required. Among fossil fuels, coal use particularly must significantly decrease. Such structural shifts will improve energy self-sufficiency, thus enhancing energy security. Under the policy scenarios, energy-source diversity as measured by the Herfindahl Index improves until 2050, after which diversity declines because of high dependence on a specific energy source (nuclear and biomass). Overall, however, it is revealed that energy security improves along with progress in climate mitigation. These improvements will also contribute to the economy by reducing energy procurement risks. PMID:26660094

Climatic water deficit, tree species ranges, and climate change in Yosemite National Park

USGS Publications Warehouse

Lutz, James A.; Van Wagtendonk, Jan W.; Franklin, Jerry F.

2010-01-01

Aim  (1) To calculate annual potential evapotranspiration (PET), actual evapotranspiration (AET) and climatic water deficit (Deficit) with high spatial resolution; (2) to describe distributions for 17 tree species over a 2300-m elevation gradient in a 3000-km2 landscape relative to AET and Deficit; (3) to examine changes in AET and Deficit between past (c. 1700), present (1971–2000) and future (2020–49) climatological means derived from proxies, observations and projections; and (4) to infer how the magnitude of changing Deficit may contribute to changes in forest structure and composition.Location  Yosemite National Park, California, USA.Methods  We calculated the water balance within Yosemite National Park using a modified Thornthwaite-type method and correlated AET and Deficit with tree species distribution. We used input data sets with different spatial resolutions parameterized for variation in latitude, precipitation, temperature, soil water-holding capacity, slope and aspect. We used climate proxies and climate projections to model AET and Deficit for past and future climate. We compared the modelled future water balance in Yosemite with current species water-balance ranges in North America.Results  We calculated species climatic envelopes over broad ranges of environmental gradients – a range of 310 mm for soil water-holding capacity, 48.3°C for mean monthly temperature (January minima to July maxima), and 918 mm yr−1 for annual precipitation. Tree species means were differentiated by AET and Deficit, and at higher levels of Deficit, species means were increasingly differentiated. Modelled Deficit for all species increased by a mean of 5% between past (c. 1700) and present (1971–2000). Projected increases in Deficit between present and future (2020–49) were 23% across all plots.Main conclusions  Modelled changes in Deficit between past, present and future climate scenarios suggest that recent past changes in forest structure and composition may accelerate in the future, with species responding individualistically to further declines in water availability. Declining water availability may disproportionately affect Pinus monticola and Tsuga mertensiana. Fine-scale heterogeneity in soil water-holding capacity, aspect and slope implies that plant water balance may vary considerably within the grid cells of kilometre-scale climate models. Sub-grid-cell soil and topographical data can partially compensate for the lack of spatial heterogeneity in gridded climate data, potentially improving vegetation-change projections in mountainous landscapes with heterogeneous topography.

Validation of Nurse Practitioner Primary Care Organizational Climate Questionnaire: A New Tool to Study Nurse Practitioner Practice Settings.

PubMed

Poghosyan, Lusine; Chaplin, William F; Shaffer, Jonathan A

2017-04-01

Favorable organizational climate in primary care settings is necessary to expand the nurse practitioner (NP) workforce and promote their practice. Only one NP-specific tool, the Nurse Practitioner Primary Care Organizational Climate Questionnaire (NP-PCOCQ), measures NP organizational climate. We confirmed NP-PCOCQ's factor structure and established its predictive validity. A crosssectional survey design was used to collect data from 314 NPs in Massachusetts in 2012. Confirmatory factor analysis and regression models were used. The 4-factor model characterized NP-PCOCQ. The NP-PCOCQ score predicted job satisfaction (beta = .36; p < .001) and intent to leave job (odds ratio = .28; p = .011). NP-PCOCQ can be used by researchers to produce new evidence and by administrators to assess organizational climate in their clinics. Further testing of NP-PCOCQ is needed.

Hydrologic resilience and Amazon productivity.

PubMed

Ahlström, Anders; Canadell, Josep G; Schurgers, Guy; Wu, Minchao; Berry, Joseph A; Guan, Kaiyu; Jackson, Robert B

2017-08-30

The Amazon rainforest is disproportionately important for global carbon storage and biodiversity. The system couples the atmosphere and land, with moist forest that depends on convection to sustain gross primary productivity and growth. Earth system models that estimate future climate and vegetation show little agreement in Amazon simulations. Here we show that biases in internally generated climate, primarily precipitation, explain most of the uncertainty in Earth system model results; models, empirical data and theory converge when precipitation biases are accounted for. Gross primary productivity, above-ground biomass and tree cover align on a hydrological relationship with a breakpoint at ~2000 mm annual precipitation, where the system transitions between water and radiation limitation of evapotranspiration. The breakpoint appears to be fairly stable in the future, suggesting resilience of the Amazon to climate change. Changes in precipitation and land use are therefore more likely to govern biomass and vegetation structure in Amazonia.Earth system model simulations of future climate in the Amazon show little agreement. Here, the authors show that biases in internally generated climate explain most of this uncertainty and that the balance between water-saturated and water-limited evapotranspiration controls the Amazon resilience to climate change.

Spherical harmonic analysis of a synoptic climatology generated with a global general circulation model

NASA Technical Reports Server (NTRS)

Christidis, Z. D.; Spar, J.

1980-01-01

Spherical harmonic analysis was used to analyze the observed climatological (C) fields of temperature at 850 mb, geopotential height at 500 mb, and sea level pressure. The spherical harmonic method was also applied to the corresponding "model climatological" fields (M) generated by a general circulation model, the "GISS climate model." The climate model was initialized with observed data for the first of December 1976 at 00. GMT and allowed to generate five years of meteorological history. Monthly means of the above fields for the five years were computed and subjected to spherical harmonic analysis. It was found from the comparison of the spectral components of both sets, M and C, that the climate model generated reasonable 500 mb geopotential heights. The model temperature field at 850 mb exhibited a generally correct structure. However, the meridional temperature gradient was overestimated and overheating of the continents was observed in summer.

Effects of cumulus entrainment and multiple cloud types on a January global climate model simulation

NASA Technical Reports Server (NTRS)

Yao, Mao-Sung; Del Genio, Anthony D.

1989-01-01

An improved version of the GISS Model II cumulus parameterization designed for long-term climate integrations is used to study the effects of entrainment and multiple cloud types on the January climate simulation. Instead of prescribing convective mass as a fixed fraction of the cloud base grid-box mass, it is calculated based on the closure assumption that the cumulus convection restores the atmosphere to a neutral moist convective state at cloud base. This change alone significantly improves the distribution of precipitation, convective mass exchanges, and frequencies in the January climate. The vertical structure of the tropical atmosphere exhibits quasi-equilibrium behavior when this closure is used, even though there is no explicit constraint applied above cloud base.

Precipitation projections under GCMs perspective and Turkish Water Foundation (TWF) statistical downscaling model procedures

NASA Astrophysics Data System (ADS)

Dabanlı, İsmail; Şen, Zekai

2018-04-01

The statistical climate downscaling model by the Turkish Water Foundation (TWF) is further developed and applied to a set of monthly precipitation records. The model is structured by two phases as spatial (regional) and temporal downscaling of global circulation model (GCM) scenarios. The TWF model takes into consideration the regional dependence function (RDF) for spatial structure and Markov whitening process (MWP) for temporal characteristics of the records to set projections. The impact of climate change on monthly precipitations is studied by downscaling Intergovernmental Panel on Climate Change-Special Report on Emission Scenarios (IPCC-SRES) A2 and B2 emission scenarios from Max Plank Institute (EH40PYC) and Hadley Center (HadCM3). The main purposes are to explain the TWF statistical climate downscaling model procedures and to expose the validation tests, which are rewarded in same specifications as "very good" for all stations except one (Suhut) station in the Akarcay basin that is in the west central part of Turkey. Eventhough, the validation score is just a bit lower at the Suhut station, the results are "satisfactory." It is, therefore, possible to say that the TWF model has reasonably acceptable skill for highly accurate estimation regarding standard deviation ratio (SDR), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS) criteria. Based on the validated model, precipitation predictions are generated from 2011 to 2100 by using 30-year reference observation period (1981-2010). Precipitation arithmetic average and standard deviation have less than 5% error for EH40PYC and HadCM3 SRES (A2 and B2) scenarios.

The changing strength and nature of fire-climate relationships in the northern Rocky Mountains, U.S.A., 1902-2008

USGS Publications Warehouse

Littell, Jeremy

2015-01-01

Time-varying fire-climate relationships may represent an important component of fire-regime variability, relevant for understanding the controls of fire and projecting fire activity under global-change scenarios. We used time-varying statistical models to evaluate if and how fire-climate relationships varied from 1902-2008, in one of the most flammable forested regions of the western U.S.A. Fire-danger and water-balance metrics yielded the best combination of calibration accuracy and predictive skill in modeling annual area burned. The strength of fire-climate relationships varied markedly at multi-decadal scales, with models explaining < 40% to 88% of the variation in annual area burned. The early 20th century (1902-1942) and the most recent two decades (1985-2008) exhibited strong fire-climate relationships, with weaker relationships for much of the mid 20th century (1943-1984), coincident with diminished burning, less fire-conducive climate, and the initiation of modern fire fighting. Area burned and the strength of fire-climate relationships increased sharply in the mid 1980s, associated with increased temperatures and longer potential fire seasons. Unlike decades with high burning in the early 20th century, models developed using fire-climate relationships from recent decades overpredicted area burned when applied to earlier periods. This amplified response of fire to climate is a signature of altered fire-climate-relationships, and it implicates non-climatic factors in this recent shift. Changes in fuel structure and availability following 40+ yr of unusually low fire activity, and possibly land use, may have resulted in increased fire vulnerability beyond expectations from climatic factors alone. Our results highlight the potential for non-climatic factors to alter fire-climate relationships, and the need to account for such dynamics, through adaptable statistical or processes-based models, for accurately predicting future fire activity.

The Changing Strength and Nature of Fire-Climate Relationships in the Northern Rocky Mountains, U.S.A., 1902-2008

PubMed Central

Higuera, Philip E.; Abatzoglou, John T.; Littell, Jeremy S.; Morgan, Penelope

2015-01-01

Time-varying fire-climate relationships may represent an important component of fire-regime variability, relevant for understanding the controls of fire and projecting fire activity under global-change scenarios. We used time-varying statistical models to evaluate if and how fire-climate relationships varied from 1902-2008, in one of the most flammable forested regions of the western U.S.A. Fire-danger and water-balance metrics yielded the best combination of calibration accuracy and predictive skill in modeling annual area burned. The strength of fire-climate relationships varied markedly at multi-decadal scales, with models explaining < 40% to 88% of the variation in annual area burned. The early 20th century (1902-1942) and the most recent two decades (1985-2008) exhibited strong fire-climate relationships, with weaker relationships for much of the mid 20th century (1943-1984), coincident with diminished burning, less fire-conducive climate, and the initiation of modern fire fighting. Area burned and the strength of fire-climate relationships increased sharply in the mid 1980s, associated with increased temperatures and longer potential fire seasons. Unlike decades with high burning in the early 20th century, models developed using fire-climate relationships from recent decades overpredicted area burned when applied to earlier periods. This amplified response of fire to climate is a signature of altered fire-climate-relationships, and it implicates non-climatic factors in this recent shift. Changes in fuel structure and availability following 40+ yr of unusually low fire activity, and possibly land use, may have resulted in increased fire vulnerability beyond expectations from climatic factors alone. Our results highlight the potential for non-climatic factors to alter fire-climate relationships, and the need to account for such dynamics, through adaptable statistical or processes-based models, for accurately predicting future fire activity. PMID:26114580

The Changing Strength and Nature of Fire-Climate Relationships in the Northern Rocky Mountains, U.S.A., 1902-2008.

PubMed

Higuera, Philip E; Abatzoglou, John T; Littell, Jeremy S; Morgan, Penelope

2015-01-01

Time-varying fire-climate relationships may represent an important component of fire-regime variability, relevant for understanding the controls of fire and projecting fire activity under global-change scenarios. We used time-varying statistical models to evaluate if and how fire-climate relationships varied from 1902-2008, in one of the most flammable forested regions of the western U.S.A. Fire-danger and water-balance metrics yielded the best combination of calibration accuracy and predictive skill in modeling annual area burned. The strength of fire-climate relationships varied markedly at multi-decadal scales, with models explaining < 40% to 88% of the variation in annual area burned. The early 20th century (1902-1942) and the most recent two decades (1985-2008) exhibited strong fire-climate relationships, with weaker relationships for much of the mid 20th century (1943-1984), coincident with diminished burning, less fire-conducive climate, and the initiation of modern fire fighting. Area burned and the strength of fire-climate relationships increased sharply in the mid 1980s, associated with increased temperatures and longer potential fire seasons. Unlike decades with high burning in the early 20th century, models developed using fire-climate relationships from recent decades overpredicted area burned when applied to earlier periods. This amplified response of fire to climate is a signature of altered fire-climate-relationships, and it implicates non-climatic factors in this recent shift. Changes in fuel structure and availability following 40+ yr of unusually low fire activity, and possibly land use, may have resulted in increased fire vulnerability beyond expectations from climatic factors alone. Our results highlight the potential for non-climatic factors to alter fire-climate relationships, and the need to account for such dynamics, through adaptable statistical or processes-based models, for accurately predicting future fire activity.

The Pliocene Model Intercomparison Project (PlioMIP) Phase 2: scientific objectives and experimental design

NASA Astrophysics Data System (ADS)

Haywood, Alan M.; Dowsett, Harry J.; Dolan, Aisling M.; Rowley, David; Abe-Ouchi, Ayako; Otto-Bliesner, Bette; Chandler, Mark A.; Hunter, Stephen J.; Lunt, Daniel J.; Pound, Matthew; Salzmann, Ulrich

2016-03-01

The Pliocene Model Intercomparison Project (PlioMIP) is a co-ordinated international climate modelling initiative to study and understand climate and environments of the Late Pliocene, as well as their potential relevance in the context of future climate change. PlioMIP examines the consistency of model predictions in simulating Pliocene climate and their ability to reproduce climate signals preserved by geological climate archives. Here we provide a description of the aim and objectives of the next phase of the model intercomparison project (PlioMIP Phase 2), and we present the experimental design and boundary conditions that will be utilized for climate model experiments in Phase 2. Following on from PlioMIP Phase 1, Phase 2 will continue to be a mechanism for sampling structural uncertainty within climate models. However, Phase 1 demonstrated the requirement to better understand boundary condition uncertainties as well as uncertainty in the methodologies used for data-model comparison. Therefore, our strategy for Phase 2 is to utilize state-of-the-art boundary conditions that have emerged over the last 5 years. These include a new palaeogeographic reconstruction, detailing ocean bathymetry and land-ice surface topography. The ice surface topography is built upon the lessons learned from offline ice sheet modelling studies. Land surface cover has been enhanced by recent additions of Pliocene soils and lakes. Atmospheric reconstructions of palaeo-CO2 are emerging on orbital timescales, and these are also incorporated into PlioMIP Phase 2. New records of surface and sea surface temperature change are being produced that will be more temporally consistent with the boundary conditions and forcings used within models. Finally we have designed a suite of prioritized experiments that tackle issues surrounding the basic understanding of the Pliocene and its relevance in the context of future climate change in a discrete way.

The Pliocene Model Intercomparison Project (PlioMIP) Phase 2: Scientific Objectives and Experimental Design

NASA Technical Reports Server (NTRS)

Haywood, Alan M.; Dowsett, Harry J.; Dolan, Aisling M.; Rowley, David; Abe-Ouchi, Ayako; Otto-Bliesner, Bette; Chandler, Mark A.; Hunter, Stephen J.; Lunt, Daniel J.; Pound, Matthew;

Simulating 2,368 temperate lakes reveals weak coherence in stratification phenology

USGS Publications Warehouse

Read, Jordan S.; Winslow, Luke A.; Hansen, Gretchen J. A.; Van Den Hoek, Jamon; Hanson, Paul C.; Bruce, Louise C; Markfort, Corey D.

2014-01-01

Changes in water temperatures resulting from climate warming can alter the structure and function of aquatic ecosystems. Lake-specific physical characteristics may play a role in mediating individual lake responses to climate. Past mechanistic studies of lake-climate interactions have simulated generic lake classes at large spatial scales or performed detailed analyses of small numbers of real lakes. Understanding the diversity of lake responses to climate change across landscapes requires a hybrid approach that couples site-specific lake characteristics with broad-scale environmental drivers. This study provides a substantial advancement in lake ecosystem modeling by combining open-source tools with freely available continental-scale data to mechanistically model daily temperatures for 2,368 Wisconsin lakes over three decades (1979-2011). The model accurately predicted observed surface layer temperatures (RMSE: 1.74°C) and the presence/absence of stratification (81.1% agreement). Among-lake coherence was strong for surface temperatures and weak for the timing of stratification, suggesting individual lake characteristics mediate some - but not all - ecologically relevant lake responses to climate.

Bullying among nurses and its relationship with burnout and organizational climate.

PubMed

Giorgi, Gabriele; Mancuso, Serena; Fiz Perez, Francisco; Castiello D'Antonio, Andrea; Mucci, Nicola; Cupelli, Vincenzo; Arcangeli, Giulio

2016-04-01

Workplace bullying is one of the most common work-related psychological problems. Bullying costs seem higher for organizations composed of health-care workers who perform direct-contact patients-complex tasks. Only a few studies have been carried out among nurses in Italy and integrated models of bullying antecedents and consequences are particularly missing. The aim of this study was to develop a bullying model focused on the interaction between bullying and burnout in the setting of a climate-health relationship. Research involved 658 nurses who completed a survey on health, burnout, bullying and organizational climate. Structural equation modeling was used to test the hypothesis. Results suggest that workplace bullying partially mediates the relationship between organizational climate and burnout and that bullying does not affect health directly, but only indirectly, via the mediation of burnout. Our study demonstrates the key-role of workplace bullying and burnout in the climate-health relationship in order to understand and to improve nurses' health. © 2015 John Wiley & Sons Australia, Ltd.

Food web structure shaped by habitat size and climate across a latitudinal gradient.

PubMed

Romero, Gustavo Q; Piccoli, Gustavo C O; de Omena, Paula M; Gonçalves-Souza, Thiago

2016-10-01

Habitat size and climate are known to affect the trophic structure and dynamics of communities, but their interactive effects are poorly understood. Organisms from different trophic levels vary in terms of metabolic requirements and heat dissipation. Indeed, larger species such as keystone predators require more stable climatic conditions than their prey. Likewise, habitat size disproportionally affects large-sized predators, which require larger home ranges and are thus restricted to larger habitats. Therefore, food web structure in patchy ecosystems is expected to be shaped by habitat size and climate variations. Here we investigate this prediction using natural aquatic microcosm (bromeliad phytotelmata) food webs composed of litter resources (mainly detritus), detritivores, mesopredators, and top predators (damselflies). We surveyed 240 bromeliads of varying sizes (water retention capacity) across 12 open restingas in SE Brazil spread across a wide range of tropical latitudes (-12.6° to -27.6°, ca. 2,000 km) and climates (Δ mean annual temperature = 5.3°C). We found a strong increase in predator-to-detritivore mass ratio with habitat size, which was representative of a typical inverted trophic pyramid in larger ecosystems. However, this relationship was contingent among the restingas; slopes of linear models were steeper in more stable and favorable climates, leading to inverted trophic pyramids (and top-down control) being more pronounced in environments with more favorable climatic conditions. By contrast, detritivore-resource and mesopredator-detritivore mass ratios were not affected by habitat size or climate variations across latitudes. Our results highlight that the combined effects of habitat size, climate and predator composition are pivotal to understanding the impacts of multiple environmental factors on food web structure and dynamics. © 2016 by the Ecological Society of America.

Projecting Climate Change Impacts on Wildfire Probabilities

NASA Astrophysics Data System (ADS)

Westerling, A. L.; Bryant, B. P.; Preisler, H.

2008-12-01

We present preliminary results of the 2008 Climate Change Impact Assessment for wildfire in California, part of the second biennial science report to the California Climate Action Team organized via the California Climate Change Center by the California Energy Commission's Public Interest Energy Research Program pursuant to Executive Order S-03-05 of Governor Schwarzenegger. In order to support decision making by the State pertaining to mitigation of and adaptation to climate change and its impacts, we model wildfire occurrence monthly from 1950 to 2100 under a range of climate scenarios from the Intergovernmental Panel on Climate Change. We use six climate change models (GFDL CM2.1, NCAR PCM1, CNRM CM3, MPI ECHAM5, MIROC3.2 med, NCAR CCSM3) under two emissions scenarios--A2 (C02 850ppm max atmospheric concentration) and B1(CO2 550ppm max concentration). Climate model output has been downscaled to a 1/8 degree (~12 km) grid using two alternative methods: a Bias Correction and Spatial Donwscaling (BCSD) and a Constructed Analogues (CA) downscaling. Hydrologic variables have been simulated from temperature, precipitation, wind and radiation forcing data using the Variable Infiltration Capacity (VIC) Macroscale Hydrologic Model. We model wildfire as a function of temperature, moisture deficit, and land surface characteristics using nonlinear logistic regression techniques. Previous work on wildfire climatology and seasonal forecasting has demonstrated that these variables account for much of the inter-annual and seasonal variation in wildfire. The results of this study are monthly gridded probabilities of wildfire occurrence by fire size class, and estimates of the number of structures potentially affected by fires. In this presentation we will explore the range of modeled outcomes for wildfire in California, considering the effects of emissions scenarios, climate model sensitivities, downscaling methods, hydrologic simulations, statistical model specifications for california wildfire, and their intersection with a range of development scenarios for California.

A novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China

PubMed Central

Yang, Yanzheng; Zhu, Qiuan; Peng, Changhui; Wang, Han; Xue, Wei; Lin, Guanghui; Wen, Zhongming; Chang, Jie; Wang, Meng; Liu, Guobin; Li, Shiqing

2016-01-01

Increasing evidence indicates that current dynamic global vegetation models (DGVMs) have suffered from insufficient realism and are difficult to improve, particularly because they are built on plant functional type (PFT) schemes. Therefore, new approaches, such as plant trait-based methods, are urgently needed to replace PFT schemes when predicting the distribution of vegetation and investigating vegetation sensitivity. As an important direction towards constructing next-generation DGVMs based on plant functional traits, we propose a novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China. The results demonstrated that a Gaussian mixture model (GMM) trained with a LMA-Nmass-LAI data combination yielded an accuracy of 72.82% in simulating vegetation distribution, providing more detailed parameter information regarding community structures and ecosystem functions. The new approach also performed well in analyses of vegetation sensitivity to different climatic scenarios. Although the trait-climate relationship is not the only candidate useful for predicting vegetation distributions and analysing climatic sensitivity, it sheds new light on the development of next-generation trait-based DGVMs. PMID:27052108

Dynamics of climate-based malaria transmission model with age-structured human population

NASA Astrophysics Data System (ADS)

Addawe, Joel; Pajimola, Aprimelle Kris

2016-10-01

In this paper, we proposed to study the dynamics of malaria transmission with periodic birth rate of the vector and an age-structure for the human population. The human population is divided into two compartments: pre-school (0-5 years) and the rest of the human population. We showed the existence of a disease-free equilibrium point. Using published epidemiological parameters, we use numerical simulations to show potential effect of climate change in the dynamics of age-structured malaria transmission. Numerical simulations suggest that there exists an asymptotically attractive solution that is positive and periodic.

Characteristics of Quasi-Biennial Oscillation simulation in the Meteorological Research Institute earth system model

NASA Astrophysics Data System (ADS)

Yoshida, K.; Naoe, H.

2016-12-01

Whether climate models drive Quasi-Biennial Oscillation (QBO) appropriately is important to assess QBO impact on climate change such as global warming and solar related variation. However, there were few models generating QBO in the Coupled Model Intercomparison Project Phase 5 (CMIP5). This study focuses on dynamical structure of the QBO and its sensitivity to background wind pattern and model configuration. We present preliminary results of experiments designed by "Towards Improving the QBO in Global Climate Models (QBOi)", which is derived from the Stratosphere-troposphere processes and their role in climate (SPARC), in the Meteorological Research Institute earth system model, MRI-ESM2. The simulations were performed in present-day climate condition, repeated annual cycle condition with various CO2 level and sea surface temperatures, and QBO hindcast. In the present climate simulation, zonal wind in the equatorial stratosphere generally exhibits realistic behavior of the QBO. Equatorial zonal wind variability associated with QBO is overestimated in upper stratosphere and underestimated in lower stratosphere. In the MRI-ESM2, the QBO behavior is mainly driven by gravity wave drag parametrization (GWDP) introduced in Hines (1997). Comparing to reanalyses, shortage of resolved wave forcing is found especially in equatorial lower stratosphere. These discrepancies can be attributed to difference in wave forcing, background wind pattern and model configuration. We intend to show results of additional sensitivity experiments to examine how model configuration and background wind pattern affect resolved wave source, wave propagation characteristics, and QBO behavior.

From projected species distribution to food-web structure under climate change.

PubMed

Albouy, Camille; Velez, Laure; Coll, Marta; Colloca, Francesco; Le Loc'h, François; Mouillot, David; Gravel, Dominique

2014-03-01

Climate change is inducing deep modifications in species geographic ranges worldwide. However, the consequences of such changes on community structure are still poorly understood, particularly the impacts on food-web properties. Here, we propose a new framework, coupling species distribution and trophic models, to predict climate change impacts on food-web structure across the Mediterranean Sea. Sea surface temperature was used to determine the fish climate niches and their future distributions. Body size was used to infer trophic interactions between fish species. Our projections reveal that 54 fish species of 256 endemic and native species included in our analysis would disappear by 2080-2099 from the Mediterranean continental shelf. The number of feeding links between fish species would decrease on 73.4% of the continental shelf. However, the connectance of the overall fish web would increase on average, from 0.26 to 0.29, mainly due to a differential loss rate of feeding links and species richness. This result masks a systematic decrease in predator generality, estimated here as the number of prey species, from 30.0 to 25.4. Therefore, our study highlights large-scale impacts of climate change on marine food-web structure with potential deep consequences on ecosystem functioning. However, these impacts will likely be highly heterogeneous in space, challenging our current understanding of climate change impact on local marine ecosystems. © 2013 John Wiley & Sons Ltd.

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

Eyring, Veronika; Bony, Sandrine; Meehl, Gerald A.

By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) andmore » CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.« less

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

NASA Technical Reports Server (NTRS)

Makowski, David; Asseng, Senthold; Ewert, Frank; Bassu, Simona; Durand, Jean-Louis; Martre, Pierre; Adam, Myriam; Aggarwal, Pramod K.; Angulo, Carlos; Baron, Chritian;

Multiple Scales of Control on the Structure and Spatial Distribution of Woody Vegetation in African Savanna Watersheds

PubMed Central

Vaughn, Nicholas R.; Asner, Gregory P.; Smit, Izak P. J.; Riddel, Edward S.

2015-01-01

Factors controlling savanna woody vegetation structure vary at multiple spatial and temporal scales, and as a consequence, unraveling their combined effects has proven to be a classic challenge in savanna ecology. We used airborne LiDAR (light detection and ranging) to map three-dimensional woody vegetation structure throughout four savanna watersheds, each contrasting in geologic substrate and climate, in Kruger National Park, South Africa. By comparison of the four watersheds, we found that geologic substrate had a stronger effect than climate in determining watershed-scale differences in vegetation structural properties, including cover, height and crown density. Generalized Linear Models were used to assess the spatial distribution of woody vegetation structural properties, including cover, height and crown density, in relation to mapped hydrologic, topographic and fire history traits. For each substrate and climate combination, models incorporating topography, hydrology and fire history explained up to 30% of the remaining variation in woody canopy structure, but inclusion of a spatial autocovariate term further improved model performance. Both crown density and the cover of shorter woody canopies were determined more by unknown factors likely to be changing on smaller spatial scales, such as soil texture, herbivore abundance or fire behavior, than by our mapped regional-scale changes in topography and hydrology. We also detected patterns in spatial covariance at distances up to 50–450 m, depending on watershed and structural metric. Our results suggest that large-scale environmental factors play a smaller role than is often attributed to them in determining woody vegetation structure in southern African savannas. This highlights the need for more spatially-explicit, wide-area analyses using high resolution remote sensing techniques. PMID:26660502

Multiple Scales of Control on the Structure and Spatial Distribution of Woody Vegetation in African Savanna Watersheds.

PubMed

Vaughn, Nicholas R; Asner, Gregory P; Smit, Izak P J; Riddel, Edward S

2015-01-01

Factors controlling savanna woody vegetation structure vary at multiple spatial and temporal scales, and as a consequence, unraveling their combined effects has proven to be a classic challenge in savanna ecology. We used airborne LiDAR (light detection and ranging) to map three-dimensional woody vegetation structure throughout four savanna watersheds, each contrasting in geologic substrate and climate, in Kruger National Park, South Africa. By comparison of the four watersheds, we found that geologic substrate had a stronger effect than climate in determining watershed-scale differences in vegetation structural properties, including cover, height and crown density. Generalized Linear Models were used to assess the spatial distribution of woody vegetation structural properties, including cover, height and crown density, in relation to mapped hydrologic, topographic and fire history traits. For each substrate and climate combination, models incorporating topography, hydrology and fire history explained up to 30% of the remaining variation in woody canopy structure, but inclusion of a spatial autocovariate term further improved model performance. Both crown density and the cover of shorter woody canopies were determined more by unknown factors likely to be changing on smaller spatial scales, such as soil texture, herbivore abundance or fire behavior, than by our mapped regional-scale changes in topography and hydrology. We also detected patterns in spatial covariance at distances up to 50-450 m, depending on watershed and structural metric. Our results suggest that large-scale environmental factors play a smaller role than is often attributed to them in determining woody vegetation structure in southern African savannas. This highlights the need for more spatially-explicit, wide-area analyses using high resolution remote sensing techniques.

Influence of safety motivation and climate on safety behaviour and outcomes: evidence from the Saudi Arabian construction industry.

PubMed

Panuwatwanich, Kriengsak; Al-Haadir, Saeed; Stewart, Rodney A

2017-03-01

Over the last three decades, safety literature has focused on safety climate and its role in forecasting injuries and accidents. However, research findings regarding the relationships between safety climate and other key outcome constructs are somewhat inconsistent. Recent safety climate literature suggests that examining the role of safety motivation may help provide a better explanation of such relationships. The research presented in this article aimed to empirically analyse the relationships among safety motivation, safety climate, safety behaviour and safety outcomes within the context of the Saudi Arabian construction industry. A conceptual model was developed to examine the relationships among four main constructs: safety motivation, safety climate, safety behaviour and safety outcomes. Based on the survey data collected in Saudi Arabia from site engineers and project managers (n = 295), statistical analyses were carried out, including confirmatory and exploratory factor analysis, and structural equation modelling to assess the model and test the hypotheses. The main results indicated that safety motivation could positively influence safety behaviour through safety climate, which plays a mediating role for this mechanism. The results also confirmed that safety behaviour could predict safety outcomes within the context of the Saudi Arabian construction industry.

Authoritative School Climate and High School Dropout Rates

ERIC Educational Resources Information Center

Jia, Yuane; Konold, Timothy R.; Cornell, Dewey

2016-01-01

This study tested the association between school-wide measures of an authoritative school climate and high school dropout rates in a statewide sample of 315 high schools. Regression models at the school level of analysis used teacher and student measures of disciplinary structure, student support, and academic expectations to predict overall high…

Relationships between School Climate, Bullying and Delinquent Behaviours

ERIC Educational Resources Information Center

Aldridge, Jill M.; McChesney, Katrina; Afari, Ernest

2018-01-01

Given that schools are, potentially, powerful sites for influencing adolescent behaviour, it is important that there is greater understanding of the psychosocial aspects of the school climate that can be leveraged for this purpose. The research reported in this article used structural equation modelling (with data from a sample of 6120 students at…

A method to encapsulate model structural uncertainty in ensemble projections of future climate: EPIC v1.0

NASA Astrophysics Data System (ADS)

Lewis, Jared; Bodeker, Greg E.; Kremser, Stefanie; Tait, Andrew

2017-12-01

A method, based on climate pattern scaling, has been developed to expand a small number of projections of fields of a selected climate variable (X) into an ensemble that encapsulates a wide range of indicative model structural uncertainties. The method described in this paper is referred to as the Ensemble Projections Incorporating Climate model uncertainty (EPIC) method. Each ensemble member is constructed by adding contributions from (1) a climatology derived from observations that represents the time-invariant part of the signal; (2) a contribution from forced changes in X, where those changes can be statistically related to changes in global mean surface temperature (Tglobal); and (3) a contribution from unforced variability that is generated by a stochastic weather generator. The patterns of unforced variability are also allowed to respond to changes in Tglobal. The statistical relationships between changes in X (and its patterns of variability) and Tglobal are obtained in a training phase. Then, in an implementation phase, 190 simulations of Tglobal are generated using a simple climate model tuned to emulate 19 different global climate models (GCMs) and 10 different carbon cycle models. Using the generated Tglobal time series and the correlation between the forced changes in X and Tglobal, obtained in the training phase, the forced change in the X field can be generated many times using Monte Carlo analysis. A stochastic weather generator is used to generate realistic representations of weather which include spatial coherence. Because GCMs and regional climate models (RCMs) are less likely to correctly represent unforced variability compared to observations, the stochastic weather generator takes as input measures of variability derived from observations, but also responds to forced changes in climate in a way that is consistent with the RCM projections. This approach to generating a large ensemble of projections is many orders of magnitude more computationally efficient than running multiple GCM or RCM simulations. Such a large ensemble of projections permits a description of a probability density function (PDF) of future climate states rather than a small number of individual story lines within that PDF, which may not be representative of the PDF as a whole; the EPIC method largely corrects for such potential sampling biases. The method is useful for providing projections of changes in climate to users wishing to investigate the impacts and implications of climate change in a probabilistic way. A web-based tool, using the EPIC method to provide probabilistic projections of changes in daily maximum and minimum temperatures for New Zealand, has been developed and is described in this paper.

GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales

DOE PAGES

Schiro, Kathleen A.; Ahmed, Fiaz; Giangrande, Scott E.; ...

2018-04-17

Representations of strongly precipitating deep-convective systems in climate models are among the most important factors in their simulation. Parameterizations of these motions face the dual challenge of unclear pathways to including mesoscale organization and high sensitivity of convection to approximations of turbulent entrainment of environmental air. Ill-constrained entrainment processes can even affect global average climate sensitivity under global warming. Multiinstrument observations from the Department of Energy GoAmazon2014/5 field campaign suggest that an alternative formulation from radar-derived dominant updraft structure yields a strong relationship of precipitation to buoyancy in both mesoscale and smaller-scale convective systems. This simultaneously provides a key stepmore » toward representing the influence of mesoscale convection in climate models and sidesteps a problematic dependence on traditional entrainment rates. A substantial fraction of precipitation is associated with mesoscale convective systems (MCSs), which are currently poorly represented in climate models. Convective parameterizations are highly sensitive to the assumptions of an entraining plume model, in which high equivalent potential temperature air from the boundary layer is modified via turbulent entrainment. Here we show, using multiinstrument evidence from the Green Ocean Amazon field campaign (2014–2015; GoAmazon2014/5), that an empirically constrained weighting for inflow of environmental air based on radar wind profiler estimates of vertical velocity and mass flux yields a strong relationship between resulting buoyancy measures and precipitation statistics. This deep-inflow weighting has no free parameter for entrainment in the conventional sense, but to a leading approximation is simply a statement of the geometry of the inflow. The structure further suggests the weighting could consistently apply even for coherent inflow structures noted in field campaign studies for MCSs over tropical oceans. For radar precipitation retrievals averaged over climate model grid scales at the GoAmazon2014/5 site, the use of deep-inflow mixing yields a sharp increase in the probability and magnitude of precipitation with increasing buoyancy. Furthermore, this applies for both mesoscale and smaller-scale convection. Results from reanalysis and satellite data show that this holds more generally: Deep-inflow mixing yields a strong precipitation–buoyancy relation across the tropics. Lastly, deep-inflow mixing may thus circumvent inadequacies of current parameterizations while helping to bridge the gap toward representing mesoscale convection in climate models.« less

GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales

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

Schiro, Kathleen A.; Ahmed, Fiaz; Giangrande, Scott E.

Representations of strongly precipitating deep-convective systems in climate models are among the most important factors in their simulation. Parameterizations of these motions face the dual challenge of unclear pathways to including mesoscale organization and high sensitivity of convection to approximations of turbulent entrainment of environmental air. Ill-constrained entrainment processes can even affect global average climate sensitivity under global warming. Multiinstrument observations from the Department of Energy GoAmazon2014/5 field campaign suggest that an alternative formulation from radar-derived dominant updraft structure yields a strong relationship of precipitation to buoyancy in both mesoscale and smaller-scale convective systems. This simultaneously provides a key stepmore » toward representing the influence of mesoscale convection in climate models and sidesteps a problematic dependence on traditional entrainment rates. A substantial fraction of precipitation is associated with mesoscale convective systems (MCSs), which are currently poorly represented in climate models. Convective parameterizations are highly sensitive to the assumptions of an entraining plume model, in which high equivalent potential temperature air from the boundary layer is modified via turbulent entrainment. Here we show, using multiinstrument evidence from the Green Ocean Amazon field campaign (2014–2015; GoAmazon2014/5), that an empirically constrained weighting for inflow of environmental air based on radar wind profiler estimates of vertical velocity and mass flux yields a strong relationship between resulting buoyancy measures and precipitation statistics. This deep-inflow weighting has no free parameter for entrainment in the conventional sense, but to a leading approximation is simply a statement of the geometry of the inflow. The structure further suggests the weighting could consistently apply even for coherent inflow structures noted in field campaign studies for MCSs over tropical oceans. For radar precipitation retrievals averaged over climate model grid scales at the GoAmazon2014/5 site, the use of deep-inflow mixing yields a sharp increase in the probability and magnitude of precipitation with increasing buoyancy. Furthermore, this applies for both mesoscale and smaller-scale convection. Results from reanalysis and satellite data show that this holds more generally: Deep-inflow mixing yields a strong precipitation–buoyancy relation across the tropics. Lastly, deep-inflow mixing may thus circumvent inadequacies of current parameterizations while helping to bridge the gap toward representing mesoscale convection in climate models.« less

High-resolution climate and land surface interactions modeling over Belgium: current state and decennial scale projections

NASA Astrophysics Data System (ADS)

Jacquemin, Ingrid; Henrot, Alexandra-Jane; Beckers, Veronique; Berckmans, Julie; Debusscher, Bos; Dury, Marie; Minet, Julien; Hamdi, Rafiq; Dendoncker, Nicolas; Tychon, Bernard; Hambuckers, Alain; François, Louis

2016-04-01

The interactions between land surface and climate are complex. Climate changes can affect ecosystem structure and functions, by altering photosynthesis and productivity or inducing thermal and hydric stresses on plant species. These changes then impact socio-economic systems, through e.g., lower farming or forestry incomes. Ultimately, it can lead to permanent changes in land use structure, especially when associated with other non-climatic factors, such as urbanization pressure. These interactions and changes have feedbacks on the climate systems, in terms of changing: (1) surface properties (albedo, roughness, evapotranspiration, etc.) and (2) greenhouse gas emissions (mainly CO2, CH4, N2O). In the framework of the MASC project (« Modelling and Assessing Surface Change impacts on Belgian and Western European climate »), we aim at improving regional climate model projections at the decennial scale over Belgium and Western Europe by combining high-resolution models of climate, land surface dynamics and socio-economic processes. The land surface dynamics (LSD) module is composed of a dynamic vegetation model (CARAIB) calculating the productivity and growth of natural and managed vegetation, and an agent-based model (CRAFTY), determining the shifts in land use and land cover. This up-scaled LSD module is made consistent with the surface scheme of the regional climate model (RCM: ALARO) to allow simulations of the RCM with a fully dynamic land surface for the recent past and the period 2000-2030. In this contribution, we analyze the results of the first simulations performed with the CARAIB dynamic vegetation model over Belgium at a resolution of 1km. This analysis is performed at the species level, using a set of 17 species for natural vegetation (trees and grasses) and 10 crops, especially designed to represent the Belgian vegetation. The CARAIB model is forced with surface atmospheric variables derived from the monthly global CRU climatology or ALARO outputs (from a 4 km resolution simulation) for the recent past and the decennial projections. Evidently, these simulations lead to a first analysis of the impact of climate change on carbon stocks (e.g., biomass, soil carbon) and fluxes (e.g., gross and net primary productivities (GPP and NPP) and net ecosystem production (NEP)). The surface scheme is based on two land use/land cover databases, ECOPLAN for the Flemish region and, for the Walloon region, the COS-Wallonia database and the Belgian agricultural statistics for agricultural land. Land use and land cover are fixed through time (reference year: 2007) in these simulations, but a first attempt of coupling between CARAIB and CRAFTY will be made to establish dynamic land use change scenarios for the next decades. A simulation with variable land use would allow an analysis of land use change impacts not only on crop yields and the land carbon budget, but also on climate relevant parameters, such as surface albedo, roughness length and evapotranspiration towards a coupling with the RCM.

The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations

NASA Astrophysics Data System (ADS)

Alexander, K.; Easterbrook, S. M.

2015-01-01

We analyse the source code of eight coupled climate models, selected from those that participated in the CMIP5 (Taylor et al., 2012) or EMICAR5 (Eby et al., 2013; Zickfeld et al., 2013) intercomparison projects. For each model, we sort the preprocessed code into components and subcomponents based on dependency structure. We then create software architecture diagrams which show the relative sizes of these components/subcomponents and the flow of data between them. The diagrams also illustrate several major classes of climate model design; the distribution of complexity between components, which depends on historical development paths as well as the conscious goals of each institution; and the sharing of components between different modelling groups. These diagrams offer insights into the similarities and differences between models, and have the potential to be useful tools for communication between scientists, scientific institutions, and the public.

The adventures of climate science in the sweet land of idle arguments

NASA Astrophysics Data System (ADS)

Winsberg, Eric; Goodwin, William Mark

2016-05-01

In a recent series of papers Roman Frigg, Leonard Smith, and several coauthors have developed a general epistemological argument designed to cast doubt on the capacity of a broad range of mathematical models to generate "decision relevant predictions." The presumptive targets of their argument are at least some of the modeling projects undertaken in contemporary climate science. In this paper, we trace and contrast two very different readings of the scope of their argument. We do this by considering the very different implications for climate science that these interpretations would have. Then, we lay out the structure of their argument-an argument by analogy-with an eye to identifying points at which certain epistemically significant distinctions might limit the force of the analogy. Finally, some of these epistemically significant distinctions are introduced and defended as relevant to a great many of the predictive mathematical modeling projects employed in contemporary climate science.

The Signature of Southern Hemisphere Atmospheric Circulation Patterns in Antarctic Precipitation

PubMed Central

Thompson, David W. J.; van den Broeke, Michiel R.

2017-01-01

Abstract We provide the first comprehensive analysis of the relationships between large‐scale patterns of Southern Hemisphere climate variability and the detailed structure of Antarctic precipitation. We examine linkages between the high spatial resolution precipitation from a regional atmospheric model and four patterns of large‐scale Southern Hemisphere climate variability: the southern baroclinic annular mode, the southern annular mode, and the two Pacific‐South American teleconnection patterns. Variations in all four patterns influence the spatial configuration of precipitation over Antarctica, consistent with their signatures in high‐latitude meridional moisture fluxes. They impact not only the mean but also the incidence of extreme precipitation events. Current coupled‐climate models are able to reproduce all four patterns of atmospheric variability but struggle to correctly replicate their regional impacts on Antarctic climate. Thus, linking these patterns directly to Antarctic precipitation variability may allow a better estimate of future changes in precipitation than using model output alone. PMID:29398735

Analyzing the responses of species assemblages to climate change across the Great Basin, USA.

NASA Astrophysics Data System (ADS)

Henareh Khalyani, A.; Falkowski, M. J.; Crookston, N.; Yousef, F.

2016-12-01

The potential impacts of climate change on the future distribution of tree species in not well understood. Climate driven changes in tree species distribution could cause significant changes in realized species niches, potentially resulting in the loss of ecotonal species as well as the formation on novel assemblages of overlapping tree species. In an effort to gain a better understating of how the geographic distribution of tree species may respond to climate change, we model the potential future distribution of 50 different tree species across 70 million ha in the Great Basin, USA. This is achieved by leveraging a species realized niche model based on non-parametric analysis of species occurrences across climatic, topographic, and edaphic variables. Spatially explicit, high spatial resolution (30 m) climate variables (e.g., precipitation, and minimum, maximum, and mean temperature) and associated climate indices were generated on an annual basis between 1981-2010 by integrating climate station data with digital elevation data (Shuttle Radar Topographic Mission (SRTM) data) in a thin plate spline interpolation algorithm (ANUSPLIN). Bioclimate models of species niches in in the cotemporary period and three following 30 year periods were then generated by integrating the climate variables, soil data, and CMIP 5 general circulation model projections. Our results suggest that local scale contemporary variations in species realized niches across space are influenced by edaphic and topographic variables as well as climatic variables. The local variability in soil properties and topographic variability across space also affect the species responses to climate change through time and potential formation of species assemblages in future. The results presented here in will aid in the development of adaptive forest management techniques aimed at mitigating negative impacts of climate change on forest composition, structure, and function.

Socio-economic and climate change impacts on agriculture: an integrated assessment, 1990–2080

PubMed Central

Fischer, Günther; Shah, Mahendra; N. Tubiello, Francesco; van Velhuizen, Harrij

2005-01-01

A comprehensive assessment of the impacts of climate change on agro-ecosystems over this century is developed, up to 2080 and at a global level, albeit with significant regional detail. To this end an integrated ecological–economic modelling framework is employed, encompassing climate scenarios, agro-ecological zoning information, socio-economic drivers, as well as world food trade dynamics. Specifically, global simulations are performed using the FAO/IIASA agro-ecological zone model, in conjunction with IIASAs global food system model, using climate variables from five different general circulation models, under four different socio-economic scenarios from the intergovernmental panel on climate change. First, impacts of different scenarios of climate change on bio-physical soil and crop growth determinants of yield are evaluated on a 5′×5′ latitude/longitude global grid; second, the extent of potential agricultural land and related potential crop production is computed. The detailed bio-physical results are then fed into an economic analysis, to assess how climate impacts may interact with alternative development pathways, and key trends expected over this century for food demand and production, and trade, as well as key composite indices such as risk of hunger and malnutrition, are computed. This modelling approach connects the relevant bio-physical and socio-economic variables within a unified and coherent framework to produce a global assessment of food production and security under climate change. The results from the study suggest that critical impact asymmetries due to both climate and socio-economic structures may deepen current production and consumption gaps between developed and developing world; it is suggested that adaptation of agricultural techniques will be central to limit potential damages under climate change. PMID:16433094

Adapting the Biome-BGC Model to New Zealand Pastoral Agriculture: Climate Change and Land-Use Change

NASA Astrophysics Data System (ADS)

Keller, E. D.; Baisden, W. T.; Timar, L.

2011-12-01

We have adapted the Biome-BGC model to make climate change and land-use scenario estimates of New Zealand's pasture production in 2020 and 2050, with comparison to a 2005 baseline. We take an integrated modelling approach with the aim of enabling the model's use for policy assessments across broadly related issues such as climate change mitigation and adaptation, land-use change, and greenhouse gas projections. The Biome-BGC model is a biogeochemical model that simulates carbon, water, and nitrogen cycles in terrestrial ecosystems. We introduce two new 'ecosystems', sheep/beef and dairy pasture, within the existing structure of the Biome-BGC model and calibrate its ecophysiological parameters against pasture clipping data from diverse sites around New Zealand to form a baseline estimate of total New Zealand pasture production. Using downscaled AR4 climate projections, we construct mid- and upper-range climate change scenarios in 2020 and 2050. We produce land-use change scenarios in the same years by combining the Biome-BGC model with the Land Use in Rural New Zealand (LURNZ) model. The LURNZ model uses econometric approaches to predict future land-use change driven by changes in net profits driven by expected pricing, including the introduction of an emission trading system. We estimate the relative change in national pasture production from our 2005 baseline levels for both sheep/beef and dairy systems under each scenario.

The Mars Dust and Water Cycles: Investigating the Influence of Clouds on the Vertical Distribution and Meridional Transport of Dust and Water.

NASA Technical Reports Server (NTRS)

Kahre, M. A.; Haberle, R. M.; Hollingsworth, J. L.; Brecht, A. S.; Urata, R.

2015-01-01

The dust and water cycles are critical to the current Martian climate, and they interact with each other through cloud formation. Dust modulates the thermal structure of the atmosphere and thus greatly influences atmospheric circulation. Clouds provide radiative forcing and control the net hemispheric transport of water through the alteration of the vertical distributions of water and dust. Recent advancements in the quality and sophistication of both climate models and observations enable an increased understanding of how the coupling between the dust and water cycles (through cloud formation) impacts the dust and water cycles. We focus here on the effects of clouds on the vertical distributions of dust and water and how those vertical distributions control the net meridional transport of water. We utilize observations of temperature, dust and water ice from the Mars Climate Sounder (MCS) on the Mars Reconnaissance Orbiter (MRO) and the NASA ARC Mars Global Climate Model (MGCM) to show that the magnitude and nature of the hemispheric exchange of water during NH summer is sensitive to the vertical structure of the simulated aphelion cloud belt. Further, we investigate how clouds influence atmospheric temperatures and thus the vertical structure of the cloud belt. Our goal is to isolate and understand the importance of radiative/dynamic feedbacks due to the physical processes involved with cloud formation and evolution on the current climate of Mars.

The Mars Dust and Water Cycles: Investigating the Influence of Clouds on the Vertical Distribution and Meridional Transport of Dust and Water

NASA Astrophysics Data System (ADS)

Kahre, Melinda A.; Haberle, Robert M.; Hollingsworth, Jeffery L.; Brecht, Amanda S.; Urata, Richard A.

2015-11-01

The dust and water cycles are critical to the current Martian climate, and they interact with each other through cloud formation. Dust modulates the thermal structure of the atmosphere and thus greatly influences atmospheric circulation. Clouds provide radiative forcing and control the net hemispheric transport of water through the alteration of the vertical distributions of water and dust. Recent advancements in the quality and sophistication of both climate models and observations enable an increased understanding of how the coupling between the dust and water cycles (through cloud formation) impacts the dust and water cycles. We focus here on the effects of clouds on the vertical distributions of dust and water and how those vertical distributions control the net meridional transport of water. We utilize observations of temperature, dust and water ice from the Mars Climate Sounder (MCS) on the Mars Reconnaissance Orbiter (MRO) and the NASA ARC Mars Global Climate Model (MGCM) to show that the magnitude and nature of the hemispheric exchange of water during NH summer is sensitive to the vertical structure of the simulated aphelion cloud belt. Further, we investigate how clouds influence atmospheric temperatures and thus the vertical structure of the cloud belt. Our goal is to isolate and understand the importance of radiative/dynamic feedbacks due to the physical processes involved with cloud formation and evolution on the current climate of Mars.

Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing

NASA Astrophysics Data System (ADS)

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

2006-05-01

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

Model confirmation in climate economics

PubMed Central

Millner, Antony; McDermott, Thomas K. J.

2016-01-01

Benefit–cost integrated assessment models (BC-IAMs) inform climate policy debates by quantifying the trade-offs between alternative greenhouse gas abatement options. They achieve this by coupling simplified models of the climate system to models of the global economy and the costs and benefits of climate policy. Although these models have provided valuable qualitative insights into the sensitivity of policy trade-offs to different ethical and empirical assumptions, they are increasingly being used to inform the selection of policies in the real world. To the extent that BC-IAMs are used as inputs to policy selection, our confidence in their quantitative outputs must depend on the empirical validity of their modeling assumptions. We have a degree of confidence in climate models both because they have been tested on historical data in hindcasting experiments and because the physical principles they are based on have been empirically confirmed in closely related applications. By contrast, the economic components of BC-IAMs often rely on untestable scenarios, or on structural models that are comparatively untested on relevant time scales. Where possible, an approach to model confirmation similar to that used in climate science could help to build confidence in the economic components of BC-IAMs, or focus attention on which components might need refinement for policy applications. We illustrate the potential benefits of model confirmation exercises by performing a long-run hindcasting experiment with one of the leading BC-IAMs. We show that its model of long-run economic growth—one of its most important economic components—had questionable predictive power over the 20th century. PMID:27432964

The influence of climatic niche preferences on the population genetic structure of a mistletoe species complex.

PubMed

Ramírez-Barahona, Santiago; González, Clementina; González-Rodríguez, Antonio; Ornelas, Juan Francisco

2017-06-01

The prevalent view on genetic structuring in parasitic plants is that host-race formation is caused by varying degrees of host specificity. However, the relative importance of ecological niche divergence and host specificity to population differentiation remains poorly understood. We evaluated the factors associated with population differentiation in mistletoes of the Psittacanthus schiedeanus complex (Loranthaceae) in Mexico. We used genetic data from chloroplast sequences and nuclear microsatellites to study population genetic structure and tested its association with host preferences and climatic niche variables. Pairwise genetic differentiation was associated with environmental and host preferences, independent of geography. However, environmental predictors appeared to be more important than host preferences to explain genetic structure, supporting the hypothesis that the occurrence of the parasite is largely determined by its own climatic niche and, to a lesser degree, by host specificity. Genetic structure is significant within this mistletoe species complex, but the processes associated with this structure appear to be more complex than previously thought. Although host specificity was not supported as the major determinant of population differentiation, we consider this to be part of a more comprehensive ecological model of mistletoe host-race formation that incorporates the effects of climatic niche evolution. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

A population genetic transect of Panicum hallii (Poaceae).

PubMed

Lowry, David B; Purmal, Colin T; Juenger, Thomas E

2013-03-01

Understanding the relationship between climate, adaptation, and population structure is of fundamental importance to botanists because these factors are crucial for the evolution of biodiversity and the response of species to future climate change. Panicum hallii is an emerging model system for perennial grass and bioenergy research, yet very little is known about the relationship between climate and population structure in this system. • We analyzed geographic population differentiation across 39 populations of P. hallii along a longitudinal transect from the savannas of central Texas through the deserts of Arizona and New Mexico. A combination of morphological and genetic (microsatellite) analysis was used to explore patterns of population structure. • We found strong differentiation between high elevation western desert populations and lower elevation eastern populations of P. hallii, with a pronounced break in structure occurring in western Texas. In addition, we confirmed that there are high levels of morphological and genetic structure between previous recognized varieties (var. hallii and var. filipes) within this species. • The results of this study suggest that patterns of population structure within P. hallii may be driven by climatic variation over space. Overall, this study lays the groundwork for future studies on the genetics of local adaptation and reproductive isolation in this system.

Predictive models of forest dynamics.

PubMed

Purves, Drew; Pacala, Stephen

2008-06-13

Dynamic global vegetation models (DGVMs) have shown that forest dynamics could dramatically alter the response of the global climate system to increased atmospheric carbon dioxide over the next century. But there is little agreement between different DGVMs, making forest dynamics one of the greatest sources of uncertainty in predicting future climate. DGVM predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated by recent advances in the mathematics of forest modeling, ecological understanding of diverse forest communities, and the availability of forest inventory data.

A weather-driven model of malaria transmission

PubMed Central

Hoshen, Moshe B; Morse, Andrew P

2004-01-01

Background Climate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission. Methods This paper describes a mathematical-biological model of the parasite dynamics, comprising both the weather-dependent within-vector stages and the weather-independent within-host stages. Results Numerical evaluations of the model in both time and space show that it qualitatively reconstructs the prevalence of infection. Conclusion A process-based modelling structure has been developed that may be suitable for the simulation of malaria forecasts based on seasonal weather forecasts. PMID:15350206

SPAGETTA: a Multi-Purpose Gridded Stochastic Weather Generator

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Huth, R.; Rotach, M. W.; Dabhi, H.

2017-12-01

SPAGETTA is a new multisite/gridded multivariate parametric stochastic weather generator (WG). Site-specific precipitation occurrence and amount are modelled by Markov chain and Gamma distribution, the non-precipitation variables are modelled by an autoregressive (AR) model conditioned on precipitation occurrence, and the spatial coherence of all variables is modelled following the Wilks' (2009) approach. SPAGETTA may be run in two modes. Mode 1: it is run as a classical WG, which is calibrated using weather series from multiple sites, and only then it may produce arbitrarily long synthetic series mimicking the spatial and temporal structure of the calibration data. To generate the weather series representing the future climate, the WG parameters are modified according to the climate change scenario, typically derived from GCM or RCM simulations. Mode 2: the user provides only basic information (not necessarily to be realistic) on the temporal and spatial auto-correlation structure of the weather variables and their mean annual cycle; the generator itself derives the parameters of the underlying AR model, which produces the multi-site weather series. Optionally, the user may add the spatially varying trend, which is superimposed to the synthetic series. The contribution consists of following parts: (a) Model of the WG. (b) Validation of WG in terms of the spatial temperature and precipitation characteristics, including characteristics of spatial hot/cold/dry/wet spells. (c) Results of the climate change impact experiment, in which the WG parameters representing the spatial and temporal variability are modified using the climate change scenarios and the effect on the above spatial validation indices is analysed. In this experiment, the WG is calibrated using the E-OBS gridded daily weather data for several European regions, and the climate change scenarios are derived from the selected RCM simulations (CORDEX database). (d) The second mode of operation will be demonstrated by results obtained while developing the methodology for assessing collective significance of trends in multi-site weather series. The performance of the proposed test statistics is assessed based on large number of realisations of synthetic series produced by WG assuming a given statistical structure and trend of the weather series.

A combined remote sensing and modeling based approach to identify sustainable pathways for urban and peri-urban agriculture in China

NASA Astrophysics Data System (ADS)

Wattenbach, M.; Delgado, J. M.; Roessner, S.; Bochow, M.; Güntner, A.; Kropp, J.; Cantu Ros, A. G.; Hattermann, F.; Kolbe, T.; Sodoudi, S.; Cubasch, U. Ulrich; Zeitz, J.; Ross, L.; Böckel, K.; Fang, C.; Bo, L.; Pan, G.

2012-04-01

As the world's biggest economy, China is becoming the biggest consumer of resources globally. Given this trend, the over-proportional fast increase in urbanization presents China with fundamental problems. Among the most urgent ones is the increasing loss of agricultural land as urbanization takes place in the most productive regions along the coast. The latter is being responsible for a shift in agriculture production towards climatically less favorable areas. At the same time, the loss of green areas in and around growing cities is increasing the effect of the urban heat island. The perception of the potential risks related to this phenomenon, in the context of climate change, has led the Shanghai city administration to increase its urban-greening efforts, expanding the per capita area of green from 1m2 in 1990 to 12.5m2 in 2008. In this context, this paper aims at identifying the influence of urban and peri-urban agriculture (UPA) on the sustainability of the urban regions of Shanghai and Nanjing. In particular, it focuses on the effects of UPA on the greenhouse gas (GHG) emissions, soil nutrients and water balances, local climate and the structure and functions of the urbanized areas. We propose an interdisciplinary framework combining remote sensing, model simulations and GHG field observations and targeted at identifying "win-win" strategies for sustainable planning pathways showing high potentials for UPA. The framework is based on spatial scenario modeling, automatic classification of urban structure types and on a prototype of a high-quality spatial database consisting of a 3D city model. Dynamic boundary conditions for climate and urban development are provided by state of the art models. These approaches meet the needs of stakeholders and planners in China. A special emphasis is put on interdependencies between small holder farming in the urban and peri-urban zone and climate change adaptation and mitigation strategies focusing on improved management of local water and nutrient cycles. The whole database generated will be structured and made accessible for planners and stakeholders in the form of a 3D city visualization model.

Ecosystem management can mitigate vegetation shifts induced by climate change in African savannas

NASA Astrophysics Data System (ADS)

Scheiter, Simon; Savadogo, Patrice

2017-04-01

The welfare of people in the tropics and sub-tropics strongly depends on goods and services that ecosystems supply. Flows of these ecosystem services are strongly influenced by interactions between climate change and land use. A prominent example are savannas, covering approximately 20% of the Earth's land surface. Key ecosystem services in these areas are fuel wood for cooking and heating, food production and livestock. Changes in the structure and dynamics of savanna vegetation may strongly influence local people's living conditions, as well as the climate system and biogeochemical cycles. We used a dynamic vegetation model to explore interactive effects of climate and land use on the vegetation structure, distribution and carbon cycling of African savannas under current and future conditions. More specifically, we simulate long term impacts of fire management, grazing and fuel wood harvesting. The model projects that under future climate without human land use impacts, large savanna areas would shift towards more wood dominated vegetation due to CO2 fertilization effects and changes in water use efficiency. However, land use activities can mitigate climate change impacts on vegetation to maintain desired ecosystem states that ensure fluxes of important ecosystem services. We then use optimization algorithms to identify sustainable land use strategies that maximize the utility of people managing savannas while preserving a stable vegetation state. Our results highlight that the development of land use policy for tropical and sub-tropical areas needs to account for climate change impacts on vegetation.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Cholera and shigellosis in Bangladesh: similarities and differences in population dynamics under climate forcing

NASA Astrophysics Data System (ADS)

Pascual, M.; Cash, B.; Reiner, R.; King, A.; Emch, M.; Yunus, M.; Faruque, A. S.

2012-12-01

The influence of climate variability on the population dynamics of infectious diseases is considered a large scale, regional, phenomenon, and as such, has been previously addressed for cholera with temporal models that do not incorporate fine-scale spatial structure. In our previous work, evidence for a role of ENSO (El Niño Southern Oscillation) on cholera in Bangladesh was elucidated, and shown to influence the regional climate through precipitation. With a probabilistic spatial model for cholera dynamics in the megacity of Dhaka, we found that the action of climate variability (ENSO and flooding) is localized: there is a climate-sensitive urban core that acts to propagate risk to the rest of the city. Here, we consider long-term surveillance data for shigellosis, another diarrheal disease that coexists with cholera in Bangladesh. We compare the patterns of association with climate variables for these two diseases in a rural setting, as well as the spatial structure in their spatio-temporal dynamics in an urban one. Evidence for similar patterns is presented, and discussed in the context of the differences in the routes of transmission of the two diseases and the proposed role of an environmental reservoir in cholera. The similarities provide evidence for a more general influence of hydrology and of socio-economic factors underlying human susceptibility and sanitary conditions.

Evaluation of Oceanic Surface Observation for Reproducing the Upper Ocean Structure in ECHAM5/MPI-OM

NASA Astrophysics Data System (ADS)

Luo, Hao; Zheng, Fei; Zhu, Jiang

2017-12-01

Better constraints of initial conditions from data assimilation are necessary for climate simulations and predictions, and they are particularly important for the ocean due to its long climate memory; as such, ocean data assimilation (ODA) is regarded as an effective tool for seasonal to decadal predictions. In this work, an ODA system is established for a coupled climate model (ECHAM5/MPI-OM), which can assimilate all available oceanic observations using an ensemble optimal interpolation approach. To validate and isolate the performance of different surface observations in reproducing air-sea climate variations in the model, a set of observing system simulation experiments (OSSEs) was performed over 150 model years. Generally, assimilating sea surface temperature, sea surface salinity, and sea surface height (SSH) can reasonably reproduce the climate variability and vertical structure of the upper ocean, and assimilating SSH achieves the best results compared to the true states. For the El Niño-Southern Oscillation (ENSO), assimilating different surface observations captures true aspects of ENSO well, but assimilating SSH can further enhance the accuracy of ENSO-related feedback processes in the coupled model, leading to a more reasonable ENSO evolution and air-sea interaction over the tropical Pacific. For ocean heat content, there are still limitations in reproducing the long time-scale variability in the North Atlantic, even if SSH has been taken into consideration. These results demonstrate the effectiveness of assimilating surface observations in capturing the interannual signal and, to some extent, the decadal signal but still highlight the necessity of assimilating profile data to reproduce specific decadal variability.

Representing Northern Peatland Hydrology and Biogeochemistry with ALM Land Surface Model

NASA Astrophysics Data System (ADS)

Shi, X.; Ricciuto, D. M.; Thornton, P. E.; Hanson, P. J.; Xu, X.; Mao, J.; Warren, J.; Yuan, F.; Norby, R. J.; Sebestyen, S.; Griffiths, N.; Weston, D. J.; Walker, A.

2017-12-01

Northern peatlands are likely to be important in future carbon cycle-climate feedbacks due to their large carbon pool and vulnerability to hydrological change. Predictive understanding of northern peatland hydrology is a necessary precursor to understanding the fate of massive carbon stores in these systems under the influence of present and future climate change. Current models have begun to address microtopographic controls on peatland hydrology, but none have included a prognostic calculation of peatland water table depth for a vegetated wetland, independent of prescribed regional water tables. Firstly, we introduce a new configuration of the land model (ALM) of Accelerated Climate model for Energy (ACME), which includes a fully prognostic water table calculation for a vegetated peatland. Secondly, we couple our new hydrology treatment with vertically structured soil organic matter pool, and the addition of components from methane biogeochemistry. Thirdly, we introduce a new PFT for mosses and implement the water content dynamics and physiology of mosses. We inform and test our model based on SPRUCE experiment to get the reasonable results for the seasonal dynamics water table depths, water content dynamics and physiology of mosses, and correct soil carbon profiles. Then, we use our new model structure to test the how the water table depth and CH4 emission will respond to elevated CO2 and different warming scenarios.

Emergent properties of climate-vegetation feedbacks in the North American Monsoon Macrosystem

NASA Astrophysics Data System (ADS)

Mathias, A.; Niu, G.; Zeng, X.

2012-12-01

The ability of ecosystems to adapt naturally to climate change and associated disturbances (e.g. wildfires, spread of invasive species) is greatly affected by the stability of feedback interactions between climate and vegetation. In order to study climate-vegetation interactions, such as CO2 and H2O exchange in the North American Monsoon System (NAMS), we plan to couple a community land surface model (NoahMP or CLM) used in regional climate models (WRF) with an individual based, spatially explicit vegetation model (ECOTONE). Individual based modeling makes it possible to link individual plant traits with properties of plant communities. Community properties, such as species composition and species distribution arise from dynamic interactions of individual plants with each other, and with their environment. Plants interact with each other through intra- and interspecific competition for resources (H2O, nitrogen), and the outcome of these interactions depends on the properties of the plant community and the environment itself. In turn, the environment is affected by the resulting change in community structure, which may have an impact on the drivers of climate change. First, we performed sensitivity tests of ECOTONE to assess its ability to reproduce vegetation distribution in the NAMS. We compared the land surface model and ECOTONE with regard to their capability to accurately simulate soil moisture, CO2 flux and above ground biomass. For evaluating the models we used the eddy-correlation sensible and latent heat fluxes, CO2 flux and observations of other climate and environmental variables (e.g. soil temperature and moisture) from the Santa Rita experimental range. The model intercomparison helped us understand the advantages and disadvantages of each model, providing us guidance for coupling the community land surface model (NoahMP or CLM) with ECOTONE.

Climate models predict increasing temperature variability in poor countries.

PubMed

Bathiany, Sebastian; Dakos, Vasilis; Scheffer, Marten; Lenton, Timothy M

2018-05-01

Extreme events such as heat waves are among the most challenging aspects of climate change for societies. We show that climate models consistently project increases in temperature variability in tropical countries over the coming decades, with the Amazon as a particular hotspot of concern. During the season with maximum insolation, temperature variability increases by ~15% per degree of global warming in Amazonia and Southern Africa and by up to 10%°C -1 in the Sahel, India, and Southeast Asia. Mechanisms include drying soils and shifts in atmospheric structure. Outside the tropics, temperature variability is projected to decrease on average because of a reduced meridional temperature gradient and sea-ice loss. The countries that have contributed least to climate change, and are most vulnerable to extreme events, are projected to experience the strongest increase in variability. These changes would therefore amplify the inequality associated with the impacts of a changing climate.

Climate models predict increasing temperature variability in poor countries

PubMed Central

Dakos, Vasilis; Scheffer, Marten

2018-01-01

Extreme events such as heat waves are among the most challenging aspects of climate change for societies. We show that climate models consistently project increases in temperature variability in tropical countries over the coming decades, with the Amazon as a particular hotspot of concern. During the season with maximum insolation, temperature variability increases by ~15% per degree of global warming in Amazonia and Southern Africa and by up to 10%°C−1 in the Sahel, India, and Southeast Asia. Mechanisms include drying soils and shifts in atmospheric structure. Outside the tropics, temperature variability is projected to decrease on average because of a reduced meridional temperature gradient and sea-ice loss. The countries that have contributed least to climate change, and are most vulnerable to extreme events, are projected to experience the strongest increase in variability. These changes would therefore amplify the inequality associated with the impacts of a changing climate. PMID:29732409

The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): Understanding sea ice through climate-model simulations

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

Notz, Dirk; Jahn, Alexandra; Holland, Marika

A better understanding of the role of sea ice for the changing climate of our planet is the central aim of the diagnostic Coupled Model Intercomparison Project 6 (CMIP6)-endorsed Sea-Ice Model Intercomparison Project (SIMIP). To reach this aim, SIMIP requests sea-ice-related variables from climate-model simulations that allow for a better understanding and, ultimately, improvement of biases and errors in sea-ice simulations with large-scale climate models. This then allows us to better understand to what degree CMIP6 model simulations relate to reality, thus improving our confidence in answering sea-ice-related questions based on these simulations. Furthermore, the SIMIP protocol provides a standardmore » for sea-ice model output that will streamline and hence simplify the analysis of the simulated sea-ice evolution in research projects independent of CMIP. To reach its aims, SIMIP provides a structured list of model output that allows for an examination of the three main budgets that govern the evolution of sea ice, namely the heat budget, the momentum budget, and the mass budget. Furthermore, we explain the aims of SIMIP in more detail and outline how its design allows us to answer some of the most pressing questions that sea ice still poses to the international climate-research community.« less

The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): Understanding sea ice through climate-model simulations

DOE PAGES

Notz, Dirk; Jahn, Alexandra; Holland, Marika; ...

2016-09-23

A better understanding of the role of sea ice for the changing climate of our planet is the central aim of the diagnostic Coupled Model Intercomparison Project 6 (CMIP6)-endorsed Sea-Ice Model Intercomparison Project (SIMIP). To reach this aim, SIMIP requests sea-ice-related variables from climate-model simulations that allow for a better understanding and, ultimately, improvement of biases and errors in sea-ice simulations with large-scale climate models. This then allows us to better understand to what degree CMIP6 model simulations relate to reality, thus improving our confidence in answering sea-ice-related questions based on these simulations. Furthermore, the SIMIP protocol provides a standardmore » for sea-ice model output that will streamline and hence simplify the analysis of the simulated sea-ice evolution in research projects independent of CMIP. To reach its aims, SIMIP provides a structured list of model output that allows for an examination of the three main budgets that govern the evolution of sea ice, namely the heat budget, the momentum budget, and the mass budget. Furthermore, we explain the aims of SIMIP in more detail and outline how its design allows us to answer some of the most pressing questions that sea ice still poses to the international climate-research community.« less

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Evaluation of Boreal Summer Monsoon Intraseasonal Variability in the GASS-YOTC Multi-Model Physical Processes Experiment

NASA Astrophysics Data System (ADS)

Mani, N. J.; Waliser, D. E.; Jiang, X.

2014-12-01

While the boreal summer monsoon intraseasonal variability (BSISV) exerts profound influence on the south Asian monsoon, the capability of present day dynamical models in simulating and predicting the BSISV is still limited. The global model evaluation project on vertical structure and diabatic processes of the Madden Julian Oscillations (MJO) is a joint venture, coordinated by the Working Group on Numerical Experimentation (WGNE) MJO Task Force and GEWEX Atmospheric System Study (GASS) program, for assessing the model deficiencies in simulating the ISV and for improving our understanding of the underlying processes. In this study the simulation of the northward propagating BSISV is investigated in 26 climate models with special focus on the vertical diabatic heating structure and clouds. Following parallel lines of inquiry as the MJO Task Force has done with the eastward propagating MJO, we utilize previously proposed and newly developed model performance metrics and process diagnostics and apply them to the global climate model simulations of BSISV.

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts.

PubMed

Crase, Beth; Liedloff, Adam; Vesk, Peter A; Fukuda, Yusuke; Wintle, Brendan A

2014-08-01

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions. © 2014 John Wiley & Sons Ltd.

Climate model simulations of the mid-Pliocene: Earth's last great interval of global warmth

USGS Publications Warehouse

Dolan, A.M.; Haywood, A.M.; Dowsett, H.J.

2012-01-01

Pliocene Model Intercomparison Project Workshop; Reston, Virginia, 2–4 August 2011 The Pliocene Model Intercomparison Project (PlioMIP), supported by the U.S. Geological Survey's (USGS) Pliocene Research, Interpretation and Synoptic Mapping (PRISM) project and Powell Center, is an integral part of a third iteration of the Paleoclimate Modelling Intercomparison Project (PMIP3). PlioMIP's aim is to systematically compare structurally different climate models. This is done in the context of the mid-Pliocene (~3.3–3.0 million years ago), a geological interval when the global annual mean temperature was similar to predictions for the next century.

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

NASA Astrophysics Data System (ADS)

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

2010-12-01

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

Climatic influence on anthrax suitability in warming northern latitudes.

PubMed

Walsh, Michael G; de Smalen, Allard W; Mor, Siobhan M

2018-06-18

Climate change is impacting ecosystem structure and function, with potentially drastic downstream effects on human and animal health. Emerging zoonotic diseases are expected to be particularly vulnerable to climate and biodiversity disturbance. Anthrax is an archetypal zoonosis that manifests its most significant burden on vulnerable pastoralist communities. The current study sought to investigate the influence of temperature increases on geographic anthrax suitability in the temperate, boreal, and arctic North, where observed climate impact has been rapid. This study also explored the influence of climate relative to more traditional factors, such as livestock distribution, ungulate biodiversity, and soil-water balance, in demarcating risk. Machine learning was used to model anthrax suitability in northern latitudes. The model identified climate, livestock density and wild ungulate species richness as the most influential features in predicting suitability. These findings highlight the significance of warming temperatures for anthrax ecology in northern latitudes, and suggest potential mitigating effects of interventions targeting megafauna biodiversity conservation in grassland ecosystems, and animal health promotion among small to midsize livestock herds.

Ethical climate as a moderator between organizational trust and whistle-blowing among nurses and secretaries

PubMed Central

Aydan, Seda; Kaya, Sidika

2018-01-01

Objectives: To reveal the effect of perception of ethical climate by nurses and secretaries and their level of organizational trust on their whistleblowing intention. Methods: Nurses and secretaries working in a University Hospital in Ankara, Turkey, were enrolled in the study conducted in 2016. Responses were received from 369 nurses and secretaries working at Clinics and Polyclinics. Path analysis, investigation of structural equation models used while multi-regression analysis was also applied. Results: According to the regression model, ethical climate dimensions, profession, gender, and work place had significant impact on the whistleblowing intention. According to Path analysis, ethical climate had direct impact of 69% on whistleblowing intention. It was seen that organizational trust had an indirect impact of 27% on the whistleblowing score when ethical climate had a moderator role. Conclusion: In order to promote whistleblowing in organizations, it is important to keep the ethical climate perception of employees and the level of their organizational trust at high levels. PMID:29805421

Ethical climate as a moderator between organizational trust and whistle-blowing among nurses and secretaries.

PubMed

Aydan, Seda; Kaya, Sidika

2018-01-01

To reveal the effect of perception of ethical climate by nurses and secretaries and their level of organizational trust on their whistleblowing intention. Nurses and secretaries working in a University Hospital in Ankara, Turkey, were enrolled in the study conducted in 2016. Responses were received from 369 nurses and secretaries working at Clinics and Polyclinics. Path analysis, investigation of structural equation models used while multi-regression analysis was also applied. According to the regression model, ethical climate dimensions, profession, gender, and work place had significant impact on the whistleblowing intention. According to Path analysis, ethical climate had direct impact of 69% on whistleblowing intention. It was seen that organizational trust had an indirect impact of 27% on the whistleblowing score when ethical climate had a moderator role. In order to promote whistleblowing in organizations, it is important to keep the ethical climate perception of employees and the level of their organizational trust at high levels.

Extreme weather and climate events with ecological relevance: a review

PubMed Central

Meehl, Gerald A.

2017-01-01

Robust evidence exists that certain extreme weather and climate events, especially daily temperature and precipitation extremes, have changed in regard to intensity and frequency over recent decades. These changes have been linked to human-induced climate change, while the degree to which climate change impacts an individual extreme climate event (ECE) is more difficult to quantify. Rapid progress in event attribution has recently been made through improved understanding of observed and simulated climate variability, methods for event attribution and advances in numerical modelling. Attribution for extreme temperature events is stronger compared with other event types, notably those related to the hydrological cycle. Recent advances in the understanding of ECEs, both in observations and their representation in state-of-the-art climate models, open new opportunities for assessing their effect on human and natural systems. Improved spatial resolution in global climate models and advances in statistical and dynamical downscaling now provide climatic information at appropriate spatial and temporal scales. Together with the continued development of Earth System Models that simulate biogeochemical cycles and interactions with the biosphere at increasing complexity, these make it possible to develop a mechanistic understanding of how ECEs affect biological processes, ecosystem functioning and adaptation capabilities. Limitations in the observational network, both for physical climate system parameters and even more so for long-term ecological monitoring, have hampered progress in understanding bio-physical interactions across a range of scales. New opportunities for assessing how ECEs modulate ecosystem structure and functioning arise from better scientific understanding of ECEs coupled with technological advances in observing systems and instrumentation. This article is part of the themed issue ‘Behavioural, ecological and evolutionary responses to extreme climatic events’. PMID:28483866

Extreme weather and climate events with ecological relevance: a review.

PubMed

Ummenhofer, Caroline C; Meehl, Gerald A

2017-06-19

Robust evidence exists that certain extreme weather and climate events, especially daily temperature and precipitation extremes, have changed in regard to intensity and frequency over recent decades. These changes have been linked to human-induced climate change, while the degree to which climate change impacts an individual extreme climate event (ECE) is more difficult to quantify. Rapid progress in event attribution has recently been made through improved understanding of observed and simulated climate variability, methods for event attribution and advances in numerical modelling. Attribution for extreme temperature events is stronger compared with other event types, notably those related to the hydrological cycle. Recent advances in the understanding of ECEs, both in observations and their representation in state-of-the-art climate models, open new opportunities for assessing their effect on human and natural systems. Improved spatial resolution in global climate models and advances in statistical and dynamical downscaling now provide climatic information at appropriate spatial and temporal scales. Together with the continued development of Earth System Models that simulate biogeochemical cycles and interactions with the biosphere at increasing complexity, these make it possible to develop a mechanistic understanding of how ECEs affect biological processes, ecosystem functioning and adaptation capabilities. Limitations in the observational network, both for physical climate system parameters and even more so for long-term ecological monitoring, have hampered progress in understanding bio-physical interactions across a range of scales. New opportunities for assessing how ECEs modulate ecosystem structure and functioning arise from better scientific understanding of ECEs coupled with technological advances in observing systems and instrumentation.This article is part of the themed issue 'Behavioural, ecological and evolutionary responses to extreme climatic events'. © 2017 The Author(s).

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

PubMed

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

2010-07-12

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

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Challenges in identifying sites climatically matched to the native ranges of animal invaders.

PubMed

Rodda, Gordon H; Jarnevich, Catherine S; Reed, Robert N

2011-02-09

Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk.

Challenges in Identifying Sites Climatically Matched to the Native Ranges of Animal Invaders

PubMed Central

Rodda, Gordon H.; Jarnevich, Catherine S.; Reed, Robert N.

2011-01-01

Background Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Methodology/Principal Findings Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. Conclusions/Significance When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk. PMID:21347411

Challenges in identifying sites climatically matched to the native ranges of animal invaders

USGS Publications Warehouse

Rodda, G.H.; Jarnevich, C.S.; Reed, R.N.

2011-01-01

Background: Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Methodology/Principal Findings: Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. Conclusions/Significance: When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk.

Simulating dynamic and mixed-severity fire regimes: a process-based fire extension for LANDIS-II

Treesearch

Brian R. Sturtevant; Robert M. Scheller; Brian R. Miranda; Douglas Shinneman; Alexandra Syphard

2009-01-01

Fire regimes result from reciprocal interactions between vegetation and fire that may be further affected by other disturbances, including climate, landform, and terrain. In this paper, we describe fire and fuel extensions for the forest landscape simulation model, LANDIS-II, that allow dynamic interactions among fire, vegetation, climate, and landscape structure, and...

Climate change effects on historical range and variability of two large landscapes in western Montana, USA

Treesearch

Robert E. Keane; Lisa M. Holsinger; Russell A. Parsons; Kathy Gray

2008-01-01

Quantifying the historical range and variability of landscape composition and structure using simulation modeling is becoming an important means of assessing current landscape condition and prioritizing landscapes for ecosystem restoration. However, most simulated time series are generated using static climate conditions which fail to account for the predicted major...

Whole-farm models to quantify greenhouse gas emissions and their potential use for linking climate change mitigation and adaptation in temperate grassland ruminant-based farming systems.

PubMed

Del Prado, A; Crosson, P; Olesen, J E; Rotz, C A

2013-06-01

The farm level is the most appropriate scale for evaluating options for mitigating greenhouse gas (GHG) emissions, because the farm represents the unit at which management decisions in livestock production are made. To date, a number of whole farm modelling approaches have been developed to quantify GHG emissions and explore climate change mitigation strategies for livestock systems. This paper analyses the limitations and strengths of the different existing approaches for modelling GHG mitigation by considering basic model structures, approaches for simulating GHG emissions from various farm components and the sensitivity of GHG outputs and mitigation measures to different approaches. Potential challenges for linking existing models with the simulation of impacts and adaptation measures under climate change are explored along with a brief discussion of the effects on other ecosystem services.

ENES the European Network for Earth System modelling and its infrastructure projects IS-ENES

NASA Astrophysics Data System (ADS)

Guglielmo, Francesca; Joussaume, Sylvie; Parinet, Marie

2016-04-01

The scientific community working on climate modelling is organized within the European Network for Earth System modelling (ENES). In the past decade, several European university departments, research centres, meteorological services, computer centres, and industrial partners engaged in the creation of ENES with the purpose of working together and cooperating towards the further development of the network, by signing a Memorandum of Understanding. As of 2015, the consortium counts 47 partners. The climate modelling community, and thus ENES, faces challenges which are both science-driven, i.e. analysing of the full complexity of the Earth System to improve our understanding and prediction of climate changes, and have multi-faceted societal implications, as a better representation of climate change on regional scales leads to improved understanding and prediction of impacts and to the development and provision of climate services. ENES, promoting and endorsing projects and initiatives, helps in developing and evaluating of state-of-the-art climate and Earth system models, facilitates model inter-comparison studies, encourages exchanges of software and model results, and fosters the use of high performance computing facilities dedicated to high-resolution multi-model experiments. ENES brings together public and private partners, integrates countries underrepresented in climate modelling studies, and reaches out to different user communities, thus enhancing European expertise and competitiveness. In this need of sophisticated models, world-class, high-performance computers, and state-of-the-art software solutions to make efficient use of models, data and hardware, a key role is played by the constitution and maintenance of a solid infrastructure, developing and providing services to the different user communities. ENES has investigated the infrastructural needs and has received funding from the EU FP7 program for the IS-ENES (InfraStructure for ENES) phase I and II projects. We present here the case study of an existing network of institutions brought together toward common goals by a non-binding agreement, ENES, and of its two IS-ENES projects. These latter will be discussed in their double role as a means to provide and/or maintain the actual infrastructure (hardware, software, skilled human resources, services) to achieve ENES scientific goals -fulfilling the aims set in a strategy document-, but also to inform and provide to the network a structured way of working and of interacting with the extended community. The genesis and evolution of the network and the interaction network/projects will also be analysed in terms of long-term sustainability.

An exploration of workplace social capital as an antecedent of occupational safety and health climate and outcomes in the Chinese education sector.

PubMed

Tang, Jessica Janice; Leka, Stavroula; Hunt, Nigel; MacLennan, Sara

2014-07-01

It is widely acknowledged that teachers are at greater risk of work-related health problems. At the same time, employee perceptions of different dimensions of organizational climate can influence their attitudes, performance, and well-being at work. This study applied and extended a safety climate model in the context of the education sector in Hong Kong. Apart from safety considerations alone, the study included occupational health considerations and social capital and tested their relationships with occupational safety and health (OSH) outcomes. Seven hundred and four Hong Kong teachers completed a range of questionnaires exploring social capital, OSH climate, OSH knowledge, OSH performance (compliance and participation), general health, and self-rated health complaints and injuries. Structural equation modeling (SEM) was used to analyze the relationships between predictive and outcome variables. SEM analysis revealed a high level of goodness of fit, and the hypothesized model including social capital yielded a better fit than the original model. Social capital, OSH climate, and OSH performance were determinants of both positive and negative outcome variables. In addition, social capital not only significantly predicted general health directly, but also had a predictive effect on the OSH climate-behavior-outcome relationship. This study makes a contribution to the workplace social capital and OSH climate literature by empirically assessing their relationship in the Chinese education sector.

Overview of the Special Issue: A Multi-Model Framework to ...

EPA Pesticide Factsheets

The Climate Change Impacts and Risk Analysis (CIRA) project establishes a new multi-model framework to systematically assess the impacts, economic damages, and risks from climate change in the United States. The primary goal of this framework to estimate how climate change impacts and damages in the United States are avoided or reduced due to global greenhouse gas (GHG) emissions mitigation scenarios. Scenarios are designed to explore key uncertainties around the measurement of these changes. The modeling exercise presented in this Special Issue includes two integrated assessment models and 15 sectoral models encompassing six broad impacts sectors - water resources, electric power, infrastructure, human health, ecosystems, and forests. Three consistent emissions scenarios are used to analyze the benefits of global GHG mitigation targets: a reference and two policy scenarios, with total radiative forcing in 2100 of 10.0W/m2, 4.5W/m2, and 3.7W/m2. A range of climate sensitivities, climate models, natural variability measures, and structural uncertainties of sectoral models are examined to explore the implications of key uncertainties. This overview paper describes the motivations, goals, design, and academic contribution of the CIRA modeling exercise and briefly summarizes the subsequent papers in this Special Issue. A summary of results across impact sectors is provided showing that: GHG mitigation provides benefits to the United States that increase over

Coastal Wetland Ecosystem Responses to Climate Change: the Role of Macroclimatic Drivers along the Northern Gulf of Mexico

NASA Astrophysics Data System (ADS)

Osland, M. J.; Enwright, N.; Day, R. H.; Gabler, C. A.; Stagg, C. L.; From, A. S.

2014-12-01

Across the globe, macroclimatic drivers greatly influence coastal wetland ecosystem structure and function. However, changing macroclimatic conditions are rarely incorporated into coastal wetland vulnerability assessments. Here, we quantify the influence of macroclimatic drivers upon coastal wetland ecosystems along the Northern Gulf of Mexico (NGOM) coast. From a global perspective, the NGOM coast provides several excellent opportunities to examine the effects of climate change upon coastal wetlands. The abundant coastal wetland ecosystems in the region span two major climatic gradients: (1) a winter temperature gradient that crosses temperate to tropical climatic zones; and (2) a precipitation gradient that crosses humid to semi-arid zones. We present analyses where we used geospatial data (historical climate, hydrology, and coastal wetland coverage) and field data (soil, elevation, and plant community composition and structure) to quantify climate-mediated ecological transitions. We identified winter climate and precipitation-based thresholds that separate mangrove forests from salt marshes and vegetated wetlands from unvegetated wetlands, respectively. We used simple distribution and abundance models to evaluate the potential ecological effects of alternative future climate change scenarios. Our results illustrate and quantify the importance of macroclimatic drivers and indicate that climate change could result in landscape-scale changes in coastal wetland ecosystem structure and function. These macroclimate-mediated ecological changes could affect the supply of some ecosystem goods and services as well as the resilience of these ecosystems to stressors, including accelerated sea level rise. Collectively, our findings highlight the importance of incorporating macroclimatic drivers within future-focused coastal wetland vulnerability assessments.

Climate change or climate cycles? Snowpack trends in the Olympic and Cascade Mountains, Washington, USA.

PubMed

Barry, Dwight; McDonald, Shea

2013-01-01

Climate change could significantly influence seasonal streamflow and water availability in the snowpack-fed watersheds of Washington, USA. Descriptions of snowpack decline often use linear ordinary least squares (OLS) models to quantify this change. However, the region's precipitation is known to be related to climate cycles. If snowpack decline is more closely related to these cycles, an OLS model cannot account for this effect, and thus both descriptions of trends and estimates of decline could be inaccurate. We used intervention analysis to determine whether snow water equivalent (SWE) in 25 long-term snow courses within the Olympic and Cascade Mountains are more accurately described by OLS (to represent gradual change), stationary (to represent no change), or step-stationary (to represent climate cycling) models. We used Bayesian information-theoretic methods to determine these models' relative likelihood, and we found 90 models that could plausibly describe the statistical structure of the 25 snow courses' time series. Posterior model probabilities of the 29 "most plausible" models ranged from 0.33 to 0.91 (mean = 0.58, s = 0.15). The majority of these time series (55%) were best represented as step-stationary models with a single breakpoint at 1976/77, coinciding with a major shift in the Pacific Decadal Oscillation. However, estimates of SWE decline differed by as much as 35% between statistically plausible models of a single time series. This ambiguity is a critical problem for water management policy. Approaches such as intervention analysis should become part of the basic analytical toolkit for snowpack or other climatic time series data.

Macroclimatic change expected to transform coastal wetland ecosystems this century

USGS Publications Warehouse

Gabler, Christopher A.; Osland, Michael J.; Grace, James B.; Stagg, Camille L.; Day, Richard H.; Hartley, Stephen B.; Enwright, Nicholas M.; From, Andrew; McCoy, Meagan L.; McLeod, Jennie L.

2017-01-01

Coastal wetlands, existing at the interface between land and sea, are highly vulnerable to climate change. Macroclimate (for example, temperature and precipitation regimes) greatly influences coastal wetland ecosystem structure and function. However, research on climate change impacts in coastal wetlands has concentrated primarily on sea-level rise and largely ignored macroclimatic drivers, despite their power to transform plant community structure and modify ecosystem goods and services. Here, we model wetland plant community structure based on macroclimate using field data collected across broad temperature and precipitation gradients along the northern Gulf of Mexico coast. Our analyses quantify strongly nonlinear temperature thresholds regulating the potential for marsh-to-mangrove conversion. We also identify precipitation thresholds for dominance by various functional groups, including succulent plants and unvegetated mudflats. Macroclimate-driven shifts in foundation plant species abundance will have large effects on certain ecosystem goods and services. Based on current and projected climatic conditions, we project that transformative ecological changes are probable throughout the region this century, even under conservative climate scenarios. Coastal wetland ecosystems are functionally similar worldwide, so changes in this region are indicative of potential future changes in climatically similar regions globally.

CIM-EARTH: Community Integrated Model of Economic and Resource Trajectories for Humankind

NASA Astrophysics Data System (ADS)

Foster, I.; Elliott, J.; Munson, T.; Judd, K.; Moyer, E. J.; Sanstad, A. H.

2010-12-01

We report here on the development of an open source software framework termed CIM-EARTH that is intended to aid decision-making in climate and energy policy. Numerical modeling in support of evaluating policies to address climate change is difficult not only because of inherent uncertainties but because of the differences in scale and modeling approach required for various subcomponents of the system. Economic and climate models are structured quite differently, and while climate forcing can be assumed to be roughly global, climate impacts and the human response to them occur on small spatial scales. Mitigation policies likewise can be applied on scales ranging from the better part of a continent (e.g. a carbon cap-and-trade program for the entire U.S.) to a few hundred km (e.g. statewide renewable portfolio standards and local gasoline taxes). Both spatial and time resolution requirements can be challenging for global economic models. CIM-EARTH is a modular framework based around dynamic general equilibrium models. It is designed as a community tool that will enable study of the environmental benefits, transition costs, capitalization effects, and other consequences of both mitigation policies and unchecked climate change. Modularity enables both integration of highly resolved component sub-models for energy and other key systems and also user-directed choice of tradeoffs between e.g. spatial, sectoral, and time resolution. This poster describes the framework architecture, the current realized version, and plans for future releases. As with other open-source models familiar to the climate community (e.g. CCSM), deliverables will be made publicly available on a regular schedule, and community input is solicited for development of new features and modules.

A hybrid-domain approach for modeling climate data time series

NASA Astrophysics Data System (ADS)

Wen, Qiuzi H.; Wang, Xiaolan L.; Wong, Augustine

2011-09-01

In order to model climate data time series that often contain periodic variations, trends, and sudden changes in mean (mean shifts, mostly artificial), this study proposes a hybrid-domain (HD) algorithm, which incorporates a time domain test and a newly developed frequency domain test through an iterative procedure that is analogue to the well known backfitting algorithm. A two-phase competition procedure is developed to address the confounding issue between modeling periodic variations and mean shifts. A variety of distinctive features of climate data time series, including trends, periodic variations, mean shifts, and a dependent noise structure, can be modeled in tandem using the HD algorithm. This is particularly important for homogenization of climate data from a low density observing network in which reference series are not available to help preserve climatic trends and long-term periodic variations, preventing them from being mistaken as artificial shifts. The HD algorithm is also powerful in estimating trend and periodicity in a homogeneous data time series (i.e., in the absence of any mean shift). The performance of the HD algorithm (in terms of false alarm rate and hit rate in detecting shifts/cycles, and estimation accuracy) is assessed via a simulation study. Its power is further illustrated through its application to a few climate data time series.

Urban amplification of the global warming in Moscow megacity

NASA Astrophysics Data System (ADS)

Kislov, Alexander; Konstantinov, Pavel; Varentsov, Mikhail; Samsonov, Timofey; Gorlach, Irina; Trusilova, Kristina

2015-04-01

Climate changes in the large cities are very important and requires better understanding. The focus of this paper is climate change of the Moscow megacity. Its urban features strongly influence the atmospheric boundary layer above the Moscow agglomeration area and determine the microclimatic features of the local environment, such as urban heat island (UHI). Available meteorological observations within the Moscow urban area and surrounding territory allow us to assess the natural climate variations and human-induced climate warming separately. To obtain more precisely viewing on the UHI structure we have included into the analysis the satellite data (Meteosat-10), providing temperature and humidity profiles with high resolution. To investigate the mechanism of the urban amplification we realized the regional climate model COSMO-CLM+TEB. Apart from detailed climate research the model runs will be planned for climate projecting of Moscow agglomeration area. Climate change differences between urban and rural areas are determined by changes of the shape of the UHI and their relationships with changes of building height and density. Therefore, the urban module of COSMO-CLM+TEB model is fed by information from special GIS database contenting both geometric characteristics of the urban canyons and other characteristics of the urban surface. The sources of information were maps belonging to the OpenStreetMap, and digital elevation models SRTM90 and ASTER GDEM v.2 as well. The multiscale GIS database allows us to generate such kind of information with different spatial resolution (200, 500 and 1000 meters).

Risky business: The impact of climate and climate variability on human population dynamics in Western Europe during the Last Glacial Maximum

NASA Astrophysics Data System (ADS)

Burke, Ariane; Kageyama, Masa; Latombe, Guilllaume; Fasel, Marc; Vrac, Mathieu; Ramstein, Gilles; James, Patrick M. A.

2017-05-01

The extent to which climate change has affected the course of human evolution is an enduring question. The ability to maintain spatially extensive social networks and a fluid social structure allows human foragers to ;map onto; the landscape, mitigating the impact of ecological risk and conferring resilience. But what are the limits of resilience and to which environmental variables are foraging populations sensitive? We address this question by testing the impact of a suite of environmental variables, including climate variability, on the distribution of human populations in Western Europe during the Last Glacial Maximum (LGM). Climate variability affects the distribution of plant and animal resources unpredictably, creating an element of risk for foragers for whom mobility comes at a cost. We produce a model of habitat suitability that allows us to generate predictions about the probable distribution of human populations and discuss the implications of these predictions for the structure of human populations and their social and cultural evolution during the LGM.

Does climate directly influence NPP globally?

PubMed

Chu, Chengjin; Bartlett, Megan; Wang, Youshi; He, Fangliang; Weiner, Jacob; Chave, Jérôme; Sack, Lawren

2016-01-01

The need for rigorous analyses of climate impacts has never been more crucial. Current textbooks state that climate directly influences ecosystem annual net primary productivity (NPP), emphasizing the urgent need to monitor the impacts of climate change. A recent paper challenged this consensus, arguing, based on an analysis of NPP for 1247 woody plant communities across global climate gradients, that temperature and precipitation have negligible direct effects on NPP and only perhaps have indirect effects by constraining total stand biomass (Mtot ) and stand age (a). The authors of that study concluded that the length of the growing season (lgs ) might have a minor influence on NPP, an effect they considered not to be directly related to climate. In this article, we describe flaws that affected that study's conclusions and present novel analyses to disentangle the effects of stand variables and climate in determining NPP. We re-analyzed the same database to partition the direct and indirect effects of climate on NPP, using three approaches: maximum-likelihood model selection, independent-effects analysis, and structural equation modeling. These new analyses showed that about half of the global variation in NPP could be explained by Mtot combined with climate variables and supported strong and direct influences of climate independently of Mtot , both for NPP and for net biomass change averaged across the known lifetime of the stands (ABC = average biomass change). We show that lgs is an important climate variable, intrinsically correlated with, and contributing to mean annual temperature and precipitation (Tann and Pann ), all important climatic drivers of NPP. Our analyses provide guidance for statistical and mechanistic analyses of climate drivers of ecosystem processes for predictive modeling and provide novel evidence supporting the strong, direct role of climate in determining vegetation productivity at the global scale. © 2015 John Wiley & Sons Ltd.

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

NASA Astrophysics Data System (ADS)

Bates, S. C.; Strand, G.

2013-12-01

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

Uncertainty in projected point precipitation extremes for hydrological impact analysis of climate change

NASA Astrophysics Data System (ADS)

Van Uytven, Els; Willems, Patrick

2017-04-01

Current trends in the hydro-meteorological variables indicate the potential impact of climate change on hydrological extremes. Therefore, they trigger an increased importance climate adaptation strategies in water management. The impact of climate change on hydro-meteorological and hydrological extremes is, however, highly uncertain. This is due to uncertainties introduced by the climate models, the internal variability inherent to the climate system, the greenhouse gas scenarios and the statistical downscaling methods. In view of the need to define sustainable climate adaptation strategies, there is a need to assess these uncertainties. This is commonly done by means of ensemble approaches. Because more and more climate models and statistical downscaling methods become available, there is a need to facilitate the climate impact and uncertainty analysis. A Climate Perturbation Tool has been developed for that purpose, which combines a set of statistical downscaling methods including weather typing, weather generator, transfer function and advanced perturbation based approaches. By use of an interactive interface, climate impact modelers can apply these statistical downscaling methods in a semi-automatic way to an ensemble of climate model runs. The tool is applicable to any region, but has been demonstrated so far to cases in Belgium, Suriname, Vietnam and Bangladesh. Time series representing future local-scale precipitation, temperature and potential evapotranspiration (PET) conditions were obtained, starting from time series of historical observations. Uncertainties on the future meteorological conditions are represented in two different ways: through an ensemble of time series, and a reduced set of synthetic scenarios. The both aim to span the full uncertainty range as assessed from the ensemble of climate model runs and downscaling methods. For Belgium, for instance, use was made of 100-year time series of 10-minutes precipitation observations and daily temperature and PET observations at Uccle and a large ensemble of 160 global climate model runs (CMIP5). They cover all four representative concentration pathway based greenhouse gas scenarios. While evaluating the downscaled meteorological series, particular attention was given to the performance of extreme value metrics (e.g. for precipitation, by means of intensity-duration-frequency statistics). Moreover, the total uncertainty was decomposed in the fractional uncertainties for each of the uncertainty sources considered. Research assessing the additional uncertainty due to parameter and structural uncertainties of the hydrological impact model is ongoing.

Atmospheric rivers in a hierarchy of high resolution global climate models: results from the UPSCALE simulation campaign

NASA Astrophysics Data System (ADS)

Demory, Marie-Estelle; Vidale, Pier-Luigi; Schiemann, Reinhard; Roberts, Malcolm; Mizielinski, Matthew

2014-05-01

A traceable hierarchy of global climate models (based on the Met Office Unified Model, GA3 formulation), with mesh sizes ranging from 130km to 25km, has been developed in order to study the impact of improved representation of small-scale processes on the mean climate, its variability and extremes. Five-member ensembles of atmosphere-only integrations were completed at these resolutions, each 27 years in length, using both present day forcing and a future climate scenario. These integrations, collectively known as the "UPSCALE campaign", were completed using time provided by the European PrACE project on supercomputer HERMIT (HLRS Stuttgart). A wide variety of processes are being studied to assess these integrations, in particular with regards to the role of resolution. It has been shown that the relatively coarse resolution of atmospheric general circulation models (AGCMs) limits their ability to represent moisture transport from ocean to land. Understanding of the processes underlying this observed improvement with higher resolution remains insufficient. Atmospheric Rivers (ARs) are an important process of moisture transport onto land in mid-latitude eddies and have been shown by Lavers et al. (2012) to be involved in creating the moisture supply that sustains extreme precipitation events. We investigated the ability of a state-of-the art climate model to represent the location, frequency and 3D structure of atmospheric rivers affecting Western Europe, with a focus on the UK. We show that the climatology of atmospheric rivers, in particular frequency, is underrepresented in the GCM at standard resolution and that this is slightly improved at high resolution (25km): our results are in better agreement with reanalysis data, even if sizable biases remain. The three-dimensional structure of the atmospheric rivers is also more credibly represented at high-resolution. Some aspects of the relationship between the improved simulation in current climate conditions, and how this impacts on changes in the future climate, with much larger atmospheric moisture availability, will also be discussed. In particular, we aim to quantify the relative roles of atmospheric transport and increased precipitation rates in the higher quantiles.

Formation of Valley Networks in a Cold and Icy Early Mars Climate: Predictions for Erosion Rates and Channel Morphology

NASA Astrophysics Data System (ADS)

Cassanelli, J.

2017-12-01

Mars is host to a diverse array of valley networks, systems of linear-to-sinuous depressions which are widely distributed across the surface and which exhibit branching patterns similar to the dendritic drainage patterns of terrestrial fluvial systems. Characteristics of the valley networks are indicative of an origin by fluvial activity, providing among the most compelling evidence for the past presence of flowing liquid water on the surface of Mars. Stratigraphic and crater age dating techniques suggest that the formation of the valley networks occurred predominantly during the early geologic history of Mars ( 3.7 Ga). However, whether the valley networks formed predominantly by rainfall in a relatively warm and wet early Mars climate, or by snowmelt and episodic rainfall in an ambient cold and icy climate, remains disputed. Understanding the formative environment of the valley networks will help distinguish between these warm and cold end-member early Mars climate models. Here we test a conceptual model for channel incision and evolution under cold and icy conditions with a substrate characterized by the presence of an ice-free dry active layer and subjacent ice-cemented regolith, similar to that found in the Antarctic McMurdo Dry Valleys. We implement numerical thermal models, quantitative erosion and transport estimates, and morphometric analyses in order to outline predictions for (1) the precise nature and structure of the substrate, (2) fluvial erosion/incision rates, and (3) channel morphology. Model predictions are compared against morphologic and morphometric observational data to evaluate consistency with the assumed cold climate scenario. In the cold climate scenario, the substrate is predicted to be characterized by a kilometers-thick globally-continuous cryosphere below a 50-100 meter thick desiccated ice-free zone. Initial results suggest that, with the predicted substrate structure, fluvial channel erosion and morphology in a cold early Mars climate exposed to episodic high temperatures will not differ significantly from that in a warm climate. The fundamentally different hydrologic conditions are likely to influence other aspects of valley network morphology and morphometry including: drainage density, drainage pattern, and stream orders.

The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6

DOE PAGES

Webb, Mark J.; Andrews, Timothy; Bodas-Salcedo, Alejandro; ...

2017-01-01

Our primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud–climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. But, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions Howmore » does the Earth system respond to forcing? and What are the origins and consequences of systematic model biases? and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity.A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO 2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO 2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO 2 forcing and sea surface warming?CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions. How well do clouds and other relevant variables simulated by models agree with observations?What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models?Which models have the most credible representations of processes relevant to the simulation of clouds?How do clouds and their changes interact with other elements of the climate system?« less

The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6.

NASA Technical Reports Server (NTRS)

Webb, Mark J.; Andrews, Timothy; Bodas-Salcedo, Alejandro; Bony, Sandrine; Bretherton, Christopher S.; Chadwick, Robin; Chepfer, Helene; Douville, Herve; Good, Peter; Kay, Jennifer E.;

The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6

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

Webb, Mark J.; Andrews, Timothy; Bodas-Salcedo, Alejandro

Our primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud–climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. But, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions Howmore » does the Earth system respond to forcing? and What are the origins and consequences of systematic model biases? and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity.A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO 2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO 2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO 2 forcing and sea surface warming?CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions. How well do clouds and other relevant variables simulated by models agree with observations?What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models?Which models have the most credible representations of processes relevant to the simulation of clouds?How do clouds and their changes interact with other elements of the climate system?« less

Mapping past, present, and future climatic suitability for invasive Aedes aegypti and Aedes albopictus in the United States: a process-based modeling approach using CMIP5 downscaled climate scenarios

NASA Astrophysics Data System (ADS)

Donnelly, M. A. P.; Marcantonio, M.; Melton, F. S.; Barker, C. M.

2016-12-01

The ongoing spread of the mosquitoes, Aedes aegypti and Aedes albopictus, in the continental United States leaves new areas at risk for local transmission of dengue, chikungunya, and Zika viruses. All three viruses have caused major disease outbreaks in the Americas with infected travelers returning regularly to the U.S. The expanding range of these mosquitoes raises questions about whether recent spread has been enabled by climate change or other anthropogenic influences. In this analysis, we used downscaled climate scenarios from the NASA Earth Exchange Global Daily Downscaled Projections (NEX GDDP) dataset to model Ae. aegypti and Ae. albopictus population growth rates across the United States. We used a stage-structured matrix population model to understand past and present climatic suitability for these vectors, and to project future suitability under CMIP5 climate change scenarios. Our results indicate that much of the southern U.S. is suitable for both Ae. aegypti and Ae. albopictus year-round. In addition, a large proportion of the U.S. is seasonally suitable for mosquito population growth, creating the potential for periodic incursions into new areas. Changes in climatic suitability in recent decades for Ae. aegypti and Ae. albopictus have occurred already in many regions of the U.S., and model projections of future climate suggest that climate change will continue to reshape the range of Ae. aegypti and Ae. albopictus in the U.S., and potentially the risk of the viruses they transmit.

Mapping Past, Present, and Future Climatic Suitability for Invasive Aedes Aegypti and Aedes Albopictus in the United States: A Process-Based Modeling Approach Using CMIP5 Downscaled Climate Scenarios

NASA Technical Reports Server (NTRS)

Donnelly, Marisa Anne Pella; Marcantonio, Matteo; Melton, Forrest S.; Barker, Christopher M.

2016-01-01

The ongoing spread of the mosquitoes, Aedes aegypti and Aedes albopictus, in the continental United States leaves new areas at risk for local transmission of dengue, chikungunya, and Zika viruses. All three viruses have caused major disease outbreaks in the Americas with infected travelers returning regularly to the U.S. The expanding range of these mosquitoes raises questions about whether recent spread has been enabled by climate change or other anthropogenic influences. In this analysis, we used downscaled climate scenarios from the NASA Earth Exchange Global Daily Downscaled Projections (NEX GDDP) dataset to model Ae. aegypti and Ae. albopictus population growth rates across the United States. We used a stage-structured matrix population model to understand past and present climatic suitability for these vectors, and to project future suitability under CMIP5 climate change scenarios. Our results indicate that much of the southern U.S. is suitable for both Ae. aegypti and Ae. albopictus year-round. In addition, a large proportion of the U.S. is seasonally suitable for mosquito population growth, creating the potential for periodic incursions into new areas. Changes in climatic suitability in recent decades for Ae. aegypti and Ae. albopictus have occurred already in many regions of the U.S., and model projections of future climate suggest that climate change will continue to reshape the range of Ae. aegypti and Ae. albopictus in the U.S., and potentially the risk of the viruses they transmit.

Profiles of Student Perceptions of School Climate: Relations with Risk Behaviors and Academic Outcomes.

PubMed

Shukla, Kathan; Konold, Timothy; Cornell, Dewey

2016-06-01

School climate has been linked to a variety of positive student outcomes, but there may be important within-school differences among students in their experiences of school climate. This study examined within-school heterogeneity among 47,631 high school student ratings of their school climate through multilevel latent class modeling. Student profiles across 323 schools were generated on the basis of multiple indicators of school climate: disciplinary structure, academic expectations, student willingness to seek help, respect for students, affective and cognitive engagement, prevalence of teasing and bullying, general victimization, bullying victimization, and bullying perpetration. Analyses identified four meaningfully different student profile types that were labeled positive climate, medium climate-low bullying, medium climate-high bullying, and negative climate. Contrasts among these profile types on external criteria revealed meaningful differences for race, grade-level, parent education level, educational aspirations, and frequency of risk behaviors. © Society for Community Research and Action 2016.

Untangling climate signals from autogenic changes in long-term peatland development

NASA Astrophysics Data System (ADS)

Morris, Paul J.; Baird, Andy J.; Young, Dylan M.; Swindles, Graeme T.

2015-12-01

Peatlands represent important archives of Holocene paleoclimatic information. However, autogenic processes may disconnect peatland hydrological behavior from climate and overwrite climatic signals in peat records. We use a simulation model of peatland development driven by a range of Holocene climate reconstructions to investigate climate signal preservation in peat records. Simulated water-table depths and peat decomposition profiles exhibit homeostatic recovery from prescribed changes in rainfall, whereas changes in temperature cause lasting alterations to peatland structure and function. Autogenic ecohydrological feedbacks provide both high- and low-pass filters for climatic information, particularly rainfall. Large-magnitude climatic changes of an intermediate temporal scale (i.e., multidecadal to centennial) are most readily preserved in our simulated peat records. Simulated decomposition signals are offset from the climatic changes that generate them due to a phenomenon known as secondary decomposition. Our study provides the mechanistic foundations for a framework to separate climatic and autogenic signals in peat records.

The Impact of Transformational Leadership on Safety Climate and Individual Safety Behavior on Construction Sites.

PubMed

Shen, Yuzhong; Ju, Chuanjing; Koh, Tas Yong; Rowlinson, Steve; Bridge, Adrian J

2017-01-05

Unsafe acts contribute dominantly to construction accidents, and increasing safety behavior is essential to reduce accidents. Previous research conceptualized safety behavior as an interaction between proximal individual differences (safety knowledge and safety motivation) and distal contextual factors (leadership and safety climate). However, relatively little empirical research has examined this conceptualization in the construction sector. Given the cultural background of the sample, this study makes a slight modification to the conceptualization and views transformational leadership as an antecedent of safety climate. Accordingly, this study establishes a multiple mediator model showing the mechanisms through which transformational leadership translates into safety behavior. The multiple mediator model is estimated by the structural equation modeling (SEM) technique, using individual questionnaire responses from a random sample of construction personnel based in Hong Kong. As hypothesized, transformational leadership has a significant impact on safety climate which is mediated by safety-specific leader-member exchange (LMX), and safety climate in turn impacts safety behavior through safety knowledge. The results suggest that future safety climate interventions should be more effective if supervisors exhibit transformational leadership, encourage construction personnel to voice safety concerns without fear of retaliation, and repeatedly remind them about safety on the job.

The Impact of Transformational Leadership on Safety Climate and Individual Safety Behavior on Construction Sites

PubMed Central

Shen, Yuzhong; Ju, Chuanjing; Koh, Tas Yong; Rowlinson, Steve; Bridge, Adrian J.

2017-01-01

Unsafe acts contribute dominantly to construction accidents, and increasing safety behavior is essential to reduce accidents. Previous research conceptualized safety behavior as an interaction between proximal individual differences (safety knowledge and safety motivation) and distal contextual factors (leadership and safety climate). However, relatively little empirical research has examined this conceptualization in the construction sector. Given the cultural background of the sample, this study makes a slight modification to the conceptualization and views transformational leadership as an antecedent of safety climate. Accordingly, this study establishes a multiple mediator model showing the mechanisms through which transformational leadership translates into safety behavior. The multiple mediator model is estimated by the structural equation modeling (SEM) technique, using individual questionnaire responses from a random sample of construction personnel based in Hong Kong. As hypothesized, transformational leadership has a significant impact on safety climate which is mediated by safety-specific leader–member exchange (LMX), and safety climate in turn impacts safety behavior through safety knowledge. The results suggest that future safety climate interventions should be more effective if supervisors exhibit transformational leadership, encourage construction personnel to voice safety concerns without fear of retaliation, and repeatedly remind them about safety on the job. PMID:28067775

Competitive and demographic leverage points of community shifts under climate warming

PubMed Central

Sorte, Cascade J. B.; White, J. Wilson

2013-01-01

Accelerating rates of climate change and a paucity of whole-community studies of climate impacts limit our ability to forecast shifts in ecosystem structure and dynamics, particularly because climate change can lead to idiosyncratic responses via both demographic effects and altered species interactions. We used a multispecies model to predict which processes and species' responses are likely to drive shifts in the composition of a space-limited benthic marine community. Our model was parametrized from experimental manipulations of the community. Model simulations indicated shifts in species dominance patterns as temperatures increase, with projected shifts in composition primarily owing to the temperature dependence of growth, mortality and competition for three critical species. By contrast, warming impacts on two other species (rendering them weaker competitors for space) and recruitment rates of all species were of lesser importance in determining projected community changes. Our analysis reveals the importance of temperature-dependent competitive interactions for predicting effects of changing climate on such communities. Furthermore, by identifying processes and species that could disproportionately leverage shifts in community composition, our results contribute to a mechanistic understanding of climate change impacts, thereby allowing more insightful predictions of future biodiversity patterns. PMID:23658199

Multi-model comparison highlights consistency in predicted effect of warming on a semi-arid shrub

USGS Publications Warehouse

Renwick, Katherine M.; Curtis, Caroline; Kleinhesselink, Andrew R.; Schlaepfer, Daniel R.; Bradley, Bethany A.; Aldridge, Cameron L.; Poulter, Benjamin; Adler, Peter B.

2018-01-01

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi-model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi-model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.

Oceanography and life history predict contrasting genetic population structure in two Antarctic fish species.

PubMed

Young, Emma F; Belchier, Mark; Hauser, Lorenz; Horsburgh, Gavin J; Meredith, Michael P; Murphy, Eugene J; Pascoal, Sonia; Rock, Jennifer; Tysklind, Niklas; Carvalho, Gary R

2015-06-01

Understanding the key drivers of population connectivity in the marine environment is essential for the effective management of natural resources. Although several different approaches to evaluating connectivity have been used, they are rarely integrated quantitatively. Here, we use a 'seascape genetics' approach, by combining oceanographic modelling and microsatellite analyses, to understand the dominant influences on the population genetic structure of two Antarctic fishes with contrasting life histories, Champsocephalus gunnari and Notothenia rossii. The close accord between the model projections and empirical genetic structure demonstrated that passive dispersal during the planktonic early life stages is the dominant influence on patterns and extent of genetic structuring in both species. The shorter planktonic phase of C. gunnari restricts direct transport of larvae between distant populations, leading to stronger regional differentiation. By contrast, geographic distance did not affect differentiation in N. rossii, whose longer larval period promotes long-distance dispersal. Interannual variability in oceanographic flows strongly influenced the projected genetic structure, suggesting that shifts in circulation patterns due to climate change are likely to impact future genetic connectivity and opportunities for local adaptation, resilience and recovery from perturbations. Further development of realistic climate models is required to fully assess such potential impacts.

Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models.

PubMed

Jenouvrier, Stéphanie; Holland, Marika; Stroeve, Julienne; Barbraud, Christophe; Weimerskirch, Henri; Serreze, Mark; Caswell, Hal

2012-09-01

Sea ice conditions in the Antarctic affect the life cycle of the emperor penguin (Aptenodytes forsteri). We present a population projection for the emperor penguin population of Terre Adélie, Antarctica, by linking demographic models (stage-structured, seasonal, nonlinear, two-sex matrix population models) to sea ice forecasts from an ensemble of IPCC climate models. Based on maximum likelihood capture-mark-recapture analysis, we find that seasonal sea ice concentration anomalies (SICa ) affect adult survival and breeding success. Demographic models show that both deterministic and stochastic population growth rates are maximized at intermediate values of annual SICa , because neither the complete absence of sea ice, nor heavy and persistent sea ice, would provide satisfactory conditions for the emperor penguin. We show that under some conditions the stochastic growth rate is positively affected by the variance in SICa . We identify an ensemble of five general circulation climate models whose output closely matches the historical record of sea ice concentration in Terre Adélie. The output of this ensemble is used to produce stochastic forecasts of SICa , which in turn drive the population model. Uncertainty is included by incorporating multiple climate models and by a parametric bootstrap procedure that includes parameter uncertainty due to both model selection and estimation error. The median of these simulations predicts a decline of the Terre Adélie emperor penguin population of 81% by the year 2100. We find a 43% chance of an even greater decline, of 90% or more. The uncertainty in population projections reflects large differences among climate models in their forecasts of future sea ice conditions. One such model predicts population increases over much of the century, but overall, the ensemble of models predicts that population declines are far more likely than population increases. We conclude that climate change is a significant risk for the emperor penguin. Our analytical approach, in which demographic models are linked to IPCC climate models, is powerful and generally applicable to other species and systems. © 2012 Blackwell Publishing Ltd.

Climate impact on spreading of airborne infectious diseases. Complex network based modeling of climate influences on influenza like illnesses

NASA Astrophysics Data System (ADS)

Brenner, Frank; Marwan, Norbert; Hoffmann, Peter

2017-06-01

In this study we combined a wide range of data sets to simulate the outbreak of an airborne infectious disease that is directly transmitted from human to human. The basis is a complex network whose structures are inspired by global air traffic data (from openflights.org) containing information about airports, airport locations, direct flight connections and airplane types. Disease spreading inside every node is realized with a Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model. Disease transmission rates in our model are depending on the climate environment and therefore vary in time and from node to node. To implement the correlation between water vapor pressure and influenza transmission rate [J. Shaman, M. Kohn, Proc. Natl. Acad. Sci. 106, 3243 (2009)], we use global available climate reanalysis data (WATCH-Forcing-Data-ERA-Interim, WFDEI). During our sensitivity analysis we found that disease spreading dynamics are strongly depending on network properties, the climatic environment of the epidemic outbreak location, and the season during the year in which the outbreak is happening.

Modeling climate change impacts on combined sewer overflow using synthetic precipitation time series.

PubMed

Bendel, David; Beck, Ferdinand; Dittmer, Ulrich

2013-01-01

In the presented study climate change impacts on combined sewer overflows (CSOs) in Baden-Wuerttemberg, Southern Germany, were assessed based on continuous long-term rainfall-runoff simulations. As input data, synthetic rainfall time series were used. The applied precipitation generator NiedSim-Klima accounts for climate change effects on precipitation patterns. Time series for the past (1961-1990) and future (2041-2050) were generated for various locations. Comparing the simulated CSO activity of both periods we observe significantly higher overflow frequencies for the future. Changes in overflow volume and overflow duration depend on the type of overflow structure. Both values will increase at simple CSO structures that merely divide the flow, whereas they will decrease when the CSO structure is combined with a storage tank. However, there is a wide variation between the results of different precipitation time series (representative for different locations).

Relationships between bullying, school climate, and student risk behaviors.

PubMed

Klein, Jennifer; Cornell, Dewey; Konold, Timothy

2012-09-01

This study examined whether characteristics of a positive school climate were associated with lower student risk behavior in a sample of 3,687 high school students who completed the School Climate Bullying Survey and questions about risk behavior from the Youth Risk Behavior Surveillance Survey (YRBS). Confirmatory factor analyses established fit for 20 items with three hypothesized school climate scales measuring (1) prevalence of bullying and teasing; (2) aggressive attitudes; and (3) student willingness to seek help. Structural equation modeling established the relationship of these measures with student reports of risk behavior. Multigroup analyses identified differential effects across gender and race. A positive school climate could be an important protective factor in preventing student risk behavior.

Tropical Atlantic climate response to different freshwater input in high latitudes with an ocean-only general circulation model

NASA Astrophysics Data System (ADS)

Men, Guang; Wan, Xiuquan; Liu, Zedong

2016-10-01

Tropical Atlantic climate change is relevant to the variation of Atlantic meridional overturning circulation (AMOC) through different physical processes. Previous coupled climate model simulation suggested a dipole-like SST structure cooling over the North Atlantic and warming over the South Tropical Atlantic in response to the slowdown of the AMOC. Using an ocean-only global ocean model here, an attempt was made to separate the total influence of various AMOC change scenarios into an oceanic-induced component and an atmospheric-induced component. In contrast with previous freshwater-hosing experiments with coupled climate models, the ocean-only modeling presented here shows a surface warming in the whole tropical Atlantic region and the oceanic-induced processes may play an important role in the SST change in the equatorial south Atlantic. Our result shows that the warming is partly governed by oceanic process through the mechanism of oceanic gateway change, which operates in the regime where freshwater forcing is strong, exceeding 0.3 Sv. Strong AMOC change is required for the gateway mechanism to work in our model because only when the AMOC is sufficiently weak, the North Brazil Undercurrent can flow equatorward, carrying warm and salty north Atlantic subtropical gyre water into the equatorial zone. This threshold is likely to be model-dependent. An improved understanding of these issues may have help with abrupt climate change prediction later.

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

NASA Astrophysics Data System (ADS)

Parsons, Luke Alexander

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

Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change

USGS Publications Warehouse

Sork, Victoria L.; Davis, Frank W.; Westfall, Robert; Flint, Alan L.; Ikegami, Makihiko; Wang, Hongfang; Grivet, Delphine

2010-01-01

Rapid climate change jeopardizes tree populations by shifting current climate zones. To avoid extinction, tree populations must tolerate, adapt, or migrate. Here we investigate geographic patterns of genetic variation in valley oak, Quercus lobata N??e, to assess how underlying genetic structure of populations might influence this species' ability to survive climate change. First, to understand how genetic lineages shape spatial genetic patterns, we examine historical patterns of colonization. Second, we examine the correlation between multivariate nuclear genetic variation and climatic variation. Third, to illustrate how geographic genetic variation could interact with regional patterns of 21st Century climate change, we produce region-specific bioclimatic distributions of valley oak using Maximum Entropy (MAXENT) models based on downscaled historical (1971-2000) and future (2070-2100) climate grids. Future climatologies are based on a moderate-high (A2) carbon emission scenario and two different global climate models. Chloroplast markers indicate historical range-wide connectivity via colonization, especially in the north. Multivariate nuclear genotypes show a strong association with climate variation that provides opportunity for local adaptation to the conditions within their climatic envelope. Comparison of regional current and projected patterns of climate suitability indicates that valley oaks grow in distinctly different climate conditions in different parts of their range. Our models predict widely different regional outcomes from local displacement of a few kilometres to hundreds of kilometres. We conclude that the relative importance of migration, adaptation, and tolerance are likely to vary widely for populations among regions, and that late 21st Century conditions could lead to regional extinctions. ?? 2010 Blackwell Publishing Ltd.

Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change.

PubMed

Sork, Victoria L; Davis, Frank W; Westfall, Robert; Flint, Alan; Ikegami, Makihiko; Wang, Hongfang; Grivet, Delphine

2010-09-01

Rapid climate change jeopardizes tree populations by shifting current climate zones. To avoid extinction, tree populations must tolerate, adapt, or migrate. Here we investigate geographic patterns of genetic variation in valley oak, Quercus lobata Née, to assess how underlying genetic structure of populations might influence this species' ability to survive climate change. First, to understand how genetic lineages shape spatial genetic patterns, we examine historical patterns of colonization. Second, we examine the correlation between multivariate nuclear genetic variation and climatic variation. Third, to illustrate how geographic genetic variation could interact with regional patterns of 21st Century climate change, we produce region-specific bioclimatic distributions of valley oak using Maximum Entropy (MAXENT) models based on downscaled historical (1971-2000) and future (2070-2100) climate grids. Future climatologies are based on a moderate-high (A2) carbon emission scenario and two different global climate models. Chloroplast markers indicate historical range-wide connectivity via colonization, especially in the north. Multivariate nuclear genotypes show a strong association with climate variation that provides opportunity for local adaptation to the conditions within their climatic envelope. Comparison of regional current and projected patterns of climate suitability indicates that valley oaks grow in distinctly different climate conditions in different parts of their range. Our models predict widely different regional outcomes from local displacement of a few kilometres to hundreds of kilometres. We conclude that the relative importance of migration, adaptation, and tolerance are likely to vary widely for populations among regions, and that late 21st Century conditions could lead to regional extinctions.

Reconstructing Holocene climate using a climate model: Model strategy and preliminary results

NASA Astrophysics Data System (ADS)

Haberkorn, K.; Blender, R.; Lunkeit, F.; Fraedrich, K.

2009-04-01

An Earth system model of intermediate complexity (Planet Simulator; PlaSim) is used to reconstruct Holocene climate based on proxy data. The Planet Simulator is a user friendly general circulation model (GCM) suitable for palaeoclimate research. Its easy handling and the modular structure allow for fast and problem dependent simulations. The spectral model is based on the moist primitive equations conserving momentum, mass, energy and moisture. Besides the atmospheric part, a mixed layer-ocean with sea ice and a land surface with biosphere are included. The present-day climate of PlaSim, based on an AMIP II control-run (T21/10L resolution), shows reasonable agreement with ERA-40 reanalysis data. Combining PlaSim with a socio-technological model (GLUES; DFG priority project INTERDYNAMIK) provides improved knowledge on the shift from hunting-gathering to agropastoral subsistence societies. This is achieved by a data assimilation approach, incorporating proxy time series into PlaSim to initialize palaeoclimate simulations during the Holocene. For this, the following strategy is applied: The sensitivities of the terrestrial PlaSim climate are determined with respect to sea surface temperature (SST) anomalies. Here, the focus is the impact of regionally varying SST both in the tropics and the Northern Hemisphere mid-latitudes. The inverse of these sensitivities is used to determine the SST conditions necessary for the nudging of land and coastal proxy climates. Preliminary results indicate the potential, the uncertainty and the limitations of the method.

Alleviating tropical Atlantic sector biases in the Kiel climate model by enhancing horizontal and vertical atmosphere model resolution: climatology and interannual variability

NASA Astrophysics Data System (ADS)

Harlaß, Jan; Latif, Mojib; Park, Wonsun

2018-04-01

We investigate the quality of simulating tropical Atlantic (TA) sector climatology and interannual variability in integrations of the Kiel climate model (KCM) with varying atmosphere model resolution. The ocean model resolution is kept fixed. A reasonable simulation of TA sector annual-mean climate, seasonal cycle and interannual variability can only be achieved at sufficiently high horizontal and vertical atmospheric resolution. Two major reasons for the improvements are identified. First, the western equatorial Atlantic westerly surface wind bias in spring can be largely eliminated, which is explained by a better representation of meridional and especially vertical zonal momentum transport. The enhanced atmospheric circulation along the equator in turn greatly improves the thermal structure of the upper equatorial Atlantic with much reduced warm sea surface temperature (SST) biases. Second, the coastline in the southeastern TA and steep orography are better resolved at high resolution, which improves wind structure and in turn reduces warm SST biases in the Benguela upwelling region. The strongly diminished wind and SST biases at high atmosphere model resolution allow for a more realistic latitudinal position of the intertropical convergence zone. Resulting stronger cross-equatorial winds, in conjunction with a shallower thermocline, enable a rapid cold tongue development in the eastern TA in boreal spring. This enables simulation of realistic interannual SST variability and its seasonal phase locking in the KCM, which primarily is the result of a stronger thermocline feedback. Our findings suggest that enhanced atmospheric resolution, both vertical and horizontal, could be a key to achieving more realistic simulation of TA climatology and interannual variability in climate models.

Changes in Concurrent Risk of Warm and Dry Years under Impact of Climate Change

NASA Astrophysics Data System (ADS)

Sarhadi, A.; Wiper, M.; Touma, D. E.; Ausín, M. C.; Diffenbaugh, N. S.

2017-12-01

Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena. The changing concurrence of multiple climatic extremes (warm and dry years) may result in intensification of undesirable consequences for water resources, human and ecosystem health, and environmental equity. The present study assesses how global warming influences the probability that warm and dry years co-occur in a global scale. In the first step of the study a designed multivariate Mann-Kendall trend analysis is used to detect the areas in which the concurrence of warm and dry years has increased in the historical climate records and also climate models in the global scale. The next step investigates the concurrent risk of the extremes under dynamic nonstationary conditions. A fully generalized multivariate risk framework is designed to evolve through time under dynamic nonstationary conditions. In this methodology, Bayesian, dynamic copulas are developed to model the time-varying dependence structure between the two different climate extremes (warm and dry years). The results reveal an increasing trend in the concurrence risk of warm and dry years, which are in agreement with the multivariate trend analysis from historical and climate models. In addition to providing a novel quantification of the changing probability of compound extreme events, the results of this study can help decision makers develop short- and long-term strategies to prepare for climate stresses now and in the future.

Desert mammal populations are limited by introduced predators rather than future climate change

PubMed Central

Wardle, Glenda M.; Dickman, Chris R.

2017-01-01

Climate change is predicted to place up to one in six species at risk of extinction in coming decades, but extinction probability is likely to be influenced further by biotic interactions such as predation. We use structural equation modelling to integrate results from remote camera trapping and long-term (17–22 years) regional-scale (8000 km2) datasets on vegetation and small vertebrates (greater than 38 880 captures) to explore how biotic processes and two key abiotic drivers influence the structure of a diverse assemblage of desert biota in central Australia. We use our models to predict how changes in rainfall and wildfire are likely to influence the cover and productivity of the dominant vegetation and the impacts of predators on their primary rodent prey over a 100-year timeframe. Our results show that, while vegetation cover may decline due to climate change, the strongest negative effect on prey populations in this desert system is top-down suppression from introduced predators. PMID:29291051

Farmers' climate information needs for long-term adaptive decisions: A case study of almonds in CA

NASA Astrophysics Data System (ADS)

Jagannathan, K. A.; Jones, A. D.; Pathak, T. B.; Kerr, A. C.; Doll, D.

2016-12-01

Despite advances in climate modeling and projections, several sources report that current tools and models are not widely used in the agriculture sector. Farmers, depending on their local context, require information on very specific climatic metrics such as start of rains during the planting season, number of low temperature days during the growing season, etc. However, such specific climatic information is either not available, and/or is not synthesized and communicated in a manner that is accessible to these decision-makers. This research aims to bridge the gap between climate information and decision-making needs, by providing an improved understanding of what farmers' consider as relevant climate information, and how these needs compare with current modeling capabilities. Almond is a perennial crop, so any changes in climate within its 25-30 year lifetime can have an adverse impact on crop yield. This makes almond growers vulnerable to medium and long-term climate change. Hence, providing appropriate information on future climate projections can help guide their decisions on crop types & varieties, as well as management practices that are better adapted to future climatic conditions. Semi-structured exploratory interviews have been conducted with almond growers, farm advisors, and other industry stakeholders, with three goals: (1) to understand how growers have used climate information in the past; (2) to identify key climatic variables that are relevant - including appropriate temporal scales and acceptable uncertainty levels; and (3) to understand communication methods that could improve the usability of climate information for farm-level decision-making. The interviews showcased a great diversity amongst growers in terms of how they used weather/climate information. Discussions also indicated that there was a potential for climate information to impact long-term decisions, but only if it is provided within the right context, terminology, and communication channels. The findings offer valuable bottom-up insights into farmers' perspectives on relevance of climate information. These results will also be compared with current modeling capabilities in order to synthesize conclusions for improving the usability of climate science for agricultural decision-makers.

Genus age, provincial area and the taxonomic structure of marine faunas.

PubMed

Harnik, Paul G; Jablonski, David; Krug, Andrew Z; Valentine, James W

2010-11-22

Species are unevenly distributed among genera within clades and regions, with most genera species-poor and few species-rich. At regional scales, this structure to taxonomic diversity is generated via speciation, extinction and geographical range dynamics. Here, we use a global database of extant marine bivalves to characterize the taxonomic structure of climate zones and provinces. Our analyses reveal a general, Zipf-Mandelbrot form to the distribution of species among genera, with faunas from similar climate zones exhibiting similar taxonomic structure. Provinces that contain older taxa and/or encompass larger areas are expected to be more species-rich. Although both median genus age and provincial area correlate with measures of taxonomic structure, these relationships are interdependent, nonlinear and driven primarily by contrasts between tropical and extra-tropical faunas. Provincial area and taxonomic structure are largely decoupled within climate zones. Counter to the expectation that genus age and species richness should positively covary, diverse and highly structured provincial faunas are dominated by young genera. The marked differences between tropical and temperate faunas suggest strong spatial variation in evolutionary rates and invasion frequencies. Such variation contradicts biogeographic models that scale taxonomic diversity to geographical area.

Genus age, provincial area and the taxonomic structure of marine faunas

PubMed Central

Harnik, Paul G.; Jablonski, David; Krug, Andrew Z.; Valentine, James W.

2010-01-01

Species are unevenly distributed among genera within clades and regions, with most genera species-poor and few species-rich. At regional scales, this structure to taxonomic diversity is generated via speciation, extinction and geographical range dynamics. Here, we use a global database of extant marine bivalves to characterize the taxonomic structure of climate zones and provinces. Our analyses reveal a general, Zipf–Mandelbrot form to the distribution of species among genera, with faunas from similar climate zones exhibiting similar taxonomic structure. Provinces that contain older taxa and/or encompass larger areas are expected to be more species-rich. Although both median genus age and provincial area correlate with measures of taxonomic structure, these relationships are interdependent, nonlinear and driven primarily by contrasts between tropical and extra-tropical faunas. Provincial area and taxonomic structure are largely decoupled within climate zones. Counter to the expectation that genus age and species richness should positively covary, diverse and highly structured provincial faunas are dominated by young genera. The marked differences between tropical and temperate faunas suggest strong spatial variation in evolutionary rates and invasion frequencies. Such variation contradicts biogeographic models that scale taxonomic diversity to geographical area. PMID:20534619

A likelihood-based time series modeling approach for application in dendrochronology to examine the growth-climate relations and forest disturbance history.

PubMed

Lee, E Henry; Wickham, Charlotte; Beedlow, Peter A; Waschmann, Ronald S; Tingey, David T

2017-10-01

A time series intervention analysis (TSIA) of dendrochronological data to infer the tree growth-climate-disturbance relations and forest disturbance history is described. Maximum likelihood is used to estimate the parameters of a structural time series model with components for climate and forest disturbances (i.e., pests, diseases, fire). The statistical method is illustrated with a tree-ring width time series for a mature closed-canopy Douglas-fir stand on the west slopes of the Cascade Mountains of Oregon, USA that is impacted by Swiss needle cast disease caused by the foliar fungus, Phaecryptopus gaeumannii (Rhode) Petrak. The likelihood-based TSIA method is proposed for the field of dendrochronology to understand the interaction of temperature, water, and forest disturbances that are important in forest ecology and climate change studies.

A Comparative Study of Organizational Climate and Campus Leadership at Bakersfield College, Based on the Roueche-Baker Community College Excellence Model.

ERIC Educational Resources Information Center

Nusz, Phyllis Jane

A study was conducted at Bakersfield College to identify the strengths and weaknesses of the college's organizational structure and to determine to what extent the institution possessed specific elements of organizational climate and campus leadership that research has identified to be vital to educational quality. The survey instrument used to…

pyhector: A Python interface for the simple climate model Hector

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

N Willner, Sven; Hartin, Corinne; Gieseke, Robert

2017-04-01

Pyhector is a Python interface for the simple climate model Hector (Hartin et al. 2015) developed in C++. Simple climate models like Hector can, for instance, be used in the analysis of scenarios within integrated assessment models like GCAM1, in the emulation of complex climate models, and in uncertainty analyses. Hector is an open-source, object oriented, simple global climate carbon cycle model. Its carbon cycle consists of a one pool atmosphere, three terrestrial pools which can be broken down into finer biomes or regions, and four carbon pools in the ocean component. The terrestrial carbon cycle includes primary production andmore » respiration fluxes. The ocean carbon cycle circulates carbon via a simplified thermohaline circulation, calculating air-sea fluxes as well as the marine carbonate system (Hartin et al. 2016). The model input is time series of greenhouse gas emissions; as example scenarios for these the Pyhector package contains the Representative Concentration Pathways (RCPs)2. These were developed to cover the range of baseline and mitigation emissions scenarios and are widely used in climate change research and model intercomparison projects. Using DataFrames from the Python library Pandas (McKinney 2010) as a data structure for the scenarios simplifies generating and adapting scenarios. Other parameters of the Hector model can easily be modified when running the model. Pyhector can be installed using pip from the Python Package Index.3 Source code and issue tracker are available in Pyhector's GitHub repository4. Documentation is provided through Readthedocs5. Usage examples are also contained in the repository as a Jupyter Notebook (Pérez and Granger 2007; Kluyver et al. 2016). Courtesy of the Mybinder project6, the example Notebook can also be executed and modified without installing Pyhector locally.« less

Stochastic Hourly Weather Generator HOWGH: Validation and its Use in Pest Modelling under Present and Future Climates

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Hirschi, M.; Spirig, C.

2014-12-01

To quantify impact of the climate change on a specific pest (or any weather-dependent process) in a specific site, we may use a site-calibrated pest (or other) model and compare its outputs obtained with site-specific weather data representing present vs. perturbed climates. The input weather data may be produced by the stochastic weather generator. Apart from the quality of the pest model, the reliability of the results obtained in such experiment depend on an ability of the generator to represent the statistical structure of the real world weather series, and on the sensitivity of the pest model to possible imperfections of the generator. This contribution deals with the multivariate HOWGH weather generator, which is based on a combination of parametric and non-parametric statistical methods. Here, HOWGH is used to generate synthetic hourly series of three weather variables (solar radiation, temperature and precipitation) required by a dynamic pest model SOPRA to simulate the development of codling moth. The contribution presents results of the direct and indirect validation of HOWGH. In the direct validation, the synthetic series generated by HOWGH (various settings of its underlying model are assumed) are validated in terms of multiple climatic characteristics, focusing on the subdaily wet/dry and hot/cold spells. In the indirect validation, we assess the generator in terms of characteristics derived from the outputs of SOPRA model fed by the observed vs. synthetic series. The weather generator may be used to produce weather series representing present and future climates. In the latter case, the parameters of the generator may be modified by the climate change scenarios based on Global or Regional Climate Models. To demonstrate this feature, the results of codling moth simulations for future climate will be shown. Acknowledgements: The weather generator is developed and validated within the frame of projects WG4VALUE (project LD12029 sponsored by the Ministry of Education, Youth and Sports of CR), and VALUE (COST ES 1102 action).

An integrated assessment modeling framework for uncertainty studies in global and regional climate change: the MIT IGSM-CAM (version 1.0)

NASA Astrophysics Data System (ADS)

Monier, E.; Scott, J. R.; Sokolov, A. P.; Forest, C. E.; Schlosser, C. A.

2013-12-01

This paper describes a computationally efficient framework for uncertainty studies in global and regional climate change. In this framework, the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity to a human activity model, is linked to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Since the MIT IGSM-CAM framework (version 1.0) incorporates a human activity model, it is possible to analyze uncertainties in emissions resulting from both uncertainties in the underlying socio-economic characteristics of the economic model and in the choice of climate-related policies. Another major feature is the flexibility to vary key climate parameters controlling the climate system response to changes in greenhouse gases and aerosols concentrations, e.g., climate sensitivity, ocean heat uptake rate, and strength of the aerosol forcing. The IGSM-CAM is not only able to realistically simulate the present-day mean climate and the observed trends at the global and continental scale, but it also simulates ENSO variability with realistic time scales, seasonality and patterns of SST anomalies, albeit with stronger magnitudes than observed. The IGSM-CAM shares the same general strengths and limitations as the Coupled Model Intercomparison Project Phase 3 (CMIP3) models in simulating present-day annual mean surface temperature and precipitation. Over land, the IGSM-CAM shows similar biases to the NCAR Community Climate System Model (CCSM) version 3, which shares the same atmospheric model. This study also presents 21st century simulations based on two emissions scenarios (unconstrained scenario and stabilization scenario at 660 ppm CO2-equivalent) similar to, respectively, the Representative Concentration Pathways RCP8.5 and RCP4.5 scenarios, and three sets of climate parameters. Results of the simulations with the chosen climate parameters provide a good approximation for the median, and the 5th and 95th percentiles of the probability distribution of 21st century changes in global mean surface air temperature from previous work with the IGSM. Because the IGSM-CAM framework only considers one particular climate model, it cannot be used to assess the structural modeling uncertainty arising from differences in the parameterization suites of climate models. However, comparison of the IGSM-CAM projections with simulations of 31 CMIP5 models under the RCP4.5 and RCP8.5 scenarios show that the range of warming at the continental scale shows very good agreement between the two ensemble simulations, except over Antarctica, where the IGSM-CAM overestimates the warming. This demonstrates that by sampling the climate system response, the IGSM-CAM, even though it relies on one single climate model, can essentially reproduce the range of future continental warming simulated by more than 30 different models. Precipitation changes projected in the IGSM-CAM simulations and the CMIP5 multi-model ensemble both display a large uncertainty at the continental scale. The two ensemble simulations show good agreement over Asia and Europe. However, the ranges of precipitation changes do not overlap - but display similar size - over Africa and South America, two continents where models generally show little agreement in the sign of precipitation changes and where CCSM3 tends to be an outlier. Overall, the IGSM-CAM provides an efficient and consistent framework to explore the large uncertainty in future projections of global and regional climate change associated with uncertainty in the climate response and projected emissions.

Quantifying uncertainties of permafrost carbon-climate feedbacks

NASA Astrophysics Data System (ADS)

Burke, Eleanor J.; Ekici, Altug; Huang, Ye; Chadburn, Sarah E.; Huntingford, Chris; Ciais, Philippe; Friedlingstein, Pierre; Peng, Shushi; Krinner, Gerhard

2017-06-01

The land surface models JULES (Joint UK Land Environment Simulator, two versions) and ORCHIDEE-MICT (Organizing Carbon and Hydrology in Dynamic Ecosystems), each with a revised representation of permafrost carbon, were coupled to the Integrated Model Of Global Effects of climatic aNomalies (IMOGEN) intermediate-complexity climate and ocean carbon uptake model. IMOGEN calculates atmospheric carbon dioxide (CO2) and local monthly surface climate for a given emission scenario with the land-atmosphere CO2 flux exchange from either JULES or ORCHIDEE-MICT. These simulations include feedbacks associated with permafrost carbon changes in a warming world. Both IMOGEN-JULES and IMOGEN-ORCHIDEE-MICT were forced by historical and three alternative future-CO2-emission scenarios. Those simulations were performed for different climate sensitivities and regional climate change patterns based on 22 different Earth system models (ESMs) used for CMIP3 (phase 3 of the Coupled Model Intercomparison Project), allowing us to explore climate uncertainties in the context of permafrost carbon-climate feedbacks. Three future emission scenarios consistent with three representative concentration pathways were used: RCP2.6, RCP4.5 and RCP8.5. Paired simulations with and without frozen carbon processes were required to quantify the impact of the permafrost carbon feedback on climate change. The additional warming from the permafrost carbon feedback is between 0.2 and 12 % of the change in the global mean temperature (ΔT) by the year 2100 and 0.5 and 17 % of ΔT by 2300, with these ranges reflecting differences in land surface models, climate models and emissions pathway. As a percentage of ΔT, the permafrost carbon feedback has a greater impact on the low-emissions scenario (RCP2.6) than on the higher-emissions scenarios, suggesting that permafrost carbon should be taken into account when evaluating scenarios of heavy mitigation and stabilization. Structural differences between the land surface models (particularly the representation of the soil carbon decomposition) are found to be a larger source of uncertainties than differences in the climate response. Inertia in the permafrost carbon system means that the permafrost carbon response depends on the temporal trajectory of warming as well as the absolute amount of warming. We propose a new policy-relevant metric - the frozen carbon residence time (FCRt) in years - that can be derived from these complex land surface models and used to quantify the permafrost carbon response given any pathway of global temperature change.

National Variation in Crop Yield Production Functions

NASA Astrophysics Data System (ADS)

Devineni, N.; Rising, J. A.

2017-12-01

A new multilevel model for yield prediction at the county scale using regional climate covariates is presented in this paper. A new crop specific water deficit index, growing degree days, extreme degree days, and time-trend as an approximation of technology improvements are used as predictors to estimate annual crop yields for each county from 1949 to 2009. Every county in the United States is allowed to have unique parameters describing how these weather predictors are related to yield outcomes. County-specific parameters are further modeled as varying according to climatic characteristics, allowing the prediction of parameters in regions where crops are not currently grown and into the future. The structural relationships between crop yield and regional climate as well as trends are estimated simultaneously. All counties are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. The model captures up to 60% of the variability in crop yields after removing the effect of technology, does well in out of sample predictions and is useful in relating the climate responses to local bioclimatic factors. We apply the predicted growing models in a cost-benefit analysis to identify the most economically productive crop in each county.

Logit-normal mixed model for Indian Monsoon rainfall extremes

NASA Astrophysics Data System (ADS)

Dietz, L. R.; Chatterjee, S.

2014-03-01

Describing the nature and variability of Indian monsoon rainfall extremes is a topic of much debate in the current literature. We suggest the use of a generalized linear mixed model (GLMM), specifically, the logit-normal mixed model, to describe the underlying structure of this complex climatic event. Several GLMM algorithms are described and simulations are performed to vet these algorithms before applying them to the Indian precipitation data procured from the National Climatic Data Center. The logit-normal model was applied with fixed covariates of latitude, longitude, elevation, daily minimum and maximum temperatures with a random intercept by weather station. In general, the estimation methods concurred in their suggestion of a relationship between the El Niño Southern Oscillation (ENSO) and extreme rainfall variability estimates. This work provides a valuable starting point for extending GLMM to incorporate the intricate dependencies in extreme climate events.

Tropical Tree Trait Diversity Enhances Forest Biomass Resilience in a Dynamic Global Vegetation Model

NASA Astrophysics Data System (ADS)

Sakschewski, B.; Kirsten, T.; von Bloh, W.; Poorter, L.; Pena-Claros, M.; Boit, A.

2016-12-01

Functional diversity of ecosystems has been found to increase ecosystem functions and therefore enhance ecosystem resilience against environmental stressors. However, global carbon-cycle and biosphere models still classify the global vegetation into a relatively small number of distinct plant functional types (PFT) with constant features over space and time. Therefore, those models might underestimate the resilience and adaptive capacity of natural vegetation under climate change by ignoring positive effects that functional diversity might bring about. We diversified a set a of selected tree traits in a dynamic global vegetation model (LPJmL). In the new subversion, called LPJmL-FIT, Amazon region biomass stocks and forest structure appear significantly more resilient against climate change. Enhanced tree trait diversity enables the simulated rainforests to adjust to new environmental conditions via ecological sorting. These results may stimulate a new debate on the value of biodiversity for climate change mitigation.

A network-based approach for semi-quantitative knowledge mining and its application to yield variability

NASA Astrophysics Data System (ADS)

Schauberger, Bernhard; Rolinski, Susanne; Müller, Christoph

2016-12-01

Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. A systematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.

Forecasting the combined effects of urbanization and climate change on stream ecosystems: from impacts to management options

PubMed Central

Nelson, Kären C; Palmer, Margaret A; Pizzuto, James E; Moglen, Glenn E; Angermeier, Paul L; Hilderbrand, Robert H; Dettinger, Michael; Hayhoe, Katharine

2009-01-01

Streams collect runoff, heat, and sediment from their watersheds, making them highly vulnerable to anthropogenic disturbances such as urbanization and climate change. Forecasting the effects of these disturbances using process-based models is critical to identifying the form and magnitude of likely impacts. Here, we integrate a new biotic model with four previously developed physical models (downscaled climate projections, stream hydrology, geomorphology, and water temperature) to predict how stream fish growth and reproduction will most probably respond to shifts in climate and urbanization over the next several decades. The biotic submodel couples dynamics in fish populations and habitat suitability to predict fish assemblage composition, based on readily available biotic information (preferences for habitat, temperature, and food, and characteristics of spawning) and day-to-day variability in stream conditions. We illustrate the model using Piedmont headwater streams in the Chesapeake Bay watershed of the USA, projecting ten scenarios: Baseline (low urbanization; no on-going construction; and present-day climate); one Urbanization scenario (higher impervious surface, lower forest cover, significant construction activity); four future climate change scenarios [Hadley CM3 and Parallel Climate Models under medium-high (A2) and medium-low (B2) emissions scenarios]; and the same four climate change scenarios plus Urbanization. Urbanization alone depressed growth or reproduction of 8 of 39 species, while climate change alone depressed 22 to 29 species. Almost every recreationally important species (i.e. trouts, basses, sunfishes) and six of the ten currently most common species were predicted to be significantly stressed. The combined effect of climate change and urbanization on adult growth was sometimes large compared to the effect of either stressor alone. Thus, the model predicts considerable change in fish assemblage composition, including loss of diversity. Synthesis and applications. The interaction of climate change and urban growth may entail significant reconfiguring of headwater streams, including a loss of ecosystem structure and services, which will be more costly than climate change alone. On local scales, stakeholders cannot control climate drivers but they can mitigate stream impacts via careful land use. Therefore, to conserve stream ecosystems, we recommend that proactive measures be taken to insure against species loss or severe population declines. Delays will inevitably exacerbate the impacts of both climate change and urbanization on headwater systems. PMID:19536343

Developing a global mixed-canopy, height-variable vegetation structure dataset for estimating global vegetation albedo and biomass in the NASA Ent Terrestrial Biosphere Model and GISS GCM

NASA Astrophysics Data System (ADS)

Montes, C.; Kiang, N. Y.; Yang, W.; Ni-Meister, W.; Schaaf, C.; Aleinov, I. D.; Jonas, J.; Zhao, F. A.; Yao, T.; Wang, Z.; Sun, Q.

2015-12-01

Processes determining biosphere-atmosphere coupling are strongly influenced by vegetation structure. Thus, ecosystem carbon sequestration and evapotranspiration affecting global carbon and water balances will depend upon the spatial extent of vegetation, its vertical structure, and its physiological variability. To represent this globally, Dynamic Global Vegetation Models (DGVMs) coupled to General Circulation Models (GCMs) make use of satellite and/or model-based vegetation classifications often composed by homogeneous communities. This work aims at developing a new Global Vegetation Structure Dataset (GVSD) by incorporating varying vegetation heights for mixed plant communities to be used as input to the Ent Terrestrial Biosphere Model (TBM), the DGVM coupled to the NASA Goddard Institute for Space Studies (GISS) GCM. Information sources include the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover and plant functional types (PFTs) (Friedl et al., 2010), vegetation height from the Geoscience Laser Altimeter System (GLAS) on board ICESat (Ice, Cloud, and land Elevation Satellite) (Simard et al., 2011; Tang et al., 2014) along with the Global Data Sets of Vegetation Leaf Area Index (LAI)3g (Zhu et al. 2013). Further PFT partitioning is performed according to a climate classification utilizing the Climate Research Unit (CRU) and the NOAA Global Precipitation Climatology Centre (GPCC) data. Final products are a GVSD consisting of mixed plant communities (e.g. mixed forests, savannas, mixed PFTs) following the Ecosystem Demography model (Moorcroft et al., 2001) approach represented by multi-cohort community patches at the sub-grid level of the GCM, which are ensembles of identical individuals whose differences are represented by PFTs, canopy height, density and vegetation structure sensitivity to allometric parameters. To assess the sensitivity of the GISS GCM to vegetation structure, we produce a range of estimates of Ent TBM biomass and plant densities by varying allometric specifications. Ultimately, this GVSD will serve as a template for community data sets, and be used as boundary conditions to the Ent TBM for prediction of canopy albedo in the Analytical Clumped Two-Stream canopy radiative transfer scheme, biomass, primary productivity, respiration, and GISS GCM climate.

Social norms and efficacy beliefs drive the Alarmed segment’s public-sphere climate actions

NASA Astrophysics Data System (ADS)

Doherty, Kathryn L.; Webler, Thomas N.

2016-09-01

Surprisingly few individuals who are highly concerned about climate change take action to influence public policies. To assess social-psychological and cognitive drivers of public-sphere climate actions of Global Warming’s Six Americas `Alarmed’ segment, we developed a behaviour model and tested it using structural equation modelling of survey data from Vermont, USA (N = 702). Our model, which integrates social cognitive theory, social norms research, and value belief norm theory, explains 36-64% of the variance in five behaviours. Here we show descriptive social norms, self-efficacy, personal response efficacy, and collective response efficacy as strong driving forces of: voting, donating, volunteering, contacting government officials, and protesting about climate change. The belief that similar others took action increased behaviour and strengthened efficacy beliefs, which also led to greater action. Our results imply that communication efforts targeting Alarmed individuals and their public actions should include strategies that foster beliefs about positive descriptive social norms and efficacy.

NCPP's Use of Standard Metadata to Promote Open and Transparent Climate Modeling

NASA Astrophysics Data System (ADS)

Treshansky, A.; Barsugli, J. J.; Guentchev, G.; Rood, R. B.; DeLuca, C.

2012-12-01

The National Climate Predictions and Projections (NCPP) Platform is developing comprehensive regional and local information about the evolving climate to inform decision making and adaptation planning. This includes both creating and providing tools to create metadata about the models and processes used to create its derived data products. NCPP is using the Common Information Model (CIM), an ontology developed by a broad set of international partners in climate research, as its metadata language. This use of a standard ensures interoperability within the climate community as well as permitting access to the ecosystem of tools and services emerging alongside the CIM. The CIM itself is divided into a general-purpose (UML & XML) schema which structures metadata documents, and a project or community-specific (XML) Controlled Vocabulary (CV) which constraints the content of metadata documents. NCPP has already modified the CIM Schema to accommodate downscaling models, simulations, and experiments. NCPP is currently developing a CV for use by the downscaling community. Incorporating downscaling into the CIM will lead to several benefits: easy access to the existing CIM Documents describing CMIP5 models and simulations that are being downscaled, access to software tools that have been developed in order to search, manipulate, and visualize CIM metadata, and coordination with national and international efforts such as ES-DOC that are working to make climate model descriptions and datasets interoperable. Providing detailed metadata descriptions which include the full provenance of derived data products will contribute to making that data (and, the models and processes which generated that data) more open and transparent to the user community.

Arctic cities and climate change: climate-induced changes in stability of Russian urban infrastructure built on permafrost

NASA Astrophysics Data System (ADS)

Shiklomanov, Nikolay; Streletskiy, Dmitry; Swales, Timothy

2014-05-01

Planned socio-economic development during the Soviet period promoted migration into the Arctic and work force consolidation in urbanized settlements to support mineral resources extraction and transportation industries. These policies have resulted in very high level of urbanization in the Soviet Arctic. Despite the mass migration from the northern regions during the 1990s following the collapse of the Soviet Union and the diminishing government support, the Russian Arctic population remains predominantly urban. In five Russian Administrative regions underlined by permafrost and bordering the Arctic Ocean 66 to 82% (depending on region) of the total population is living in Soviet-era urban communities. The political, economic and demographic changes in the Russian Arctic over the last 20 years are further complicated by climate change which is greatly amplified in the Arctic region. One of the most significant impacts of climate change on arctic urban landscapes is the warming and degradation of permafrost which negatively affects the structural integrity of infrastructure. The majority of structures in the Russian Arctic are built according to the passive principle, which promotes equilibrium between the permafrost thermal regime and infrastructure foundations. This presentation is focused on quantitative assessment of potential changes in stability of Russian urban infrastructure built on permafrost in response to ongoing and future climatic changes using permafrost - geotechnical model forced by GCM-projected climate. To address the uncertainties in GCM projections we have utilized results from 6 models participated in most recent IPCC model inter-comparison project. The analysis was conducted for entire extent of Russian permafrost-affected area and on several representative urban communities. Our results demonstrate that significant observed reduction in urban infrastructure stability throughout the Russian Arctic can be attributed to climatic changes and that projected future climatic changes will further negatively affect communities on permafrost. However, the uncertainties in magnitude and spatial and temporal patterns of projected climate change produced by individual GCMs translate to substantial variability of the future state of infrastructure built on permafrost.

Using Models to Understand Sea Level Rise

ERIC Educational Resources Information Center

Barth-Cohen, Lauren; Medina, Edwing

2017-01-01

Important science phenomena--such as atomic structure, evolution, and climate change--are often hard to observe directly. That's why an important scientific practice is to use scientific models to represent one's current understanding of a system. Using models has been included as an essential science and engineering practice in the "Next…

Structure and vulnerability of Pacific Northwest tidal wetlands – A summary of wetland climate change research by the Western Ecology Division, U.S. EPA

USGS Publications Warehouse

Folger, Christina L; Lee, Henry; Janousek, Christopher N.; Reusser, Deborah A.

2014-01-01

Climate change poses a serious threat to the tidal wetlands of the Pacific Northwest (PNW) region of the U.S. In response to this threat, scientists at the Western Ecology Division of the U.S. EPA at and the Western Fisheries Research Center of the U.S. Geological Survey, along with other partners, initiated a series of studies on the structure and vulnerability of tidal wetlands to climate change. One research thrust was to evaluate community structure of PNW marshes, experimentally assess the vulnerability of marsh plants to inundation and salinity stress (as would happen with sea level rise), and evaluate the utility of the National Wetland Inventory (NWI) classification system. Another research thrust was to develop tools that provide insights into possible impacts of climate change. This effort included enhancing the Sea Level Affecting Marshes Model (SLAMM) to predict the effects of sea level rise on submerged aquatic vegetation (Zostera marina) distributions, evaluating changes in river flow into coastal estuaries in response to precipitation changes, and synthesizing Pacific Coast estuary, watershed, and climate data in a downloadable tool. Because the research resulting from these efforts was published in multiple venues, we summarized them in this document. We anticipate that future research efforts by the U.S. EPA will continue with a focus on climate change impacts on a regional scale.

Emotional Intelligence, Motivational Climate and Levels of Anxiety in Athletes from Different Categories of Sports: Analysis through Structural Equations.

PubMed

Castro-Sánchez, Manuel; Zurita-Ortega, Félix; Chacón-Cuberos, Ramón; López-Gutiérrez, Carlos Javier; Zafra-Santos, Edson

2018-05-01

(1) Background: Psychological factors can strongly affect the athletes’ performance. Therefore, currently the role of the sports psychologist is particularly relevant, being in charge of training the athlete’s psychological factors. This study aims at analysing the connections between motivational climate in sport, anxiety and emotional intelligence depending on the type of sport practised (individual/team) by means of a multigroup structural equations analysis. (2) 372 semi-professional Spanish athletes took part in this investigation, analysing motivational climate (PMCSQ-2), emotional intelligence (SSRI) and levels of anxiety (STAI). A model of multigroup structural equations was carried out which fitted accordingly (χ² = 586.77; df = 6.37; p < 0.001; Comparative Fit Index (CFI) = 0.951; Normed Fit Index (NFI) = 0.938; Incremental Fit Index (IFI) = 0.947; Root Mean Square Error of Approximation (RMSEA) = 0.069). (3) Results: A negative and direct connection has been found between ego oriented climate and task oriented climate, which is stronger and more differentiated in team sports. The most influential indicator in ego oriented climate is intra-group rivalry, exerting greater influence in individual sports. For task-oriented climate the strongest indicator is having an important role in individual sports, while in team sports it is cooperative learning. Emotional intelligence dimensions correlate more strongly in team sports than in individual sports. In addition, there was a negative and indirect relation between task oriented climate and trait-anxiety in both categories of sports. (4) Conclusions: This study shows how the task-oriented motivational climate or certain levels of emotional intelligence can act preventively in the face of anxiety states in athletes. Therefore, the development of these psychological factors could prevent anxiety states and improve performance in athletes.

Emotional Intelligence, Motivational Climate and Levels of Anxiety in Athletes from Different Categories of Sports: Analysis through Structural Equations

PubMed Central

López-Gutiérrez, Carlos Javier; Zafra-Santos, Edson

2018-01-01

(1) Background: Psychological factors can strongly affect the athletes’ performance. Therefore, currently the role of the sports psychologist is particularly relevant, being in charge of training the athlete’s psychological factors. This study aims at analysing the connections between motivational climate in sport, anxiety and emotional intelligence depending on the type of sport practised (individual/team) by means of a multigroup structural equations analysis. (2) 372 semi-professional Spanish athletes took part in this investigation, analysing motivational climate (PMCSQ-2), emotional intelligence (SSRI) and levels of anxiety (STAI). A model of multigroup structural equations was carried out which fitted accordingly (χ2 = 586.77; df = 6.37; p < 0.001; Comparative Fit Index (CFI) = 0.951; Normed Fit Index (NFI) = 0.938; Incremental Fit Index (IFI) = 0.947; Root Mean Square Error of Approximation (RMSEA) = 0.069). (3) Results: A negative and direct connection has been found between ego oriented climate and task oriented climate, which is stronger and more differentiated in team sports. The most influential indicator in ego oriented climate is intra-group rivalry, exerting greater influence in individual sports. For task-oriented climate the strongest indicator is having an important role in individual sports, while in team sports it is cooperative learning. Emotional intelligence dimensions correlate more strongly in team sports than in individual sports. In addition, there was a negative and indirect relation between task oriented climate and trait-anxiety in both categories of sports. (4) Conclusions: This study shows how the task-oriented motivational climate or certain levels of emotional intelligence can act preventively in the face of anxiety states in athletes. Therefore, the development of these psychological factors could prevent anxiety states and improve performance in athletes. PMID:29724008

Simulating post-wildfire forest trajectories under alternative climate and management scenarios.

PubMed

Tarancón, Alicia Azpeleta; Fulé, Peter Z; Shive, Kristen L; Sieg, Carolyn H; Meador, Andrew Sánchez; Strom, Barbara

Post-fire predictions of forest recovery under future climate change and management actions are necessary for forest managers to make decisions about treatments. We applied the Climate-Forest Vegetation Simulator (Climate-FVS), a new version of a widely used forest management model, to compare alternative climate and management scenarios in a severely burned multispecies forest of Arizona, USA. The incorporation of seven combinations of General Circulation Models (GCM) and emissions scenarios altered long-term (100 years) predictions of future forest condition compared to a No Climate Change (NCC) scenario, which forecast a gradual increase to high levels of forest density and carbon stock. In contrast, emissions scenarios that included continued high greenhouse gas releases led to near-complete deforestation by 2111. GCM-emissions scenario combinations that were less severe reduced forest structure and carbon stock relative to NCC. Fuel reduction treatments that had been applied prior to the severe wildfire did have persistent effects, especially under NCC, but were overwhelmed by increasingly severe climate change. We tested six management strategies aimed at sustaining future forests: prescribed burning at 5, 10, or 20-year intervals, thinning 40% or 60% of stand basal area, and no treatment. Severe climate change led to deforestation under all management regimes, but important differences emerged under the moderate scenarios: treatments that included regular prescribed burning fostered low density, wildfire-resistant forests composed of the naturally dominant species, ponderosa pine. Non-fire treatments under moderate climate change were forecast to become dense and susceptible to severe wildfire, with a shift to dominance by sprouting species. Current U.S. forest management requires modeling of future scenarios but does not mandate consideration of climate change effects. However, this study showed substantial differences in model outputs depending on climate and management actions. Managers should incorporate climate change into the process of analyzing the environmental effects of alternative actions.

Estimation of climate change impact on dead fuel moisture at local scale by using weather generators

NASA Astrophysics Data System (ADS)

Pellizzaro, Grazia; Bortolu, Sara; Dubrovsky, Martin; Arca, Bachisio; Ventura, Andrea; Duce, Pierpaolo

2015-04-01

The moisture content of dead fuel is an important variable in fire ignition and fire propagation. Moisture exchange in dead materials is controlled by physical processes, and is clearly dependent on atmospheric changes. According to projections of future climate in Southern Europe, changes in temperature, precipitation and extreme events are expected. More prolonged drought seasons could influence fuel moisture content and, consequently, the number of days characterized by high ignition danger in Mediterranean ecosystems. The low resolution of the climate data provided by the general circulation models (GCMs) represents a limitation for evaluating climate change impacts at local scale. For this reason, the climate research community has called to develop appropriate downscaling techniques. One of the downscaling approaches, which transform the raw outputs from the climate models (GCMs or RCMs) into data with more realistic structure, is based on linking a stochastic weather generator with the climate model outputs. Weather generators linked to climate change scenarios can therefore be used to create synthetic weather series (air temperature and relative humidity, wind speed and precipitation) representing present and future climates at local scale. The main aims of this work are to identify useful tools to determine potential impacts of expected climate change on dead fuel status in Mediterranean shrubland and, in particular, to estimate the effect of climate changes on the number of days characterized by critical values of dead fuel moisture. Measurements of dead fuel moisture content (FMC) in Mediterranean shrubland were performed by using humidity sensors in North Western Sardinia (Italy) for six years. Meteorological variables were also recorded. Data were used to determine the accuracy of the Canadian Fine Fuels Moisture Code (FFM code) in modelling moisture dynamics of dead fuel in Mediterranean vegetation. Critical threshold values of FFM code for Mediterranean climate were identified by percentile analysis, and new fuel moisture code classes were also defined. A stochastic weather generator (M&Rfi), linked to climate change scenarios derived from 17 available General Circulation Models (GCMs), was used to produce synthetic weather series, representing present and future climates, for four selected sites located in North Western Sardinia, Italy. The number of days with critical FFM code values for present and future climate were calculated and the potential impact of future climate change was analysed.

Forecasting Distributional Responses of Limber Pine to Climate Change at Management-Relevant Scales in Rocky Mountain National Park

PubMed Central

Monahan, William B.; Cook, Tammy; Melton, Forrest; Connor, Jeff; Bobowski, Ben

2013-01-01

Resource managers at parks and other protected areas are increasingly expected to factor climate change explicitly into their decision making frameworks. However, most protected areas are small relative to the geographic ranges of species being managed, so forecasts need to consider local adaptation and community dynamics that are correlated with climate and affect distributions inside protected area boundaries. Additionally, niche theory suggests that species' physiological capacities to respond to climate change may be underestimated when forecasts fail to consider the full breadth of climates occupied by the species rangewide. Here, using correlative species distribution models that contrast estimates of climatic sensitivity inferred from the two spatial extents, we quantify the response of limber pine (Pinus flexilis) to climate change in Rocky Mountain National Park (Colorado, USA). Models are trained locally within the park where limber pine is the community dominant tree species, a distinct structural-compositional vegetation class of interest to managers, and also rangewide, as suggested by niche theory. Model forecasts through 2100 under two representative concentration pathways (RCP 4.5 and 8.5 W/m2) show that the distribution of limber pine in the park is expected to move upslope in elevation, but changes in total and core patch area remain highly uncertain. Most of this uncertainty is biological, as magnitudes of projected change are considerably more variable between the two spatial extents used in model training than they are between RCPs, and novel future climates only affect local model predictions associated with RCP 8.5 after 2091. Combined, these results illustrate the importance of accounting for unknowns in species' climatic sensitivities when forecasting distributional scenarios that are used to inform management decisions. We discuss how our results for limber pine may be interpreted in the context of climate change vulnerability and used to help guide adaptive management. PMID:24391742

Forecasting the effects of land use scenarios on farmland birds reveal a potential mitigation of climate change impacts.

PubMed

Princé, Karine; Lorrillière, Romain; Barbet-Massin, Morgane; Léger, François; Jiguet, Frédéric

2015-01-01

Climate and land use changes are key drivers of current biodiversity trends, but interactions between these drivers are poorly modeled, even though they could amplify or mitigate negative impacts of climate change. Here, we attempt to predict the impacts of different agricultural change scenarios on common breeding birds within farmland included in the potential future climatic suitable areas for these species. We used the Special Report on Emissions Scenarios (SRES) to integrate likely changes in species climatic suitability, based on species distribution models, and changes in area of farmland, based on the IMAGE model, inside future climatic suitable areas. We also developed six farmland cover scenarios, based on expert opinion, which cover a wide spectrum of potential changes in livestock farming and cropping patterns by 2050. We ran generalized linear mixed models to calibrate the effects of farmland cover and climate change on bird specific abundance within 386 small agricultural regions. We used model outputs to predict potential changes in bird populations on the basis of predicted changes in regional farmland cover, in area of farmland and in species climatic suitability. We then examined the species sensitivity according to their habitat requirements. A scenario based on extensification of agricultural systems (i.e., low-intensity agriculture) showed the greatest potential to reduce reverse current declines in breeding birds. To meet ecological requirements of a larger number of species, agricultural policies accounting for regional disparities and landscape structure appear more efficient than global policies uniformly implemented at national scale. Interestingly, we also found evidence that farmland cover changes can mitigate the negative effect of climate change. Here, we confirm that there is a potential for countering negative effects of climate change by adaptive management of landscape. We argue that such studies will help inform sustainable agricultural policies for the future.

Forecasting distributional responses of limber pine to climate change at management-relevant scales in Rocky Mountain National Park.

PubMed

Monahan, William B; Cook, Tammy; Melton, Forrest; Connor, Jeff; Bobowski, Ben

2013-01-01

Resource managers at parks and other protected areas are increasingly expected to factor climate change explicitly into their decision making frameworks. However, most protected areas are small relative to the geographic ranges of species being managed, so forecasts need to consider local adaptation and community dynamics that are correlated with climate and affect distributions inside protected area boundaries. Additionally, niche theory suggests that species' physiological capacities to respond to climate change may be underestimated when forecasts fail to consider the full breadth of climates occupied by the species rangewide. Here, using correlative species distribution models that contrast estimates of climatic sensitivity inferred from the two spatial extents, we quantify the response of limber pine (Pinus flexilis) to climate change in Rocky Mountain National Park (Colorado, USA). Models are trained locally within the park where limber pine is the community dominant tree species, a distinct structural-compositional vegetation class of interest to managers, and also rangewide, as suggested by niche theory. Model forecasts through 2100 under two representative concentration pathways (RCP 4.5 and 8.5 W/m(2)) show that the distribution of limber pine in the park is expected to move upslope in elevation, but changes in total and core patch area remain highly uncertain. Most of this uncertainty is biological, as magnitudes of projected change are considerably more variable between the two spatial extents used in model training than they are between RCPs, and novel future climates only affect local model predictions associated with RCP 8.5 after 2091. Combined, these results illustrate the importance of accounting for unknowns in species' climatic sensitivities when forecasting distributional scenarios that are used to inform management decisions. We discuss how our results for limber pine may be interpreted in the context of climate change vulnerability and used to help guide adaptive management.

Projecting Poverty at the Household Scale to Assess the Impact of Climate Change on Poor People

NASA Astrophysics Data System (ADS)

Hallegatte, S.; Rozenberg, J.

2015-12-01

This paper quantifies the potential impacts of climate change on poverty in 2030 and 2050, in 92 countries covering 90% of the developing world population. It accounts for the deep uncertainties that characterize future socio-economic evolutions and the lack of data regarding the condition and livelihood of poor people. It also considers many impacts of climate change, another source of uncertainty. We use a micro-simulation model based on household surveys and explore a wide range of uncertainties on future structural change, productivity growth or demographic changes. This results, for each country, in the creation of several hundred scenarios for future income growth and income distribution. We then explore the resulting space of possible futures and use scenario discovery techniques to identify the main drivers of inequalities and poverty reduction. We find that redistribution and structural change are powerful drivers of poverty and inequality reduction, except in low-income countries. In the poorest countries in Africa, reducing poverty cannot rely on redistribution but requires low population growth and productivity growth in agriculture. Once we have explored the space of possible outcomes for poverty and inequalities, we choose two representative scenarios of the best and worst cases and model the impacts of climate change in each of these two scenarios. Climate change impacts are modeled through 4 channels. First, climate change has an impact on labor productivity growth for people who work outside because of higher temperatures. Second, climate change has an impact on human capital because of more severe stunting in some places. Third, climate change has an impact on physical capital via more frequent natural disasters. Fourth, climate change has an impact on consumption because of changes in food prices. Impacts are very heterogeneous across countries and are mostly concentrated in African and South-East Asian countries. For high radiative forcing (RCP8.5), the impact of climate change on poverty is 6 times larger in the pessimistic scenario than in the optimistic scenario, illustrating how development and poverty reduction are powerful adaptation tools. Our results stress the urgency of achieving poverty eradication by 2030 in order to limit the negative impacts of climate change on the poor.

Importance of anthropogenic climate impact, sampling error and urban development in sewer system design.

PubMed

Egger, C; Maurer, M

2015-04-15

Urban drainage design relying on observed precipitation series neglects the uncertainties associated with current and indeed future climate variability. Urban drainage design is further affected by the large stochastic variability of precipitation extremes and sampling errors arising from the short observation periods of extreme precipitation. Stochastic downscaling addresses anthropogenic climate impact by allowing relevant precipitation characteristics to be derived from local observations and an ensemble of climate models. This multi-climate model approach seeks to reflect the uncertainties in the data due to structural errors of the climate models. An ensemble of outcomes from stochastic downscaling allows for addressing the sampling uncertainty. These uncertainties are clearly reflected in the precipitation-runoff predictions of three urban drainage systems. They were mostly due to the sampling uncertainty. The contribution of climate model uncertainty was found to be of minor importance. Under the applied greenhouse gas emission scenario (A1B) and within the period 2036-2065, the potential for urban flooding in our Swiss case study is slightly reduced on average compared to the reference period 1981-2010. Scenario planning was applied to consider urban development associated with future socio-economic factors affecting urban drainage. The impact of scenario uncertainty was to a large extent found to be case-specific, thus emphasizing the need for scenario planning in every individual case. The results represent a valuable basis for discussions of new drainage design standards aiming specifically to include considerations of uncertainty. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Historical (1860) forest structure in ponderosa pine forests of the northern Front Range, Colorado

Treesearch

Peter M. Brown; Michael A. Battaglia; Paula J. Fornwalt; Benjamin Gannon; Laurie S. Huckaby; Chad Julian; Antony S. Cheng

2015-01-01

Management of many dry conifer forests in western North America is focused on promoting resilience to future wildfires, climate change, and land use impacts through restoration of historical patterns of forest structure and disturbance processes. Historical structural data provide models for past resilient conditions that inform the design of silvicultural treatments...

Multifractal analysis of a GCM climate

NASA Astrophysics Data System (ADS)

Carl, P.

2003-04-01

Multifractal analysis using the Wavelet Transform Modulus Maxima (WTMM) approach is being applied to the climate of a Mintz--Arakawa type, coarse resolution, two--layer AGCM. The model shows a backwards running period multiplication scenario throughout the northern summer, subsequent to a 'hard', subcritical Hopf bifurcation late in spring. This 'route out of chaos' (seen in cross sections of a toroidal phase space structure) is born in the planetary monsoon system which inflates the seasonal 'cycle' into these higher order structures and is blamed for the pronounced intraseasonal--to--centennial model climate variability. Previous analyses of the latter using advanced modal decompositions showed regularity based patterns in the time--frequency plane which are qualitatively similar to those obtained from the real world. The closer look here at the singularity structures, as a fundamental diagnostic supplement, aims at both more complete understanding (and quantification) of the model's qualitative dynamics and search for further tools of model intercomparison and verification in this respect. Analysing wavelet is the 10th derivative of the Gaussian which might suffice to suppress regular patterns in the data. Intraseasonal attractors, studied in time series of model precipitation over Central India, show shifting and braodening singularity spectra towards both more violent extreme events (premonsoon--monsoon transition) and weaker events (late summer to postmonsoon transition). Hints at a fractal basin boundary are found close to transition from period--2 to period--1 in the monsoon activity cycle. Interannual analyses are provided for runs with varied solar constants. To address the (in--)stationarity issue, first results are presented with a windowed multifractal analysis of longer--term runs ("singularity spectrogram").

Leadership, Organizational Climate, and Working Alliance in a Children's Mental Health Service System

PubMed Central

Green, Amy E.; Albanese, Brian J.; Cafri, Guy; Aarons, Gregory A.

2014-01-01

The goal of this study was to examine the relationships of transformational leadership and organizational climate with working alliance, in a children's mental health service system. Using multilevel structural equation modeling, the effect of leadership on working alliance was mediated by organizational climate. These results suggest that supervisors may be able to impact quality of care through improving workplace climate. Organizational factors should be considered in efforts to improve public sector services. Understanding these issues is important for program leaders, mental health service providers, and consumers because they can affect both the way services are delivered and ultimately, clinical outcomes. PMID:24323137

Leadership, organizational climate, and working alliance in a children's mental health service system.

PubMed

Green, Amy E; Albanese, Brian J; Cafri, Guy; Aarons, Gregory A

2014-10-01

The goal of this study was to examine the relationships of transformational leadership and organizational climate with working alliance, in a children's mental health service system. Using multilevel structural equation modeling, the effect of leadership on working alliance was mediated by organizational climate. These results suggest that supervisors may be able to impact quality of care through improving workplace climate. Organizational factors should be considered in efforts to improve public sector services. Understanding these issues is important for program leaders, mental health service providers, and consumers because they can affect both the way services are delivered and ultimately, clinical outcomes.

Linking climate change and karst hydrology to evaluate species vulnerability: The Edwards and Madison aquifers (Invited)

NASA Astrophysics Data System (ADS)

Mahler, B. J.; Long, A. J.; Stamm, J. F.; Poteet, M.; Symstad, A.

2013-12-01

Karst aquifers present an extreme case of flow along structurally variable pathways, making them highly dynamic systems and therefore likely to respond rapidly to climate change. In turn, many biological communities and ecosystems associated with karst are sensitive to hydrologic changes. We explored how three sites in the Edwards aquifer (Texas) and two sites in the Madison aquifer (South Dakota) might respond to projected climate change from 2011 to 2050. Ecosystems associated with these karst aquifers support federally listed endangered and threatened species and state-listed species of concern, including amphibians, birds, insects, and plants. The vulnerability of selected species associated with projected climate change was assessed. The Advanced Research Weather and Research Forecasting (WRF) model was used to simulate projected climate at a 36-km grid spacing for three weather stations near the study sites, using boundary and initial conditions from the global climate model Community Climate System Model (CCSM3) and an A2 emissions scenario. Daily temperature and precipitation projections from the WRF model were used as input for the hydrologic Rainfall-Response Aquifer and Watershed Flow (RRAWFLOW) model and the Climate Change Vulnerability Index (CCVI) model. RRAWFLOW is a lumped-parameter model that simulates hydrologic response at a single site, combining the responses of quick and slow flow that commonly characterize karst aquifers. CCVI uses historical and projected climate and hydrologic metrics to determine the vulnerability of selected species on the basis of species exposure to climate change, sensitivity to factors associated with climate change, and capacity to adapt to climate change. An upward trend in temperature was projected for 2011-2050 at all three weather stations; there was a trend (downward) in annual precipitation only for the weather station in Texas. A downward trend in mean annual spring flow or groundwater level was projected for all of the Edwards sites, but there was no significant trend for the Madison sites. Of 16 Edwards aquifer species evaluated (four amphibians, six arthropods, one fish, one mollusk, and four plants), 12 were scored as highly or moderately vulnerable under the projected climate change scenario. In contrast, all of the 8 Madison aquifer species evaluated (two mammals, one bird, one mollusk, and four plants) were scored as moderately vulnerable, stable, or intermediate between the two. The inclusion of hydrologic projections in the vulnerability assessment was essential for interpreting the effects of climate change on aquatic species of conservations concern, such as endemic salamanders. The linkage of climate, hydrologic, and vulnerability models provided a bridge to project the effects of global climate change on local karst aquifer and stream systems and selected species.

Estimation and modeling of forest attributes across large spatial scales using BiomeBGC, high-resolution imagery, LiDAR data, and inventory data

NASA Astrophysics Data System (ADS)

Golinkoff, Jordan Seth

The accurate estimation of forest attributes at many different spatial scales is a critical problem. Forest landowners may be interested in estimating timber volume, forest biomass, and forest structure to determine their forest's condition and value. Counties and states may be interested to learn about their forests to develop sustainable management plans and policies related to forests, wildlife, and climate change. Countries and consortiums of countries need information about their forests to set global and national targets to deal with issues of climate change and deforestation as well as to set national targets and understand the state of their forest at a given point in time. This dissertation approaches these questions from two perspectives. The first perspective uses the process model Biome-BGC paired with inventory and remote sensing data to make inferences about a current forest state given known climate and site variables. Using a model of this type, future climate data can be used to make predictions about future forest states as well. An example of this work applied to a forest in northern California is presented. The second perspective of estimating forest attributes uses high resolution aerial imagery paired with light detection and ranging (LiDAR) remote sensing data to develop statistical estimates of forest structure. Two approaches within this perspective are presented: a pixel based approach and an object based approach. Both approaches can serve as the platform on which models (either empirical growth and yield models or process models) can be run to generate inferences about future forest state and current forest biogeochemical cycling.

Disentangling the effects of climate variability and functional change on ecosystem carbon dynamics using semi-empirical modelling

NASA Astrophysics Data System (ADS)

Wu, J.; van der Linden, L.; Lasslop, G.; Carvalhais, N.; Pilegaard, K.; Beier, C.; Ibrom, A.

2012-04-01

The ecosystem carbon balance is affected by both external climatic forcing (e.g. solar radiation, air temperature and humidity) and internal dynamics in the ecosystem functional properties (e.g. canopy structure, leaf photosynthetic capacity and carbohydrate reserve). In order to understand to what extent and at which temporal scale, climatic variability and functional changes regulated the interannual variation (IAV) in the net ecosystem exchange of CO2 (NEE), data-driven analysis and semi-empirical modelling (Lasslop et al. 2010) were performed based on a 13 year NEE record in a temperate deciduous forest (Pilegaard et al 2011, Wu et al. 2012). We found that the sensitivity of carbon fluxes to climatic variability was significantly higher at shorter than at longer time scales and changed seasonally. This implied that the changing distribution of climate anomalies during the vegetation period could have stronger impacts on future ecosystem carbon balances than changes in average climate. At the annual time scale, approximately 80% of the interannual variability in NEE was attributed to the variation in the model parameters, indicating the observed IAV in the carbon dynamics at the investigated site was dominated by changes in ecosystem functioning. In general this study showed the need for understanding the mechanisms of ecosystem functional change. The method can be applied at other sites to explore ecosystem behavior across different plant functional types and climate gradients. Incorporating ecosystem functional change into process based models will reduce the uncertainties in long-term predictions of ecosystem carbon balances in global climate change projections. Acknowledgements. This work was supported by the EU FP7 project CARBO-Extreme, the DTU Climate Centre and the Danish national project ECOCLIM (Danish Council for Strategic Research).

Tracking climate change in a dispersal-limited species: reduced spatial and genetic connectivity in a montane salamander.

PubMed

Velo-Antón, G; Parra, J L; Parra-Olea, G; Zamudio, K R

2013-06-01

Tropical montane taxa are often locally adapted to very specific climatic conditions, contributing to their lower dispersal potential across complex landscapes. Climate and landscape features in montane regions affect population genetic structure in predictable ways, yet few empirical studies quantify the effects of both factors in shaping genetic structure of montane-adapted taxa. Here, we considered temporal and spatial variability in climate to explain contemporary genetic differentiation between populations of the montane salamander, Pseudoeurycea leprosa. Specifically, we used ecological niche modelling (ENM) and measured spatial connectivity and gene flow (using both mtDNA and microsatellite markers) across extant populations of P. leprosa in the Trans-Mexican Volcanic Belt (TVB). Our results indicate significant spatial and genetic isolation among populations, but we cannot distinguish between isolation by distance over time or current landscape barriers as mechanisms shaping population genetic divergences. Combining ecological niche modelling, spatial connectivity analyses, and historical and contemporary genetic signatures from different classes of genetic markers allows for inference of historical evolutionary processes and predictions of the impacts future climate change will have on the genetic diversity of montane taxa with low dispersal rates. Pseudoeurycea leprosa is one montane species among many endemic to this region and thus is a case study for the continued persistence of spatially and genetically isolated populations in the highly biodiverse TVB of central Mexico. © 2013 John Wiley & Sons Ltd.

Striking Seasonality in the Secular Warming of the Northern Continents: Structure and Mechanisms

NASA Astrophysics Data System (ADS)

Nigam, S.; Thomas, N. P.

2017-12-01

The linear trend in twentieth-century surface air temperature (SAT)—a key secular warming signal— exhibits striking seasonal variations over Northern Hemisphere continents; SAT trends are pronounced in winter and spring but notably weaker in summer and fall. The SAT trends in historical twentieth-century climate simulations informing the Intergovernmental Panel for Climate Change's Fifth Assessment show varied (and often unrealistic) strength and structure, and markedly weaker seasonal variation. The large intra-ensemble spread of winter SAT trends in some historical simulations was surprising, especially in the context of century-long linear trends, with implications for the detection of the secular warming signal. The striking seasonality of observed secular warming over northern continents warrants an explanation and the representation of related processes in climate models. Here, the seasonality of SAT trends over North America is shown to result from land surface-hydroclimate interactions and, to an extent, also from the secular change in low-level atmospheric circulation and related thermal advection. It is argued that the winter dormancy and summer vigor of the hydrologic cycle over middle- to high-latitude continents permit different responses to the additional incident radiative energy from increasing greenhouse gas concentrations. The seasonal cycle of climate, despite its monotony, provides an expanded phase space for the exposition of the dynamical and thermodynamical processes generating secular warming, and an exceptional cost-effective opportunity for benchmarking climate projection models.

Benchmarking novel approaches for modelling species range dynamics

PubMed Central

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

2016-01-01

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

Benchmarking novel approaches for modelling species range dynamics.

PubMed

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

2016-08-01

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

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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 eliminated the outliers. Since the criteria of selecting the "best" models are somewhat loose, we constructed several regional ensembles consisting of different number of high-ranked models and compared results from these optimized ensembles with observations and with the ensemble of all models. We tested our approach in specific regional application of the terrestrial Russian Arctic, considering permafrost and Artic biomes as key regional climate-dependent systems, and temperature and precipitation characteristics governing their state as target meteorological parameters. Results of this case study are deposited on the web portal www.permafrost.su/gcms

Using synchronization in multi-model ensembles to improve prediction

NASA Astrophysics Data System (ADS)

Hiemstra, P.; Selten, F.

2012-04-01

In recent decades, many climate models have been developed to understand and predict the behavior of the Earth's climate system. Although these models are all based on the same basic physical principles, they still show different behavior. This is for example caused by the choice of how to parametrize sub-grid scale processes. One method to combine these imperfect models, is to run a multi-model ensemble. The models are given identical initial conditions and are integrated forward in time. A multi-model estimate can for example be a weighted mean of the ensemble members. We propose to go a step further, and try to obtain synchronization between the imperfect models by connecting the multi-model ensemble, and exchanging information. The combined multi-model ensemble is also known as a supermodel. The supermodel has learned from observations how to optimally exchange information between the ensemble members. In this study we focused on the density and formulation of the onnections within the supermodel. The main question was whether we could obtain syn-chronization between two climate models when connecting only a subset of their state spaces. Limiting the connected subspace has two advantages: 1) it limits the transfer of data (bytes) between the ensemble, which can be a limiting factor in large scale climate models, and 2) learning the optimal connection strategy from observations is easier. To answer the research question, we connected two identical quasi-geostrohic (QG) atmospheric models to each other, where the model have different initial conditions. The QG model is a qualitatively realistic simulation of the winter flow on the Northern hemisphere, has three layers and uses a spectral imple-mentation. We connected the models in the original spherical harmonical state space, and in linear combinations of these spherical harmonics, i.e. Empirical Orthogonal Functions (EOFs). We show that when connecting through spherical harmonics, we only need to connect 28% of the state variables to obtain synchronization. In addition, when connecting through EOFs, we can reduce this percentage even more to 12%. This reduction is caused by the more efficient description of the model state variables when using EOFs. The connected state variables center around the medium scale structures in the model. Small and large scale structures need not be connected in order to obtain synchronization. This could be related to the baroclinic instabilities in the QG model which are located in the medium scale structures of the model. The baroclinic instabilities are the main source of divergence between the two connected models.

Effects of future climate conditions on terrestrial export from coastal southern California

NASA Astrophysics Data System (ADS)

Feng, D.; Zhao, Y.; Raoufi, R.; Beighley, E.; Melack, J. M.

2015-12-01

The Santa Barbara Coastal - Long Term Ecological Research Project (SBC-LTER) is focused on investigating the relative importance of land and ocean processes in structuring giant kelp forest ecosystems. Understanding how current and future climate conditions influence terrestrial export is a central theme for the project. Here we combine the Hillslope River Routing (HRR) model and daily precipitation and temperature downscaled using statistical downscaling based on localized constructed Analogs (LOCA) to estimate recent streamflow dynamics (2000 to 2014) and future conditions (2015 to 2100). The HRR model covers the SBC-LTER watersheds from just west of the Ventura River to Point Conception; a land area of roughly 800 km2 with 179 watersheds ranging from 0.1 to 123 km2. The downscaled climate conditions have a spatial resolution of 6 km by 6 km. Here, we use the Penman-Monteith method with the Food and Agriculture Organization of the United Nations (FAO) limited climate data approximations and land surface conditions (albedo, leaf area index, land cover) measured from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites to estimate potential evapotranspiration (PET). The HRR model is calibrated for the period 2000 to 2014 using USGS and LTER streamflow. An automated calibration technique is used. For future climate scenarios, we use mean 8-day land cover conditions. Future streamflow, ET and soil moisture statistics are presented and based on downscaled P and T from ten climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5).

Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

NASA Astrophysics Data System (ADS)

Najafi, Ehsan; Devineni, Naresh; Khanbilvardi, Reza M.; Kogan, Felix

2018-03-01

During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large-scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño-southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country-based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out-of-sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.

The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6

NASA Astrophysics Data System (ADS)

Webb, Mark J.; Andrews, Timothy; Bodas-Salcedo, Alejandro; Bony, Sandrine; Bretherton, Christopher S.; Chadwick, Robin; Chepfer, Hélène; Douville, Hervé; Good, Peter; Kay, Jennifer E.; Klein, Stephen A.; Marchand, Roger; Medeiros, Brian; Pier Siebesma, A.; Skinner, Christopher B.; Stevens, Bjorn; Tselioudis, George; Tsushima, Yoko; Watanabe, Masahiro

2017-01-01

The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud-climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions How does the Earth system respond to forcing? and What are the origins and consequences of systematic model biases? and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity.A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming?CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions.

1. How well do clouds and other relevant variables simulated by models agree with observations?

2. What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models?

3. Which models have the most credible representations of processes relevant to the simulation of clouds?

4. How do clouds and their changes interact with other elements of the climate system?

Role of model structure on the response of soil biogeochemistry to hydro-climatic fluctuations

NASA Astrophysics Data System (ADS)

Manzoni, S.; Porporato, A.

2005-05-01

Soil carbon and nutrient cycles are strongly affected by hydro-climatic variability, which interacts with the internal ecosystem structure. Here we test the implications of biogeochemical model structure on such dynamics by extending an existing model by the authors and coworkers. When forced by hydro-climatic fluctuations, the different model structures induce specific preferential nutrient paths among the soil pools, which in turn affect nutrient distribution and availability to microbes and plants. In particular, if it is assumed that microbes can directly assimilate organic nitrogen, plants tend to be inferior competitors for nutrients even in well-watered conditions, while if a certain amount of organic nitrogen is assumed to be mineralized without being first incorporated into microbial cells, vegetation can be advantaged over a wide range of soil moisture values. We also investigate the intensification of competition for nutrients (e.g., nitrogen) between plant and soil microbial communities under extreme hydrologic conditions, such as droughts and intense storms. Frequent rainfall events may determine ideal soil moisture conditions for plant uptake, enhancing nitrogen leaching while lowering oxygen concentration and inhibiting microbial activity. During droughts, the soil water potential often drops to the point of hampering the plant nutrient uptake while still remaining high enough for microbial decomposition and nitrogen immobilization. The interplay of microbe and vegetation water stress is investigated in depth as it controls the ability of one community (e.g., plants or soil microbes) to establish competitive advantage on the other. The long-term effects of these dynamics of competition and nutrient allocation are explored under steady-state and stochastic soil moisture conditions to analyze the feedbacks between soil organic matter and vegetation dynamics.

Associated and Mediating Variables Related to Job Satisfaction among Professionals from Mental Health Teams.

PubMed

Fleury, Marie-Josée; Grenier, Guy; Bamvita, Jean-Marie; Chiocchio, François

2018-06-01

Using a structural analysis, this study examines the relationship between job satisfaction among 315 mental health professionals from the province of Quebec (Canada) and a wide range of variables related to provider characteristics, team characteristics, processes, and emergent states, and organizational culture. We used the Job Satisfaction Survey to assess job satisfaction. Our conceptual framework integrated numerous independent variables adapted from the input-mediator-output-input (IMOI) model and the Integrated Team Effectiveness Model (ITEM). The structural equation model predicted 47% of the variance of job satisfaction. Job satisfaction was associated with eight variables: strong team support, participation in the decision-making process, closer collaboration, fewer conflicts among team members, modest knowledge production (team processes), firm affective commitment, multifocal identification (emergent states) and belonging to the nursing profession (provider characteristics). Team climate had an impact on six job satisfaction variables (team support, knowledge production, conflicts, affective commitment, collaboration, and multifocal identification). Results show that team processes and emergent states were mediators between job satisfaction and team climate. To increase job satisfaction among professionals, health managers need to pursue strategies that foster a positive climate within mental health teams.

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

Zarzycki, Colin M.; Thatcher, Diana R.; Jablonowski, Christiane

This paper describes an objective technique for detecting the extratropical transition (ET) of tropical cyclones (TCs) in high-resolution gridded climate data. The algorithm is based on previous observational studies using phase spaces to define the symmetry and vertical thermal structure of cyclones. Storm tracking is automated, allowing for direct analysis of climate data. Tracker performance in the North Atlantic is assessed using 23 years of data from the variable-resolution Community Atmosphere Model (CAM) at two different resolutions (DX 55 km and 28 km), the Climate Forecast System Reanalysis (CFSR, DX 38 km), and the ERA-Interim Reanalysis (ERA-I, DX 80 km).more » The mean spatiotemporal climatologies and seasonal cycles of objectively detected ET in the observationally constrained CFSR and ERA-I are well matched to previous observational studies, demonstrating the capability of the scheme to adequately find events. High resolution CAM reproduces TC and ET statistics that are in general agreement with reanalyses. One notable model bias, however, is significantly longer time between ET onset and ET completion in CAM, particularly for TCs that lose symmetry prior to developing a cold-core structure and becoming extratropical cyclones, demonstrating the capability of this method to expose model biases in simulated cyclones beyond the tropical phase.« less

Obliquity-driven expansion of North Atlantic sea ice controls structure of the last glacial

NASA Astrophysics Data System (ADS)

Turney, Chris; Thomas, Zoe; Hutchinson, David; Bradshaw, Corey; Brook, Barry; England, Matthew; Fogwill, Christopher; Jones, Richard; Palmer, Jonathan; Hughen, Konrad; Cooper, Alan

2015-04-01

North Atlantic late-Pleistocene climate was characterised by a series of abrupt climate changes, the most extreme of which were the Dansgaard-Oeschger (D-O) events; millennial-scale oscillations that switched rapidly between cold and warm atmospheric conditions of up to Δ16°C, most strongly expressed during the period 60-30 ka. Time series analysis of palaeoclimate ice core records is one of the best ways to detect threshold behaviour in the climate system; however, some of these techniques can be age model dependent. Spectral analysis of a new Greenland-Cariaco GICC05 age model (GICC05-CB), generated by combining the GICC05 and Cariaco ∂18O chronologies, reveals a change in the dominant periodicities at ~31 ka, consistent with the cessation of the D-O events. While the GICC05-CB has the same ∂18O structure as GICC05, the different periodicity profile reveals a change in the climate system at 31 ka. Stability analysis of the ∂18O time series over the last 60 ka determines the number of states the climate experienced over time, and reveals a bifurcation in the climate system at 31 ka, switching from a bistable to a monostable state. Early warning signals of this bifurcation are also detected starting 10,000 years before the shift in the form of increasing autocorrelation and variance. This is consistent with the climate system experiencing a slow forcing towards a critical threshold. These signals are found in both the GICC05-CB and GICC05 chronologies, though the timing of the bifurcation point varies slightly. We suggest that this bifurcation is linked to a minima in obliquity, causing greatly expanded sea ice in the Labrador sea. Modelling runs from the CSIRO Mk3L Earth-system model indicates that extensive sea ice cover is established in the Labrador Sea and North Pacific at the obliquity minima centred on 28.5 ka. This expanded sea ice is thus responsible for shifting the Northern Hemisphere westerlies southwards and reducing the strength of the AMOC, preventing the establishment of the cold state from 31 ka.

The impact of volcanic aerosol on the Northern Hemisphere stratospheric polar vortex: mechanisms and sensitivity to forcing structure

NASA Astrophysics Data System (ADS)

Toohey, M.; Krüger, K.; Bittner, M.; Timmreck, C.; Schmidt, H.

2014-12-01

Observations and simple theoretical arguments suggest that the Northern Hemisphere (NH) stratospheric polar vortex is stronger in winters following major volcanic eruptions. However, recent studies show that climate models forced by prescribed volcanic aerosol fields fail to reproduce this effect. We investigate the impact of volcanic aerosol forcing on stratospheric dynamics, including the strength of the NH polar vortex, in ensemble simulations with the Max Planck Institute Earth System Model. The model is forced by four different prescribed forcing sets representing the radiative properties of stratospheric aerosol following the 1991 eruption of Mt. Pinatubo: two forcing sets are based on observations, and are commonly used in climate model simulations, and two forcing sets are constructed based on coupled aerosol-climate model simulations. For all forcings, we find that simulated temperature and zonal wind anomalies in the NH high latitudes are not directly impacted by anomalous volcanic aerosol heating. Instead, high-latitude effects result from enhancements in stratospheric residual circulation, which in turn result, at least in part, from enhanced stratospheric wave activity. High-latitude effects are therefore much less robust than would be expected if they were the direct result of aerosol heating. Both observation-based forcing sets result in insignificant changes in vortex strength. For the model-based forcing sets, the vortex response is found to be sensitive to the structure of the forcing, with one forcing set leading to significant strengthening of the polar vortex in rough agreement with observation-based expectations. Differences in the dynamical response to the forcing sets imply that reproducing the polar vortex responses to past eruptions, or predicting the response to future eruptions, depends on accurate representation of the space-time structure of the volcanic aerosol forcing.

IN11B-1621: Quantifying How Climate Affects Vegetation in the Amazon Rainforest

NASA Technical Reports Server (NTRS)

Das, Kamalika; Kodali, Anuradha; Szubert, Marcin; Ganguly, Sangram; Bongard, Joshua

2016-01-01

Amazon droughts in 2005 and 2010 have raised serious concern about the future of the rainforest. Amazon forests are crucial because of their role as the largest carbon sink in the world which would effect the global warming phenomena with decreased photosynthesis activity. Especially, after a decline in plant growth in 1.68 million km2 forest area during the once-in-a-century severe drought in 2010, it is of primary importance to understand the relationship between different climatic variables and vegetation. In an earlier study, we have shown that non-linear models are better at capturing the relation dynamics of vegetation and climate variables such as temperature and precipitation, compared to linear models. In this research, we learn precise models between vegetation and climatic variables (temperature, precipitation) for normal conditions in the Amazon region using genetic programming based symbolic regression. This is done by removing high elevation and drought affected areas and also considering the slope of the region as one of the important factors while building the model. The model learned reveals new and interesting ways historical and current climate variables affect the vegetation at any location. MAIAC data has been used as a vegetation surrogate in our study. For temperature and precipitation, we have used TRMM and MODIS Land Surface Temperature data sets while learning the non-linear regression model. However, to generalize the model to make it independent of the data source, we perform transfer learning where we regress a regularized least squares to learn the parameters of the non-linear model using other data sources such as the precipitation and temperature from the Climatic Research Center (CRU). This new model is very similar in structure and performance compared to the original learned model and verifies the same claims about the nature of dependency between these climate variables and the vegetation in the Amazon region. As a result of this study, we are able to learn, for the very first time how exactly different climate factors influence vegetation at any location in the Amazon rainforests, independent of the specific sources from which the data has been obtained.

Quantifying How Climate Affects Vegetation in the Amazon Rainforest

NASA Astrophysics Data System (ADS)

Das, K.; Kodali, A.; Szubert, M.; Ganguly, S.; Bongard, J.

2016-12-01

Amazon droughts in 2005 and 2010 have raised serious concern about the future of the rainforest. Amazon forests are crucial because of their role as the largest carbon sink in the world which would effect the global warming phenomena with decreased photosynthesis activity. Especially, after a decline in plant growth in 1.68 million km2 forest area during the once-in-a-century severe drought in 2010, it is of primary importance to understand the relationship between different climatic variables and vegetation. In an earlier study, we have shown that non-linear models are better at capturing the relation dynamics of vegetation and climate variables such as temperature and precipitation, compared to linear models. In this research, we learn precise models between vegetation and climatic variables (temperature, precipitation) for normal conditions in the Amazon region using genetic programming based symbolic regression. This is done by removing high elevation and drought affected areas and also considering the slope of the region as one of the important factors while building the model. The model learned reveals new and interesting ways historical and current climate variables affect the vegetation at any location. MAIAC data has been used as a vegetation surrogate in our study. For temperature and precipitation, we have used TRMM and MODIS Land Surface Temperature data sets while learning the non-linear regression model. However, to generalize the model to make it independent of the data source, we perform transfer learning where we regress a regularized least squares to learn the parameters of the non-linear model using other data sources such as the precipitation and temperature from the Climatic Research Center (CRU). This new model is very similar in structure and performance compared to the original learned model and verifies the same claims about the nature of dependency between these climate variables and the vegetation in the Amazon region. As a result of this study, we are able to learn, for the very first time how exactly different climate factors influence vegetation at any location in the Amazon rainforests, independent of the specific sources from which the data has been obtained.

School environments and obesity: The mediating role of personal stress.

PubMed

Milam, Adam J; Jones, Chandria D; Debnam, Katrina J; Bradshaw, Catherine P

2017-01-01

Youth spend a large amount of time in the school environment. Given the multiple influences of teachers, peers, and food and physical activity options, youth are likely to experience stressors that can influence their weight. This study examines the association between school climate and weight status. Students ( n = 28,582; 58 schools) completed an online, anonymous school climate survey as part of the Maryland Safe and Supportive Schools Project. Multilevel structural equation modeling was used to explore the association between school climate, personal stress, and obesity. Analyses were stratified by gender. At the individual level, poor school climate (bullying, physical safety, and lack of whole-school connectedness) was associated with an increased likelihood of being overweight among females ( β =.115, p = .019) but not males ( β = .138; p =.244), after controlling for age, race, and physical activity. There was no association between school climate at the school level and being overweight among males or females. A second model included stress as a potential mediator; stress attenuated the relationship between poor school-related climate and being overweight ( β = .039; p = .048) among females. Findings suggest that stress related to school climate can play a role in the health and weight status of youth.

Expansion Under Climate Change: The Genetic Consequences.

PubMed

Garnier, Jimmy; Lewis, Mark A

2016-11-01

Range expansion and range shifts are crucial population responses to climate change. Genetic consequences are not well understood but are clearly coupled to ecological dynamics that, in turn, are driven by shifting climate conditions. We model a population with a deterministic reaction-diffusion model coupled to a heterogeneous environment that develops in time due to climate change. We decompose the resulting travelling wave solution into neutral genetic components to analyse the spatio-temporal dynamics of its genetic structure. Our analysis shows that range expansions and range shifts under slow climate change preserve genetic diversity. This is because slow climate change creates range boundaries that promote spatial mixing of genetic components. Mathematically, the mixing leads to so-called pushed travelling wave solutions. This mixing phenomenon is not seen in spatially homogeneous environments, where range expansion reduces genetic diversity through gene surfing arising from pulled travelling wave solutions. However, the preservation of diversity is diminished when climate change occurs too quickly. Using diversity indices, we show that fast expansions and range shifts erode genetic diversity more than slow range expansions and range shifts. Our study provides analytical insight into the dynamics of travelling wave solutions in heterogeneous environments.

Challenges of Representing Sub-Grid Physics in an Adaptive Mesh Refinement Atmospheric Model

NASA Astrophysics Data System (ADS)

O'Brien, T. A.; Johansen, H.; Johnson, J. N.; Rosa, D.; Benedict, J. J.; Keen, N. D.; Collins, W.; Goodfriend, E.

2015-12-01

Some of the greatest potential impacts from future climate change are tied to extreme atmospheric phenomena that are inherently multiscale, including tropical cyclones and atmospheric rivers. Extremes are challenging to simulate in conventional climate models due to existing models' coarse resolutions relative to the native length-scales of these phenomena. Studying the weather systems of interest requires an atmospheric model with sufficient local resolution, and sufficient performance for long-duration climate-change simulations. To this end, we have developed a new global climate code with adaptive spatial and temporal resolution. The dynamics are formulated using a block-structured conservative finite volume approach suitable for moist non-hydrostatic atmospheric dynamics. By using both space- and time-adaptive mesh refinement, the solver focuses computational resources only where greater accuracy is needed to resolve critical phenomena. We explore different methods for parameterizing sub-grid physics, such as microphysics, macrophysics, turbulence, and radiative transfer. In particular, we contrast the simplified physics representation of Reed and Jablonowski (2012) with the more complex physics representation used in the System for Atmospheric Modeling of Khairoutdinov and Randall (2003). We also explore the use of a novel macrophysics parameterization that is designed to be explicitly scale-aware.

Direct and indirect climate controls predict heterogeneous early-mid 21st century wildfire burned area across western and boreal North America

PubMed Central

Falk, Donald A.; Westerling, Anthony L.; Swetnam, Thomas W.

2017-01-01

Predicting wildfire under future conditions is complicated by complex interrelated drivers operating across large spatial scales. Annual area burned (AAB) is a useful index of global wildfire activity. Current and antecedent seasonal climatic conditions, and the timing of snowpack melt, have been suggested as important drivers of AAB. As climate warms, seasonal climate and snowpack co-vary in intricate ways, influencing fire at continental and sub-continental scales. We used independent records of seasonal climate and snow cover duration (last date of permanent snowpack, LDPS) and cell-based Structural Equation Models (SEM) to separate direct (climatic) and indirect (snow cover) effects on relative changes in AAB under future climatic scenarios across western and boreal North America. To isolate seasonal climate variables with the greatest effect on AAB, we ran multiple regression models of log-transformed AAB on seasonal climate variables and LDPS. We used the results of multiple regressions to project future AAB using GCM ensemble climate variables and LDPS, and validated model predictions with recent AAB trends. Direct influences of spring and winter temperatures on AAB are larger and more widespread than the indirect effect mediated by changes in LDPS in most areas. Despite significant warming trends and reductions in snow cover duration, projected responses of AAB to early-mid 21st century are heterogeneous across the continent. Changes in AAB range from strongly increasing (one order of magnitude increases in AAB) to moderately decreasing (more than halving of baseline AAB). Annual wildfire area burned in coming decades is likely to be highly geographically heterogeneous, reflecting interacting regional and seasonal climate drivers of fire occurrence and spread. PMID:29244839

Vegetation-climate feedbacks modulate rainfall patterns in Africa under future climate change

NASA Astrophysics Data System (ADS)

Wu, Minchao; Schurgers, Guy; Rummukainen, Markku; Smith, Benjamin; Samuelsson, Patrick; Jansson, Christer; Siltberg, Joe; May, Wilhelm

2016-07-01

Africa has been undergoing significant changes in climate and vegetation in recent decades, and continued changes may be expected over this century. Vegetation cover and composition impose important influences on the regional climate in Africa. Climate-driven changes in vegetation structure and the distribution of forests versus savannah and grassland may feed back to climate via shifts in the surface energy balance, hydrological cycle and resultant effects on surface pressure and larger-scale atmospheric circulation. We used a regional Earth system model incorporating interactive vegetation-atmosphere coupling to investigate the potential role of vegetation-mediated biophysical feedbacks on climate dynamics in Africa in an RCP8.5-based future climate scenario. The model was applied at high resolution (0.44 × 0.44°) for the CORDEX-Africa domain with boundary conditions from the CanESM2 general circulation model. We found that increased tree cover and leaf-area index (LAI) associated with a CO2 and climate-driven increase in net primary productivity, particularly over subtropical savannah areas, not only imposed important local effect on the regional climate by altering surface energy fluxes but also resulted in remote effects over central Africa by modulating the land-ocean temperature contrast, Atlantic Walker circulation and moisture inflow feeding the central African tropical rainforest region with precipitation. The vegetation-mediated feedbacks were in general negative with respect to temperature, dampening the warming trend simulated in the absence of feedbacks, and positive with respect to precipitation, enhancing rainfall reduction over the rainforest areas. Our results highlight the importance of accounting for vegetation-atmosphere interactions in climate projections for tropical and subtropical Africa.

Direct and indirect climate controls predict heterogeneous early-mid 21st century wildfire burned area across western and boreal North America.

PubMed

Kitzberger, Thomas; Falk, Donald A; Westerling, Anthony L; Swetnam, Thomas W

2017-01-01

Predicting wildfire under future conditions is complicated by complex interrelated drivers operating across large spatial scales. Annual area burned (AAB) is a useful index of global wildfire activity. Current and antecedent seasonal climatic conditions, and the timing of snowpack melt, have been suggested as important drivers of AAB. As climate warms, seasonal climate and snowpack co-vary in intricate ways, influencing fire at continental and sub-continental scales. We used independent records of seasonal climate and snow cover duration (last date of permanent snowpack, LDPS) and cell-based Structural Equation Models (SEM) to separate direct (climatic) and indirect (snow cover) effects on relative changes in AAB under future climatic scenarios across western and boreal North America. To isolate seasonal climate variables with the greatest effect on AAB, we ran multiple regression models of log-transformed AAB on seasonal climate variables and LDPS. We used the results of multiple regressions to project future AAB using GCM ensemble climate variables and LDPS, and validated model predictions with recent AAB trends. Direct influences of spring and winter temperatures on AAB are larger and more widespread than the indirect effect mediated by changes in LDPS in most areas. Despite significant warming trends and reductions in snow cover duration, projected responses of AAB to early-mid 21st century are heterogeneous across the continent. Changes in AAB range from strongly increasing (one order of magnitude increases in AAB) to moderately decreasing (more than halving of baseline AAB). Annual wildfire area burned in coming decades is likely to be highly geographically heterogeneous, reflecting interacting regional and seasonal climate drivers of fire occurrence and spread.

Multi-time scale Climate Informed Stochastic Hybrid Simulation-Optimization Model (McISH model) for Multi-Purpose Reservoir System

NASA Astrophysics Data System (ADS)

Lu, M.; Lall, U.

2013-12-01

In order to mitigate the impacts of climate change, proactive management strategies to operate reservoirs and dams are needed. A multi-time scale climate informed stochastic model is developed to optimize the operations for a multi-purpose single reservoir by simulating decadal, interannual, seasonal and sub-seasonal variability. We apply the model to a setting motivated by the largest multi-purpose dam in N. India, the Bhakhra reservoir on the Sutlej River, a tributary of the Indus. This leads to a focus on timing and amplitude of the flows for the monsoon and snowmelt periods. The flow simulations are constrained by multiple sources of historical data and GCM future projections, that are being developed through a NSF funded project titled 'Decadal Prediction and Stochastic Simulation of Hydroclimate Over Monsoon Asia'. The model presented is a multilevel, nonlinear programming model that aims to optimize the reservoir operating policy on a decadal horizon and the operation strategy on an updated annual basis. The model is hierarchical, in terms of having a structure that two optimization models designated for different time scales are nested as a matryoshka doll. The two optimization models have similar mathematical formulations with some modifications to meet the constraints within that time frame. The first level of the model is designated to provide optimization solution for policy makers to determine contracted annual releases to different uses with a prescribed reliability; the second level is a within-the-period (e.g., year) operation optimization scheme that allocates the contracted annual releases on a subperiod (e.g. monthly) basis, with additional benefit for extra release and penalty for failure. The model maximizes the net benefit of irrigation, hydropower generation and flood control in each of the periods. The model design thus facilitates the consistent application of weather and climate forecasts to improve operations of reservoir systems. The decadal flow simulations are re-initialized every year with updated climate projections to improve the reliability of the operation rules for the next year, within which the seasonal operation strategies are nested. The multi-level structure can be repeated for monthly operation with weekly subperiods to take advantage of evolving weather forecasts and seasonal climate forecasts. As a result of the hierarchical structure, sub-seasonal even weather time scale updates and adjustment can be achieved. Given an ensemble of these scenarios, the McISH reservoir simulation-optimization model is able to derive the desired reservoir storage levels, including minimum and maximum, as a function of calendar date, and the associated release patterns. The multi-time scale approach allows adaptive management of water supplies acknowledging the changing risks, meeting both the objectives over the decade in expected value and controlling the near term and planning period risk through probabilistic reliability constraints. For the applications presented, the target season is the monsoon season from June to September. The model also includes a monthly flood volume forecast model, based on a Copula density fit to the monthly flow and the flood volume flow. This is used to guide dynamic allocation of the flood control volume given the forecasts.

Mean-state acceleration of cloud-resolving models and large eddy simulations

DOE PAGES

Jones, C. R.; Bretherton, C. S.; Pritchard, M. S.

2015-10-29

In this study, large eddy simulations and cloud-resolving models (CRMs) are routinely used to simulate boundary layer and deep convective cloud processes, aid in the development of moist physical parameterization for global models, study cloud-climate feedbacks and cloud-aerosol interaction, and as the heart of superparameterized climate models. These models are computationally demanding, placing practical constraints on their use in these applications, especially for long, climate-relevant simulations. In many situations, the horizontal-mean atmospheric structure evolves slowly compared to the turnover time of the most energetic turbulent eddies. We develop a simple scheme to reduce this time scale separation to accelerate themore » evolution of the mean state. Using this approach we are able to accelerate the model evolution by a factor of 2–16 or more in idealized stratocumulus, shallow and deep cumulus convection without substantial loss of accuracy in simulating mean cloud statistics and their sensitivity to climate change perturbations. As a culminating test, we apply this technique to accelerate the embedded CRMs in the Superparameterized Community Atmosphere Model by a factor of 2, thereby showing that the method is robust and stable to realistic perturbations across spatial and temporal scales typical in a GCM.« less

Next Generation Climate Change Experiments Needed to Advance Knowledge and for Assessment of CMIP6

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

Katzenberger, John; Arnott, James; Wright, Alyson

2014-10-30

The Aspen Global Change Institute hosted a technical science workshop entitled, “Next generation climate change experiments needed to advance knowledge and for assessment of CMIP6,” on August 4-9, 2013 in Aspen, CO. Jerry Meehl (NCAR), Richard Moss (PNNL), and Karl Taylor (LLNL) served as co-chairs for the workshop which included the participation of 32 scientists representing most of the major climate modeling centers for a total of 160 participant days. In August 2013, AGCI gathered a high level meeting of representatives from major climate modeling centers around the world to assess achievements and lessons learned from the most recent generationmore » of coordinated modeling experiments known as the Coupled Model Intercomparison Project – 5 (CMIP5) as well as to scope out the science questions and coordination structure desired for the next anticipated phase of modeling experiments called CMIP6. The workshop allowed for reflection on the coordination of the CMIP5 process as well as intercomparison of model results, such as were assessed in the most recent IPCC 5th Assessment Report, Working Group 1. For example, this slide from Masahiro Watanabe examines performance on a range of models capturing Atlantic Meridional Overturning Circulation (AMOC).« less

Unraveling the martian water cycle with high-resolution global climate simulations

NASA Astrophysics Data System (ADS)

Pottier, Alizée; Forget, François; Montmessin, Franck; Navarro, Thomas; Spiga, Aymeric; Millour, Ehouarn; Szantai, André; Madeleine, Jean-Baptiste

2017-07-01

Global climate modeling of the Mars water cycle is usually performed at relatively coarse resolution (200 - 300km), which may not be sufficient to properly represent the impact of waves, fronts, topography effects on the detailed structure of clouds and surface ice deposits. Here, we present new numerical simulations of the annual water cycle performed at a resolution of 1° × 1° (∼ 60 km in latitude). The model includes the radiative effects of clouds, whose influence on the thermal structure and atmospheric dynamics is significant, thus we also examine simulations with inactive clouds to distinguish the direct impact of resolution on circulation and winds from the indirect impact of resolution via water ice clouds. To first order, we find that the high resolution does not dramatically change the behavior of the system, and that simulations performed at ∼ 200 km resolution capture well the behavior of the simulated water cycle and Mars climate. Nevertheless, a detailed comparison between high and low resolution simulations, with reference to observations, reveal several significant changes that impact our understanding of the water cycle active today on Mars. The key northern cap edge dynamics are affected by an increase in baroclinic wave strength, with a complication of northern summer dynamics. South polar frost deposition is modified, with a westward longitudinal shift, since southern dynamics are also influenced. Baroclinic wave mode transitions are observed. New transient phenomena appear, like spiral and streak clouds, already documented in the observations. Atmospheric circulation cells in the polar region exhibit a large variability and are fine structured, with slope winds. Most modeled phenomena affected by high resolution give a picture of a more turbulent planet, inducing further variability. This is challenging for long-period climate studies.

Shifts in the climate space of temperate cyprinid fishes due to climate change are coupled with altered body sizes and growth rates.

PubMed

Ruiz-Navarro, Ana; Gillingham, Phillipa K; Britton, J Robert

2016-09-01

Predictions of species responses to climate change often focus on distribution shifts, although responses can also include shifts in body sizes and population demographics. Here, shifts in the distributional ranges ('climate space'), body sizes (as maximum theoretical body sizes, L∞) and growth rates (as rate at which L∞ is reached, K) were predicted for five fishes of the Cyprinidae family in a temperate region over eight climate change projections. Great Britain was the model area, and the model species were Rutilus rutilus, Leuciscus leuciscus, Squalius cephalus, Gobio gobio and Abramis brama. Ensemble models predicted that the species' climate spaces would shift in all modelled projections, with the most drastic changes occurring under high emissions; all range centroids shifted in a north-westerly direction. Predicted climate space expanded for R. rutilus and A. brama, contracted for S. cephalus, and for L. leuciscus and G. gobio, expanded under low-emission scenarios but contracted under high emissions, suggesting the presence of some climate-distribution thresholds. For R. rutilus, A. brama, S. cephalus and G. gobio, shifts in their climate space were coupled with predicted shifts to significantly smaller maximum body sizes and/or faster growth rates, aligning strongly to aspects of temperature-body size theory. These predicted shifts in L∞ and K had considerable consequences for size-at-age per species, suggesting substantial alterations in population age structures and abundances. Thus, when predicting climate change outcomes for species, outputs that couple shifts in climate space with altered body sizes and growth rates provide considerable insights into the population and community consequences, especially for species that cannot easily track their thermal niches. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Linking Science and Management in an Interactive Geospatial, Mutli-Criterion, Structured Decision Support Framework: Use Case Studies of the "Future Forests Geo-visualization and Decision Support Tool

NASA Astrophysics Data System (ADS)

Pontius, J.; Duncan, J.

2017-12-01

Land managers are often faced with balancing management activities to accomplish a diversity of management objectives, in systems faced with many stress agents. Advances in ecosystem modeling provide a rich source of information to inform management. Coupled with advances in decision support techniques and computing capabilities, interactive tools are now accessible for a broad audience of stakeholders. Here we present one such tool designed to capture information on how climate change may impact forested ecosystems, and how that impact varies spatially across the landscape. This tool integrates empirical models of current and future forest structure and function in a structured decision framework that allows users to customize weights for multiple management objectives and visualize suitability outcomes across the landscape. Combined with climate projections, the resulting products allow stakeholders to compare the relative success of various management objectives on a pixel by pixel basis and identify locations where management outcomes are most likely to be met. Here we demonstrate this approach with the integration of several of the preliminary models developed to map species distributions, sugar maple health, forest fragmentation risk and hemlock vulnerability to hemlock woolly adelgid under current and future climate scenarios. We compare three use case studies with objective weightings designed to: 1) Identify key parcels for sugarbush conservation and management, 2) Target state lands that may serve as hemlock refugia from hemlock woolly adelgid induced mortality, and 3) Examine how climate change may alter the success of managing for both sugarbush and hemlock across privately owned lands. This tool highlights the value of flexible models that can be easily run with customized weightings in a dynamic, integrated assessment that allows users to hone in on their potentially complex management objectives, and to visualize and prioritize locations across the landscape. It also demonstrates the importance of including climate considerations for long-term management. This merging of scientific knowledge with the diversity of stakeholder needs is an important step towards using science to inform management and policy decisions.

Determining the predictors of innovation implementation in healthcare: a quantitative analysis of implementation effectiveness.

PubMed

Jacobs, Sara R; Weiner, Bryan J; Reeve, Bryce B; Hofmann, David A; Christian, Michael; Weinberger, Morris

2015-01-22

The failure rates for implementing complex innovations in healthcare organizations are high. Estimates range from 30% to 90% depending on the scope of the organizational change involved, the definition of failure, and the criteria to judge it. The innovation implementation framework offers a promising approach to examine the organizational factors that determine effective implementation. To date, the utility of this framework in a healthcare setting has been limited to qualitative studies and/or group level analyses. Therefore, the goal of this study was to quantitatively examine this framework among individual participants in the National Cancer Institute's Community Clinical Oncology Program using structural equation modeling. We examined the innovation implementation framework using structural equation modeling (SEM) among 481 physician participants in the National Cancer Institute's Community Clinical Oncology Program (CCOP). The data sources included the CCOP Annual Progress Reports, surveys of CCOP physician participants and administrators, and the American Medical Association Physician Masterfile. Overall the final model fit well. Our results demonstrated that not only did perceptions of implementation climate have a statistically significant direct effect on implementation effectiveness, but physicians' perceptions of implementation climate also mediated the relationship between organizational implementation policies and practices (IPP) and enrollment (p <0.05). In addition, physician factors such as CCOP PI status, age, radiological oncologists, and non-oncologist specialists significantly influenced enrollment as well as CCOP organizational size and structure, which had indirect effects on implementation effectiveness through IPP and implementation climate. Overall, our results quantitatively confirmed the main relationship postulated in the innovation implementation framework between IPP, implementation climate, and implementation effectiveness among individual physicians. This finding is important, as although the model has been discussed within healthcare organizations before, the studies have been predominately qualitative in nature and/or at the organizational level. In addition, our findings have practical applications. Managers looking to increase implementation effectiveness of an innovation should focus on creating an environment that physicians perceive as encouraging implementation. In addition, managers should consider instituting specific organizational IPP aimed at increasing positive perceptions of implementation climate. For example, IPP should include specific expectations, support, and rewards for innovation use.

Agricultural legacy, climate, and soil influence the restoration and carbon potential of woody regrowth in Australia.

PubMed

Dwyer, John M; Fensham, Rod J; Buckley, Yvonne M

2010-10-01

Opportunities for dual restoration and carbon benefits from naturally regenerating woody ecosystems in agricultural landscapes have been highlighted recently. The restoration capacity of woody ecosystems depends on the magnitude and duration of ecosystem modification, i.e., the "agricultural legacy." However, this legacy may not influence carbon sequestration in the same way as restoration because carbon potential depends primarily on biomass accumulation, with little consideration of other attributes and functions of the ecosystem. Our present study simultaneously assesses the restoration and carbon potential of Acacia harpophylla regrowth, an extensive regrowth ecosystem in northeastern Australia. We used a landscape-scale survey of A. harpophylla regrowth to test the following hypotheses: (1) management history, in combination with climatic and edaphic factors, has long-term effects on stem densities, and (2) higher-density stands have lower restoration and carbon potential, which is also influenced by climatic and edaphic factors. We focused on the restoration of forest structure, which was characterized using stem density, aboveground biomass, stem heights, and stem diameters. Data were analyzed using multilevel models within the hierarchical Bayesian model (HBM) framework. We found strong support for both hypotheses. Repeated attempts at clearing Brigalow (A. harpophylla ecosystem) regrowth increases stem densities, and these densities remain high over the long term, particularly in high-rainfall areas and on gilgaied, high-clay soils (hypothesis 1). In models testing hypothesis 2, interactions between stem density and stand age indicate that higher-density stands have slower biomass accumulation and structural development in the long term. After accounting for stem density and stand age, annual rainfall had a positive effect on biomass accumulation and structural development. Other climate and soil variables were retained in the various models but had weaker effects. Spatial extrapolations of the HBMs indicated that the central and eastern parts of the study region are most suitable for biomass accumulation; however, these may not correspond to the areas that historically supported the highest biomass Brigalow forests. We conclude that carbon and restoration goals are largely congruent within areas of similar climate. At the regional scale, however, spatial prioritization of restoration and carbon projects may only be aligned where carbon benefits will be high.

Constraining the models' response of tropical low clouds to SST forcings using CALIPSO observations

NASA Astrophysics Data System (ADS)

Cesana, G.; Del Genio, A. D.; Ackerman, A. S.; Brient, F.; Fridlind, A. M.; Kelley, M.; Elsaesser, G.

2017-12-01

Low-cloud response to a warmer climate is still pointed out as being the largest source of uncertainty in the last generation of climate models. To date there is no consensus among the models on whether the tropical low cloudiness would increase or decrease in a warmer climate. In addition, it has been shown that - depending on their climate sensitivity - the models either predict deeper or shallower low clouds. Recently, several relationships between inter-model characteristics of the present-day climate and future climate changes have been highlighted. These so-called emergent constraints aim to target relevant model improvements and to constrain models' projections based on current climate observations. Here we propose to use - for the first time - 10 years of CALIPSO cloud statistics to assess the ability of the models to represent the vertical structure of tropical low clouds for abnormally warm SST. We use a simulator approach to compare observations and simulations and focus on the low-layered clouds (i.e. z < 3.2km) as well the more detailed level perspective of clouds (40 levels from 0 to 19km). Results show that in most models an increase of the SST leads to a decrease of the low-layer cloud fraction. Vertically, the clouds deepen namely by decreasing the cloud fraction in the lowest levels and increasing it around the top of the boundary-layer. This feature is coincident with an increase of the high-level cloud fraction (z > 6.5km). Although the models' spread is large, the multi-model mean captures the observed variations but with a smaller amplitude. We then employ the GISS model to investigate how changes in cloud parameterizations affect the response of low clouds to warmer SSTs on the one hand; and how they affect the variations of the model's cloud profiles with respect to environmental parameters on the other hand. Finally, we use CALIPSO observations to constrain the model by determining i) what set of parameters allows reproducing the observed relationships and ii) what are the consequences on the cloud feedbacks. These results point toward process-oriented constraints of low-cloud responses to surface warming and environmental parameters.

Time-varying Concurrent Risk of Extreme Droughts and Heatwaves in California

NASA Astrophysics Data System (ADS)

Sarhadi, A.; Diffenbaugh, N. S.; Ausin, M. C.

2016-12-01

Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena such as droughts and heatwaves. The concurrent of these nature-changing climatic extremes may result in intensifying undesirable consequences in terms of human health and destructive effects in water resources. The present study assesses the risk of concurrent extreme droughts and heatwaves under dynamic nonstationary conditions arising from climate change in California. For doing so, a generalized fully Bayesian time-varying multivariate risk framework is proposed evolving through time under dynamic human-induced environment. In this methodology, an extreme, Bayesian, dynamic copula (Gumbel) is developed to model the time-varying dependence structure between the two different climate extremes. The time-varying extreme marginals are previously modeled using a Generalized Extreme Value (GEV) distribution. Bayesian Markov Chain Monte Carlo (MCMC) inference is integrated to estimate parameters of the nonstationary marginals and copula using a Gibbs sampling method. Modelled marginals and copula are then used to develop a fully Bayesian, time-varying joint return period concept for the estimation of concurrent risk. Here we argue that climate change has increased the chance of concurrent droughts and heatwaves over decades in California. It is also demonstrated that a time-varying multivariate perspective should be incorporated to assess realistic concurrent risk of the extremes for water resources planning and management in a changing climate in this area. The proposed generalized methodology can be applied for other stochastic nature-changing compound climate extremes that are under the influence of climate change.

Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as `field' or `global' significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Daily precipitation climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.

Wood production response to climate change will depend critically on forest composition and structure.

PubMed

Coomes, David A; Flores, Olivier; Holdaway, Robert; Jucker, Tommaso; Lines, Emily R; Vanderwel, Mark C

2014-12-01

Established forests currently function as a major carbon sink, sequestering as woody biomass about 26% of global fossil fuel emissions. Whether forests continue to act as a global sink will depend on many factors, including the response of aboveground wood production (AWP; MgC ha(-1 ) yr(-1) ) to climate change. Here, we explore how AWP in New Zealand's natural forests is likely to change. We start by statistically modelling the present-day growth of 97 199 individual trees within 1070 permanently marked inventory plots as a function of tree size, competitive neighbourhood and climate. We then use these growth models to identify the factors that most influence present-day AWP and to predict responses to medium-term climate change under different assumptions. We find that if the composition and structure of New Zealand's forests were to remain unchanged over the next 30 years, then AWP would increase by 6-23%, primarily as a result of physiological responses to warmer temperatures (with no appreciable effect of changing rainfall). However, if warmth-requiring trees were able to migrate into currently cooler areas and if denser canopies were able to form, then a different AWP response is likely: forests growing in the cool mountain environments would show a 30% increase in AWP, while those in the lowland would hardly respond (on average, -3% when mean annual temperature exceeds 8.0 °C). We conclude that response of wood production to anthropogenic climate change is not only dependent on the physiological responses of individual trees, but is highly contingent on whether forests adjust in composition and structure. © 2014 John Wiley & Sons Ltd.

Revealing, Reducing, and Representing Uncertainties in New Hydrologic Projections for Climate-changed Futures

NASA Astrophysics Data System (ADS)

Arnold, Jeffrey; Clark, Martyn; Gutmann, Ethan; Wood, Andy; Nijssen, Bart; Rasmussen, Roy

2016-04-01

The United States Army Corps of Engineers (USACE) has had primary responsibility for multi-purpose water resource operations on most of the major river systems in the U.S. for more than 200 years. In that time, the USACE projects and programs making up those operations have proved mostly robust against the range of natural climate variability encountered over their operating life spans. However, in some watersheds and for some variables, climate change now is known to be shifting the hydroclimatic baseline around which that natural variability occurs and changing the range of that variability as well. This makes historical stationarity an inappropriate basis for assessing continued project operations under climate-changed futures. That means new hydroclimatic projections are required at multiple scales to inform decisions about specific threats and impacts, and for possible adaptation responses to limit water-resource vulnerabilities and enhance operational resilience. However, projections of possible future hydroclimatologies have myriad complex uncertainties that require explicit guidance for interpreting and using them to inform those decisions about climate vulnerabilities and resilience. Moreover, many of these uncertainties overlap and interact. Recent work, for example, has shown the importance of assessing the uncertainties from multiple sources including: global model structure [Meehl et al., 2005; Knutti and Sedlacek, 2013]; internal climate variability [Deser et al., 2012; Kay et al., 2014]; climate downscaling methods [Gutmann et al., 2012; Mearns et al., 2013]; and hydrologic models [Addor et al., 2014; Vano et al., 2014; Mendoza et al., 2015]. Revealing, reducing, and representing these uncertainties is essential for defining the plausible quantitative climate change narratives required to inform water-resource decision-making. And to be useful, such quantitative narratives, or storylines, of climate change threats and hydrologic impacts must sample from the full range of uncertainties associated with all parts of the simulation chain, from global climate models with simulations of natural climate variability, through regional climate downscaling, and on to modeling of affected hydrologic processes and downstream water resources impacts. This talk will present part of the work underway now both to reveal and reduce some important uncertainties and to develop explicit guidance for future generation of quantitative hydroclimatic storylines. Topics will include: 1- model structural and parameter-set limitations of some methods widely used to quantify climate impacts to hydrologic processes [Gutmann et al., 2014; Newman et al., 2015]; 2- development and evaluation of new, spatially consistent, U.S. national-scale climate downscaling and hydrologic simulation capabilities directly relevant at the multiple scales of water-resource decision-making [Newman et al., 2015; Mizukami et al., 2015; Gutmann et al., 2016]; and 3- development and evaluation of advanced streamflow forecasting methods to reduce and represent integrated uncertainties in a tractable way [Wood et al., 2014; Wood et al., 2015]. A key focus will be areas where climatologic and hydrologic science is currently under-developed to inform decisions - or is perhaps wrongly scaled or misapplied in practice - indicating the need for additional fundamental science and interpretation.

Nonlinear Dynamical Modes as a Basis for Short-Term Forecast of Climate Variability

NASA Astrophysics Data System (ADS)

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

2017-12-01

We study abilities of data-driven stochastic models constructed by nonlinear dynamical decomposition of spatially distributed data to quantitative (short-term) forecast of climate characteristics. We compare two data processing techniques: (i) widely used empirical orthogonal function approach, and (ii) nonlinear dynamical modes (NDMs) framework [1,2]. We also make comparison of two kinds of the prognostic models: (i) traditional autoregression (linear) model and (ii) model in the form of random ("stochastic") nonlinear dynamical system [3]. We apply all combinations of the above-mentioned data mining techniques and kinds of models to short-term forecasts of climate indices based on sea surface temperature (SST) data. We use NOAA_ERSST_V4 dataset (monthly SST with space resolution 20 × 20) covering the tropical belt and starting from the year 1960. We demonstrate that NDM-based nonlinear model shows better prediction skill versus EOF-based linear and nonlinear models. Finally we discuss capability of NDM-based nonlinear model for long-term (decadal) prediction of climate variability. [1] D. Mukhin, A. Gavrilov, E. Loskutov , A.Feigin, J.Kurths, 2015: Principal nonlinear dynamical modes of climate variability, Scientific Reports, rep. 5, 15510; doi: 10.1038/srep15510. [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J., 2016: Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101. [3] Ya. Molkov, D. Mukhin, E. Loskutov, A. Feigin, 2012: Random dynamical models from time series. Phys. Rev. E, Vol. 85, n.3.

Evaluation of uncertainty in capturing the spatial variability and magnitudes of extreme hydrological events for the uMngeni catchment, South Africa

NASA Astrophysics Data System (ADS)

Kusangaya, Samuel; Warburton Toucher, Michele L.; van Garderen, Emma Archer

2018-02-01

Downscaled General Circulation Models (GCMs) output are used to forecast climate change and provide information used as input for hydrological modelling. Given that our understanding of climate change points towards an increasing frequency, timing and intensity of extreme hydrological events, there is therefore the need to assess the ability of downscaled GCMs to capture these extreme hydrological events. Extreme hydrological events play a significant role in regulating the structure and function of rivers and associated ecosystems. In this study, the Indicators of Hydrologic Alteration (IHA) method was adapted to assess the ability of simulated streamflow (using downscaled GCMs (dGCMs)) in capturing extreme river dynamics (high and low flows), as compared to streamflow simulated using historical climate data from 1960 to 2000. The ACRU hydrological model was used for simulating streamflow for the 13 water management units of the uMngeni Catchment, South Africa. Statistically downscaled climate models obtained from the Climate System Analysis Group at the University of Cape Town were used as input for the ACRU Model. Results indicated that, high flows and extreme high flows (one in ten year high flows/large flood events) were poorly represented both in terms of timing, frequency and magnitude. Simulated streamflow using dGCMs data also captures more low flows and extreme low flows (one in ten year lowest flows) than that captured in streamflow simulated using historical climate data. The overall conclusion was that although dGCMs output can reasonably be used to simulate overall streamflow, it performs poorly when simulating extreme high and low flows. Streamflow simulation from dGCMs must thus be used with caution in hydrological applications, particularly for design hydrology, as extreme high and low flows are still poorly represented. This, arguably calls for the further improvement of downscaling techniques in order to generate climate data more relevant and useful for hydrological applications such as in design hydrology. Nevertheless, the availability of downscaled climatic output provide the potential of exploring climate model uncertainties in different hydro climatic regions at local scales where forcing data is often less accessible but more accurate at finer spatial scales and with adequate spatial detail.

Wavelet-based time series bootstrap model for multidecadal streamflow simulation using climate indicators

NASA Astrophysics Data System (ADS)

Erkyihun, Solomon Tassew; Rajagopalan, Balaji; Zagona, Edith; Lall, Upmanu; Nowak, Kenneth

2016-05-01

A model to generate stochastic streamflow projections conditioned on quasi-oscillatory climate indices such as Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO) is presented. Recognizing that each climate index has underlying band-limited components that contribute most of the energy of the signals, we first pursue a wavelet decomposition of the signals to identify and reconstruct these features from annually resolved historical data and proxy based paleoreconstructions of each climate index covering the period from 1650 to 2012. A K-Nearest Neighbor block bootstrap approach is then developed to simulate the total signal of each of these climate index series while preserving its time-frequency structure and marginal distributions. Finally, given the simulated climate signal time series, a K-Nearest Neighbor bootstrap is used to simulate annual streamflow series conditional on the joint state space defined by the simulated climate index for each year. We demonstrate this method by applying it to simulation of streamflow at Lees Ferry gauge on the Colorado River using indices of two large scale climate forcings: Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO), which are known to modulate the Colorado River Basin (CRB) hydrology at multidecadal time scales. Skill in stochastic simulation of multidecadal projections of flow using this approach is demonstrated.

Landscape fragmentation affects responses of avian communities to climate change.

PubMed

Jarzyna, Marta A; Porter, William F; Maurer, Brian A; Zuckerberg, Benjamin; Finley, Andrew O

2015-08-01

Forecasting the consequences of climate change is contingent upon our understanding of the relationship between biodiversity patterns and climatic variability. While the impacts of climate change on individual species have been well-documented, there is a paucity of studies on climate-mediated changes in community dynamics. Our objectives were to investigate the relationship between temporal turnover in avian biodiversity and changes in climatic conditions and to assess the role of landscape fragmentation in affecting this relationship. We hypothesized that community turnover would be highest in regions experiencing the most pronounced changes in climate and that these patterns would be reduced in human-dominated landscapes. To test this hypothesis, we quantified temporal turnover in avian communities over a 20-year period using data from the New York State Breeding Atlases collected during 1980-1985 and 2000-2005. We applied Bayesian spatially varying intercept models to evaluate the relationship between temporal turnover and temporal trends in climatic conditions and landscape fragmentation. We found that models including interaction terms between climate change and landscape fragmentation were superior to models without the interaction terms, suggesting that the relationship between avian community turnover and changes in climatic conditions was affected by the level of landscape fragmentation. Specifically, we found weaker associations between temporal turnover and climatic change in regions with prevalent habitat fragmentation. We suggest that avian communities in fragmented landscapes are more robust to climate change than communities found in contiguous habitats because they are comprised of species with wider thermal niches and thus are less susceptible to shifts in climatic variability. We conclude that highly fragmented regions are likely to undergo less pronounced changes in composition and structure of faunal communities as a result of climate change, whereas those changes are likely to be greater in contiguous and unfragmented habitats. © 2015 John Wiley & Sons Ltd.

A climate change context for the decline of a foundation tree species in south-western Australia: insights from phylogeography and species distribution modelling.

PubMed

Dalmaris, Eleftheria; Ramalho, Cristina E; Poot, Pieter; Veneklaas, Erik J; Byrne, Margaret

2015-11-01

A worldwide increase in tree decline and mortality has been linked to climate change and, where these represent foundation species, this can have important implications for ecosystem functions. This study tests a combined approach of phylogeographic analysis and species distribution modelling to provide a climate change context for an observed decline in crown health and an increase in mortality in Eucalyptus wandoo, an endemic tree of south-western Australia. Phylogeographic analyses were undertaken using restriction fragment length polymorphism analysis of chloroplast DNA in 26 populations across the species distribution. Parsimony analysis of haplotype relationships was conducted, a haplotype network was prepared, and haplotype and nucleotide diversity were calculated. Species distribution modelling was undertaken using Maxent models based on extant species occurrences and projected to climate models of the last glacial maximum (LGM). A structured pattern of diversity was identified, with the presence of two groups that followed a climatic gradient from mesic to semi-arid regions. Most populations were represented by a single haplotype, but many haplotypes were shared among populations, with some having widespread distributions. A putative refugial area with high haplotype diversity was identified at the centre of the species distribution. Species distribution modelling showed high climatic suitability at the LGM and high climatic stability in the central region where higher genetic diversity was found, and low suitability elsewhere, consistent with a pattern of range contraction. Combination of phylogeography and paleo-distribution modelling can provide an evolutionary context for climate-driven tree decline, as both can be used to cross-validate evidence for refugia and contraction under harsh climatic conditions. This approach identified a central refugial area in the test species E. wandoo, with more recent expansion into peripheral areas from where it had contracted at the LGM. This signature of contraction from lower rainfall areas is consistent with current observations of decline on the semi-arid margin of the range, and indicates low capacity to tolerate forecast climatic change. Identification of a paleo-historical context for current tree decline enables conservation interventions to focus on maintaining genetic diversity, which provides the evolutionary potential for adaptation to climate change. © The Author 2015. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Effects of climate and forest structure on palms, bromeliads and bamboos in Atlantic Forest fragments of Northeastern Brazil.

PubMed

Hilário, R R; Toledo, J J

2016-01-01

Palms, bromeliads and bamboos are key elements of tropical forests and understanding the effects of climate, anthropogenic pressure and forest structure on these groups is crucial to forecast structural changes in tropical forests. Therefore, we investigated the effects of these factors on the abundance of these groups in 22 Atlantic forest fragments of Northeastern Brazil. Abundance of bromeliads and bamboos were assessed through indexes. Palms were counted within a radius of 20 m. We also obtained measures of vegetation structure, fragment size, annual precipitation, precipitation seasonality and human population density. We tested the effects of these predictors on plant groups using path analysis. Palm abundance was higher in taller forests with larger trees, closed canopy and sparse understory, which may be a result of the presence of seed dispersers and specific attributes of local palm species. Bromeliads were negatively affected by both annual precipitation and precipitation seasonality, what may reflect adaptations of these plants to use water efficiently, but also the need to capture water in a regular basis. Bamboos were not related to any predictor variable. As climate and forest structure affected the abundance of bromeliads and palms, human-induced climatic changes and disturbances in forest structure may modify the abundance of these groups. In addition, soil properties and direct measurements of human disturbance should be used in future studies in order to improve the predictability of models about plant groups in Northeastern Atlantic Forest.

Oceanography and life history predict contrasting genetic population structure in two Antarctic fish species

PubMed Central

Young, Emma F; Belchier, Mark; Hauser, Lorenz; Horsburgh, Gavin J; Meredith, Michael P; Murphy, Eugene J; Pascoal, Sonia; Rock, Jennifer; Tysklind, Niklas; Carvalho, Gary R

2015-01-01

Understanding the key drivers of population connectivity in the marine environment is essential for the effective management of natural resources. Although several different approaches to evaluating connectivity have been used, they are rarely integrated quantitatively. Here, we use a ‘seascape genetics’ approach, by combining oceanographic modelling and microsatellite analyses, to understand the dominant influences on the population genetic structure of two Antarctic fishes with contrasting life histories, Champsocephalus gunnari and Notothenia rossii. The close accord between the model projections and empirical genetic structure demonstrated that passive dispersal during the planktonic early life stages is the dominant influence on patterns and extent of genetic structuring in both species. The shorter planktonic phase of C. gunnari restricts direct transport of larvae between distant populations, leading to stronger regional differentiation. By contrast, geographic distance did not affect differentiation in N. rossii, whose longer larval period promotes long-distance dispersal. Interannual variability in oceanographic flows strongly influenced the projected genetic structure, suggesting that shifts in circulation patterns due to climate change are likely to impact future genetic connectivity and opportunities for local adaptation, resilience and recovery from perturbations. Further development of realistic climate models is required to fully assess such potential impacts. PMID:26029262

Forecasting carbon budget under climate change and CO2 fertilization for subtropical region in China using integrated biosphere simulator (IBIS) model

USGS Publications Warehouse

Zhu, Q.; Jiang, H.; Liu, J.; Peng, C.; Fang, X.; Yu, S.; Zhou, G.; Wei, X.; Ju, W.

2011-01-01

The regional carbon budget of the climatic transition zone may be very sensitive to climate change and increasing atmospheric CO2 concentrations. This study simulated the carbon cycles under these changes using process-based ecosystem models. The Integrated Biosphere Simulator (IBIS), a Dynamic Global Vegetation Model (DGVM), was used to evaluate the impacts of climate change and CO2 fertilization on net primary production (NPP), net ecosystem production (NEP), and the vegetation structure of terrestrial ecosystems in Zhejiang province (area 101,800 km2, mainly covered by subtropical evergreen forest and warm-temperate evergreen broadleaf forest) which is located in the subtropical climate area of China. Two general circulation models (HADCM3 and CGCM3) representing four IPCC climate change scenarios (HC3AA, HC3GG, CGCM-sresa2, and CGCM-sresb1) were used as climate inputs for IBIS. Results show that simulated historical biomass and NPP are consistent with field and other modelled data, which makes the analysis of future carbon budget reliable. The results indicate that NPP over the entire Zhejiang province was about 55 Mt C yr-1 during the last half of the 21st century. An NPP increase of about 24 Mt C by the end of the 21st century was estimated with the combined effects of increasing CO2 and climate change. A slight NPP increase of about 5 Mt C was estimated under the climate change alone scenario. Forests in Zhejiang are currently acting as a carbon sink with an average NEP of about 2.5 Mt C yr-1. NEP will increase to about 5 Mt C yr-1 by the end of the 21st century with the increasing atmospheric CO2 concentration and climate change. However, climate change alone will reduce the forest carbon sequestration of Zhejiang's forests. Future climate warming will substantially change the vegetation cover types; warm-temperate evergreen broadleaf forest will be gradually substituted by subtropical evergreen forest. An increasing CO2 concentration will have little contribution to vegetation changes. Simulated NPP shows geographic patterns consistent with temperature to a certain extent, and precipitation is not the limiting factor for forest NPP in the subtropical climate conditions. There is no close relationship between the spatial pattern of NEP and climate condition.

Forecasting carbon budget under climate change and CO 2 fertilization for subtropical region in China using integrated biosphere simulator (IBIS) model

USGS Publications Warehouse

Zhu, Q.; Jiang, H.; Liu, J.; Peng, C.; Fang, X.; Yu, S.; Zhou, G.; Wei, X.; Ju, W.

2011-01-01

The regional carbon budget of the climatic transition zone may be very sensitive to climate change and increasing atmospheric CO 2 concentrations. This study simulated the carbon cycles under these changes using process-based ecosystem models. The Integrated Biosphere Simulator (IBIS), a Dynamic Global Vegetation Model (DGVM), was used to evaluate the impacts of climate change and CO 2 fertilization on net primary production (NPP), net ecosystem production (NEP), and the vegetation structure of terrestrial ecosystems in Zhejiang province (area 101,800 km 2, mainly covered by subtropical evergreen forest and warm-temperate evergreen broadleaf forest) which is located in the subtropical climate area of China. Two general circulation models (HADCM3 and CGCM3) representing four IPCC climate change scenarios (HC3AA, HC3GG, CGCM-sresa2, and CGCM-sresb1) were used as climate inputs for IBIS. Results show that simulated historical biomass and NPP are consistent with field and other modelled data, which makes the analysis of future carbon budget reliable. The results indicate that NPP over the entire Zhejiang province was about 55 Mt C yr -1 during the last half of the 21 st century. An NPP increase of about 24 Mt C by the end of the 21 st century was estimated with the combined effects of increasing CO 2 and climate change. A slight NPP increase of about 5 Mt C was estimated under the climate change alone scenario. Forests in Zhejiang are currently acting as a carbon sink with an average NEP of about 2.5 Mt C yr -1. NEP will increase to about 5 Mt C yr -1 by the end of the 21 st century with the increasing atmospheric CO 2 concentration and climate change. However, climate change alone will reduce the forest carbon sequestration of Zhejiang's forests. Future climate warming will substantially change the vegetation cover types; warm-temperate evergreen broadleaf forest will be gradually substituted by subtropical evergreen forest. An increasing CO 2 concentration will have little contribution to vegetation changes. Simulated NPP shows geographic patterns consistent with temperature to a certain extent, and precipitation is not the limiting factor for forest NPP in the subtropical climate conditions. There is no close relationship between the spatial pattern of NEP and climate condition.

Challenges in global modeling of wetland extent and wetland methane dynamics

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Catchments as non-linear filters: evaluating data-driven approaches for spatio-temporal predictions in ungauged basins

NASA Astrophysics Data System (ADS)

Bellugi, D. G.; Tennant, C.; Larsen, L.

2016-12-01

Catchment and climate heterogeneity complicate prediction of runoff across time and space, and resulting parameter uncertainty can lead to large accumulated errors in hydrologic models, particularly in ungauged basins. Recently, data-driven modeling approaches have been shown to avoid the accumulated uncertainty associated with many physically-based models, providing an appealing alternative for hydrologic prediction. However, the effectiveness of different methods in hydrologically and geomorphically distinct catchments, and the robustness of these methods to changing climate and changing hydrologic processes remain to be tested. Here, we evaluate the use of machine learning techniques to predict daily runoff across time and space using only essential climatic forcing (e.g. precipitation, temperature, and potential evapotranspiration) time series as model input. Model training and testing was done using a high quality dataset of daily runoff and climate forcing data for 25+ years for 600+ minimally-disturbed catchments (drainage area range 5-25,000 km2, median size 336 km2) that cover a wide range of climatic and physical characteristics. Preliminary results using Support Vector Regression (SVR) suggest that in some catchments this nonlinear-based regression technique can accurately predict daily runoff, while the same approach fails in other catchments, indicating that the representation of climate inputs and/or catchment filter characteristics in the model structure need further refinement to increase performance. We bolster this analysis by using Sparse Identification of Nonlinear Dynamics (a sparse symbolic regression technique) to uncover the governing equations that describe runoff processes in catchments where SVR performed well and for ones where it performed poorly, thereby enabling inference about governing processes. This provides a robust means of examining how catchment complexity influences runoff prediction skill, and represents a contribution towards the integration of data-driven inference and physically-based models.

Mesoscale weather and climate modeling with the global non-hydrostatic Goddard Earth Observing System Model (GEOS-5) at cloud-permitting resolutions

NASA Astrophysics Data System (ADS)

Putman, W. M.; Suarez, M.

2009-12-01

The Goddard Earth Observing System Model (GEOS-5), an earth system model developed in the NASA Global Modeling and Assimilation Office (GMAO), has integrated the non-hydrostatic finite-volume dynamical core on the cubed-sphere grid. The extension to a non-hydrostatic dynamical framework and the quasi-uniform cubed-sphere geometry permits the efficient exploration of global weather and climate modeling at cloud permitting resolutions of 10- to 4-km on today's high performance computing platforms. We have explored a series of incremental increases in global resolution with GEOS-5 from it's standard 72-level 27-km resolution (~5.5 million cells covering the globe from the surface to 0.1 hPa) down to 3.5-km (~3.6 billion cells). We will present results from a series of forecast experiments exploring the impact of the non-hydrostatic dynamics at transition resolutions of 14- to 7-km, and the influence of increased horizontal/vertical resolution on convection and physical parameterizations within GEOS-5. Regional and mesoscale features of 5- to 10-day weather forecasts will be presented and compared with satellite observations. Our results will highlight the impact of resolution on the structure of cloud features including tropical convection and tropical cyclone predicability, cloud streets, von Karman vortices, and the marine stratocumulus cloud layer. We will also present experiment design and early results from climate impact experiments for global non-hydrostatic models using GEOS-5. Our climate experiments will focus on support for the Year of Tropical Convection (YOTC). We will also discuss a seasonal climate time-slice experiment design for downscaling coarse resolution century scale climate simulations to global non-hydrostatic resolutions of 14- to 7-km with GEOS-5.

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Annual grass invasion in sagebrush-steppe: The relative importance of climate, soil properties and biotic interactions

USGS Publications Warehouse

Bansal, Sheel; Sheley, Roger L.

2016-01-01

The invasion by winter-annual grasses (AGs) such as Bromus tectorum into sagebrush steppe throughout the western USA is a classic example of a biological invasion with multiple, interacting climate, soil and biotic factors driving the invasion, although few studies have examined all components together. Across a 6000-km2 area of the northern Great Basin, we conducted a field assessment of 100 climate, soil, and biotic (functional group abundances, diversity) factors at each of 90 sites that spanned an invasion gradient ranging from 0 to 100 % AG cover. We first determined which biotic and abiotic factors had the strongest correlative relationships with AGs and each resident functional group. We then used regression and structural equation modeling to explore how multiple ecological factors interact to influence AG abundance. Among biotic interactions, we observed negative relationships between AGs and biodiversity, perennial grass cover, resident species richness, biological soil crust cover and shrub density, whereas perennial and annual forb cover, tree cover and soil microbial biomass had no direct linkage to AG. Among abiotic factors, AG cover was strongly related to climate (increasing cover with increasing temperature and aridity), but had weak relationships with soil factors. Our structural equation model showed negative effects of perennial grasses and biodiversity on AG cover while integrating the negative effects of warmer climate and positive influence of belowground processes on resident functional groups. Our findings illustrate the relative importance of biotic interactions and climate on invasive abundance, while soil properties appear to have stronger relationships with resident biota than with invasives.

Climate Variability In The Euro-atlantic Sector As Simulated By Echam4

NASA Astrophysics Data System (ADS)

Menezes, I.; Corte-Real, J.; Ramos, A.; Conde, F.

The atmosphere is a fundamental component of the climate system and its influence in local and global climates results from its composition, structure and motion. The best available tools to simulate future climates are coupled atmosphere-ocean general circulation models (AOGCMs), ECHAM4 (T42 L19)[1] being a very relevant exam- ple of such a model due to its elaborated parametrizations of physical processes. The purpose of this work is twofold : (1) to assess the ability of ECHAM4 in reproducing the reference climate of 1961-1990, over the Euro-Atlantic sector (29N-71N; 67W- 59E) in terms of mean sea level pressure, surface temperature and total precipitation; (2) to evaluate the expected changes of the same climate elements in a warmer world. To attain the first goal the ECHAMSs control run output is compared with observed data obtained from the Climatic Research Unit (CRU data set)[2-5]; to achieve the second objective, the modelSs control run is compared with its transient run forced by greenhouse gases. In both cases, comparisons are made in terms of mean values, variability in space and time and extremes. References [1] E. Roeckner, K. Arpe, L. Bengtsson, M. Christoph, M. Claussen, L. Dümenil, M. Esch, M. Giorgetta, U. Schlese, and U. Schulzweida, 1996: The atmospheric gen- eral circulation model ECHAM4: Model description and simulation of present-day climate. Max Planck Institut für Meteorologie, Report No. 218, Hamburg, Germany, 90 pp. [2] M. Hulme, D. Conway, P.D. Jones, T. Jiang, E.M. Barrow, and C. Turney (1995), Construction of a 1961-90 European climatology for climate change impacts and mod- elling applications, Int. J. Climatol., 15, 1333-1363. [3] M. Hulme (1994), The cost of climate data U a European experience, Weather, 49, 168-175. [4] M. Hulme, and M.G. New (1997), Dependence of large-scale precipitation clima- tologies on temporal and spatial sampling, J. Climate, 10, 1099-1113. 1 [5] C.J. Willmot, S.M. Robeson and M.J. Janis (1996), Comparison of approaches for estimating time-averaged precipitation using data from the United States, Int. J. Cli- matol., 16, 1103-1115. 2

Simulated and Inferred LAI, NPP, and Biomes in North America Since the Last Glacial Maximum.

NASA Astrophysics Data System (ADS)

Zajac, L. M.; Williams, J. W.; Kaplan, J.

2004-12-01

Vegetation structure and productivity are sensitive to climate change and are an important source of feedbacks to the climate system. Here we employ multiple lines of evidence to reconstruct variations in leaf area index (LAI), net primary productivity (NPP), and biomes. LAI determines the total canopy surface area available for light interception, gas exchange, and water loss, and NPP, the increase in plant carbon per unit area, measures the flux of carbon into the terrestrial biosphere. BIOME4, an equilibrium biogeography and biogeochemistry vegetation model, is used to simulate LAI, NPP, and biome distributions in North America for the past 21,000 years at 1,000-year time-steps. BIOME4 was coupled asynchronously to the Hadley Center Unified Model with a mixed-layer ocean model forced by variations in orbital boundary conditions, physiography, and atmospheric CO2 concentration (Kaplan et al. 2002). BIOME4 models LAI as a trade-off between maximizing light interception and minimizing water loss and assigns the LAI that maximizes NPP. Past LAI's and biomes, independently estimated from fossil pollen assemblages using the modern analogue technique, are compared to model results. In unglaciated eastern North America, canopy closure of the full-glacial conifer forests and woodlands in response to ameliorating climatic conditions resulted in a 80% increase in LAI's between 21 ka and 11 ka. After 8 ka, large areas of tundra and forest-tundra developed in deglaciated regions. The BIOME4 simulations show good agreement with the LAI's and biome distribution inferred from fossil pollen records. Sensitivity analyses with BIOME4 indicate that both climate and CO2 played important roles in regulating vegetation structure and productivity.

An approach to secure weather and climate models against hardware faults

NASA Astrophysics Data System (ADS)

Düben, Peter D.; Dawson, Andrew

2017-03-01

Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelization to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. In this paper, we present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform model simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13 % for the shallow water model.

Validation and quantification of uncertainty in coupled climate models using network analysis

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

Bracco, Annalisa

We developed a fast, robust and scalable methodology to examine, quantify, and visualize climate patterns and their relationships. It is based on a set of notions, algorithms and metrics used in the study of graphs, referred to as complex network analysis. This approach can be applied to explain known climate phenomena in terms of an underlying network structure and to uncover regional and global linkages in the climate system, while comparing general circulation models outputs with observations. The proposed method is based on a two-layer network representation, and is substantially new within the available network methodologies developed for climate studies.more » At the first layer, gridded climate data are used to identify ‘‘areas’’, i.e., geographical regions that are highly homogeneous in terms of the given climate variable. At the second layer, the identified areas are interconnected with links of varying strength, forming a global climate network. The robustness of the method (i.e. the ability to separate between topological distinct fields, while identifying correctly similarities) has been extensively tested. It has been proved that it provides a reliable, fast framework for comparing and ranking the ability of climate models of reproducing observed climate patterns and their connectivity. We further developed the methodology to account for lags in the connectivity between climate patterns and refined our area identification algorithm to account for autocorrelation in the data. The new methodology based on complex network analysis has been applied to state-of-the-art climate model simulations that participated to the last IPCC (International Panel for Climate Change) assessment to verify their performances, quantify uncertainties, and uncover changes in global linkages between past and future projections. Network properties of modeled sea surface temperature and rainfall over 1956–2005 have been constrained towards observations or reanalysis data sets, and their differences quantified using two metrics. Projected changes from 2051 to 2300 under the scenario with the highest representative and extended concentration pathways (RCP8.5 and ECP8.5) have then been determined. The network of models capable of reproducing well major climate modes in the recent past, changes little during this century. In contrast, among those models the uncertainties in the projections after 2100 remain substantial, and primarily associated with divergences in the representation of the modes of variability, particularly of the El Niño Southern Oscillation (ENSO), and their connectivity, and therefore with their intrinsic predictability, more so than with differences in the mean state evolution. Additionally, we evaluated the relation between the size and the ‘strength’ of the area identified by the network analysis as corresponding to ENSO noting that only a small subset of models can reproduce realistically the observations.« less

Uncertainties in Future Regional Sea Level Trends: How to Deal with the Internal Climate Variability?

NASA Astrophysics Data System (ADS)

Becker, M.; Karpytchev, M.; Hu, A.; Deser, C.; Lennartz-Sassinek, S.

2017-12-01

Today, the Climate models (CM) are the main tools for forecasting sea level rise (SLR) at global and regional scales. The CM forecasts are accompanied by inherent uncertainties. Understanding and reducing these uncertainties is becoming a matter of increasing urgency in order to provide robust estimates of SLR impact on coastal societies, which need sustainable choices of climate adaptation strategy. These CM uncertainties are linked to structural model formulation, initial conditions, emission scenario and internal variability. The internal variability is due to complex non-linear interactions within the Earth Climate System and can induce diverse quasi-periodic oscillatory modes and long-term persistences. To quantify the effects of internal variability, most studies used multi-model ensembles or sea level projections from a single model ran with perturbed initial conditions. However, large ensembles are not generally available, or too small, and computationally expensive. In this study, we use a power-law scaling of sea level fluctuations, as observed in many other geophysical signals and natural systems, which can be used to characterize the internal climate variability. From this specific statistical framework, we (1) use the pre-industrial control run of the National Center for Atmospheric Research Community Climate System Model (NCAR-CCSM) to test the robustness of the power-law scaling hypothesis; (2) employ the power-law statistics as a tool for assessing the spread of regional sea level projections due to the internal climate variability for the 21st century NCAR-CCSM; (3) compare the uncertainties in predicted sea level changes obtained from a NCAR-CCSM multi-member ensemble simulations with estimates derived for power-law processes, and (4) explore the sensitivity of spatial patterns of the internal variability and its effects on regional sea level projections.

Diagnostic indicators for integrated assessment models of climate policy

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

Kriegler, Elmar; Petermann, Nils; Krey, Volker

2015-01-01

Integrated assessments of how climate policy interacts with energy-economic systems can be performed by a variety of models with different functional structures. This article proposes a diagnostic scheme that can be applied to a wide range of integrated assessment models to classify differences among models based on their carbon price responses. Model diagnostics can uncover patterns and provide insights into why, under a given scenario, certain types of models behave in observed ways. Such insights are informative since model behavior can have a significant impact on projections of climate change mitigation costs and other policy-relevant information. The authors propose diagnosticmore » indicators to characterize model responses to carbon price signals and test these in a diagnostic study with 11 global models. Indicators describe the magnitude of emission abatement and the associated costs relative to a harmonized baseline, the relative changes in carbon intensity and energy intensity and the extent of transformation in the energy system. This study shows a correlation among indicators suggesting that models can be classified into groups based on common patterns of behavior in response to carbon pricing. Such a classification can help to more easily explain variations among policy-relevant model results.« less

Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

NASA Technical Reports Server (NTRS)

Cohen, Charlie; Robertson, Franklin; Molod, Andrea

2014-01-01

The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of SST gradients, associated PBL baroclinicity enabled by turbulent mixing, the ensuing PBL moisture convergence, and how changes in these processes relate to convective precipitation bias growth over this short period.

Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

NASA Astrophysics Data System (ADS)

Robertson, F. R.; Cohen, C.

2014-12-01

The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of SST gradients, associated PBL baroclinicity enabled by turbulent mixing, the ensuing PBL moisture convergence, and how changes in these processes relate to convective precipitation bias growth over this short period.

Climate change and tree-line ecosystems in the Sierra Nevada: Habitat suitability modelling to inform high-elevation forest dynamics monitoring

USGS Publications Warehouse

Moore, Peggy E.; Alvarez, Otto; McKinney, Shawn T.; Li, Wenkai; Brooks, Matthew L.; Guo, Qinghua

2017-01-01

Whitebark pine and foxtail pine serve foundational roles in the subalpine zone of the Sierra Nevada. They provide the dominant structure in tree-line forests and regulate key ecosystem processes and community dynamics. Climate change models suggest that there will be changes in temperature regimes and in the timing and magnitude of precipitation within the current distribution of these species, and these changes may alter the species’ distributional limits. Other stressors include the non-native pathogen white pine blister rust and mountain pine beetle, which have played a role in the decline of whitebark pine throughout much of its range. The National Park Service is monitoring status and trends of these species. This report provides complementary information in the form of habitat suitability models to predict climate change impacts on the future distribution of these species within Sierra Nevada national parks.We used maximum entropy modeling to build habitat suitability models by relating species occurrence to environmental variables. Species occurrence was available from 328 locations for whitebark pine and 244 for foxtail pine across the species’ distributions within the parks. We constructed current climate surfaces for modeling by interpolating data from weather stations. Climate surfaces included mean, minimum, and maximum temperature and total precipitation for January, April, July, and October. We downscaled five general circulation models for the 2050s and the 2090s from ~125 km2 to 1 km2 under both an optimistic and an extreme climate scenario to bracket potential climatic change and its influence on projected suitable habitat. To describe anticipated changes in the distribution of suitable habitat, we compared, for each species, climate scenario, and time period, the current models with future models in terms of proportional change in habitat size, elevation distribution, model center points, and where habitat is predicted to expand or contract.Overall, models indicated that suitable habitats for whitebark and foxtail pine are more likely to shift geographically within the parks by 2100 rather than decline precipitously. This implies park managers might focus conservation efforts on stressors other than climate change, working toward species resilience in the face of threats from introduced disease and elevated native insect damage. More specifically, further understanding of the incidence and severity of white pine blister rust and other stressors in high elevation white pines would help assess vulnerability from threats other than climate change.

The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models

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

Zhang, Yuying; Xie, Shaocheng; Klein, Stephen A.

Clouds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have had difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors which are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the conceptmore » of instrument “simulators”, which convert model variables into pseudo-instrument observations, has evolved with the goal to improve and to facilitate the comparison of modeled clouds with observations. Many simulators have (and continue to be developed) for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the global climate modeling community to exploit satellite observations for model cloud evaluation (e.g., Klein et al. 2013; Zhang et al. 2010). This article introduces a ground-based cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to CloudSat radar observations, ARM radar measurements occur with higher temporal resolution and finer vertical resolution. This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are difficult for the CloudSat radar to detect due to surface contamination (Mace et al. 2007; Marchand et al. 2008). Therefore, the ARM ground-based cloud observations can provide important observations of clouds that complement measurements from space.« less

ClimateSpark: An in-memory distributed computing framework for big climate data analytics

NASA Astrophysics Data System (ADS)

Hu, Fei; Yang, Chaowei; Schnase, John L.; Duffy, Daniel Q.; Xu, Mengchao; Bowen, Michael K.; Lee, Tsengdar; Song, Weiwei

2018-06-01

The unprecedented growth of climate data creates new opportunities for climate studies, and yet big climate data pose a grand challenge to climatologists to efficiently manage and analyze big data. The complexity of climate data content and analytical algorithms increases the difficulty of implementing algorithms on high performance computing systems. This paper proposes an in-memory, distributed computing framework, ClimateSpark, to facilitate complex big data analytics and time-consuming computational tasks. Chunking data structure improves parallel I/O efficiency, while a spatiotemporal index is built for the chunks to avoid unnecessary data reading and preprocessing. An integrated, multi-dimensional, array-based data model (ClimateRDD) and ETL operations are developed to address big climate data variety by integrating the processing components of the climate data lifecycle. ClimateSpark utilizes Spark SQL and Apache Zeppelin to develop a web portal to facilitate the interaction among climatologists, climate data, analytic operations and computing resources (e.g., using SQL query and Scala/Python notebook). Experimental results show that ClimateSpark conducts different spatiotemporal data queries/analytics with high efficiency and data locality. ClimateSpark is easily adaptable to other big multiple-dimensional, array-based datasets in various geoscience domains.

Nursing practice environment, job outcomes and safety climate: a structural equation modelling analysis.

PubMed

Dos Santos Alves, Daniela Fernanda; da Silva, Dirceu; de Brito Guirardello, Edinêis

2017-01-01

To assess correlations between the characteristics of the nursing practice environment, job outcomes and safety climate. The nursing practice environment is critical to the well-being of professionals and to patient safety, as highlighted by national and international studies; however, there is a lack of evidence regarding this theme in paediatric units. A cross-sectional study, in two paediatric hospitals in Brazil, was conducted from December 2013 to February 2014. For data collection, we used the Nursing Work Index - Revised, Safety Attitudes Questionnaire - Short Form 2006 and the Maslach Burnout Inventory, and for analysis Spearman's correlation coefficient and structural equation modelling were used. Two hundred and sixty-seven professional nurses participated in the study. Autonomy, control over the work environment and the relationship between nursing and medical staff are factors associated with job outcomes and safety climate and can be considered their predictors. Professional nurses with greater autonomy, good working relationships and control over their work environment have lower levels of emotional exhaustion, higher job satisfaction, less intention of leaving the job and the safety climate is positive. Initiatives to improve the professional practice environment can improve the safety of paediatric patients and the well-being of professional nurses. © 2016 John Wiley & Sons Ltd.

Assessing Potential Future Carbon Dynamics with Climate Change and Fire Management in a Mountainous Landscape on the Olympic Peninsula, Washington, USA

NASA Astrophysics Data System (ADS)

Kennedy, R. S.

2010-12-01

Forests of the mountainous landscapes of the maritime Pacific Northwestern USA may have high carbon sequestration potential via their high productivity and moderate to infrequent fire regimes. With climate change, there may be shifts in incidence and severity of fire, especially in the drier areas of the region, via changes to forest productivity and hydrology, and consequent effects to C sequestration and forest structure. To explore this issue, I assessed potential effects of fire management (little fire suppression/wildland fire management/highly effective fire suppression) under two climate change scenarios on future C sequestration dynamics (amounts and spatial pattern) in Olympic National Park, WA, over a 500-year simulation period. I used the simulation platform FireBGCv2, which contains a mechanistic, individual tree succession model, a spatially explicit climate-based biophysical model that uses daily weather data, and a spatially explicit fire model incorporating ignition, spread, and effects on ecosystem components. C sequestration patterns varied over time and spatial and temporal patterns differed somewhat depending on the climate change scenario applied and the fire management methods employed. Under the more extreme climate change scenario with little fire suppression, fires were most frequent and severe and C sequestration decreased. General trends were similar under the more moderate climate change scenario, as compared to current climate, but spatial patterns differed. Both climate change scenarios under highly effective fire suppression showed about 50% of starting total C after the initial transition phase, whereas with 10% fire suppression both scenarios exhibited about 10% of starting amounts. Areas of the landscape that served as refugia for older forest under increasing frequency of high severity fire were also hotspots for C sequestration in a landscape experiencing increasing frequency of disturbance with climate change.

Effects of biotic feedback and harvest management on boreal forest fire activity under climate change.

PubMed

Krawchuk, Meg A; Cumming, Steve G

2011-01-01

Predictions of future fire activity over Canada's boreal forests have primarily been generated from climate data following assumptions that direct effects of weather will stand alone in contributing to changes in burning. However, this assumption needs explicit testing. First, areas recently burned can be less likely to burn again in the near term, and this endogenous regulation suggests the potential for self-limiting, negative biotic feedback to regional climate-driven increases in fire. Second, forest harvest is ongoing, and resulting changes in vegetation structure have been shown to affect fire activity. Consequently, we tested the assumption that fire activity will be driven by changes in fire weather without regulation by biotic feedback or regional harvest-driven changes in vegetation structure in the mixedwood boreal forest of Alberta, Canada, using a simulation experiment that includes the interaction of fire, stand dynamics, climate change, and clear cut harvest management. We found that climate change projected with fire weather indices calculated from the Canadian Regional Climate Model increased fire activity, as expected, and our simulations established evidence that the magnitude of regional increase in fire was sufficient to generate negative feedback to subsequent fire activity. We illustrate a 39% (1.39-fold) increase in fire initiation and 47% (1.47-fold) increase in area burned when climate and stand dynamics were included in simulations, yet 48% (1.48-fold) and 61% (1.61-fold) increases, respectively, when climate was considered alone. Thus, although biotic feedbacks reduced burned area estimates in important ways, they were secondary to the direct effect of climate on fire. We then show that ongoing harvest management in this region changed landscape composition in a way that led to reduced fire activity, even in the context of climate change. Although forest harvesting resulted in decreased regional fire activity when compared to unharvested conditions, forest composition and age structure was shifted substantially, illustrating a trade-off between management goals to minimize fire and conservation goals to emulate natural disturbance.

Exploring eco-hydrological consequences of the Amazonian ecosystems under climate and land-use changes in the 21st century

NASA Astrophysics Data System (ADS)

Zhang, K.; Castanho, A. D.; Moghim, S.; Bras, R. L.; Coe, M. T.; Costa, M. H.; Levine, N. M.; Longo, M.; McKnight, S.; Wang, J.; Moorcroft, P. R.

2012-12-01

Deforestation and drought have imposed regional-scale perturbations onto Amazonian ecosystems and are predicted to cause larger negative impacts on the Amazonian ecosystems and associated regional carbon dynamics in the 21st century. However, global climate models (GCMs) vary greatly in their projections of future climate change in Amazonia, giving rise to uncertainty in the expected fate of the Amazon over the coming century. In this study, we explore the possible eco-hydrological consequences of the Amazonian ecosystems under projected climate and land-use changes in the 21st century using two state-of-the-art terrestrial ecosystem models—Ecosystem Demography Model 2.1(ED2.1) and Integrated Biosphere Simulator model (IBIS)—driven by three representative, bias-corrected climate projections from three IPCC GCMs (NCARPCM1, NCARCCSM3 and HadCM3), coupled with two land-use change scenarios (a business-as-usual and a strict governance scenario). We also analyze the relative roles of climate change, CO2 fertilization, land-use change and fire in driving the projected composition and structure of the Amazonian ecosystems. Our results show that CO2 fertilization enhances vegetation productivity and above-ground biomass (AGB) in the region, while land-use change and fire cause AGB loss and the replacement of forests by the savanna-like vegetation. The impacts of climate change depend strongly on the direction and severity of projected precipitation changes in the region. In particular, when intensified water stress is superimposed on unregulated deforestation, both ecosystem models predict large-scale dieback of Amazonian rainforests.

Estimation of the possible influence of future climate changes on biodiversity in terrestrial ecosystem

NASA Astrophysics Data System (ADS)

Noda, H. M.; Nishina, K.; Ito, A.

2015-12-01

In recent decades, climate change has progressed worldwide and their influences on ecosystem structure and function that provide various goods and services to humans' well-being are of the greatest concerns. The ecosystem function and services are tightly coupled with the biodiversity, particularly via food web and biogeochemical cycles and here carbon is one of the central elements. The photosynthetic carbon fixation by plants, which forms the basis of the food web, is known to be highly sensitive to meteorological changes including radiation, temperature, precipitation and CO2 concentration. Thus an analysis of the effect of future climate change on the carbon cycle processes including photosynthetic production in a biogeographical region, which is important from the viewpoint of the biodiversity conservation, such as "biodiversity hotspot", might enable us to discuss the relevance between climate change and biodiversity.In ISI-MIP (Inter-Sectoral Impact Model Intercomparison Project) phase 1, we have estimated NPP (net primary production), plant biomass and soil organic carbon by seven global biome models under climate conditions from 1901 to 2100 based on four RCPs (Representative Concentration Pathways for 2.6, 4.5, 6.0, and 8.5 W m-2 stabilization targets) and five global climate models. In the present study, we analyzed these outputs to reveal the effects of changes on NPP, plant biomass and soil organic carbon in 20 biodiversity hotspots in various climatic regions. Although NPP of whole world tended to increase under RCP 8.5 W m-2 scenario, some biome models have shown that NPP of the hotspots in tropical regions decrease.

Assessing the impact of future climate extremes on the US corn and soybean production

NASA Astrophysics Data System (ADS)

Jin, Z.

2015-12-01

Future climate changes will place big challenges to the US agricultural system, among which increasing heat stress and precipitation variability were the two major concerns. Reliable prediction of crop productions in response to the increasingly frequent and severe extreme climate is a prerequisite for developing adaptive strategies on agricultural risk management. However, the progress has been slow on quantifying the uncertainty of computational predictions at high spatial resolutions. Here we assessed the risks of future climate extremes on the US corn and soybean production using the Agricultural Production System sIMulator (APSIM) model under different climate scenarios. To quantify the uncertainty due to conceptual representations of heat, drought and flooding stress in crop models, we proposed a new strategy of algorithm ensemble in which different methods for simulating crop responses to those extreme climatic events were incorporated into the APSIM. This strategy allowed us to isolate irrelevant structure differences among existing crop models but only focus on the process of interest. Future climate inputs were derived from high-spatial-resolution (12km × 12km) Weather Research and Forecasting (WRF) simulations under Representative Concentration Pathways 4.5 (RCP 4.5) and 8.5 (RCP 8.5). Based on crop model simulations, we analyzed the magnitude and frequency of heat, drought and flooding stress for the 21st century. We also evaluated the water use efficiency and water deficit on regional scales if farmers were to boost their yield by applying more fertilizers. Finally we proposed spatially explicit adaptation strategies of irrigation and fertilizing for different management zones.

An approach to secure weather and climate models against hardware faults

NASA Astrophysics Data System (ADS)

Düben, Peter; Dawson, Andrew

2017-04-01

Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelisation to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. We present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13% for the shallow water model.

On the utilization of hydrological modelling for road drainage design under climate and land use change.

PubMed

Kalantari, Zahra; Briel, Annemarie; Lyon, Steve W; Olofsson, Bo; Folkeson, Lennart

2014-03-15

Road drainage structures are often designed using methods that do not consider process-based representations of a landscape's hydrological response. This may create inadequately sized structures as coupled land cover and climate changes can lead to an amplified hydrological response. This study aims to quantify potential increases of runoff in response to future extreme rain events in a 61 km(2) catchment (40% forested) in southwest Sweden using a physically-based hydrological modelling approach. We simulate peak discharge and water level (stage) at two types of pipe bridges and one culvert, both of which are commonly used at Swedish road/stream intersections, under combined forest clear-cutting and future climate scenarios for 2050 and 2100. The frequency of changes in peak flow and water level varies with time (seasonality) and storm size. These changes indicate that the magnitude of peak flow and the runoff response are highly correlated to season rather than storm size. In all scenarios considered, the dimensions of the current culvert are insufficient to handle the increase in water level estimated using a physically-based modelling approach. It also appears that the water level at the pipe bridges changes differently depending on the size and timing of the storm events. The findings of the present study and the approach put forward should be considered when planning investigations on and maintenance for areas at risk of high water flows. In addition, the research highlights the utility of physically-based hydrological models to identify the appropriateness of road drainage structure dimensioning. Copyright © 2014 Elsevier B.V. All rights reserved.

Predicting climate change: Uncertainties and prospects for surmounting them

NASA Astrophysics Data System (ADS)

Ghil, Michael

2008-03-01

General circulation models (GCMs) are among the most detailed and sophisticated models of natural phenomena in existence. Still, the lack of robust and efficient subgrid-scale parametrizations for GCMs, along with the inherent sensitivity to initial data and the complex nonlinearities involved, present a major and persistent obstacle to narrowing the range of estimates for end-of-century warming. Estimating future changes in the distribution of climatic extrema is even more difficult. Brute-force tuning the large number of GCM parameters does not appear to help reduce the uncertainties. Andronov and Pontryagin (1937) proposed structural stability as a way to evaluate model robustness. Unfortunately, many real-world systems proved to be structurally unstable. We illustrate these concepts with a very simple model for the El Niño--Southern Oscillation (ENSO). Our model is governed by a differential delay equation with a single delay and periodic (seasonal) forcing. Like many of its more or less detailed and realistic precursors, this model exhibits a Devil's staircase. We study the model's structural stability, describe the mechanisms of the observed instabilities, and connect our findings to ENSO phenomenology. In the model's phase-parameter space, regions of smooth dependence on parameters alternate with rough, fractal ones. We then apply the tools of random dynamical systems and stochastic structural stability to the circle map and a torus map. The effect of noise with compact support on these maps is fairly intuitive: it is the most robust structures in phase-parameter space that survive the smoothing introduced by the noise. The nature of the stochastic forcing matters, thus suggesting that certain types of stochastic parametrizations might be better than others in achieving GCM robustness. This talk represents joint work with M. Chekroun, E. Simonnet and I. Zaliapin.

Projected Changes on the Global Surface Wave Drift Climate towards the END of the Twenty-First Century

NASA Astrophysics Data System (ADS)

Carrasco, Ana; Semedo, Alvaro; Behrens, Arno; Weisse, Ralf; Breivik, Øyvind; Saetra, Øyvind; Håkon Christensen, Kai

2016-04-01

The global wave-induced current (the Stokes Drift - SD) is an important feature of the ocean surface, with mean values close to 10 cm/s along the extra-tropical storm tracks in both hemispheres. Besides the horizontal displacement of large volumes of water the SD also plays an important role in the ocean mix-layer turbulence structure, particularly in stormy or high wind speed areas. The role of the wave-induced currents in the ocean mix-layer and in the sea surface temperature (SST) is currently a hot topic of air-sea interaction research, from forecast to climate ranges. The SD is mostly driven by wind sea waves and highly sensitive to changes in the overlaying wind speed and direction. The impact of climate change in the global wave-induced current climate will be presented. The wave model WAM has been forced by the global climate model (GCM) ECHAM5 wind speed (at 10 m height) and ice, for present-day and potential future climate conditions towards the end of the end of the twenty-first century, represented by the Intergovernmental Panel for Climate Change (IPCC) CMIP3 (Coupled Model Inter-comparison Project phase 3) A1B greenhouse gas emission scenario (usually referred to as a ''medium-high emissions'' scenario). Several wave parameters were stored as output in the WAM model simulations, including the wave spectra. The 6 hourly and 0.5°×0.5°, temporal and space resolution, wave spectra were used to compute the SD global climate of two 32-yr periods, representative of the end of the twentieth (1959-1990) and twenty-first (1969-2100) centuries. Comparisons of the present climate run with the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-40 reanalysis are used to assess the capability of the WAM-ECHAM5 runs to produce realistic SD results. This study is part of the WRCP-JCOMM COWCLIP (Coordinated Ocean Wave Climate Project) effort.

The Data Platform for Climate Research and Action: Introducing Climate Watch

NASA Astrophysics Data System (ADS)

Hennig, R. J.; Ge, M.; Friedrich, J.; Lebling, K.; Carlock, G.; Arcipowska, A.; Mangan, E.; Biru, H.; Tankou, A.; Chaudhury, M.

2017-12-01

The Paris Agreement, adopted through Decision 1/CP.21, brings all nations together to take on ambitious efforts to combat climate change. Open access to climate data supporting climate research, advancing knowledge, and informing decision making is key to encourage and strengthen efforts of stakeholders at all levels to address and respond to effects of climate change. Climate Watch is a robust online data platform developed in response to the urgent needs of knowledge and tools to empower climate research and action, including those of researchers, policy makers, the private sector, civil society, and all other non-state actors. Building on the rapid growing technology of open data and information sharing, Climate Watch is equipped with extensive amount of climate data, informative visualizations, concise yet efficient user interface, and connection to resources users need to gather insightful information on national and global progress towards delivering on the objective of the Convention and the Paris Agreement. Climate Watch brings together hundreds of quantitative and qualitative indicators for easy explore, visualize, compare, download at global, national, and sectoral levels: Greenhouse gas (GHG) emissions for more than 190 countries over the1850-2014 time period, covering all seven Kyoto Gases following IPCC source/sink categories; Structured information on over 150 NDCs facilitating the clarity, understanding and transparency of countries' contributions to address climate change; Over 6500 identified linkages between climate actions in NDCs across the 169 targets of the sustainable development goals (SDG); Over 200 indicators describing low carbon pathways from models and scenarios by integrated assessment models (IAMs) and national sources; and Data on vulnerability and risk, policies, finance, and many more. Climate Watch platform is developed as part of the broader efforts within the World Resources Institute, the NDC Partnership, and in collaboration with GIZ, UNFCCC, World Bank, and Climate Analytics.

Land Conversion in Amazonia and Northern South America; Influences on Regional Hydrology and Ecosystem Response

NASA Astrophysics Data System (ADS)

Knox, Ryan Gary

A numerical model of the terrestrial biosphere (Ecosystem Demography Model) is compbined with an atmospheric model (Brazilian Regional Atmospheric Modeling System) to investigate how land conversion in the Amazon and Northern South America have changed the hydrology of the region, and to see if those changes are significant enough to produce an ecological response. Two numerical realizations of the structure and composition of terrestrial vegetation are used as boundary conditions in a simulation of the regional land surface and atmosphere. One realization seeks to capture the present day vegetation condition that includes human deforestation and land-conversion, the other is an estimate of the potential structure and composition of the region without human influence. Model output is assessed for consistent and significant differences in hydrometeorology. Locations that show compelling differences are taken as case studies. The seasonal biases in precipitation at these locations are then used to create perturbations to long-term climate datasets. These perturbations then drive long-term simulations of dynamic vegetation to see if the climate consistent with a potential regional vegetation could elicit a change in the vegetation equilibrium at the site. Results show that South American land conversion has had consistent impacts on the regional patterning of precipitation. At some locations, changes in precipitation are persistent and constitute a significant fraction of total precipitation. Land-conversion has decreased mean continental evaporation and increased mean moisture convergence. Case study simulations of long term vegetation dynamic indicate that a hydrologic climate consistent with regional potential vegetation can indeed have significant influence on ecosystem structure and composition, particularly in water limited growth conditions. (Copies available exclusively from MIT Libraries, libraries.mit.edu/docs - docs@mit.edu)

A Scaling Model for the Anthropocene Climate Variability with Projections to 2100

NASA Astrophysics Data System (ADS)

Hébert, Raphael; Lovejoy, Shaun

2017-04-01

The determination of the climate sensitivity to radiative forcing is a fundamental climate science problem with important policy implications. We use a scaling model, with a limited set of parameters, which can directly calculate the forced globally-average surface air temperature response to anthropogenic and natural forcings. At timescales larger than an inner scale τ, which we determine as the ocean-atmosphere coupling scale at around 2 years, the global system responds, approximately, linearly, so that the variability may be decomposed into additive forced and internal components. The Ruelle response theory extends the classical linear response theory for small perturbations to systems far from equilibrium. Our model thus relates radiative forcings to a forced temperature response by convolution with a suitable Green's function, or climate response function. Motivated by scaling symmetries which allow for long range dependence, we assume a general scaling form, a scaling climate response function (SCRF) which is able to produce a wide range of responses: a power-law truncated at τ. This allows us to analytically calculate the climate sensitivity at different time scales, yielding a one-to-one relation from the transient climate response to the equilibrium climate sensitivity which are estimated, respectively, as 1.6+0.3-0.2K and 2.4+1.3-0.6K at the 90 % confidence level. The model parameters are estimated within a Bayesian framework, with a fractional Gaussian noise error model as the internal variability, from forcing series, instrumental surface temperature datasets and CMIP5 GCMs Representative Concentration Pathways (RCP) scenario runs. This observation based model is robust and projections for the coming century are made following the RCP scenario 2.6, 4.5 and 8.5, yielding in the year 2100, respectively : 1.5 +0.3)_{-0.2K, 2.3 ± 0.4 K and 4.0 ± 0.6 K at the 90 % confidence level. For comparison, the associated projections from a CMIP5 multi-model ensemble(MME) (32 models) are: 1.7 ± 0.8 K, 2.6 ± 0.8 K and 4.8 ± 1.3 K. Therefore, our projection uncertainty is less than half the structural uncertainty of this CMIP5 MME.

Climate and Non-Climate Drivers of Dengue Epidemics in Southern Coastal Ecuador

PubMed Central

Stewart-Ibarra, Anna M.; Lowe, Rachel

2013-01-01

We report a statistical mixed model for assessing the importance of climate and non-climate drivers of interannual variability in dengue fever in southern coastal Ecuador. Local climate data and Pacific sea surface temperatures (Oceanic Niño Index [ONI]) were used to predict dengue standardized morbidity ratios (SMRs; 1995–2010). Unobserved confounding factors were accounted for using non-structured yearly random effects. We found that ONI, rainfall, and minimum temperature were positively associated with dengue, with more cases of dengue during El Niño events. We assessed the influence of non-climatic factors on dengue SMR using a subset of data (2001–2010) and found that the percent of households with Aedes aegypti immatures was also a significant predictor. Our results indicate that monitoring the climate and non-climate drivers identified in this study could provide some predictive lead for forecasting dengue epidemics, showing the potential to develop a dengue early-warning system in this region. PMID:23478584

Functional diversity supports the physiological tolerance hypothesis for plant species richness along climatic gradients

USGS Publications Warehouse

Spasojevic, Marko J.; Grace, James B.; Harrison, Susan; Damschen, Ellen Ingman

2013-01-01

1. The physiological tolerance hypothesis proposes that plant species richness is highest in warm and/or wet climates because a wider range of functional strategies can persist under such conditions. Functional diversity metrics, combined with statistical modeling, offer new ways to test whether diversity-environment relationships are consistent with this hypothesis. 2. In a classic study by R. H. Whittaker (1960), herb species richness declined from mesic (cool, moist, northerly) slopes to xeric (hot, dry, southerly) slopes. Building on this dataset, we measured four plant functional traits (plant height, specific leaf area, leaf water content and foliar C:N) and used them to calculate three functional diversity metrics (functional richness, evenness, and dispersion). We then used a structural equation model to ask if ‘functional diversity’ (modeled as the joint responses of richness, evenness, and dispersion) could explain the observed relationship of topographic climate gradients to species richness. We then repeated our model examining the functional diversity of each of the four traits individually. 3. Consistent with the physiological tolerance hypothesis, we found that functional diversity was higher in more favorable climatic conditions (mesic slopes), and that multivariate functional diversity mediated the relationship of the topographic climate gradient to plant species richness. We found similar patterns for models focusing on individual trait functional diversity of leaf water content and foliar C:N. 4. Synthesis. Our results provide trait-based support for the physiological tolerance hypothesis, suggesting that benign climates support more species because they allow for a wider range of functional strategies.

Uncertainties in assessing climate change impacts on the hydrology of Mediterranean basins

NASA Astrophysics Data System (ADS)

Ludwig, Ralf

2013-04-01

There is substantial evidence in historical and recent observations that the Mediterranean and neighboring regions are especially vulnerable to the impacts of climate change. Numerous climate projections, stemming from ensembles of global and regional climate models, agree on severe changes in the climate forcing which are likely to exacerbate subsequent ecological, economic and social impacts. Many of these causal connections are closely linked to the general expectation that water availability will decline in the already water-stressed basins of Africa, the Mediterranean region and the Near East, even though considerable regional variances must be expected. Consequently, climate change impacts on water resources are raising concerns regarding their possible management and security implications. Decreasing access to water resources and other related factors could be a cause or a 'multiplier' of tensions within and between countries. Whether security threats arise from climate impacts or options for cooperation evolve does not depend only on the severity of the impacts themselves, but on social, economic, and institutional vulnerabilities or resilience as well as factors that influence local, national and international relations. However, an assessment of vulnerability and risks hinges on natural, socio-economic, and political conditions and responses, all of which are uncertain. Multidisciplinary research is needed to tackle the multi-facet complexity of climate change impacts on water resources in the Mediterranean and neighboring countries. This is particularly true in a region of overall data scarcity and poor data management and exchange structures. The current potential to develop appropriate regional adaptation measures towards climate change impacts suffers heavily from large uncertainties. These spread along a long chain of components, starting from the definition of emission scenarios to global and regional climate modeling to impact models and a subsequent variety of management options and adaptation strategies. Therefore, the 4-year FP7-project CLIMB (Climate induced changes on the hydrology of Mediterranean basins, GA: 244151) includes a major focus on the assessment and quantification of uncertainties. First, CLIMB employs a rigorous climate change model analysis, auditing the Global and Regional Climate Model data available through the ENSEMBLES and PRUDENCE initiatives. The audits lead to select the best regional performers as compared to observed values during the climatic reference period (1971- 2000). Specific bias correction and downscaling procedures are applied to provide the driving inputs and meet the demands of the subsequent impact models, transferring a future climate signal (2041-2070) into hydrological quantities at the catchment or landscape scale. However, very limited quantitative knowledge is as yet available about the role of hydrological model complexity for climate change impact assessment, where predictive power becomes more and more important and raises the demand for process-based and spatially explicit model types. Thus, CLIMB uses hydrological model ensembles to analyze the performance of existing models and works to identify the appropriate level of model complexity, and thus to determine the data specifications required to provide robust results in a climate change context. The presentation focuses on the CLIMB multi-level strategy to uncertainty assessment and highlights latest findings in some of the seven CLIMB case studies. In particular, the presentation will demonstrate the current constraints of hydro-meteorological data availability and processing and searches for solutions that can eventually be provided by integrating hydro-meteorology and ICT research communities.

Composite Study Of Aerosol Long-Range Transport Events From East Asia And North America

NASA Astrophysics Data System (ADS)

Jiang, X.; Waliser, D. E.; Guan, B.; Xavier, P.; Petch, J.; Klingaman, N. P.; Woolnough, S.

2011-12-01

While the Madden-Julian Oscillation (MJO) exerts pronounced influences on global climate and weather systems, current general circulation models (GCMs) exhibit rather limited capability in representing this prominent tropical variability mode. Meanwhile, the fundamental physics of the MJO are still elusive. Given the central role of the diabatic heating for prevailing MJO theories and demands for reducing the model deficiencies in simulating the MJO, a global model inter-comparison project on diabatic processes and vertical heating structure associated with the MJO has been coordinated through a joint effort by the WCRP-WWRP/THORPEX YOTC MJO Task Force and GEWEX GASS Program. In this presentation, progress of this model inter-comparison project will be reported, with main focus on climate simulations from about 27 atmosphere-only and coupled GCMs. Vertical structures of heating and diabatic processes associated with the MJO based on multi-model simulations will be presented along with their reanalysis and satellite estimate counterparts. Key processes possibly responsible for a realistic simulation of the MJO, including moisture-convection interaction, gross moist stability, ocean coupling, and surface heat flux, will be discussed.

Assessing the Problem Formulation in an Integrated Assessment Model: Implications for Climate Policy Decision-Support

NASA Astrophysics Data System (ADS)

Garner, G. G.; Reed, P. M.; Keller, K.

2014-12-01

Integrated assessment models (IAMs) are often used with the intent to aid in climate change decisionmaking. Numerous studies have analyzed the effects of parametric and/or structural uncertainties in IAMs, but uncertainties regarding the problem formulation are often overlooked. Here we use the Dynamic Integrated model of Climate and the Economy (DICE) to analyze the effects of uncertainty surrounding the problem formulation. The standard DICE model adopts a single objective to maximize a weighted sum of utilities of per-capita consumption. Decisionmakers, however, may be concerned with a broader range of values and preferences that are not captured by this a priori definition of utility. We reformulate the problem by introducing three additional objectives that represent values such as (i) reliably limiting global average warming to two degrees Celsius and minimizing both (ii) the costs of abatement and (iii) the damages due to climate change. We derive a set of Pareto-optimal solutions over which decisionmakers can trade-off and assess performance criteria a posteriori. We illustrate the potential for myopia in the traditional problem formulation and discuss the capability of this multiobjective formulation to provide decision support.

Global Crop Yields, Climatic Trends and Technology Enhancement

NASA Astrophysics Data System (ADS)

Najafi, E.; Devineni, N.; Khanbilvardi, R.; Kogan, F.

2016-12-01

During the last decades the global agricultural production has soared up and technology enhancement is still making positive contribution to yield growth. However, continuing population, water crisis, deforestation and climate change threaten the global food security. Attempts to predict food availability in the future around the world can be partly understood from the impact of changes to date. A new multilevel model for yield prediction at the country scale using climate covariates and technology trend is presented in this paper. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling and/or clustering to automatically group and reduce estimation uncertainties. El Niño Southern Oscillation (ENSO), Palmer Drought Severity Index (PDSI), Geopotential height (GPH), historical CO2 level and time-trend as a relatively reliable approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2007. Results show that these indicators can explain the variability in historical crop yields for most of the countries and the model performs well under out-of-sample verifications.

Sea level rise in the Severn Estuary and Bristol Channel and impacts of a Severn Barrage

NASA Astrophysics Data System (ADS)

Ahmadian, Reza; Olbert, Agnieszka I.; Hartnett, Michael; Falconer, Roger A.

2014-05-01

Many research projects in recent years have focused on marine renewable energy devices and structures due to the growing interest in marine renewable energy. These devices and structures have very different life spans. Schemes such as the Severn Barrage in the UK, as originally proposed by the Severn Tidal Power Group (STPG), would be the largest tidal renewable energy generation project in the world and would be operational for well over a century if built. Due to the long working life of some of these marine renewable energy schemes, it is important to study the impacts of climate change on such schemes, and particularly sea level rise. This study focuses on investigating the impacts of sea level rise due to climate change on the largest macro-tidal estuary in the UK, namely the Severn Estuary and Bristol Channel, and the alterations of the impacts and the performance of the Severn Barrage as a result of climate change. A hierarchy of computer models was implemented to identify the more localised impacts of climate change in the region of the study. Moreover, the potential benefits of the barrage on reducing flood risk, as well as the impact of climate change and the barrage on intertidal mudflats were investigated. The model predictions showed that the barrage would reduce flood risk due to the sea level rise. Furthermore, annual power output and the initial reduction in flood risk of the barrage would not be affected by sea level rise.

Millennial Climatic Fluctuations Are Key to the Structure of Last Glacial Ecosystems

PubMed Central

Huntley, Brian; Allen, Judy R. M.; Collingham, Yvonne C.; Hickler, Thomas; Lister, Adrian M.; Singarayer, Joy; Stuart, Anthony J.; Sykes, Martin T.; Valdes, Paul J.

2013-01-01

Whereas fossil evidence indicates extensive treeless vegetation and diverse grazing megafauna in Europe and northern Asia during the last glacial, experiments combining vegetation models and climate models have to-date simulated widespread persistence of trees. Resolving this conflict is key to understanding both last glacial ecosystems and extinction of most of the mega-herbivores. Using a dynamic vegetation model (DVM) we explored the implications of the differing climatic conditions generated by a general circulation model (GCM) in “normal” and “hosing” experiments. Whilst the former approximate interstadial conditions, the latter, designed to mimic Heinrich Events, approximate stadial conditions. The “hosing” experiments gave simulated European vegetation much closer in composition to that inferred from fossil evidence than did the “normal” experiments. Given the short duration of interstadials, and the rate at which forest cover expanded during the late-glacial and early Holocene, our results demonstrate the importance of millennial variability in determining the character of last glacial ecosystems. PMID:23613985

Millennial climatic fluctuations are key to the structure of last glacial ecosystems.

PubMed

Huntley, Brian; Allen, Judy R M; Collingham, Yvonne C; Hickler, Thomas; Lister, Adrian M; Singarayer, Joy; Stuart, Anthony J; Sykes, Martin T; Valdes, Paul J

2013-01-01

Whereas fossil evidence indicates extensive treeless vegetation and diverse grazing megafauna in Europe and northern Asia during the last glacial, experiments combining vegetation models and climate models have to-date simulated widespread persistence of trees. Resolving this conflict is key to understanding both last glacial ecosystems and extinction of most of the mega-herbivores. Using a dynamic vegetation model (DVM) we explored the implications of the differing climatic conditions generated by a general circulation model (GCM) in "normal" and "hosing" experiments. Whilst the former approximate interstadial conditions, the latter, designed to mimic Heinrich Events, approximate stadial conditions. The "hosing" experiments gave simulated European vegetation much closer in composition to that inferred from fossil evidence than did the "normal" experiments. Given the short duration of interstadials, and the rate at which forest cover expanded during the late-glacial and early Holocene, our results demonstrate the importance of millennial variability in determining the character of last glacial ecosystems.

Ocean Hydrodynamics Numerical Model in Curvilinear Coordinates for Simulating Circulation of the Global Ocean and its Separate Basins.

NASA Astrophysics Data System (ADS)

Gusev, Anatoly; Diansky, Nikolay; Zalesny, Vladimir

2010-05-01

The original program complex is proposed for the ocean circulation sigma-model, developed in the Institute of Numerical Mathematics (INM), Russian Academy of Sciences (RAS). The complex can be used in various curvilinear orthogonal coordinate systems. In addition to ocean circulation model, the complex contains a sea ice dynamics and thermodynamics model, as well as the original system of the atmospheric forcing implementation on the basis of both prescribed meteodata and atmospheric model results. This complex can be used as the oceanic block of Earth climate model as well as for solving the scientific and practical problems concerning the World ocean and its separate oceans and seas. The developed program complex can be effectively used on parallel shared memory computational systems and on contemporary personal computers. On the base of the complex proposed the ocean general circulation model (OGCM) was developed. The model is realized in the curvilinear orthogonal coordinate system obtained by the conformal transformation of the standard geographical grid that allowed us to locate the system singularities outside the integration domain. The horizontal resolution of the OGCM is 1 degree on longitude, 0.5 degree on latitude, and it has 40 non-uniform sigma-levels in depth. The model was integrated for 100 years starting from the Levitus January climatology using the realistic atmospheric annual cycle calculated on the base of CORE datasets. The experimental results showed us that the model adequately reproduces the basic characteristics of large-scale World Ocean dynamics, that is in good agreement with both observational data and results of the best climatic OGCMs. This OGCM is used as the oceanic component of the new version of climatic system model (CSM) developed in INM RAS. The latter is now ready for carrying out the new numerical experiments on climate and its change modelling according to IPCC (Intergovernmental Panel on Climate Change) scenarios in the scope of the CMIP-5 (Coupled Model Intercomparison Project). On the base of the complex proposed the Pacific Ocean circulation eddy-resolving model was realized. The integration domain covers the Pacific from Equator to Bering Strait. The model horizontal resolution is 0.125 degree and it has 20 non-uniform sigma-levels in depth. The model adequately reproduces circulation large-scale structure and its variability: Kuroshio meandering, ocean synoptic eddies, frontal zones, etc. Kuroshio high variability is shown. The distribution of contaminant was simulated that is admittedly wasted near Petropavlovsk-Kamchatsky. The results demonstrate contaminant distribution structure and provide us understanding of hydrological fields formation processes in the North-West Pacific.

Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming

NASA Astrophysics Data System (ADS)

Ilie, Iulia; Dittrich, Peter; Carvalhais, Nuno; Jung, Martin; Heinemeyer, Andreas; Migliavacca, Mirco; Morison, James I. L.; Sippel, Sebastian; Subke, Jens-Arne; Wilkinson, Matthew; Mahecha, Miguel D.

2017-09-01

Accurate model representation of land-atmosphere carbon fluxes is essential for climate projections. However, the exact responses of carbon cycle processes to climatic drivers often remain uncertain. Presently, knowledge derived from experiments, complemented by a steadily evolving body of mechanistic theory, provides the main basis for developing such models. The strongly increasing availability of measurements may facilitate new ways of identifying suitable model structures using machine learning. Here, we explore the potential of gene expression programming (GEP) to derive relevant model formulations based solely on the signals present in data by automatically applying various mathematical transformations to potential predictors and repeatedly evolving the resulting model structures. In contrast to most other machine learning regression techniques, the GEP approach generates readable models that allow for prediction and possibly for interpretation. Our study is based on two cases: artificially generated data and real observations. Simulations based on artificial data show that GEP is successful in identifying prescribed functions, with the prediction capacity of the models comparable to four state-of-the-art machine learning methods (random forests, support vector machines, artificial neural networks, and kernel ridge regressions). Based on real observations we explore the responses of the different components of terrestrial respiration at an oak forest in south-eastern England. We find that the GEP-retrieved models are often better in prediction than some established respiration models. Based on their structures, we find previously unconsidered exponential dependencies of respiration on seasonal ecosystem carbon assimilation and water dynamics. We noticed that the GEP models are only partly portable across respiration components, the identification of a general terrestrial respiration model possibly prevented by equifinality issues. Overall, GEP is a promising tool for uncovering new model structures for terrestrial ecology in the data-rich era, complementing more traditional modelling approaches.

Implementation ambiguity: The fifth element long lost in uncertainty budgets for land biogeochemical modeling

NASA Astrophysics Data System (ADS)

Tang, J.; Riley, W. J.

2015-12-01

Previous studies have identified four major sources of predictive uncertainty in modeling land biogeochemical (BGC) processes: (1) imperfect initial conditions (e.g., assumption of preindustrial equilibrium); (2) imperfect boundary conditions (e.g., climate forcing data); (3) parameterization (type I equifinality); and (4) model structure (type II equifinality). As if that were not enough to cause substantial sleep loss in modelers, we propose here a fifth element of uncertainty that results from implementation ambiguity that occurs when the model's mathematical description is translated into computational code. We demonstrate the implementation ambiguity using the example of nitrogen down regulation, a necessary process in modeling carbon-climate feedbacks. We show that, depending on common land BGC model interpretations of the governing equations for mineral nitrogen, there are three different implementations of nitrogen down regulation. We coded these three implementations in the ACME land model (ALM), and explored how they lead to different preindustrial and contemporary land biogeochemical states and fluxes. We also show how this implementation ambiguity can lead to different carbon-climate feedback estimates across the RCP scenarios. We conclude by suggesting how to avoid such implementation ambiguity in ESM BGC models.

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

NASA Astrophysics Data System (ADS)

Matthews, B.

2003-04-01

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

On the added value of WUDAPT for Urban Climate Modelling

NASA Astrophysics Data System (ADS)

Brousse, Oscar; Martilli, Alberto; Mills, Gerald; Bechtel, Benjamin; Hammerberg, Kris; Demuzere, Matthias; Wouters, Hendrik; Van Lipzig, Nicole; Ren, Chao; Feddema, Johannes J.; Masson, Valéry; Ching, Jason

2017-04-01

Over half of the planet's population now live in cities and is expected to grow up to 65% by 2050 (United Nations, 2014), most of whom will actually occupy new emerging cities of the global South. Cities' impact on climate is known to be a key driver of environmental change (IPCC, 2014) and has been studied for decades now (Howard, 1875). Still very little is known about our cities' structure around the world, preventing urban climate simulations to be done and hence guidance to be provided for mitigation. Assessing the need to bridge the urban knowledge gap for urban climate modelling perspectives, the World Urban Database and Access Portal Tool - WUDAPT - project (Ching et al., 2015; Mills et al., 2015) developed an innovative technique to map cities globally rapidly and freely. The framework established by Bechtel and Daneke (2012) derives Local Climate Zones (Stewart and Oke, 2012) city maps out of LANDSAT 8 OLI-TIRS imagery (Bechtel et al., 2015) through a supervised classification by a Random Forest Classification algorithm (Breiman, 2001). The first attempt to implement Local Climate Zones (LCZ) out of the WUDAPT product within a major climate model was carried out by Brousse et al. (2016) over Madrid, Spain. This study proved the applicability of LCZs as an enhanced urban parameterization within the WRF model (Chen et al. 2011) employing the urban canopy model BEP-BEM (Martilli, 2002; Salamanca et al., 2010), using the averaged values of the morphological and physical parameters' ranges proposed by Stewart and Oke (2012). Other studies have now used the Local Climate Zones for urban climate modelling purposes (Alexander et al., 2016; Wouters et al. 2016; Hammerberg et al., 2017; Brousse et al., 2017) and demonstrated the added value of the WUDAPT dataset. As urban data accessibility is one of the major challenge for simulations in emerging countries, this presentation will show results of simulations using LCZs and the capacity of the WUDAPT framework to be of high relevancy in multiple regions of the world, such as Africa or Asia.

Validating the Implementation Climate Scale (ICS) in Child Welfare Organizations

PubMed Central

Ehrhart, Mark G.; Torres, Elisa M.; Wright, Lisa A.; Martinez, Sandra Y.; Aarons, Gregory A.

2015-01-01

There is increasing emphasis on the use of evidence-based practices (EBPs) in child welfare settings and growing recognition of the importance of the organizational environment, and the organization’s climate in particular, for how employees perceive and support EBP implementation. Recently, Ehrhart, Aarons, and Farahnak (2014) reported on the development and validation of a measure of EBP implementation climate, the Implementation Climate Scale (ICS), in a sample of mental health clinicians. The ICS consists of 18 items and measures six critical dimensions of implementation climate: focus on EBP, educational support for EBP, recognition for EBP, rewards for EBP, selection or EBP, and selection for openness. The goal of the current study is to extend this work by providing evidence for the factor structure, reliability, and validity of the ICS in a sample of child welfare service providers. Survey data were collected from 215 child welfare providers across three states, 12 organizations, and 43 teams. Confirmatory factor analysis demonstrated good fit to the six-factor model and the alpha reliabilities for the overall measure and its subscales was acceptable. In addition, there was general support for the invariance of the factor structure across the child welfare and mental health sectors. In conclusion, this study provides evidence for the factor structure, reliability, and validity of the ICS measure for use in child welfare service organizations. PMID:26563643

Catchment Classification: Connecting Climate, Structure and Function

NASA Astrophysics Data System (ADS)

Sawicz, K. A.; Wagener, T.; Sivapalan, M.; Troch, P. A.; Carrillo, G. A.

2010-12-01

Hydrology does not yet possess a generally accepted catchment classification framework. Such a classification framework needs to: [1] give names to things, i.e. the main classification step, [2] permit transfer of information, i.e. regionalization of information, [3] permit development of generalizations, i.e. to develop new theory, and [4] provide a first order environmental change impact assessment, i.e., the hydrologic implications of climate, land use and land cover change. One strategy is to create a catchment classification framework based on the notion of catchment functions (partitioning, storage, and release). Results of an empirical study presented here connects climate and structure to catchment function (in the form of select hydrologic signatures), based on analyzing over 300 US catchments. Initial results indicate a wide assortment of signature relationships with properties of climate, geology, and vegetation. The uncertainty in the different regionalized signatures varies widely, and therefore there is variability in the robustness of classifying ungauged basins. This research provides insight into the controls of hydrologic behavior of a catchment, and enables a classification framework applicable to gauged and ungauged across the study domain. This study sheds light on what we can expect to achieve in mapping climate, structure and function in a top-down manner. Results of this study complement work done using a bottom-up physically-based modeling framework to generalize this approach (Carrillo et al., this session).

Validating the Implementation Climate Scale (ICS) in child welfare organizations.

PubMed

Ehrhart, Mark G; Torres, Elisa M; Wright, Lisa A; Martinez, Sandra Y; Aarons, Gregory A

2016-03-01

There is increasing emphasis on the use of evidence-based practices (EBPs) in child welfare settings and growing recognition of the importance of the organizational environment, and the organization's climate in particular, for how employees perceive and support EBP implementation. Recently, Ehrhart, Aarons, and Farahnak (2014) reported on the development and validation of a measure of EBP implementation climate, the Implementation Climate Scale (ICS), in a sample of mental health clinicians. The ICS consists of 18 items and measures six critical dimensions of implementation climate: focus on EBP, educational support for EBP, recognition for EBP, rewards for EBP, selection or EBP, and selection for openness. The goal of the current study is to extend this work by providing evidence for the factor structure, reliability, and validity of the ICS in a sample of child welfare service providers. Survey data were collected from 215 child welfare providers across three states, 12 organizations, and 43 teams. Confirmatory factor analysis demonstrated good fit to the six-factor model and the alpha reliabilities for the overall measure and its subscales was acceptable. In addition, there was general support for the invariance of the factor structure across the child welfare and mental health sectors. In conclusion, this study provides evidence for the factor structure, reliability, and validity of the ICS measure for use in child welfare service organizations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Formal Provenance Representation of the Data and Information Supporting the National Climate Assessment

NASA Technical Reports Server (NTRS)

Tilmes, Curt

2014-01-01

The Global Change Information System (GCIS) provides a framework for the formal representation of structured metadata about data and information about global change. The pilot deployment of the system supports the National Climate Assessment (NCA), a major report of the U.S. Global Change Research Program (USGCRP). A consumer of that report can use the system to browse and explore that supporting information. Additionally, capturing that information into a structured data model and presenting it in standard formats through well defined open inter- faces, including query interfaces suitable for data mining and linking with other databases, the information becomes valuable for other analytic uses as well.

Ontology development for provenance tracing in National Climate Assessment of the US Global Change Research Program

NASA Astrophysics Data System (ADS)

Fu, Linyun; Ma, Xiaogang; Zheng, Jin; Goldstein, Justin; Duggan, Brian; West, Patrick; Aulenbach, Steve; Tilmes, Curt; Fox, Peter

2014-05-01

This poster will show how we used a case-driven iterative methodology to develop an ontology to represent the content structure and the associated provenance information in a National Climate Assessment (NCA) report of the US Global Change Research Program (USGCRP). We applied the W3C PROV-O ontology to implement a formal representation of provenance. We argue that the use case-driven, iterative development process and the application of a formal provenance ontology help efficiently incorporate domain knowledge from earth and environmental scientists in a well-structured model interoperable in the context of the Web of Data.

Simple Climate Model Evaluation Using Impulse Response Tests

NASA Astrophysics Data System (ADS)

Schwarber, A.; Hartin, C.; Smith, S. J.

2017-12-01

Simple climate models (SCMs) are central tools used to incorporate climate responses into human-Earth system modeling. SCMs are computationally inexpensive, making them an ideal tool for a variety of analyses, including consideration of uncertainty. Despite their wide use, many SCMs lack rigorous testing of their fundamental responses to perturbations. Here, following recommendations of a recent National Academy of Sciences report, we compare several SCMs (Hector-deoclim, MAGICC 5.3, MAGICC 6.0, and the IPCC AR5 impulse response function) to diagnose model behavior and understand the fundamental system responses within each model. We conduct stylized perturbations (emissions and forcing/concentration) of three different chemical species: CO2, CH4, and BC. We find that all 4 models respond similarly in terms of overall shape, however, there are important differences in the timing and magnitude of the responses. For example, the response to a BC pulse differs over the first 20 years after the pulse among the models, a finding that is due to differences in model structure. Such perturbation experiments are difficult to conduct in complex models due to internal model noise, making a direct comparison with simple models challenging. We can, however, compare the simplified model response from a 4xCO2 step experiment to the same stylized experiment carried out by CMIP5 models, thereby testing the ability of SCMs to emulate complex model results. This work allows an assessment of how well current understanding of Earth system responses are incorporated into multi-model frameworks by way of simple climate models.

Post-Fire Recovery of Eco-Hydrologic Behavior Given Historic and Projected Climate Variability in California Mediterranean Type Environments

NASA Astrophysics Data System (ADS)

Seaby, L. P.; Tague, C. L.; Hope, A. S.

2006-12-01

The Mediterranean type environments (MTEs) of California are characterized by a distinct wet and dry season and high variability in inter-annual climate. Water limitation in MTEs makes eco-hydrological processes highly sensitive to both climate variability and frequent fire disturbance. This research modeled post-fire eco- hydrologic behavior under historical and moderate and extreme scenarios of future climate in a semi-arid chaparral dominated southern California MTE. We used a physically-based, spatially-distributed, eco- hydrological model (RHESSys - Regional Hydro-Ecologic Simulation System), to capture linkages between water and vegetation response to the combined effects of fire and historic and future climate variability. We found post-fire eco-hydrologic behavior to be strongly influenced by the episodic nature of MTE climate, which intensifies under projected climate change. Higher rates of post-fire net primary productivity were found under moderate climate change, while more extreme climate change produced water stressed conditions which were less favorable for vegetation productivity. Precipitation variability in the historic record follows the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), and these inter-annual climate characteristics intensify under climate change. Inter-annual variation in streamflow follows these precipitation patterns. Post-fire streamflow and carbon cycling trajectories are strongly dependent on climate characteristics during the first 5 years following fire, and historic intra-climate variability during this period tends to overwhelm longer term trends and variation that might be attributable to climate change. Results have implications for water resource availability, vegetation type conversion from shrubs to grassland, and changes in ecosystem structure and function.

Mesocosms Reveal Ecological Surprises from Climate Change.

PubMed

Fordham, Damien A

2015-12-01

Understanding, predicting, and mitigating the impacts of climate change on biodiversity poses one of the most crucial challenges this century. Currently, we know more about how future climates are likely to shift across the globe than about how species will respond to these changes. Two recent studies show how mesocosm experiments can hasten understanding of the ecological consequences of climate change on species' extinction risk, community structure, and ecosystem functions. Using a large-scale terrestrial warming experiment, Bestion et al. provide the first direct evidence that future global warming can increase extinction risk for temperate ectotherms. Using aquatic mesocosms, Yvon-Durocher et al. show that human-induced climate change could, in some cases, actually enhance the diversity of local communities, increasing productivity. Blending these theoretical and empirical results with computational models will improve forecasts of biodiversity loss and altered ecosystem processes due to climate change.

IPR 1.0: an efficient method for calculating solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities

NASA Astrophysics Data System (ADS)

Zhang, Y.; Chen, W.; Li, J.

2013-12-01

Climate change may alter the spatial distribution, composition, structure, and functions of plant communities. Transitional zones between biomes, or ecotones, are particularly sensitive to climate change. Ecotones are usually heterogeneous with sparse trees. The dynamics of ecotones are mainly determined by the growth and competition of individual plants in the communities. Therefore it is necessary to calculate solar radiation absorbed by individual plants for understanding and predicting their responses to climate change. In this study, we developed an individual plant radiation model, IPR (version 1.0), to calculate solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities. The model is developed based on geometrical optical relationships assuming crowns of woody plants are rectangular boxes with uniform leaf area density. The model calculates the fractions of sunlit and shaded leaf classes and the solar radiation absorbed by each class, including direct radiation from the sun, diffuse radiation from the sky, and scattered radiation from the plant community. The solar radiation received on the ground is also calculated. We tested the model by comparing with the analytical solutions of random distributions of plants. The tests show that the model results are very close to the averages of the random distributions. This model is efficient in computation, and is suitable for ecological models to simulate long-term transient responses of plant communities to climate change.

Micro-topographic hydrologic variability due to vegetation acclimation under climate change

NASA Astrophysics Data System (ADS)

Le, P. V.; Kumar, P.

2012-12-01

Land surface micro-topography and vegetation cover have fundamental effects on the land-atmosphere interactions. The altered temperature and precipitation variability associated with climate change will affect the water and energy processes both directly and that mediated through vegetation. Since climate change induces vegetation acclimation that leads to shifts in evapotranspiration and heat fluxes, it further modifies microclimate and near-surface hydrological processes. In this study, we investigate the impacts of vegetation acclimation to climate change on micro-topographic hydrologic variability. The ability to accurately predict these impacts requires the simultaneous considerations of biochemical, ecophysiological and hydrological processes. A multilayer canopy-root-soil system model coupled with a conjunctive surface-subsurface flow model is used to capture the acclimatory responses and analyze the changes in dynamics of structure and connectivity of micro-topographic storage and in magnitudes of runoff. The study is performed using Light Detection and Ranging (LiDAR) topographic data in the Birds Point-New Madrid floodway in Missouri, U.S.A. The result indicates that both climate change and its associated vegetation acclimation play critical roles in altering the micro-topographic hydrological responses.

Harvesting Atlantic Cod under Climate Variability

NASA Astrophysics Data System (ADS)

Oremus, K. L.

2016-12-01

Previous literature links the growth of a fishery to climate variability. This study uses an age-structured bioeconomic model to compare optimal harvest in the Gulf of Maine Atlantic cod fishery under a variable climate versus a static climate. The optimal harvest path depends on the relationship between fishery growth and the interest rate, with higher interest rates dictating greater harvests now at the cost of long-term stock sustainability. Given the time horizon of a single generation of fishermen under assumptions of a static climate, the model finds that the economically optimal management strategy is to harvest the entire stock in the short term and allow the fishery to collapse. However, if the biological growth of the fishery is assumed to vary with climate conditions, such as the North Atlantic Oscillation, there will always be pulses of high growth in the stock. During some of these high-growth years, the growth of the stock and its economic yield can exceed the growth rate of the economy even under high interest rates. This implies that it is not economically optimal to exhaust the New England cod fishery if NAO is included in the biological growth function. This finding may have theoretical implications for the management of other renewable yet exhaustible resources whose growth rates are subject to climate variability.

Climate change and disturbance interactions: Workshop on climate change and disturbance interactions in western North America, Tucson, Ariz., 12-15 February 2007

USGS Publications Warehouse

McKenzie, Don; Allen, Craig D.

2007-01-01

Warming temperatures across western North America, coupled with increased drought, are expected to exacerbate disturbance regimes, particularly wildfires, insect outbreaks, and invasions of exotic species. Many ecologists and resource managers expect ecosystems to change more rapidly from disturbance effects than from the effects of a changing climate by itself. A particular challenge is to understand the interactions among disturbance regimes; for example, how will massive outbreaks of bark beetles, which kill drought-stressed trees by feeding on cambial tissues, increase the potential for large severe wildfires in a warming climate?Researchers in climatology, ecosystem science, fire and insect ecology, and landscape modeling from across western North America convened in Tucson, Ariz., for a 2 and a half day intensive workshop to identify new research directions in climate change and disturbance ecology. Four work groups focused on different aspects of the response of disturbance regimes to climate change: (1) extreme events and climatic variability (2) the effects of changing disturbance regimes on ecosystems, (3) disturbance interactions and cumulative effects, and (4) developing new landscape disturbance models. The workshop was structured with the analytic hierarchy process, a decision support method for achieving consensus from diverse groups of experts without sacrificing individual contributions.

School environments and obesity: The mediating role of personal stress

PubMed Central

Milam, Adam J.; Jones, Chandria D.; Debnam, Katrina J.; Bradshaw, Catherine P.

2018-01-01

Background Youth spend a large amount of time in the school environment. Given the multiple influences of teachers, peers, and food and physical activity options, youth are likely to experience stressors that can influence their weight. This study examines the association between school climate and weight status. Method Students (n = 28,582; 58 schools) completed an online, anonymous school climate survey as part of the Maryland Safe and Supportive Schools Project. Multilevel structural equation modeling was used to explore the association between school climate, personal stress, and obesity. Analyses were stratified by gender. Results At the individual level, poor school climate (bullying, physical safety, and lack of whole-school connectedness) was associated with an increased likelihood of being overweight among females (β =.115, p = .019) but not males (β = .138; p =.244), after controlling for age, race, and physical activity. There was no association between school climate at the school level and being overweight among males or females. A second model included stress as a potential mediator; stress attenuated the relationship between poor school-related climate and being overweight (β = .039; p = .048) among females. Conclusion Findings suggest that stress related to school climate can play a role in the health and weight status of youth. PMID:29731524

Conceptualizing Holistic Community Resilience to Climate ...

EPA Pesticide Factsheets

The concept of resilience has been evolving over the past decade as a way to address the current and future challenges nations, states, and cities face from a changing climate. Understanding how the environment (natural and built), climate event risk, societal interactions, and governance reflect community resilience for adaptive management is critical for envisioning urban and natural environments that can persist through extreme weather events and longer-term shifts in climate. To be successful, this interaction of these five domains must result in maintaining quality of life and ensuring equal access to the benefits or the protection from harm for all segments of the population. An exhaustive literature review of climate resilience approaches was conducted examining the two primary elements of resilience—vulnerability and recoverability. The results of this review were examined to determine if any existing frameworks addressed the above five major areas in an integrated manner. While some aspects of a resilience model were available for existing sources, no comprehensive approach was available. A new conceptual model for resilience to climate events is proposed that incorporates some available structures and addresses these five domains at a national, regional, state, and county spatial scale for a variety of climate-induced events ranging from superstorms to droughts and their concomitant events such as wildfires, floods, and pest invasions. This conceptua

Evidence for cryptic northern refugia in the last glacial period in Cryptomeria japonica

PubMed Central

Kimura, Megumi K.; Uchiyama, Kentaro; Nakao, Katsuhiro; Moriguchi, Yoshinari; San Jose-Maldia, Lerma; Tsumura, Yoshihiko

2014-01-01

Background and Aims Distribution shifts and natural selection during past climatic changes are important factors in determining the genetic structure of forest species. In particular, climatic fluctuations during the Quaternary appear to have caused changes in the distribution ranges of plants, and thus strongly affected their genetic structure. This study was undertaken to identify the responses of the conifer Cryptomeria japonica, endemic to the Japanese Archipelago, to past climatic changes using a combination of phylogeography and species distribution modelling (SDM) methods. Specifically, this study focused on the locations of refugia during the last glacial maximum (LGM). Methods Genetic diversity and structure were examined using 20 microsatellite markers in 37 populations of C. japonica. The locations of glacial refugia were assessed using STRUCTURE analysis, and potential habitats under current and past climate conditions were predicted using SDM. The process of genetic divergence was also examined using the approximate Bayesian computation procedure (ABC) in DIY ABC to test the divergence time between the gene pools detected by the STRUCTURE analysis. Key Results STRUCTURE analysis identified four gene pools: northern Tohoku district; from Chubu to Chugoku district; from Tohoku to Shikoku district on the Pacific Ocean side of the Archipelago; and Yakushima Island. DIY ABC analysis indicated that the four gene pools diverged at the same time before the LGM. SDM also indicated potential northern cryptic refugia. Conclusions The combined evidence from microsatellites and SDM clearly indicates that climatic changes have shaped the genetic structure of C. japonica. The gene pool detected in northern Tohoku district is likely to have been established by cryptic northern refugia on the coast of the Japan Sea to the west of the Archipelago. The gene pool in Yakushima Island can probably be explained simply by long-term isolation from the other gene pools since the LGM. These results are supported by those of SDM and the predicted divergence time determined using ABC analysis. PMID:25355521

Das Bremerhavener Grundwasser im Klimawandel - Eine FREEWAT-Fallstudie

NASA Astrophysics Data System (ADS)

Panteleit, Björn; Jensen, Sven; Seiter, Katherina; Siebert, Yvonne

2018-01-01

A 3D structural model was created for the state of Bremen based on an extensive borehole database. Parameters were assigned to the model by interpretation and interpolation of the borehole descriptions. This structural model was transferred into a flow model via the FREEWAT platform, an open-source plug-in of the free QGIS software, with connection to the MODFLOW code. This groundwater management tool is intended for long-term use. As a case study for the FREEWAT Project, possible effects of climate change on groundwater levels in the Bremerhaven area have been simulated. In addition to the calibration year 2010, scenarios with a sea-level rise and decreasing groundwater recharge were simulated for the years 2040, 2070 and 2100. In addition to seawater intrusion in the coastal area, declining groundwater levels are also a concern. Possibilities for future groundwater management already include active control of the water level of a lake and the harbor basin. With the help of a focused groundwater monitoring program based on the model results, the planned flow model can become an important forecasting tool for groundwater management within the framework of the planned continuous model management and for representing the effects of changing climatic conditions and mitigation measures.

Uncertainties in modelling the climate impact of irrigation

NASA Astrophysics Data System (ADS)

de Vrese, Philipp; Hagemann, Stefan

2017-11-01

Irrigation-based agriculture constitutes an essential factor for food security as well as fresh water resources and has a distinct impact on regional and global climate. Many issues related to irrigation's climate impact are addressed in studies that apply a wide range of models. These involve substantial uncertainties related to differences in the model's structure and its parametrizations on the one hand and the need for simplifying assumptions for the representation of irrigation on the other hand. To address these uncertainties, we used the Max Planck Institute for Meteorology's Earth System model into which a simple irrigation scheme was implemented. In order to estimate possible uncertainties with regard to the model's more general structure, we compared the climate impact of irrigation between three simulations that use different schemes for the land-surface-atmosphere coupling. Here, it can be shown that the choice of coupling scheme does not only affect the magnitude of possible impacts but even their direction. For example, when using a scheme that does not explicitly resolve spatial subgrid scale heterogeneity at the surface, irrigation reduces the atmospheric water content, even in heavily irrigated regions. Contrarily, in simulations that use a coupling scheme that resolves heterogeneity at the surface or even within the lowest layers of the atmosphere, irrigation increases the average atmospheric specific humidity. A second experiment targeted possible uncertainties related to the representation of irrigation characteristics. Here, in four simulations the irrigation effectiveness (controlled by the target soil moisture and the non-vegetated fraction of the grid box that receives irrigation) and the timing of delivery were varied. The second experiment shows that uncertainties related to the modelled irrigation characteristics, especially the irrigation effectiveness, are also substantial. In general the impact of irrigation on the state of the land surface is more than three times larger when assuming a low irrigation effectiveness than when a high effectiveness is assumed. For certain variables, such as the vertically integrated water vapour, the impact is almost an order of magnitude larger. The timing of irrigation also has non-negligible effects on the simulated climate impacts and it can strongly alter their seasonality.

Analysis of trend changes in Northern African palaeo-climate by using Bayesian inference

NASA Astrophysics Data System (ADS)

Schütz, Nadine; Trauth, Martin H.; Holschneider, Matthias

2010-05-01

Climate variability of Northern Africa is of high interest due to climate-evolutionary linkages under study. The reconstruction of the palaeo-climate over long time scales, including the expected linkages (> 3 Ma), is mainly accessible by proxy data from deep sea drilling cores. By concentrating on published data sets, we try to decipher rhythms and trends to detect correlations between different proxy time series by advanced mathematical methods. Our preliminary data is dust concentration, as an indicator for climatic changes such as humidity, from the ODP sites 659, 721 and 967 situated around Northern Africa. Our interest is in challenging the available time series with advanced statistical methods to detect significant trend changes and to compare different model assumptions. For that purpose, we want to avoid the rescaling of the time axis to obtain equidistant time steps for filtering methods. Additionally we demand an plausible description of the errors for the estimated parameters, in terms of confidence intervals. Finally, depending on what model we restrict on, we also want an insight in the parameter structure of the assumed models. To gain this information, we focus on Bayesian inference by formulating the problem as a linear mixed model, so that the expectation and deviation are of linear structure. By using the Bayesian method we can formulate the posteriori density as a function of the model parameters and calculate this probability density in the parameter space. Depending which parameters are of interest, we analytically and numerically marginalize the posteriori with respect to the remaining parameters of less interest. We apply a simple linear mixed model to calculate the posteriori densities of the ODP sites 659 and 721 concerning the last 5 Ma at maximum. From preliminary calculations on these data sets, we can confirm results gained by the method of breakfit regression combined with block bootstrapping ([1]). We obtain a significant change point around (1.63 - 1.82) Ma, which correlates with a global climate transition due to the establishment of the Walker circulation ([2]). Furthermore we detect another significant change point around (2.7 - 3.2) Ma, which correlates with the end of the Pliocene warm period (permanent El Niño-like conditions) and the onset of a colder global climate ([3], [4]). The discussion on the algorithm, the results of calculated confidence intervals, the available information about the applied model in the parameter space and the comparison of multiple change point models will be presented. [1] Trauth, M.H., et al., Quaternary Science Reviews, 28, 2009 [2] Wara, M.W., et al., Science, Vol. 309, 2005 [3] Chiang, J.C.H., Annual Review of Earth and Planetary Sciences, Vol. 37, 2009 [4] deMenocal, P., Earth and Planetary Science Letters, 220, 2004

Model-based evidence for persistent species zonation shifts in the southern Rocky Mountains under a warming climate

NASA Astrophysics Data System (ADS)

Foster, A.; Shuman, J. K.; Shugart, H. H., Jr.; Dwire, K. A.; Fornwalt, P.; Sibold, J.; Negrón, J. F.

2016-12-01

Forests in the Rocky Mountains are a crucial part of the North American carbon budget, but increases in disturbances such as insect outbreaks and fire, in conjunction with climate change, threaten their vitality. Mean annual temperatures in the western United States have increased by 2°C since 1950 and the higher elevations are warming faster than the rest of the landscape. It is predicted that this warming trend will continue, and that by the end of this century, nearly 50% of the western US landscape will have climate profiles with no current analog within that region. Individual tree-based modeling allows various climate change scenarios and their effects on forest dynamics to be tested. We use an updated individual-based gap model, the University of Virginia Forest Model Enhanced (UVAFME) at a subalpine site in the southern Rocky Mountains. UVAFME has been quantitatively and qualitatively validated in the southern Rocky Mountains, and results show that UVAFME-output on size structure, biomass, and species composition compares reasonably to inventory data and descriptions of vegetation zonation and successional dynamics for the region. We perform a climate sensitivity test in which temperature is first increased linearly by 2°C over 100 years, stabilized for 200 years, cooled back to present climate values over 100 years, and again stabilized for 200 years. This test is conducted to determine what effect elevated temperatures may have on vegetation zonation, and how persistent the changes may be if the climate is brought back to its current state. Results show that elevated temperatures within the southern Rocky Mountains may lead to decreases in biomass and changes in species composition as species migrate upslope. These changes are also likely to be fairly persistent for at least one- to two-hundred years. The results from this study suggest that UVAFME and other individual-based gap models can be used to inform forest management and climate mitigation strategies for this vitally important region.

Tools for Virtual Collaboration Designed for High Resolution Hydrologic Research with Continental-Scale Data Support

NASA Astrophysics Data System (ADS)

Duffy, Christopher; Leonard, Lorne; Shi, Yuning; Bhatt, Gopal; Hanson, Paul; Gil, Yolanda; Yu, Xuan

2015-04-01

Using a series of recent examples and papers we explore some progress and potential for virtual (cyber-) collaboration inspired by access to high resolution, harmonized public-sector data at continental scales [1]. The first example describes 7 meso-scale catchments in Pennsylvania, USA where the watershed is forced by climate reanalysis and IPCC future climate scenarios (Intergovernmental Panel on Climate Change). We show how existing public-sector data and community models are currently able to resolve fine-scale eco-hydrologic processes regarding wetland response to climate change [2]. The results reveal that regional climate change is only part of the story, with large variations in flood and drought response associated with differences in terrain, physiography, landuse and/or hydrogeology. The importance of community-driven virtual testbeds are demonstrated in the context of Critical Zone Observatories, where earth scientists from around the world are organizing hydro-geophysical data and model results to explore new processes that couple hydrologic models with land-atmosphere interaction, biogeochemical weathering, carbon-nitrogen cycle, landscape evolution and ecosystem services [3][4]. Critical Zone cyber-research demonstrates how data-driven model development requires a flexible computational structure where process modules are relatively easy to incorporate and where new data structures can be implemented [5]. From the perspective of "Big-Data" the paper points out that extrapolating results from virtual observatories to catchments at continental scales, will require centralized or cloud-based cyberinfrastructure as a necessary condition for effectively sharing petabytes of data and model results [6]. Finally we outline how innovative cyber-science is supporting earth-science learning, sharing and exploration through the use of on-line tools where hydrologists and limnologists are sharing data and models for simulating the coupled impacts of catchment hydrology on lake eco-hydrology (NSF-INSPIRE, IIS1344272). The research attempts to use a virtual environment (www.organicdatascience.org) to break down disciplinary barriers and support emergent communities of science. [1] Source: Leonard and Duffy, 2013, Environmental Modelling & Software; [2] Source: Yu et al, 2014, Computers in Geoscience; [3] Source: Duffy et al, 2014, Procedia Earth and Planetary Science; [4] Source: Shi et al, Journal of Hydrometeorology, 2014; [5] Source: Bhatt et al, 2014, Environmental Modelling & Software ; [6] Leonard and Duffy, 2014, Environmental Modelling and Software.

Climate variables explain neutral and adaptive variation within salmonid metapopulations: The importance of replication in landscape genetics

USGS Publications Warehouse

Hand, Brian K.; Muhlfeld, Clint C.; Wade, Alisa A.; Kovach, Ryan; Whited, Diane C.; Narum, Shawn R.; Matala, Andrew P.; Ackerman, Michael W.; Garner, B. A.; Kimball, John S; Stanford, Jack A.; Luikart, Gordon

2016-01-01

Understanding how environmental variation influences population genetic structure is important for conservation management because it can reveal how human stressors influence population connectivity, genetic diversity and persistence. We used riverscape genetics modelling to assess whether climatic and habitat variables were related to neutral and adaptive patterns of genetic differentiation (population-specific and pairwise FST) within five metapopulations (79 populations, 4583 individuals) of steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin, USA. Using 151 putatively neutral and 29 candidate adaptive SNP loci, we found that climate-related variables (winter precipitation, summer maximum temperature, winter highest 5% flow events and summer mean flow) best explained neutral and adaptive patterns of genetic differentiation within metapopulations, suggesting that climatic variation likely influences both demography (neutral variation) and local adaptation (adaptive variation). However, we did not observe consistent relationships between climate variables and FST across all metapopulations, underscoring the need for replication when extrapolating results from one scale to another (e.g. basin-wide to the metapopulation scale). Sensitivity analysis (leave-one-population-out) revealed consistent relationships between climate variables and FST within three metapopulations; however, these patterns were not consistent in two metapopulations likely due to small sample sizes (N = 10). These results provide correlative evidence that climatic variation has shaped the genetic structure of steelhead populations and highlight the need for replication and sensitivity analyses in land and riverscape genetics.

An integrated approach to modeling changes in land use, land cover, and disturbance and their impact on ecosystem carbon dynamics: a case study in the Sierra Nevada Mountains of California

USGS Publications Warehouse

Sleeter, Benjamin M.; Liu, Jinxun; Daniel, Colin; Frid, Leonardo; Zhu, Zhiliang

2015-01-01

Increased land-use intensity (e.g. clearing of forests for cultivation, urbanization), often results in the loss of ecosystem carbon storage, while changes in productivity resulting from climate change may either help offset or exacerbate losses. However, there are large uncertainties in how land and climate systems will evolve and interact to shape future ecosystem carbon dynamics. To address this we developed the Land Use and Carbon Scenario Simulator (LUCAS) to track changes in land use, land cover, land management, and disturbance, and their impact on ecosystem carbon storage and flux within a scenario-based framework. We have combined a state-and-transition simulation model (STSM) of land change with a stock and flow model of carbon dynamics. Land-change projections downscaled from the Intergovernmental Panel on Climate Change’s (IPCC) Special Report on Emission Scenarios (SRES) were used to drive changes within the STSM, while the Integrated Biosphere Simulator (IBIS) ecosystem model was used to derive input parameters for the carbon stock and flow model. The model was applied to the Sierra Nevada Mountains ecoregion in California, USA, a region prone to large wildfires and a forestry sector projected to intensify over the next century. Three scenario simulations were conducted, including a calibration scenario, a climate-change scenario, and an integrated climate- and land-change scenario. Based on results from the calibration scenario, the LUCAS age-structured carbon accounting model was able to accurately reproduce results obtained from the process-based biogeochemical model. Under the climate-only scenario, the ecoregion was projected to be a reliable net sink of carbon, however, when land use and disturbance were introduced, the ecoregion switched to become a net source. This research demonstrates how an integrated approach to carbon accounting can be used to evaluate various drivers of ecosystem carbon change in a robust, yet transparent modeling environment.

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

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

Evaluation of DGVMs in tropical areas: linking patterns of vegetation cover, climate and fire to ecological processes

NASA Astrophysics Data System (ADS)

D'Onofrio, Donatella; von Hardenberg, Jost; Baudena, Mara

2017-04-01

Many current Dynamic Global Vegetation Models (DGVMs), including those incorporated into Earth System Models (ESMs), are able to realistically reproduce the distribution of the most worldwide biomes. However, they display high uncertainty in predicting the forest, savanna and grassland distributions and the transitions between them in tropical areas. These biomes are the most productive terrestrial ecosystems, and owing to their different biogeophysical and biogeochemical characteristics, future changes in their distributions could have also impacts on climate states. In particular, expected increasing temperature and CO2, modified precipitation regimes, as well as increasing land-use intensity could have large impacts on global biogeochemical cycles and precipitation, affecting the land-climate interactions. The difficulty of the DGVMs in simulating tropical vegetation, especially savanna structure and occurrence, has been associated with the way they represent the ecological processes and feedbacks between biotic and abiotic conditions. The inclusion of appropriate ecological mechanisms under present climatic conditions is essential for obtaining reliable future projections of vegetation and climate states. In this work we analyse observed relationships of tree and grass cover with climate and fire, and the current ecological understanding of the mechanisms driving the forest-savanna-grassland transition in Africa to evaluate the outcomes of a current state-of-the-art DGVM and to assess which ecological processes need to be included or improved within the model. Specifically, we analyse patterns of woody and herbaceous cover and fire return times from MODIS satellite observations, rainfall annual average and seasonality from TRMM satellite measurements and tree phenology information from the ESA global land cover map, comparing them with the outcomes of the LPJ-GUESS DGVM, also used by the EC-Earth global climate model. The comparison analysis with the LPJ-GUESS simulations suggests possible improvements in the model representations of tree-grass competition for water and in the vegetation-fire interaction. The proposed method could be useful for evaluating DGVMs in tropical areas, especially in the phase of model setting-up, before the coupling with Earth System Models. This could help in improving the simulations of ecological processes and consequently of land-climate interactions.

Prototype Mcs Parameterization for Global Climate Models

NASA Astrophysics Data System (ADS)

Moncrieff, M. W.

2017-12-01

Excellent progress has been made with observational, numerical and theoretical studies of MCS processes but the parameterization of those processes remain in a dire state and are missing from GCMs. The perceived complexity of the distribution, type, and intensity of organized precipitation systems has arguably daunted attention and stifled the development of adequate parameterizations. TRMM observations imply links between convective organization and large-scale meteorological features in the tropics and subtropics that are inadequately treated by GCMs. This calls for improved physical-dynamical treatment of organized convection to enable the next-generation of GCMs to reliably address a slew of challenges. The multiscale coherent structure parameterization (MCSP) paradigm is based on the fluid-dynamical concept of coherent structures in turbulent environments. The effects of vertical shear on MCS dynamics implemented as 2nd baroclinic convective heating and convective momentum transport is based on Lagrangian conservation principles, nonlinear dynamical models, and self-similarity. The prototype MCS parameterization, a minimalist proof-of-concept, is applied in the NCAR Community Climate Model, Version 5.5 (CAM 5.5). The MCSP generates convectively coupled tropical waves and large-scale precipitation features notably in the Indo-Pacific warm-pool and Maritime Continent region, a center-of-action for weather and climate variability around the globe.

Soil Structure - A Neglected Component of Land-Surface Models

NASA Astrophysics Data System (ADS)

Fatichi, S.; Or, D.; Walko, R. L.; Vereecken, H.; Kollet, S. J.; Young, M.; Ghezzehei, T. A.; Hengl, T.; Agam, N.; Avissar, R.

2017-12-01

Soil structure is largely absent in most standard sampling and measurements and in the subsequent parameterization of soil hydraulic properties deduced from soil maps and used in Earth System Models. The apparent omission propagates into the pedotransfer functions that deduce parameters of soil hydraulic properties primarily from soil textural information. Such simple parameterization is an essential ingredient in the practical application of any land surface model. Despite the critical role of soil structure (biopores formed by decaying roots, aggregates, etc.) in defining soil hydraulic functions, only a few studies have attempted to incorporate soil structure into models. They mostly looked at the effects on preferential flow and solute transport pathways at the soil profile scale; yet, the role of soil structure in mediating large-scale fluxes remains understudied. Here, we focus on rectifying this gap and demonstrating potential impacts on surface and subsurface fluxes and system wide eco-hydrologic responses. The study proposes a systematic way for correcting the soil water retention and hydraulic conductivity functions—accounting for soil-structure—with major implications for near saturated hydraulic conductivity. Modification to the basic soil hydraulic parameterization is assumed as a function of biological activity summarized by Gross Primary Production. A land-surface model with dynamic vegetation is used to carry out numerical simulations with and without the role of soil-structure for 20 locations characterized by different climates and biomes across the globe. Including soil structure affects considerably the partition between infiltration and runoff and consequently leakage at the base of the soil profile (recharge). In several locations characterized by wet climates, a few hundreds of mm per year of surface runoff become deep-recharge accounting for soil-structure. Changes in energy fluxes, total evapotranspiration and vegetation productivity are less significant but they can reach up to 10% in specific locations. Significance for land-surface and hydrological modeling and implications for distributed domains are discussed.

Long-term strategies of climate change adaptation to manage flooding events in urban areas

NASA Astrophysics Data System (ADS)

Pouget, Laurent; Russo, Beniamino; Redaño, Angel; Ribalaygua, Jaime

2010-05-01

Heavy and sudden rainfalls regularly affect the Mediterranean area, so a great number of people and buildings are exposed to the risk of rain-generated floods. Climate change is expected to modify this risk and, in the case that extreme rainfalls increase in frequencies and intensity, this could result in important damages, particularly in urban areas. This paper presents a project that aims to determine adaptation strategies to future flood risks in urban areas. It has been developed by a panel of water companies (R+i Alliance funding), and includes the evaluation of the climate change impact on the extreme rainfall, the use of innovative modelling tools to accurately forecast the flood risk and, finally, the definition of a pro-active and long-term planning against floods. This methodology has been applied in the city of Barcelona. Current climate models give some projections that are not directly applicable for flood risk studies, either because they do not have an adequate spatial and temporal resolution, or because they do not consider some important local factors, such as orography. These points have been considered within the project, when developing the design storms corresponding to future climatic conditions (e.g. years 2030 or 2050). The methodology uses statistical downscaling techniques based on global climate models predictions, including corrections for extreme events and convective storms, as well as temporal downscaling based on historical observations. The design storms created are used in combination with the predictions of sea level rise and land use evolutions to determine the future risk of flooding in the area of study. Once the boundary conditions are known, an accurate flood hazard assessment is done. It requires a local knowledge of the flow parameters in the whole analyzed domain. In urban catchments, in order to fulfill this requirement, powerful hydrological and hydraulic tools and detailed topographic data represent the unique way for a local estimation of the flow parameters (flow depth, flow velocity, flood duration, etc.). If urban floods are caused by heavy rainfall events and a quick hydrological response of the catchment, the approach to elaborate a flood hazard assessment study should take into account the drainage system capacity, too (in terms of effectiveness of surface drainage structures, as well as storm sewerages). In these cases, the hydrological modelling of the involved subcatchments should be linked to the runoff propagation 2D modelling on the urban surface and the hydraulics of the storm sewers (dual drainage modelling) through a coupled 2D/1D approach. The design storm created and the 2D/1D modelling approach have been used to simulate the future flood risk in the city of Barcelona. From the simulation results, it is possible to understand the flooding processes and the risk associated. It is therefore possible to develop some long-term adaptation strategies to reduce the flood risk for current and future climatic conditions, such as structural measures (e.g. improvement of the stormwater network) and non-structural measures (e.g. enhancement of the flood warning system).

Construction of Gridded Daily Weather Data and its Use in Central-European Agroclimatic Study

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Trnka, M.; Skalak, P.

2013-12-01

The regional-scale simulations of weather-sensitive processes (e.g. hydrology, agriculture and forestry) for the present and/or future climate often require high resolution meteorological inputs in terms of the time series of selected surface weather characteristics (typically temperature, precipitation, solar radiation, humidity, wind) for a set of stations or on a regular grid. As even the latest Global and Regional Climate Models (GCMs and RCMs) do not provide realistic representation of statistical structure of the surface weather, the model outputs must be postprocessed (downscaled) to achieve the desired statistical structure of the weather data before being used as an input to the follow-up simulation models. One of the downscaling approaches, which is employed also here, is based on a weather generator (WG), which is calibrated using the observed weather series, interpolated, and then modified according to the GCM- or RCM-based climate change scenarios. The present contribution, in which the parametric daily weather generator M&Rfi is linked to the high-resolution RCM output (ALADIN-Climate/CZ model) and GCM-based climate change scenarios, consists of two parts: The first part focuses on a methodology. Firstly, the gridded WG representing the baseline climate is created by merging information from observations and high resolution RCM outputs. In this procedure, WG is calibrated with RCM-simulated multi-variate weather series, and the grid specific WG parameters are then de-biased by spatially interpolated correction factors based on comparison of WG parameters calibrated with RCM-simulated weather series vs. spatially scarcer observations. To represent the future climate, the WG parameters are modified according to the 'WG-friendly' climate change scenarios. These scenarios are defined in terms of changes in WG parameters and include - apart from changes in the means - changes in WG parameters, which represent the additional characteristics of the weather series (e.g. probability of wet day occurrence and lag-1 autocorrelation of daily mean temperature). The WG-friendly scenarios for the present experiment are based on comparison of future vs baseline surface weather series simulated by GCMs from a CMIP3 database. The second part will present results of climate change impact study based on an above methodology applied to Central Europe. The changes in selected climatic (focusing on the extreme precipitation and temperature characteristics) and agroclimatic (including number of days during vegetation season with heat and drought stresses) characteristics will be analysed. In discussing the results, the emphasis will be put on 'added value' of various aspects of above methodology (e.g. inclusion of changes in 'advanced' WG parameters into the climate change scenarios). Acknowledgements: The present experiment is made within the frame of projects WG4VALUE (project LD12029 sponsored by the Ministry of Education, Youth and Sports of CR), ALARO-Climate (project P209/11/2405 sponsored by the Czech Science Foundation), and VALUE (COST ES 1102 action).

Sensitivity of worst-case strom surge considering influence of climate change

NASA Astrophysics Data System (ADS)

Takayabu, Izuru; Hibino, Kenshi; Sasaki, Hidetaka; Shiogama, Hideo; Mori, Nobuhito; Shibutani, Yoko; Takemi, Tetsuya

2016-04-01

There are two standpoints when assessing risk caused by climate change. One is how to prevent disaster. For this purpose, we get probabilistic information of meteorological elements, from enough number of ensemble simulations. Another one is to consider disaster mitigation. For this purpose, we have to use very high resolution sophisticated model to represent a worst case event in detail. If we could use enough computer resources to drive many ensemble runs with very high resolution model, we can handle these all themes in one time. However resources are unfortunately limited in most cases, and we have to select the resolution or the number of simulations if we design the experiment. Applying PGWD (Pseudo Global Warming Downscaling) method is one solution to analyze a worst case event in detail. Here we introduce an example to find climate change influence on the worst case storm-surge, by applying PGWD to a super typhoon Haiyan (Takayabu et al, 2015). 1 km grid WRF model could represent both the intensity and structure of a super typhoon. By adopting PGWD method, we can only estimate the influence of climate change on the development process of the Typhoon. Instead, the changes in genesis could not be estimated. Finally, we drove SU-WAT model (which includes shallow water equation model) to get the signal of storm surge height. The result indicates that the height of the storm surge increased up to 20% owing to these 150 years climate change.

Persistence and diversification of the Holarctic shrew, Sorex tundrensis (Family Soricidae), in response to climate change

USGS Publications Warehouse

Hope, Andrew G.; Waltari, Eric; Fedorov, Vadim B.; Goropashnaya, Anna V.; Talbot, Sandra; Cook, Joseph A.

2011-01-01

Environmental processes govern demography, species movements, community turnover and diversification and yet in many respects these dynamics are still poorly understood at high latitudes. We investigate the combined effects of climate change and geography through time for a widespread Holarctic shrew, Sorex tundrensis. We include a comprehensive suite of closely related outgroup taxa and three independent loci to explore phylogeographic structure and historical demography. We then explore the implications of these findings for other members of boreal communities. The tundra shrew and its sister species, the Tien Shan shrew (Sorex asper), exhibit strong geographic population structure across Siberia and into Beringia illustrating local centres of endemism that correspond to Late Pleistocene refugia. Ecological niche predictions for both current and historical distributions indicate a model of persistence through time despite dramatic climate change. Species tree estimation under a coalescent process suggests that isolation between populations has been maintained across timeframes deeper than the periodicity of Pleistocene glacial cycling. That some species such as the tundra shrew have a history of persistence largely independent of changing climate, whereas other boreal species shifted their ranges in response to climate change, highlights the dynamic processes of community assembly at high latitudes.

Persistence and diversification of the Holarctic shrew, Sorex tundrensis (Family Soricidae), in response to climate change

USGS Publications Warehouse

Hope, Andrew G.; Waltari, Eric; Fedorov, V.B.; Goropashnaya, A.V.; Talbot, Sandra; Cook, Joseph A.

2014-01-01

Environmental processes govern demography, species movements, community turnover and diversification and yet in many respects these dynamics are still poorly understood at high latitudes. We investigate the combined effects of climate change and geography through time for a widespread Holarctic shrew, Sorex tundrensis. We include a comprehensive suite of closely related outgroup taxa and three independent loci to explore phylogeographic structure and historical demography. We then explore the implications of these findings for other members of boreal communities. The tundra shrew and its sister species, the Tien Shan shrew (Sorex asper), exhibit strong geographic population structure across Siberia and into Beringia illustrating local centres of endemism that correspond to Late Pleistocene refugia. Ecological niche predictions for both current and historical distributions indicate a model of persistence through time despite dramatic climate change. Species tree estimation under a coalescent process suggests that isolation between populations has been maintained across timeframes deeper than the periodicity of Pleistocene glacial cycling. That some species such as the tundra shrew have a history of persistence largely independent of changing climate, whereas other boreal species shifted their ranges in response to climate change, highlights the dynamic processes of community assembly at high latitudes.

Workplace support, discrimination, and person-organization fit: tests of the theory of work adjustment with LGB individuals.

PubMed

Velez, Brandon L; Moradi, Bonnie

2012-07-01

The present study explored the links of 2 workplace contextual variables--perceptions of workplace heterosexist discrimination and lesbian, gay, and bisexual (LGB)-supportive climates--with job satisfaction and turnover intentions in a sample of LGB employees. An extension of the theory of work adjustment (TWA) was used as the conceptual framework for the study; as such, perceived person-organization (P-O) fit was tested as a mediator of the relations between the workplace contextual variables and job outcomes. Data were analyzed from 326 LGB employees. Zero-order correlations indicated that perceptions of workplace heterosexist discrimination and LGB-supportive climates were correlated in expected directions with P-O fit, job satisfaction, and turnover intentions. Structural equation modeling (SEM) was used to compare multiple alternative measurement models evaluating the discriminant validity of the 2 workplace contextual variables relative to one another, and the 3 TWA job variables relative to one another; SEM was also used to test the hypothesized mediation model. Comparisons of multiple alternative measurement models supported the construct distinctiveness of the variables of interest. The test of the hypothesized structural model revealed that only LGB-supportive climates (and not workplace heterosexist discrimination) had a unique direct positive link with P-O fit and, through the mediating role of P-O fit, had significant indirect positive and negative relations with job satisfaction and turnover intentions, respectively. Moreover, P-O fit had a significant indirect negative link with turnover intentions through job satisfaction.

Landscape genomic analysis of candidate genes for climate adaptation in a California endemic oak, Quercus lobata.

PubMed

Sork, Victoria L; Squire, Kevin; Gugger, Paul F; Steele, Stephanie E; Levy, Eric D; Eckert, Andrew J

2016-01-01

The ability of California tree populations to survive anthropogenic climate change will be shaped by the geographic structure of adaptive genetic variation. Our goal is to test whether climate-associated candidate genes show evidence of spatially divergent selection in natural populations of valley oak, Quercus lobata, as preliminary indication of local adaptation. Using DNA from 45 individuals from 13 localities across the species' range, we sequenced portions of 40 candidate genes related to budburst/flowering, growth, osmotic stress, and temperature stress. Using 195 single nucleotide polymorphisms (SNPs), we estimated genetic differentiation across populations and correlated allele frequencies with climate gradients using single-locus and multivariate models. The top 5% of FST estimates ranged from 0.25 to 0.68, yielding loci potentially under spatially divergent selection. Environmental analyses of SNP frequencies with climate gradients revealed three significantly correlated SNPs within budburst/flowering genes and two SNPs within temperature stress genes with mean annual precipitation, after controlling for multiple testing. A redundancy model showed a significant association between SNPs and climate variables and revealed a similar set of SNPs with high loadings on the first axis. In the RDA, climate accounted for 67% of the explained variation, when holding climate constant, in contrast to a putatively neutral SSR data set where climate accounted for only 33%. Population differentiation and geographic gradients of allele frequencies in climate-associated functional genes in Q. lobata provide initial evidence of adaptive genetic variation and background for predicting population response to climate change. © 2016 Botanical Society of America.