Do Leadership Style, Unit Climate, and Safety Climate Contribute to Safe Medication Practices?

PubMed

Farag, Amany; Tullai-McGuinness, Susan; Anthony, Mary K; Burant, Christopher

2017-01-01

This study aims at: examining if leadership style and unit climate predict safety climate; and testing the direct, indirect, and total effect of leadership style, unit climate, and safety climate on nurses' safe medication practices. The Institute of Medicine and nursing scholars propose that safety climate is a prerequisite to safety practices. However, there is limited empirical evidence about factors contributing to the development of safety climate and about the association with nurses' safe medication practices. This cross-sectional study used survey data from 246 RNs working in a Magnet® hospital. Leadership style and unit climate predicted 20% to 50% of variance on all safety climate dimensions. Model testing revealed the indirect impact of leadership style and unit climate on nurses' safe medication practices. Our hypothesized model explained small amount of the variance on nurses' safe medication practices. This finding suggests that nurses' safe medication practices are influenced by multiple contextual and personal factors that should be further examined.

Simulated impact of climate change on hydrology of multiple watersheds using traditional and recommended snowmelt runoff model methodology

USDA-ARS?s Scientific Manuscript database

For more than three decades, researchers have utilized the Snowmelt Runoff Model (SRM) to test the impacts of climate change on streamflow of snow-fed systems. In this study, the hydrological effects of climate change are modeled over three sequential years using SRM with both typical and recommende...

Contemporary Drought and Future Effects of Climate Change on the Endangered Blunt-Nosed Leopard Lizard, Gambelia sila.

PubMed

Westphal, Michael F; Stewart, Joseph A E; Tennant, Erin N; Butterfield, H Scott; Sinervo, Barry

2016-01-01

Extreme weather events can provide unique opportunities for testing models that predict the effect of climate change. Droughts of increasing severity have been predicted under numerous models, thus contemporary droughts may allow us to test these models prior to the onset of the more extreme effects predicted with a changing climate. In the third year of an ongoing severe drought, surveys failed to detect neonate endangered blunt-nosed leopard lizards in a subset of previously surveyed populations where we expected to see them. By conducting surveys at a large number of sites across the range of the species over a short time span, we were able to establish a strong positive correlation between winter precipitation and the presence of neonate leopard lizards over geographic space. Our results are consistent with those of numerous longitudinal studies and are in accordance with predictive climate change models. We suggest that scientists can take immediate advantage of droughts while they are still in progress to test patterns of occurrence in other drought-sensitive species and thus provide for more robust models of climate change effects on biodiversity.

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

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

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

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

Effects of model spatial resolution on ecohydrologic predictions and their sensitivity to inter-annual climate variability

Treesearch

Kyongho Son; Christina Tague; Carolyn Hunsaker

2016-01-01

The effect of fine-scale topographic variability on model estimates of ecohydrologic responses to climate variability in California’s Sierra Nevada watersheds has not been adequately quantified and may be important for supporting reliable climate-impact assessments. This study tested the effect of digital elevation model (DEM) resolution on model accuracy and estimates...

The Dependencies of Ecosystem Pattern, Structure, and Dynamics on Climate, Climate Variability, and Climate Change

NASA Astrophysics Data System (ADS)

Flanagan, S.; Hurtt, G. C.; Fisk, J. P.; Rourke, O.

2012-12-01

A robust understanding of the sensitivity of the pattern, structure, and dynamics of ecosystems to climate, climate variability, and climate change is needed to predict ecosystem responses to current and projected climate change. We present results of a study designed to first quantify the sensitivity of ecosystems to climate through the use of climate and ecosystem data, and then use the results to test the sensitivity of the climate data in a state-of the art ecosystem model. A database of available ecosystem characteristics such as mean canopy height, above ground biomass, and basal area was constructed from sources like the National Biomass and Carbon Dataset (NBCD). The ecosystem characteristics were then paired by latitude and longitude with the corresponding climate characteristics temperature, precipitation, photosynthetically active radiation (PAR) and dew point that were retrieved from the North American Regional Reanalysis (NARR). The average yearly and seasonal means of the climate data, and their associated maximum and minimum values, over the 1979-2010 time frame provided by NARR were constructed and paired with the ecosystem data. The compiled results provide natural patterns of vegetation structure and distribution with regard to climate data. An advanced ecosystem model, the Ecosystem Demography model (ED), was then modified to allow yearly alterations to its mechanistic climate lookup table and used to predict the sensitivities of ecosystem pattern, structure, and dynamics to climate data. The combined ecosystem structure and climate data results were compared to ED's output to check the validity of the model. After verification, climate change scenarios such as those used in the last IPCC were run and future forest structure changes due to climate sensitivities were identified. The results of this study can be used to both quantify and test key relationships for next generation models. The sensitivity of ecosystem characteristics to climate data shown in the database construction and by the model reinforces the need for high-resolution datasets and stresses the importance of understanding and incorporating climate change scenarios into earth system models.

An empirical test of the relative and combined effects of land-cover and climate change on local colonization and extinction.

PubMed

Yalcin, Semra; Leroux, Shawn James

2018-04-14

Land-cover and climate change are two main drivers of changes in species ranges. Yet, the majority of studies investigating the impacts of global change on biodiversity focus on one global change driver and usually use simulations to project biodiversity responses to future conditions. We conduct an empirical test of the relative and combined effects of land-cover and climate change on species occurrence changes. Specifically, we examine whether observed local colonization and extinctions of North American birds between 1981-1985 and 2001-2005 are correlated with land-cover and climate change and whether bird life history and ecological traits explain interspecific variation in observed occurrence changes. We fit logistic regression models to test the impact of physical land-cover change, changes in net primary productivity, winter precipitation, mean summer temperature, and mean winter temperature on the probability of Ontario breeding bird local colonization and extinction. Models with climate change, land-cover change, and the combination of these two drivers were the top ranked models of local colonization for 30%, 27%, and 29% of species, respectively. Conversely, models with climate change, land-cover change, and the combination of these two drivers were the top ranked models of local extinction for 61%, 7%, and 9% of species, respectively. The quantitative impacts of land-cover and climate change variables also vary among bird species. We then fit linear regression models to test whether the variation in regional colonization and extinction rate could be explained by mean body mass, migratory strategy, and habitat preference of birds. Overall, species traits were weakly correlated with heterogeneity in species occurrence changes. We provide empirical evidence showing that land-cover change, climate change, and the combination of multiple global change drivers can differentially explain observed species local colonization and extinction. © 2018 John Wiley & Sons Ltd.

Interactions of Mean Climate Change and Climate Variability on Food Security Extremes

NASA Technical Reports Server (NTRS)

Ruane, Alexander C.; McDermid, Sonali; Mavromatis, Theodoros; Hudson, Nicholas; Morales, Monica; Simmons, John; Prabodha, Agalawatte; Ahmad, Ashfaq; Ahmad, Shakeel; Ahuja, Laj R.

2015-01-01

Recognizing that climate change will affect agricultural systems both through mean changes and through shifts in climate variability and associated extreme events, we present preliminary analyses of climate impacts from a network of 1137 crop modeling sites contributed to the AgMIP Coordinated Climate-Crop Modeling Project (C3MP). At each site sensitivity tests were run according to a common protocol, which enables the fitting of crop model emulators across a range of carbon dioxide, temperature, and water (CTW) changes. C3MP can elucidate several aspects of these changes and quantify crop responses across a wide diversity of farming systems. Here we test the hypothesis that climate change and variability interact in three main ways. First, mean climate changes can affect yields across an entire time period. Second, extreme events (when they do occur) may be more sensitive to climate changes than a year with normal climate. Third, mean climate changes can alter the likelihood of climate extremes, leading to more frequent seasons with anomalies outside of the expected conditions for which management was designed. In this way, shifts in climate variability can result in an increase or reduction of mean yield, as extreme climate events tend to have lower yield than years with normal climate.C3MP maize simulations across 126 farms reveal a clear indication and quantification (as response functions) of mean climate impacts on mean yield and clearly show that mean climate changes will directly affect the variability of yield. Yield reductions from increased climate variability are not as clear as crop models tend to be less sensitive to dangers on the cool and wet extremes of climate variability, likely underestimating losses from water-logging, floods, and frosts.

Relevant climate response tests for stratospheric aerosol injection: A combined ethical and scientific analysis

NASA Astrophysics Data System (ADS)

Lenferna, Georges Alexandre; Russotto, Rick D.; Tan, Amanda; Gardiner, Stephen M.; Ackerman, Thomas P.

2017-06-01

In this paper, we focus on stratospheric sulfate injection as a geoengineering scheme, and provide a combined scientific and ethical analysis of climate response tests, which are a subset of outdoor tests that would seek to impose detectable and attributable changes to climate variables on global or regional scales. We assess the current state of scientific understanding on the plausibility and scalability of climate response tests. Then, we delineate a minimal baseline against which to consider whether certain climate response tests would be relevant for a deployment scenario. Our analysis shows that some climate response tests, such as those attempting to detect changes in regional climate impacts, may not be deployable in time periods relevant to realistic geoengineering scenarios. This might pose significant challenges for justifying stratospheric sulfate aerosol injection deployment overall. We then survey some of the major ethical challenges that proposed climate response tests face. We consider what levels of confidence would be required to ethically justify approving a proposed test; whether the consequences of tests are subject to similar questions of justice, compensation, and informed consent as full-scale deployment; and whether questions of intent and hubris are morally relevant for climate response tests. We suggest further research into laboratory-based work and modeling may help to narrow the scientific uncertainties related to climate response tests, and help inform future ethical debate. However, even if such work is pursued, the ethical issues raised by proposed climate response tests are significant and manifold.

Modelling the climate and ice sheets of the mid-Pliocene warm period: a test of model dependency

NASA Astrophysics Data System (ADS)

Dolan, Aisling; Haywood, Alan; Lunt, Daniel; Hill, Daniel

2010-05-01

The mid-Pliocene warm period (MPWP; c. 3.0 - 3.3 million years ago) has been the subject of a large number of published studies during the last decade. It is an interval in Earth history, where conditions were similar to those predicted by climate models for the end of the 21st Century. Not only is it important to increase our understanding of the climate dynamics in a warmer world, it is also important to determine exactly how well numerical models can retrodict a climate significantly different from the present day, in order to have confidence in them for predicting the future climate. Previous General Circulation Model (GCM) simulations have indicated that MPWP mean annual surface temperatures were on average 2 to 3˚C warmer than the pre-industrial era. Coastal stratigraphy and benthic oxygen isotope records suggest that terrestrial ice volumes were reduced when compared to modern. Ice sheet modelling studies have supported this decrease in cryospheric extent. Generally speaking, both climate and ice sheet modelling studies have only used results from one numerical model when simulating the climate of the MPWP. However, recent projects such as PMIP (the Palaeoclimate Modelling Intercomparison Project) have emphasised the need to explore the dependency of past climate predictions on the specific climate model which is used. Here we present a comparison of MPWP climatologies produced by three atmosphere only GCMs from the Goddard Institute of Space Studies (GISS), the National Centre for Atmospheric Research (NCAR) and the Hadley Centre for Climate Prediction and Research (GCMAM3, CAM3-CLM and HadAM3 respectively). We focus on the ability of the GCMs to simulate climate fields needed to drive an offline ice sheet model to assess whether there are any significant differences between the climatologies. By taking the different temperature and precipitation predictions simulated by the three models as a forcing, and adopting GCM-specific topography, we have used the British Antarctic Survey thermomechanically coupled ice sheet model (BASISM) to test the extent to which equilibrium state ice sheets in the Northern Hemisphere are GCM dependent. Initial results which do not use GCM-specific topography suggest that employing different GCM climatologies with only small differences in surface air temperature and precipitation has a dramatic effect on the resultant Greenland ice sheet, where the end-member ice sheets vary from near modern to almost zero ice volume. As an extension of this analysis, we will also present results using a second ice sheet model (Glimmer), with a view to testing the degree to which end-member ice sheets are ice sheet model dependent, something which has not previously been addressed. Initially, BASISM and Glimmer will be internally optimised for performance, but we will also present a comparison where BASISM will be configured to the Glimmer model setup in a further test of ice sheet model dependency.

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 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

The Milankovitch theory and climate sensitivity. I - Equilibrium climate model solutions for the present surface conditions. II - Interaction between the Northern Hemisphere ice sheets and the climate system

NASA Technical Reports Server (NTRS)

Neeman, Binyamin U.; Ohring, George; Joseph, Joachim H.

1988-01-01

A seasonal climate model was developed to test the climate sensitivity and, in particular, the Milankovitch (1941) theory. Four climate model versions were implemented to investigate the range of uncertainty in the parameterizations of three basic feedback mechanisms: the ice albedo-temperature, the outgoing long-wave radiation-temperature, and the eddy transport-meridional temperature gradient. It was found that the differences between the simulation of the present climate by the four versions were generally small, especially for annually averaged results. The climate model was also used to study the effect of growing/shrinking of a continental ice sheet, bedrock sinking/uplifting, and sea level changes on the climate system, taking also into account the feedback effects on the climate of the building of the ice caps.

CLIMATE CHANGE IN THAILAND AND ITS POTENTIAL IMPACT ON RICE YIELD

EPA Science Inventory

Because of the uncertainties surrounding prediction of climate change, it is common to employ climate scenarios to estimate its impacts on a system. Climate scenarios are sets of climatic perturbations used with models to test system sensitivity to projected changes. In this stud...

Modeling long-term changes in forested landscapes and their relation to the Earth's energy balance

NASA Technical Reports Server (NTRS)

Shugart, H. H.; Emanuel, W. R.; Solomon, A. M.

1984-01-01

The dynamics of the forested parts of the Earth's surface on time scales from decades to centuries are discussed. A set of computer models developed at Oak Ridge National Laboratory and elsewhere are applied as tools. These models simulate a landscape by duplicating the dynamics of growth, death and birth of each tree living on a 0.10 ha element of the landscape. This spatial unit is generally referred to as a gap in the case of the forest models. The models were tested against and applied to a diverse array of forests and appear to provide a reasonable representation for investigating forest-cover dynamics. Because of the climate linkage, one important test is the reconstruction of paleo-landscapes. Detailed reconstructions of changes in vegetation in response to changes in climate are crucial to understanding the association of the Earth's vegetation and climate and the response of the vegetation to climate change.

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.

Application of empirical and dynamical closure methods to simple climate models

NASA Astrophysics Data System (ADS)

Padilla, Lauren Elizabeth

This dissertation applies empirically- and physically-based methods for closure of uncertain parameters and processes to three model systems that lie on the simple end of climate model complexity. Each model isolates one of three sources of closure uncertainty: uncertain observational data, large dimension, and wide ranging length scales. They serve as efficient test systems toward extension of the methods to more realistic climate models. The empirical approach uses the Unscented Kalman Filter (UKF) to estimate the transient climate sensitivity (TCS) parameter in a globally-averaged energy balance model. Uncertainty in climate forcing and historical temperature make TCS difficult to determine. A range of probabilistic estimates of TCS computed for various assumptions about past forcing and natural variability corroborate ranges reported in the IPCC AR4 found by different means. Also computed are estimates of how quickly uncertainty in TCS may be expected to diminish in the future as additional observations become available. For higher system dimensions the UKF approach may become prohibitively expensive. A modified UKF algorithm is developed in which the error covariance is represented by a reduced-rank approximation, substantially reducing the number of model evaluations required to provide probability densities for unknown parameters. The method estimates the state and parameters of an abstract atmospheric model, known as Lorenz 96, with accuracy close to that of a full-order UKF for 30-60% rank reduction. The physical approach to closure uses the Multiscale Modeling Framework (MMF) to demonstrate closure of small-scale, nonlinear processes that would not be resolved directly in climate models. A one-dimensional, abstract test model with a broad spatial spectrum is developed. The test model couples the Kuramoto-Sivashinsky equation to a transport equation that includes cloud formation and precipitation-like processes. In the test model, three main sources of MMF error are evaluated independently. Loss of nonlinear multi-scale interactions and periodic boundary conditions in closure models were dominant sources of error. Using a reduced order modeling approach to maximize energy content allowed reduction of the closure model dimension up to 75% without loss in accuracy. MMF and a comparable alternative model peformed equally well compared to direct numerical simulation.

NASA Contributions to the Development and Testing of Climate Indicators

NASA Astrophysics Data System (ADS)

Houser, P. R.; Leidner, A. K.; Tsaoussi, L.; Kaye, J. A.

2014-12-01

NASA is a major contributor the U.S. National Climate Assessment (NCA), a central component of the 2012-2022 U.S. Global Change Research Program's Strategic Plan. NASA supports a range of global climate and related environmental assessment activities through its data records, models, and model-produced data sets, as well as through involvement of agency personnel. These assessments provide important information on climate change and are used by policymakers, especially with the recent increased interest in climate vulnerability, impacts, and adaptation. Climate indicators provide a clear and concise way of communicating to the NCA audiences about not only status and trends of physical drivers of the climate system, but also the ecological and socioeconomic impacts, vulnerabilities, and responses to those drivers. NASA is enhancing its participation in future NCAs by encouraging the developing and testing of potential indicators that best address the needs expressed in the NCA indicator vision and that leverage NASA's capabilities. This presentation will highlight a suite of new climate indicators that draws significantly from NASA -produced data and/or modeling products, to support decisions related to impacts, adaptation, vulnerability, and mitigation associated with climate and global change.

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

Tradeoffs in Acceleration and Initialization of Superparameterized Global Atmospheric Models for MJO and Climate Science

NASA Astrophysics Data System (ADS)

Pritchard, M. S.; Bretherton, C. S.; DeMott, C. A.

2014-12-01

New trade-offs are discussed in the cloud superparameterization approach to explicitly representing deep convection in global climate models. Intrinsic predictability tests show that the memory of cloud-resolving-scale organization is not critical for producing desired modes of organized convection such as the Madden-Julian Oscillation (MJO). This has implications for the feasibility of data assimilation and real-world initialization for superparameterized weather forecasting. Climate simulation sensitivity tests demonstrate that 400% acceleration of cloud superparameterization is possible by restricting the 32-128 km scale regime without deteriorating the realism of the simulated MJO but the number of cloud resolving model grid columns is discovered to constrain the efficiency of vertical mixing, with consequences for the simulated liquid cloud climatology. Tuning opportunities for next generation accelerated superparameterized climate models are discussed.

COP21 climate negotiators' responses to climate model forecasts

NASA Astrophysics Data System (ADS)

Bosetti, Valentina; Weber, Elke; Berger, Loïc; Budescu, David V.; Liu, Ning; Tavoni, Massimo

2017-02-01

Policymakers involved in climate change negotiations are key users of climate science. It is therefore vital to understand how to communicate scientific information most effectively to this group. We tested how a unique sample of policymakers and negotiators at the Paris COP21 conference update their beliefs on year 2100 global mean temperature increases in response to a statistical summary of climate models' forecasts. We randomized the way information was provided across participants using three different formats similar to those used in Intergovernmental Panel on Climate Change reports. In spite of having received all available relevant scientific information, policymakers adopted such information very conservatively, assigning it less weight than their own prior beliefs. However, providing individual model estimates in addition to the statistical range was more effective in mitigating such inertia. The experiment was repeated with a population of European MBA students who, despite starting from similar priors, reported conditional probabilities closer to the provided models' forecasts than policymakers. There was also no effect of presentation format in the MBA sample. These results highlight the importance of testing visualization tools directly on the population of interest.

WRF Test on IBM BG/L:Toward High Performance Application to Regional Climate Research

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

Chin, H S

The effects of climate change will mostly be felt on local to regional scales (Solomon et al., 2007). To develop better forecast skill in regional climate change, an integrated multi-scale modeling capability (i.e., a pair of global and regional climate models) becomes crucially important in understanding and preparing for the impacts of climate change on the temporal and spatial scales that are critical to California's and nation's future environmental quality and economical prosperity. Accurate knowledge of detailed local impact on the water management system from climate change requires a resolution of 1km or so. To this end, a high performancemore » computing platform at the petascale appears to be an essential tool in providing such local scale information to formulate high quality adaptation strategies for local and regional climate change. As a key component of this modeling system at LLNL, the Weather Research and Forecast (WRF) model is implemented and tested on the IBM BG/L machine. The objective of this study is to examine the scaling feature of WRF on BG/L for the optimal performance, and to assess the numerical accuracy of WRF solution on BG/L.« less

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

USGS Publications Warehouse

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

2013-01-01

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

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

PubMed

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

2013-06-04

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

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

NASA Astrophysics Data System (ADS)

Ahmadalipour, Ali; Rana, Arun; Moradkhani, Hamid; Sharma, Ashish

2017-04-01

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.

Testing a land model in ecosystem functional space via a comparison of observed and modeled ecosystem flux responses to precipitation regimes and associated stresses in a Central U.S. forest: Test Model in Ecosystem Functional Space

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

Gu, Lianhong; Pallardy, Stephen G.; Yang, Bai

Testing complex land surface models has often proceeded by asking the question: does the model prediction agree with the observation? This approach has yet led to high-performance terrestrial models that meet the challenges of climate and ecological studies. Here we test the Community Land Model (CLM) by asking the question: does the model behave like an ecosystem? We pursue its answer by testing CLM in the ecosystem functional space (EFS) at the Missouri Ozark AmeriFlux (MOFLUX) forest site in the Central U.S., focusing on carbon and water flux responses to precipitation regimes and associated stresses. In the observed EFS, precipitationmore » regimes and associated water and heat stresses controlled seasonal and interannual variations of net ecosystem exchange (NEE) of CO 2 and evapotranspiration in this deciduous forest ecosystem. Such controls were exerted more strongly by precipitation variability than by the total precipitation amount per se. A few simply constructed climate variability indices captured these controls, suggesting a high degree of potential predictability. While the interannual fluctuation in NEE was large, a net carbon sink was maintained even during an extreme drought year. Although CLM predicted seasonal and interanual variations in evapotranspiration reasonably well, its predictions of net carbon uptake were too small across the observed range of climate variability. Also, the model systematically underestimated the sensitivities of NEE and evapotranspiration to climate variability and overestimated the coupling strength between carbon and water fluxes. Its suspected that the modeled and observed trajectories of ecosystem fluxes did not overlap in the EFS and the model did not behave like the ecosystem it attempted to simulate. A definitive conclusion will require comprehensive parameter and structural sensitivity tests in a rigorous mathematical framework. We also suggest that future model improvements should focus on better representation and parameterization of process responses to environmental stresses and on more complete and robust representations of carbon-specific processes so that adequate responses to climate variability and a proper degree of coupling between carbon and water exchanges are captured.« less

Testing a land model in ecosystem functional space via a comparison of observed and modeled ecosystem flux responses to precipitation regimes and associated stresses in a Central U.S. forest: Test Model in Ecosystem Functional Space

DOE PAGES

Gu, Lianhong; Pallardy, Stephen G.; Yang, Bai; ...

2016-07-14

Testing complex land surface models has often proceeded by asking the question: does the model prediction agree with the observation? This approach has yet led to high-performance terrestrial models that meet the challenges of climate and ecological studies. Here we test the Community Land Model (CLM) by asking the question: does the model behave like an ecosystem? We pursue its answer by testing CLM in the ecosystem functional space (EFS) at the Missouri Ozark AmeriFlux (MOFLUX) forest site in the Central U.S., focusing on carbon and water flux responses to precipitation regimes and associated stresses. In the observed EFS, precipitationmore » regimes and associated water and heat stresses controlled seasonal and interannual variations of net ecosystem exchange (NEE) of CO 2 and evapotranspiration in this deciduous forest ecosystem. Such controls were exerted more strongly by precipitation variability than by the total precipitation amount per se. A few simply constructed climate variability indices captured these controls, suggesting a high degree of potential predictability. While the interannual fluctuation in NEE was large, a net carbon sink was maintained even during an extreme drought year. Although CLM predicted seasonal and interanual variations in evapotranspiration reasonably well, its predictions of net carbon uptake were too small across the observed range of climate variability. Also, the model systematically underestimated the sensitivities of NEE and evapotranspiration to climate variability and overestimated the coupling strength between carbon and water fluxes. Its suspected that the modeled and observed trajectories of ecosystem fluxes did not overlap in the EFS and the model did not behave like the ecosystem it attempted to simulate. A definitive conclusion will require comprehensive parameter and structural sensitivity tests in a rigorous mathematical framework. We also suggest that future model improvements should focus on better representation and parameterization of process responses to environmental stresses and on more complete and robust representations of carbon-specific processes so that adequate responses to climate variability and a proper degree of coupling between carbon and water exchanges are captured.« less

Reconstructing a lost Eocene Paradise, Part II: On the utility of dynamic global vegetation models in pre-Quaternary climate studies

NASA Astrophysics Data System (ADS)

Shellito, Cindy J.; Sloan, Lisa C.

2006-02-01

Models that allow vegetation to respond to and interact with climate provide a unique method for addressing questions regarding feedbacks between the ecosystem and climate in pre-Quaternary time periods. In this paper, we consider how Dynamic Global Vegetation Models (DGVMs), which have been developed for simulations with present day climate, can be used for paleoclimate studies. We begin with a series of tests in the NCAR Land Surface Model (LSM)-DGVM with Eocene geography to examine (1) the effect of removing C 4 grasses from the available plant functional types in the model; (2) model sensitivity to a change in soil texture; and (3), model sensitivity to a change in the value of pCO 2 used in the photosynthetic rate equations. The tests were designed to highlight some of the challenges of using these models and prompt discussion of possible improvements. We discuss how lack of detail in model boundary conditions, uncertainties in the application of modern plant functional types to paleo-flora simulations, and inaccuracies in the model climatology used to drive the DGVM can affect interpretation of model results. However, we also review a number of DGVM features that can facilitate understanding of past climates and offer suggestions for improving paleo-DGVM studies.

Model-Based Development of Automotive Electronic Climate Control Software

NASA Astrophysics Data System (ADS)

Kakade, Rupesh; Murugesan, Mohan; Perugu, Bhupal; Nair, Mohanan

With increasing complexity of software in today's products, writing and maintaining thousands of lines of code is a tedious task. Instead, an alternative methodology must be employed. Model-based development is one candidate that offers several benefits and allows engineers to focus on the domain of their expertise than writing huge codes. In this paper, we discuss the application of model-based development to the electronic climate control software of vehicles. The back-to-back testing approach is presented that ensures flawless and smooth transition from legacy designs to the model-based development. Simulink report generator to create design documents from the models is presented along with its usage to run the simulation model and capture the results into the test report. Test automation using model-based development tool that support the use of unique set of test cases for several testing levels and the test procedure that is independent of software and hardware platform is also presented.

Final Report on Hierarchical Coupled Modeling and Prediction of Regional Climate Change in the Atlantic Sector

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

Saravanan, Ramalingam

2011-10-30

During the course of this project, we have accomplished the following: a) Carried out studies of climate changes in the past using a hierarchy of intermediate coupled models (Chang et al., 2008; Wan et al 2009; Wen et al., 2010a,b) b) Completed the development of a Coupled Regional Climate Model (CRCM; Patricola et al., 2011a,b) c) Carried out studies testing hypotheses testing the origin of systematic errors in the CRCM (Patricola et al., 2011a,b) d) Carried out studies of the impact of air-sea interaction on hurricanes, in the context of barrier layer interactions (Balaguru et al)

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

NASA Astrophysics Data System (ADS)

Wilby, Robert L.

2013-04-01

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

A transient stochastic weather generator incorporating climate model uncertainty

NASA Astrophysics Data System (ADS)

Glenis, Vassilis; Pinamonti, Valentina; Hall, Jim W.; Kilsby, Chris G.

2015-11-01

Stochastic weather generators (WGs), which provide long synthetic time series of weather variables such as rainfall and potential evapotranspiration (PET), have found widespread use in water resources modelling. When conditioned upon the changes in climatic statistics (change factors, CFs) predicted by climate models, WGs provide a useful tool for climate impacts assessment and adaption planning. The latest climate modelling exercises have involved large numbers of global and regional climate models integrations, designed to explore the implications of uncertainties in the climate model formulation and parameter settings: so called 'perturbed physics ensembles' (PPEs). In this paper we show how these climate model uncertainties can be propagated through to impact studies by testing multiple vectors of CFs, each vector derived from a different sample from a PPE. We combine this with a new methodology to parameterise the projected time-evolution of CFs. We demonstrate how, when conditioned upon these time-dependent CFs, an existing, well validated and widely used WG can be used to generate non-stationary simulations of future climate that are consistent with probabilistic outputs from the Met Office Hadley Centre's Perturbed Physics Ensemble. The WG enables extensive sampling of natural variability and climate model uncertainty, providing the basis for development of robust water resources management strategies in the context of a non-stationary climate.

Quantifying uncertainty in climate change science through empirical information theory.

PubMed

Majda, Andrew J; Gershgorin, Boris

2010-08-24

Quantifying the uncertainty for the present climate and the predictions of climate change in the suite of imperfect Atmosphere Ocean Science (AOS) computer models is a central issue in climate change science. Here, a systematic approach to these issues with firm mathematical underpinning is developed through empirical information theory. An information metric to quantify AOS model errors in the climate is proposed here which incorporates both coarse-grained mean model errors as well as covariance ratios in a transformation invariant fashion. The subtle behavior of model errors with this information metric is quantified in an instructive statistically exactly solvable test model with direct relevance to climate change science including the prototype behavior of tracer gases such as CO(2). Formulas for identifying the most sensitive climate change directions using statistics of the present climate or an AOS model approximation are developed here; these formulas just involve finding the eigenvector associated with the largest eigenvalue of a quadratic form computed through suitable unperturbed climate statistics. These climate change concepts are illustrated on a statistically exactly solvable one-dimensional stochastic model with relevance for low frequency variability of the atmosphere. Viable algorithms for implementation of these concepts are discussed throughout the paper.

Carbon-Temperature-Water Change Analysis for Peanut Production Under Climate Change: A Prototype for the AgMIP Coordinated Climate-Crop Modeling Project (C3MP)

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; McDermid, Sonali; Rosenzweig, Cynthia; Baigorria, Guillermo A.; Jones, James W.; Romero, Consuelo C.; Cecil, L. DeWayne

2014-01-01

Climate change is projected to push the limits of cropping systems and has the potential to disrupt the agricultural sector from local to global scales. This article introduces the Coordinated Climate-Crop Modeling Project (C3MP), an initiative of the Agricultural Model Intercomparison and Improvement Project (AgMIP) to engage a global network of crop modelers to explore the impacts of climate change via an investigation of crop responses to changes in carbon dioxide concentration ([CO2]), temperature, and water. As a demonstration of the C3MP protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO-Peanut crop model for Henry County, Alabama, to evaluate responses to the range of plausible [CO2], temperature changes, and precipitation changes projected by climate models out to the end of the 21st century. These sensitivity tests are used to derive crop model emulators that estimate changes in mean yield and the coefficient of variation for seasonal yields across a broad range of climate conditions, reproducing mean yields from sensitivity test simulations with deviations of ca. 2% for rain-fed conditions. We apply these statistical emulators to investigate how peanuts respond to projections from various global climate models, time periods, and emissions scenarios, finding a robust projection of modest (<10%) median yield losses in the middle of the 21st century accelerating to more severe (>20%) losses and larger uncertainty at the end of the century under the more severe representative concentration pathway (RCP8.5). This projection is not substantially altered by the selection of the AgMERRA global gridded climate dataset rather than the local historical observations, differences between the Third and Fifth Coupled Model Intercomparison Project (CMIP3 and CMIP5), or the use of the delta method of climate impacts analysis rather than the C3MP impacts response surface and emulator approach.

A Centroid Model of Species Distribution to Analyize Multi-directional Climate Change Finger Print in Avian Distribution in North America

NASA Astrophysics Data System (ADS)

Huang, Q.; Sauer, J.; Dubayah, R.

2015-12-01

Species distribution shift (or referred to as "fingerprint of climate change") as a primary mechanism to adapt climate change has been of great interest to ecologists and conservation practitioners. Recent meta-analyses have concluded that a wide range of animal and plant species are already shifting their distribution. However majority of the literature has focused on analyzing recent poleward and elevationally upward shift of species distribution. However if measured only in poleward shifts, the fingerprint of climate change will be underestimated significantly. In this study, we demonstrate a centroid model for range-wide analysis of distribution shifts using the North American Breeding Bird Survey. The centroid model is based on a hierarchical Bayesian framework which models population change within physiographic strata while accounting for several factors affecting species detectability. We used the centroid approach to examine large number of species permanent resident species in North America and evaluated the dreiction and magnitude of their shifting distribution. To examine the inferential ability of mean temperature and precipitation, we test a hypothesis based on climate velocity theory that species would be more likely to shift their distribution or would shift with greater magnitude in in regions with high climate change velocity. For species with significant shifts of distribution, we establish a precipitation model and a temperature model to explain their change of abundance at the strata level. Two models which are composed of mean and extreme climate indices respectively are also established to test the influences of changes in gradual and extreme climate trends.

Downscaling Global Emissions and Its Implications Derived from Climate Model Experiments

PubMed Central

Abe, Manabu; Kinoshita, Tsuguki; Hasegawa, Tomoko; Kawase, Hiroaki; Kushida, Kazuhide; Masui, Toshihiko; Oka, Kazutaka; Shiogama, Hideo; Takahashi, Kiyoshi; Tatebe, Hiroaki; Yoshikawa, Minoru

2017-01-01

In climate change research, future scenarios of greenhouse gas and air pollutant emissions generated by integrated assessment models (IAMs) are used in climate models (CMs) and earth system models to analyze future interactions and feedback between human activities and climate. However, the spatial resolutions of IAMs and CMs differ. IAMs usually disaggregate the world into 10–30 aggregated regions, whereas CMs require a grid-based spatial resolution. Therefore, downscaling emissions data from IAMs into a finer scale is necessary to input the emissions into CMs. In this study, we examined whether differences in downscaling methods significantly affect climate variables such as temperature and precipitation. We tested two downscaling methods using the same regionally aggregated sulfur emissions scenario obtained from the Asian-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model. The downscaled emissions were fed into the Model for Interdisciplinary Research on Climate (MIROC). One of the methods assumed a strong convergence of national emissions intensity (e.g., emissions per gross domestic product), while the other was based on inertia (i.e., the base-year remained unchanged). The emissions intensities in the downscaled spatial emissions generated from the two methods markedly differed, whereas the emissions densities (emissions per area) were similar. We investigated whether the climate change projections of temperature and precipitation would significantly differ between the two methods by applying a field significance test, and found little evidence of a significant difference between the two methods. Moreover, there was no clear evidence of a difference between the climate simulations based on these two downscaling methods. PMID:28076446

Climate Science: How Earth System Models are Reshaping the Science Policy Interface.

NASA Technical Reports Server (NTRS)

Ruane, Alex

2015-01-01

This talk is oriented at a general audience including the largest French utility company, and will describe the basics of climate change before moving into emissions scenarios and agricultural impacts that we can test with our earth system models and impacts models.

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

PubMed

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

2016-12-01

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

The Agriculture Model Intercomparison and Improvement Project (AgMIP) (Invited)

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2010-12-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a distributed climate-scenario simulation exercise for historical model intercomparison and future climate change conditions with participation of multiple crop and world agricultural trade modeling groups around the world. The goals of AgMIP are to improve substantially the characterization of risk of hunger and world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Historical period results will spur model improvement and interaction among major modeling groups, while future period results will lead directly to tests of adaptation and mitigation strategies across a range of scales. AgMIP will consist of a multi-scale impact assessment utilizing the latest methods for climate and agricultural scenario generation. Scenarios and modeling protocols will be distributed on the web, and multi-model results will be collated and analyzed to ensure the widest possible coverage of agricultural crops and regions. AgMIP will place regional changes in agricultural production in a global context that reflects new trading opportunities, imbalances, and shortages in world markets resulting from climate change and other driving forces for food supply. Such projections are essential inputs from the Vulnerability, Impacts, and Adaptation (VIA) research community to the Intergovernmental Panel on Climate Change Fifth Assessment (AR5), now underway, and the UN Framework Convention on Climate Change. They will set the context for local-scale vulnerability and adaptation studies, supply test scenarios for national-scale development of trade policy instruments, provide critical information on changing supply and demand for water resources, and elucidate interactive effects of climate change and land use change. AgMIP will not only provide crucially-needed new global estimates of how climate change will affect food supply and hunger in the agricultural regions of the world, but it will also build the capabilities of developing countries to estimate how climate change will affect their supply and demand for food.

Testing the Causal Links between School Climate, School Violence, and School Academic Performance: A Cross-Lagged Panel Autoregressive Model

ERIC Educational Resources Information Center

Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.

2016-01-01

The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…

Validation of catchment models for predicting land-use and climate change impacts. 1. Method

NASA Astrophysics Data System (ADS)

Ewen, J.; Parkin, G.

1996-02-01

Computer simulation models are increasingly being proposed as tools capable of giving water resource managers accurate predictions of the impact of changes in land-use and climate. Previous validation testing of catchment models is reviewed, and it is concluded that the methods used do not clearly test a model's fitness for such a purpose. A new generally applicable method is proposed. This involves the direct testing of fitness for purpose, uses established scientific techniques, and may be implemented within a quality assured programme of work. The new method is applied in Part 2 of this study (Parkin et al., J. Hydrol., 175:595-613, 1996).

High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability.

PubMed

Majda, Andrew J; Abramov, Rafail; Gershgorin, Boris

2010-01-12

Climate change science focuses on predicting the coarse-grained, planetary-scale, longtime changes in the climate system due to either changes in external forcing or internal variability, such as the impact of increased carbon dioxide. The predictions of climate change science are carried out through comprehensive, computational atmospheric, and oceanic simulation models, which necessarily parameterize physical features such as clouds, sea ice cover, etc. Recently, it has been suggested that there is irreducible imprecision in such climate models that manifests itself as structural instability in climate statistics and which can significantly hamper the skill of computer models for climate change. A systematic approach to deal with this irreducible imprecision is advocated through algorithms based on the Fluctuation Dissipation Theorem (FDT). There are important practical and computational advantages for climate change science when a skillful FDT algorithm is established. The FDT response operator can be utilized directly for multiple climate change scenarios, multiple changes in forcing, and other parameters, such as damping and inverse modelling directly without the need of running the complex climate model in each individual case. The high skill of FDT in predicting climate change, despite structural instability, is developed in an unambiguous fashion using mathematical theory as guidelines in three different test models: a generic class of analytical models mimicking the dynamical core of the computer climate models, reduced stochastic models for low-frequency variability, and models with a significant new type of irreducible imprecision involving many fast, unstable modes.

Weather Forecaster Understanding of Climate Models

NASA Astrophysics Data System (ADS)

Bol, A.; Kiehl, J. T.; Abshire, W. E.

2013-12-01

Weather forecasters, particularly those in broadcasting, are the primary conduit to the public for information on climate and climate change. However, many weather forecasters remain skeptical of model-based climate projections. To address this issue, The COMET Program developed an hour-long online lesson of how climate models work, targeting an audience of weather forecasters. The module draws on forecasters' pre-existing knowledge of weather, climate, and numerical weather prediction (NWP) models. In order to measure learning outcomes, quizzes were given before and after the lesson. Preliminary results show large learning gains. For all people that took both pre and post-tests (n=238), scores improved from 48% to 80%. Similar pre/post improvement occurred for National Weather Service employees (51% to 87%, n=22 ) and college faculty (50% to 90%, n=7). We believe these results indicate a fundamental misunderstanding among many weather forecasters of (1) the difference between weather and climate models, (2) how researchers use climate models, and (3) how they interpret model results. The quiz results indicate that efforts to educate the public about climate change need to include weather forecasters, a vital link between the research community and the general public.

Integrating geological archives and climate models for the mid-Pliocene warm period.

PubMed

Haywood, Alan M; Dowsett, Harry J; Dolan, Aisling M

2016-02-16

The mid-Pliocene Warm Period (mPWP) offers an opportunity to understand a warmer-than-present world and assess the predictive ability of numerical climate models. Environmental reconstruction and climate modelling are crucial for understanding the mPWP, and the synergy of these two, often disparate, fields has proven essential in confirming features of the past and in turn building confidence in projections of the future. The continual development of methodologies to better facilitate environmental synthesis and data/model comparison is essential, with recent work demonstrating that time-specific (time-slice) syntheses represent the next logical step in exploring climate change during the mPWP and realizing its potential as a test bed for understanding future climate change.

Integrating geological archives and climate models for the mid-Pliocene warm period

PubMed Central

Haywood, Alan M.; Dowsett, Harry J.; Dolan, Aisling M.

2016-01-01

The mid-Pliocene Warm Period (mPWP) offers an opportunity to understand a warmer-than-present world and assess the predictive ability of numerical climate models. Environmental reconstruction and climate modelling are crucial for understanding the mPWP, and the synergy of these two, often disparate, fields has proven essential in confirming features of the past and in turn building confidence in projections of the future. The continual development of methodologies to better facilitate environmental synthesis and data/model comparison is essential, with recent work demonstrating that time-specific (time-slice) syntheses represent the next logical step in exploring climate change during the mPWP and realizing its potential as a test bed for understanding future climate change. PMID:26879640

Quantifying the Value of Downscaled Climate Model Information for Adaptation Decisions: When is Downscaling a Smart Decision?

NASA Astrophysics Data System (ADS)

Terando, A. J.; Wootten, A.; Eaton, M. J.; Runge, M. C.; Littell, J. S.; Bryan, A. M.; Carter, S. L.

2015-12-01

Two types of decisions face society with respect to anthropogenic climate change: (1) whether to enact a global greenhouse gas abatement policy, and (2) how to adapt to the local consequences of current and future climatic changes. The practice of downscaling global climate models (GCMs) is often used to address (2) because GCMs do not resolve key features that will mediate global climate change at the local scale. In response, the development of downscaling techniques and models has accelerated to aid decision makers seeking adaptation guidance. However, quantifiable estimates of the value of information are difficult to obtain, particularly in decision contexts characterized by deep uncertainty and low system-controllability. Here we demonstrate a method to quantify the additional value that decision makers could expect if research investments are directed towards developing new downscaled climate projections. As a proof of concept we focus on a real-world management problem: whether to undertake assisted migration for an endangered tropical avian species. We also take advantage of recently published multivariate methods that account for three vexing issues in climate impacts modeling: maximizing climate model quality information, accounting for model dependence in ensembles of opportunity, and deriving probabilistic projections. We expand on these global methods by including regional (Caribbean Basin) and local (Puerto Rico) domains. In the local domain, we test whether a high resolution (2km) dynamically downscaled GCM reduces the multivariate error estimate compared to the original coarse-scale GCM. Initial tests show little difference between the downscaled and original GCM multivariate error. When propagated through to a species population model, the Value of Information analysis indicates that the expected utility that would accrue to the manager (and species) if this downscaling were completed may not justify the cost compared to alternative actions.

Probabilistic Evaluation of Competing Climate Models

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Chatterjee, S.; Heyman, M.; Cressie, N.

2017-12-01

A standard paradigm for assessing the quality of climate model simulations is to compare what these models produce for past and present time periods, to observations of the past and present. Many of these comparisons are based on simple summary statistics called metrics. Here, we propose an alternative: evaluation of competing climate models through probabilities derived from tests of the hypothesis that climate-model-simulated and observed time sequences share common climate-scale signals. The probabilities are based on the behavior of summary statistics of climate model output and observational data, over ensembles of pseudo-realizations. These are obtained by partitioning the original time sequences into signal and noise components, and using a parametric bootstrap to create pseudo-realizations of the noise sequences. The statistics we choose come from working in the space of decorrelated and dimension-reduced wavelet coefficients. We compare monthly sequences of CMIP5 model output of average global near-surface temperature anomalies to similar sequences obtained from the well-known HadCRUT4 data set, as an illustration.

Evaluating models of climate and forest vegetation

NASA Technical Reports Server (NTRS)

Clark, James S.

1992-01-01

Understanding how the biosphere may respond to increasing trace gas concentrations in the atmosphere requires models that contain vegetation responses to regional climate. Most of the processes ecologists study in forests, including trophic interactions, nutrient cycling, and disturbance regimes, and vital components of the world economy, such as forest products and agriculture, will be influenced in potentially unexpected ways by changing climate. These vegetation changes affect climate in the following ways: changing C, N, and S pools; trace gases; albedo; and water balance. The complexity of the indirect interactions among variables that depend on climate, together with the range of different space/time scales that best describe these processes, make the problems of modeling and prediction enormously difficult. These problems of predicting vegetation response to climate warming and potential ways of testing model predictions are the subjects of this chapter.

Extra-Tropical Cyclones at Climate Scales: Comparing Models to Observations

NASA Astrophysics Data System (ADS)

Tselioudis, G.; Bauer, M.; Rossow, W.

2009-04-01

Climate is often defined as the accumulation of weather, and weather is not the concern of climate models. Justification for this latter sentiment has long been hidden behind coarse model resolutions and blunt validation tools based on climatological maps. The spatial-temporal resolutions of today's climate models and observations are converging onto meteorological scales, however, which means that with the correct tools we can test the largely unproven assumption that climate model weather is correct enough that its accumulation results in a robust climate simulation. Towards this effort we introduce a new tool for extracting detailed cyclone statistics from observations and climate model output. These include the usual cyclone characteristics (centers, tracks), but also adaptive cyclone-centric composites. We have created a novel dataset, the MAP Climatology of Mid-latitude Storminess (MCMS), which provides a detailed 6 hourly assessment of the areas under the influence of mid-latitude cyclones, using a search algorithm that delimits the boundaries of each system from the outer-most closed SLP contour. Using this we then extract composites of cloud, radiation, and precipitation properties from sources such as ISCCP and GPCP to create a large comparative dataset for climate model validation. A demonstration of the potential usefulness of these tools in process-based climate model evaluation studies will be shown.

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.

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.

Can metric-based approaches really improve multi-model climate projections? A perfect model framework applied to summer temperature change in France.

NASA Astrophysics Data System (ADS)

Boé, Julien; Terray, Laurent

2014-05-01

Ensemble approaches for climate change projections have become ubiquitous. Because of large model-to-model variations and, generally, lack of rationale for the choice of a particular climate model against others, it is widely accepted that future climate change and its impacts should not be estimated based on a single climate model. Generally, as a default approach, the multi-model ensemble mean (MMEM) is considered to provide the best estimate of climate change signals. The MMEM approach is based on the implicit hypothesis that all the models provide equally credible projections of future climate change. This hypothesis is unlikely to be true and ideally one would want to give more weight to more realistic models. A major issue with this alternative approach lies in the assessment of the relative credibility of future climate projections from different climate models, as they can only be evaluated against present-day observations: which present-day metric(s) should be used to decide which models are "good" and which models are "bad" in the future climate? Once a supposedly informative metric has been found, other issues arise. What is the best statistical method to combine multiple models results taking into account their relative credibility measured by a given metric? How to be sure in the end that the metric-based estimate of future climate change is not in fact less realistic than the MMEM? It is impossible to provide strict answers to those questions in the climate change context. Yet, in this presentation, we propose a methodological approach based on a perfect model framework that could bring some useful elements of answer to the questions previously mentioned. The basic idea is to take a random climate model in the ensemble and treat it as if it were the truth (results of this model, in both past and future climate, are called "synthetic observations"). Then, all the other members from the multi-model ensemble are used to derive thanks to a metric-based approach a posterior estimate of climate change, based on the synthetic observation of the metric. Finally, it is possible to compare the posterior estimate to the synthetic observation of future climate change to evaluate the skill of the method. The main objective of this presentation is to describe and apply this perfect model framework to test different methodological issues associated with non-uniform model weighting and similar metric-based approaches. The methodology presented is general, but will be applied to the specific case of summer temperature change in France, for which previous works have suggested potentially useful metrics associated with soil-atmosphere and cloud-temperature interactions. The relative performances of different simple statistical approaches to combine multiple model results based on metrics will be tested. The impact of ensemble size, observational errors, internal variability, and model similarity will be characterized. The potential improvements associated with metric-based approaches compared to the MMEM is terms of errors and uncertainties will be quantified.

Analytically tractable climate-carbon cycle feedbacks under 21st century anthropogenic forcing

NASA Astrophysics Data System (ADS)

Lade, Steven J.; Donges, Jonathan F.; Fetzer, Ingo; Anderies, John M.; Beer, Christian; Cornell, Sarah E.; Gasser, Thomas; Norberg, Jon; Richardson, Katherine; Rockström, Johan; Steffen, Will

2018-05-01

Changes to climate-carbon cycle feedbacks may significantly affect the Earth system's response to greenhouse gas emissions. These feedbacks are usually analysed from numerical output of complex and arguably opaque Earth system models. Here, we construct a stylised global climate-carbon cycle model, test its output against comprehensive Earth system models, and investigate the strengths of its climate-carbon cycle feedbacks analytically. The analytical expressions we obtain aid understanding of carbon cycle feedbacks and the operation of the carbon cycle. Specific results include that different feedback formalisms measure fundamentally the same climate-carbon cycle processes; temperature dependence of the solubility pump, biological pump, and CO2 solubility all contribute approximately equally to the ocean climate-carbon feedback; and concentration-carbon feedbacks may be more sensitive to future climate change than climate-carbon feedbacks. Simple models such as that developed here also provide workbenches for simple but mechanistically based explorations of Earth system processes, such as interactions and feedbacks between the planetary boundaries, that are currently too uncertain to be included in comprehensive Earth system models.

The effects of ground hydrology on climate sensitivity to solar constant variations

NASA Technical Reports Server (NTRS)

Chou, S. H.; Curran, R. J.; Ohring, G.

1979-01-01

The effects of two different evaporation parameterizations on the climate sensitivity to solar constant variations are investigated by using a zonally averaged climate model. The model is based on a two-level quasi-geostrophic zonally averaged annual mean model. One of the evaporation parameterizations tested is a nonlinear formulation with the Bowen ratio determined by the predicted vertical temperature and humidity gradients near the earth's surface. The other is the linear formulation with the Bowen ratio essentially determined by the prescribed linear coefficient.

Climate change and Ixodes tick-borne diseases of humans

PubMed Central

Ostfeld, Richard S.; Brunner, Jesse L.

2015-01-01

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

Adaptive and plastic responses of Quercus petraea populations to climate across Europe.

PubMed

Sáenz-Romero, Cuauhtémoc; Lamy, Jean-Baptiste; Ducousso, Alexis; Musch, Brigitte; Ehrenmann, François; Delzon, Sylvain; Cavers, Stephen; Chałupka, Władysław; Dağdaş, Said; Hansen, Jon Kehlet; Lee, Steve J; Liesebach, Mirko; Rau, Hans-Martin; Psomas, Achilleas; Schneck, Volker; Steiner, Wilfried; Zimmermann, Niklaus E; Kremer, Antoine

2017-07-01

How temperate forests will respond to climate change is uncertain; projections range from severe decline to increased growth. We conducted field tests of sessile oak (Quercus petraea), a widespread keystone European forest tree species, including more than 150 000 trees sourced from 116 geographically diverse populations. The tests were planted on 23 field sites in six European countries, in order to expose them to a wide range of climates, including sites reflecting future warmer and drier climates. By assessing tree height and survival, our objectives were twofold: (i) to identify the source of differential population responses to climate (genetic differentiation due to past divergent climatic selection vs. plastic responses to ongoing climate change) and (ii) to explore which climatic variables (temperature or precipitation) trigger the population responses. Tree growth and survival were modeled for contemporary climate and then projected using data from four regional climate models for years 2071-2100, using two greenhouse gas concentration trajectory scenarios each. Overall, results indicated a moderate response of tree height and survival to climate variation, with changes in dryness (either annual or during the growing season) explaining the major part of the response. While, on average, populations exhibited local adaptation, there was significant clinal population differentiation for height growth with winter temperature at the site of origin. The most moderate climate model (HIRHAM5-EC; rcp4.5) predicted minor decreases in height and survival, while the most extreme model (CCLM4-GEM2-ES; rcp8.5) predicted large decreases in survival and growth for southern and southeastern edge populations (Hungary and Turkey). Other nonmarginal populations with continental climates were predicted to be severely and negatively affected (Bercé, France), while populations at the contemporary northern limit (colder and humid maritime regions; Denmark and Norway) will probably not show large changes in growth and survival in response to climate change. © 2017 John Wiley & Sons Ltd.

Vulnerability-based evaluation of water supply design under climate change

NASA Astrophysics Data System (ADS)

Umit Taner, Mehmet; Ray, Patrick; Brown, Casey

2015-04-01

Long-lived water supply infrastructures are strategic investments in the developing world, serving the purpose of balancing water deficits compounded by both population growth and socio-economic development. Robust infrastructure design under climate change is compelling, and often addressed by focusing on the outcomes of climate model projections ('scenario-led' planning), or by identifying design options that are less vulnerable to a wide range of plausible futures ('vulnerability-based' planning). Decision-Scaling framework combines these two approaches by first applying a climate stress test on the system to explore vulnerabilities across many traces of the future, and then employing climate projections to inform the decision-making process. In this work, we develop decision scaling's nascent risk management concepts further, directing actions on vulnerabilities identified during the climate stress test. In the process, we present a new way to inform climate vulnerability space using climate projections, and demonstrate the use of multiple decision criteria to guide to a final design recommendation. The concepts are demonstrated for a water supply project in the Mombasa Province of Kenya, planned to provide domestic and irrigation supply. Six storage design capacities (from 40 to 140 million cubic meters) are explored through a stress test, under a large number climate traces representing both natural climate variability and plausible climate changes. Design outcomes are simulated over a 40-year planning period with a coupled hydrologic-water resources systems model and using standard reservoir operation rules. Resulting performance is expressed in terms of water supply reliability and economic efficiency. Ensemble climate projections are used for assigning conditional likelihoods to the climate traces using a statistical distance measure. The final design recommendations are presented and discussed for the decision criteria of expected regret, satisficing, and conditional value-at-risk (CVaR).

Methods of Evaluating Protective Clothing Relative to Heat and Cold Stress: Thermal Manikin, Biomedical Modeling, and Human Testing

DTIC Science & Technology

2011-01-01

in the climatic chamber housing the manikin. The most widely accepted test procedures for the operation of a TM are published by ASTM International...insulation value of a complete clothing ensemble. It requires a TM surface temperature of 35◦C and a climatic chamber controlled at 23◦C, 50% relative... climatic chamber controlled at 35◦C, 40% relative humidity, with a 0.4 m/sec air velocity. In addition to the tests conducted at 0.4 m/sec, USARIEM

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.

Testing the hypothesis on cognitive evolution of modern humans' learning ability: current status of past-climatic approaches.

NASA Astrophysics Data System (ADS)

Yoneda, Minoru; Abe-Ouchi, Ayako; Kawahata, Hodaka; Yokoyama, Yusuke; Oguchi, Takashi

2014-05-01

The impact of climate change on human evolution is important and debating topic for many years. Since 2010, we have involved in a general joint project entitled "Replacement of Neanderthal by Modern Humans: Testing Evolutional Models of Learning", which based on a theoretical prediction that the cognitive ability related to individual and social learning divide fates of ancient humans in very unstable Late Pleistocene climate. This model predicts that the human populations which experienced a series of environmental changes would have higher rate of individual learners, while detailed reconstructions of global climate change have reported fluent and drastic change based on ice cores and stalagmites. However, we want to understand the difference between anatomically modern human which survived and the other archaic extinct humans including European Neanderthals and Asian Denisovans. For this purpose the global synchronized change is not useful for understanding but the regional difference in the amplitude and impact of climate change is the information required. Hence, we invited a geophysicist busing Global Circulation Model to reconstruct the climatic distribution and temporal change in a continental scale. At the same time, some geochemists and geographers construct a database of local climate changes recorded in different proxies. At last, archaeologists and anthropologists tried to interpret the emergence and disappearance of human species in Europe and Asia on the reconstructed past climate maps using some tools, such as Eco-cultural niche model. Our project will show the regional difference in climate change and related archaeological events and its impact on the evolution of learning ability of modern humans.

Exploring the possibility of a common structural model measuring associations between safety climate factors and safety behaviour in health care and the petroleum sectors.

PubMed

Olsen, Espen

2010-09-01

The aim of the present study was to explore the possibility of identifying general safety climate concepts in health care and petroleum sectors, as well as develop and test the possibility of a common cross-industrial structural model. Self-completion questionnaire surveys were administered in two organisations and sectors: (1) a large regional hospital in Norway that offers a wide range of hospital services, and (2) a large petroleum company that produces oil and gas worldwide. In total, 1919 and 1806 questionnaires were returned from the hospital and petroleum organisation, with response rates of 55 percent and 52 percent, respectively. Using a split sample procedure principal factor analysis and confirmatory factor analysis revealed six identical cross-industrial measurement concepts in independent samples-five measures of safety climate and one of safety behaviour. The factors' psychometric properties were explored with satisfactory internal consistency and concept validity. Thus, a common cross-industrial structural model was developed and tested using structural equation modelling (SEM). SEM revealed that a cross-industrial structural model could be identified among health care workers and offshore workers in the North Sea. The most significant contributing variables in the model testing stemmed from organisational management support for safety and supervisor/manager expectations and actions promoting safety. These variables indirectly enhanced safety behaviour (stop working in dangerous situations) through transitions and teamwork across units, and teamwork within units as well as learning, feedback, and improvement. Two new safety climate instruments were validated as part of the study: (1) Short Safety Climate Survey (SSCS) and (2) Hospital Survey on Patient Safety Culture-short (HSOPSC-short). Based on development of measurements and structural model assessment, this study supports the possibility of a common safety climate structural model across health care and the offshore petroleum industry. 2010 Elsevier Ltd. All rights reserved.

Drought forecasting in Luanhe River basin involving climatic indices

NASA Astrophysics Data System (ADS)

Ren, Weinan; Wang, Yixuan; Li, Jianzhu; Feng, Ping; Smith, Ronald J.

2017-11-01

Drought is regarded as one of the most severe natural disasters globally. This is especially the case in Tianjin City, Northern China, where drought can affect economic development and people's livelihoods. Drought forecasting, the basis of drought management, is an important mitigation strategy. In this paper, we evolve a probabilistic forecasting model, which forecasts transition probabilities from a current Standardized Precipitation Index (SPI) value to a future SPI class, based on conditional distribution of multivariate normal distribution to involve two large-scale climatic indices at the same time, and apply the forecasting model to 26 rain gauges in the Luanhe River basin in North China. The establishment of the model and the derivation of the SPI are based on the hypothesis of aggregated monthly precipitation that is normally distributed. Pearson correlation and Shapiro-Wilk normality tests are used to select appropriate SPI time scale and large-scale climatic indices. Findings indicated that longer-term aggregated monthly precipitation, in general, was more likely to be considered normally distributed and forecasting models should be applied to each gauge, respectively, rather than to the whole basin. Taking Liying Gauge as an example, we illustrate the impact of the SPI time scale and lead time on transition probabilities. Then, the controlled climatic indices of every gauge are selected by Pearson correlation test and the multivariate normality of SPI, corresponding climatic indices for current month and SPI 1, 2, and 3 months later are demonstrated using Shapiro-Wilk normality test. Subsequently, we illustrate the impact of large-scale oceanic-atmospheric circulation patterns on transition probabilities. Finally, we use a score method to evaluate and compare the performance of the three forecasting models and compare them with two traditional models which forecast transition probabilities from a current to a future SPI class. The results show that the three proposed models outperform the two traditional models and involving large-scale climatic indices can improve the forecasting accuracy.

Climate Verification Using Running Mann Whitney Z Statistics

USDA-ARS?s Scientific Manuscript database

A robust method previously used to detect observed intra- to multi-decadal (IMD) climate regimes was adapted to test whether climate models could reproduce IMD variations in U.S. surface temperatures during 1919-2008. This procedure, called the running Mann Whitney Z (MWZ) method, samples data ranki...

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

PubMed

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

2016-01-01

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

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

PubMed Central

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

2016-01-01

Climatic niche models for invasive plants are usually constructed with occurrence records taken from literature and collections. Because these data neither discriminate among life-cycle stages of plants (adult or juvenile) nor the origin of individuals (naturally established or man-planted), the resulting models may mispredict the distribution ranges of these species. We propose that more accurate predictions could be obtained by modelling climatic niches with data of naturally established individuals, particularly with occurrence records of juvenile plants because this would restrict the predictions of models to those sites where climatic conditions allow the recruitment of the species. To test this proposal, we focused on the Peruvian peppertree (Schinus molle), a South American species that has largely invaded Mexico. Three climatic niche models were constructed for this species using high-resolution dataset gathered in the field. The first model included all occurrence records, irrespective of the life-cycle stage or origin of peppertrees (generalized niche model). The second model only included occurrence records of naturally established mature individuals (adult niche model), while the third model was constructed with occurrence records of naturally established juvenile plants (regeneration niche model). When models were compared, the generalized climatic niche model predicted the presence of peppertrees in sites located farther beyond the climatic thresholds that naturally established individuals can tolerate, suggesting that human activities influence the distribution of this invasive species. The adult and regeneration climatic niche models concurred in their predictions about the distribution of peppertrees, suggesting that naturally established adult trees only occur in sites where climatic conditions allow the recruitment of juvenile stages. These results support the proposal that climatic niches of invasive plants should be modelled with data of naturally established individuals because this improves the accuracy of predictions about their distribution ranges. PMID:27195983

Practice and philosophy of climate model tuning across six US modeling centers

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

Schmidt, Gavin A.; Bader, David; Donner, Leo J.

Model calibration (or tuning) is a necessary part of developing and testing coupled ocean–atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major US climate modeling centers. While details differ among groups in terms of scientificmore » missions, tuning targets, and tunable parameters, there is a core commonality of approaches. Furthermore, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present-day radiative imbalance vs. the implied balance in the preindustrial era as a target.« less

Practice and philosophy of climate model tuning across six US modeling centers

NASA Astrophysics Data System (ADS)

Schmidt, Gavin A.; Bader, David; Donner, Leo J.; Elsaesser, Gregory S.; Golaz, Jean-Christophe; Hannay, Cecile; Molod, Andrea; Neale, Richard B.; Saha, Suranjana

2017-09-01

Model calibration (or tuning) is a necessary part of developing and testing coupled ocean-atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major US climate modeling centers. While details differ among groups in terms of scientific missions, tuning targets, and tunable parameters, there is a core commonality of approaches. However, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present-day radiative imbalance vs. the implied balance in the preindustrial era as a target.

Practice and philosophy of climate model tuning across six US modeling centers

DOE PAGES

Schmidt, Gavin A.; Bader, David; Donner, Leo J.; ...

2017-09-01

Model calibration (or tuning) is a necessary part of developing and testing coupled ocean–atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major US climate modeling centers. While details differ among groups in terms of scientificmore » missions, tuning targets, and tunable parameters, there is a core commonality of approaches. Furthermore, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present-day radiative imbalance vs. the implied balance in the preindustrial era as a target.« less

Climate Change Implications for Tropical Islands: Interpolating and Interpreting Statistically Downscaled GCM Projections for Management and Planning

Treesearch

Azad Henareh Khalyani; William A. Gould; Eric Harmsen; Adam Terando; Maya Quinones; Jaime A. Collazo

2016-01-01

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,…

Response of the global climate to changes in atmospheric chemical composition due to fossil fuel burning

NASA Technical Reports Server (NTRS)

Cess, R. D.; Hameed, S.; Hogan, J. S.

1980-01-01

Tropospheric ozone and methane might increase in the future as the result of increasing anthropogenic emissions of CO, NOx and CH4 due to fossil fuel burning. Since O3 and CH4 are both greenhouse gases, increases in their concentrations could augment global warming due to larger future amounts of atmospheric CO2. To test this possible climatic impact, a zonal energy-balance climate model has been combined with a vertically-averaged tropospheric chemical model. The latter model includes all relevant chemical reactions which affect species derived from H2O, O2, CH4 and NOx. The climate model correspondingly incorporates changes in the infrared heating of the surface-troposphere system resulting from chemically induced changes in tropospheric ozone and methane. This coupled climate-chemical model indicates that global climate is sensitive to changes in emissions of CO, NOx and CH4, and that future increases in these emissions could enhance global warming due to increasing atmospheric CO2.

Classroom Management, School Staff Relations, School Climate, and Academic Achievement: Testing A Model with Urban High Schools

ERIC Educational Resources Information Center

Back, Lindsey T.; Polk, Elizabeth; Keys, Christopher B.; McMahon, Susan D.

2016-01-01

Urban learning environments pose distinct instructional challenges for teachers and administrators, and can lead to lower achievement compared to suburban or rural schools. Today's educational climate increasingly emphasises a need for positive academic outcomes, often measured by standardised tests, on which student educational opportunities,…

Cross-biome transplants of plant litter show decomposition models extend to a broader climatic range but lose predictability at the decadal time scale

Treesearch

William S. Currie; Mark E. Harmon; Ingrid C. Burke; Stephen C. Hart; William J. Parton; Whendee L. Silver

2009-01-01

We analyzed results from 10-year long field incubations of foliar and fine root litter from the Long-term lntersite Decomposition Experiment Team (LIDET) study. We tested whether a variety of climate and litter quality variables could be used to develop regression models of decomposition parameters across wide ranges in litter quality and climate and whether these...

Challenges in Quantifying Pliocene Terrestrial Warming Revealed by Data-Model Discord

NASA Technical Reports Server (NTRS)

Salzmann, Ulrich; Dolan, Aisling M.; Haywood, Alan M.; Chan, Wing-Le; Voss, Jochen; Hill, Daniel J.; Abe-Ouchi, Ayako; Otto-Bliesner, Bette; Bragg, Frances J.; Chandler, Mark A.;

Employee satisfaction and theft: testing climate perceptions as a mediator.

PubMed

Kulas, John T; McInnerney, Joanne E; DeMuth, Rachel Frautschy; Jadwinski, Victoria

2007-07-01

Employee theft of both property and time is an expensive and pervasive problem for American organizations. One antecedent of theft behaviors is employee dissatisfaction, but not all dissatisfied employees engage in withdrawal or theft behaviors. The authors tested a model of theft behavior by using an organization's climate for theft as an explanatory mechanism. They found that dissatisfaction influenced employee theft behaviors through the intermediary influence of employees' individual perceptions of the organization's climate for theft. The authors encourage organizations to pay attention to such climate elements and take action to alter employee perceptions if they reflect permissive attitudes toward theft.

Impact of Future Climate on Radial Growth of Four Major Boreal Tree Species in the Eastern Canadian Boreal Forest

PubMed Central

Huang, Jian-Guo; Bergeron, Yves; Berninger, Frank; Zhai, Lihong; Tardif, Jacques C.; Denneler, Bernhard

2013-01-01

Immediate phenotypic variation and the lagged effect of evolutionary adaptation to climate change appear to be two key processes in tree responses to climate warming. This study examines these components in two types of growth models for predicting the 2010–2099 diameter growth change of four major boreal species Betula papyrifera, Pinus banksiana, Picea mariana, and Populus tremuloides along a broad latitudinal gradient in eastern Canada under future climate projections. Climate-growth response models for 34 stands over nine latitudes were calibrated and cross-validated. An adaptive response model (A-model), in which the climate-growth relationship varies over time, and a fixed response model (F-model), in which the relationship is constant over time, were constructed to predict future growth. For the former, we examined how future growth of stands in northern latitudes could be forecasted using growth-climate equations derived from stands currently growing in southern latitudes assuming that current climate in southern locations provide an analogue for future conditions in the north. For the latter, we tested if future growth of stands would be maximally predicted using the growth-climate equation obtained from the given local stand assuming a lagged response to climate due to genetic constraints. Both models predicted a large growth increase in northern stands due to more benign temperatures, whereas there was a minimal growth change in southern stands due to potentially warm-temperature induced drought-stress. The A-model demonstrates a changing environment whereas the F-model highlights a constant growth response to future warming. As time elapses we can predict a gradual transition between a response to climate associated with the current conditions (F-model) to a more adapted response to future climate (A-model). Our modeling approach provides a template to predict tree growth response to climate warming at mid-high latitudes of the Northern Hemisphere. PMID:23468879

Impact of future climate on radial growth of four major boreal tree species in the Eastern Canadian boreal forest.

PubMed

Huang, Jian-Guo; Bergeron, Yves; Berninger, Frank; Zhai, Lihong; Tardif, Jacques C; Denneler, Bernhard

2013-01-01

Immediate phenotypic variation and the lagged effect of evolutionary adaptation to climate change appear to be two key processes in tree responses to climate warming. This study examines these components in two types of growth models for predicting the 2010-2099 diameter growth change of four major boreal species Betula papyrifera, Pinus banksiana, Picea mariana, and Populus tremuloides along a broad latitudinal gradient in eastern Canada under future climate projections. Climate-growth response models for 34 stands over nine latitudes were calibrated and cross-validated. An adaptive response model (A-model), in which the climate-growth relationship varies over time, and a fixed response model (F-model), in which the relationship is constant over time, were constructed to predict future growth. For the former, we examined how future growth of stands in northern latitudes could be forecasted using growth-climate equations derived from stands currently growing in southern latitudes assuming that current climate in southern locations provide an analogue for future conditions in the north. For the latter, we tested if future growth of stands would be maximally predicted using the growth-climate equation obtained from the given local stand assuming a lagged response to climate due to genetic constraints. Both models predicted a large growth increase in northern stands due to more benign temperatures, whereas there was a minimal growth change in southern stands due to potentially warm-temperature induced drought-stress. The A-model demonstrates a changing environment whereas the F-model highlights a constant growth response to future warming. As time elapses we can predict a gradual transition between a response to climate associated with the current conditions (F-model) to a more adapted response to future climate (A-model). Our modeling approach provides a template to predict tree growth response to climate warming at mid-high latitudes of the Northern Hemisphere.

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.

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 new statistical approach to climate change detection and attribution

NASA Astrophysics Data System (ADS)

Ribes, Aurélien; Zwiers, Francis W.; Azaïs, Jean-Marc; Naveau, Philippe

2017-01-01

We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the " models are statistically indistinguishable from the truth" paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951-2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 ± 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings (-0.01± 0.02 K).

Climate induced changes on the hydrology of Mediterranean basins - assessing uncertainties and quantifying risks

NASA Astrophysics Data System (ADS)

Ludwig, Ralf

2014-05-01

According to current climate projections, the Mediterranean area is at high risk for severe changes in the hydrological budget and extremes. With innovative scientific measures, integrated hydrological modeling and novel field geophysical field monitoring techniques, the FP7 project CLIMB (Climate Induced Changes on the Hydrology of Mediterranean Basins; GA: 244151) assessed the impacts of climate change on the hydrology in seven basins in the Mediterranean area, in Italy, France, Turkey, Tunisia, Egypt and the Gaza Strip, and quantified uncertainties and risks for the main stakeholders of each test site. Intensive climate model auditing selected four regional climate models, whose data was bias corrected and downscaled to serve as climate forcing for a set of hydrological models in each site. The results of the multi-model hydro-climatic ensemble and socio-economic factor analysis were applied to develop a risk model building upon spatial vulnerability and risk assessment. Findings generally reveal an increasing risk for water resources management in the test sites, yet at different rates and severity in the investigated sectors, with highest impacts likely to occur in the transition months. Most important elements of this research include the following aspects: • Climate change contributes, yet in strong regional variation, to water scarcity in the Mediterranean; other factors, e.g. pollution or poor management practices, are regionally still dominant pressures on water resources. • Rain-fed agriculture needs to adapt to seasonal changes; stable or increasing productivity likely depends on additional irrigation. • Tourism could benefit in shoulder seasons, but may expect income losses in the summer peak season due to increasing heat stress. • Local & regional water managers and water users, lack, as yet, awareness of climate change induced risks; emerging focus areas are supplies of domestic drinking water, irrigation, hydropower and livestock. • Data and knowledge gaps in climate change impact and risk assessment are still widespread and ask for extended and coordinated monitoring programs. In order to discover, visualize and provide access the results of the project, the CLIMB-Portal has been established, serving as a platform for dissemination of project results, including communication and planning for local and regional stakeholders.

Future projections of temperature and precipitation climatology for CORDEX-MENA domain using RegCM4.4

NASA Astrophysics Data System (ADS)

Ozturk, Tugba; Turp, M. Tufan; Türkeş, Murat; Kurnaz, M. Levent

2018-07-01

In this study, we investigate changes in seasonal temperature and precipitation climatology of CORDEX Middle East and North Africa (MENA) region for three periods of 2010-2040, 2040-2070 and 2070-2100 with respect to the control period of 1970-2000 by using regional climate model simulations. Projections of future climate conditions are modeled by forcing Regional Climate Model, RegCM4.4 of the International Centre for Theoretical Physics (ICTP) with two different CMIP5 global climate models. HadGEM2-ES global climate model of the Met Office Hadley Centre and MPI-ESM-MR global climate model of the Max Planck Institute for Meteorology were used to generate 50 km resolution data for the Coordinated Regional Climate Downscaling Experiment (CORDEX) Region 13. We test the seasonal time-scale performance of RegCM4.4 in simulating the observed climatology over domain of the MENA by using the output of two different global climate models. The projection results show relatively high increase of average temperatures from 3 °C up to 9 °C over the domain for far future (2070-2100). A strong decrease in precipitation is projected in almost all parts of the domain according to the output of the regional model forced by scenario outputs of two global models. Therefore, warmer and drier than present climate conditions are projected to occur more intensely over the CORDEX-MENA domain.

Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming, USA

USGS Publications Warehouse

West, Amanda; Kumar, Sunil; Jarnevich, Catherine S.

2016-01-01

Regional analysis of large wildfire potential given climate change scenarios is crucial to understanding areas most at risk in the future, yet wildfire models are not often developed and tested at this spatial scale. We fit three historical climate suitability models for large wildfires (i.e. ≥ 400 ha) in Colorado andWyoming using topography and decadal climate averages corresponding to wildfire occurrence at the same temporal scale. The historical models classified points of known large wildfire occurrence with high accuracies. Using a novel approach in wildfire modeling, we applied the historical models to independent climate and wildfire datasets, and the resulting sensitivities were 0.75, 0.81, and 0.83 for Maxent, Generalized Linear, and Multivariate Adaptive Regression Splines, respectively. We projected the historic models into future climate space using data from 15 global circulation models and two representative concentration pathway scenarios. Maps from these geospatial analyses can be used to evaluate the changing spatial distribution of climate suitability of large wildfires in these states. April relative humidity was the most important covariate in all models, providing insight to the climate space of large wildfires in this region. These methods incorporate monthly and seasonal climate averages at a spatial resolution relevant to land management (i.e. 1 km2) and provide a tool that can be modified for other regions of North America, or adapted for other parts of the world.

Pathogen-Host Associations and Predicted Range Shifts of Human Monkeypox in Response to Climate Change in Central Africa

PubMed Central

Thomassen, Henri A.; Fuller, Trevon; Asefi-Najafabady, Salvi; Shiplacoff, Julia A. G.; Mulembakani, Prime M.; Blumberg, Seth; Johnston, Sara C.; Kisalu, Neville K.; Kinkela, Timothée L.; Fair, Joseph N.; Wolfe, Nathan D.; Shongo, Robert L.; LeBreton, Matthew; Meyer, Hermann; Wright, Linda L.; Muyembe, Jean-Jacques; Buermann, Wolfgang; Okitolonda, Emile; Hensley, Lisa E.; Lloyd-Smith, James O.; Smith, Thomas B.; Rimoin, Anne W.

2013-01-01

Climate change is predicted to result in changes in the geographic ranges and local prevalence of infectious diseases, either through direct effects on the pathogen, or indirectly through range shifts in vector and reservoir species. To better understand the occurrence of monkeypox virus (MPXV), an emerging Orthopoxvirus in humans, under contemporary and future climate conditions, we used ecological niche modeling techniques in conjunction with climate and remote-sensing variables. We first created spatially explicit probability distributions of its candidate reservoir species in Africa's Congo Basin. Reservoir species distributions were subsequently used to model current and projected future distributions of human monkeypox (MPX). Results indicate that forest clearing and climate are significant driving factors of the transmission of MPX from wildlife to humans under current climate conditions. Models under contemporary climate conditions performed well, as indicated by high values for the area under the receiver operator curve (AUC), and tests on spatially randomly and non-randomly omitted test data. Future projections were made on IPCC 4th Assessment climate change scenarios for 2050 and 2080, ranging from more conservative to more aggressive, and representing the potential variation within which range shifts can be expected to occur. Future projections showed range shifts into regions where MPX has not been recorded previously. Increased suitability for MPX was predicted in eastern Democratic Republic of Congo. Models developed here are useful for identifying areas where environmental conditions may become more suitable for human MPX; targeting candidate reservoir species for future screening efforts; and prioritizing regions for future MPX surveillance efforts. PMID:23935820

Assessing the impacts of climate change in Mediterranean catchments under conditions of data scarcity

NASA Astrophysics Data System (ADS)

Meyer, Swen; Ludwig, Ralf

2013-04-01

According to current climate projections, Mediterranean countries are at high risk for an even pronounced susceptibility to changes in the hydrological budget and extremes. While there is scientific consensus that climate induced changes on the hydrology of Mediterranean regions are presently occurring and are projected to amplify in the future, very little knowledge is available about the quantification of these changes, which is hampered by a lack of suitable and cost effective hydrological monitoring and modeling systems. The European FP7-project CLIMB is aiming to analyze climate induced changes on the hydrology of the Mediterranean Basins by investigating 7 test sites located in the countries Italy, France, Turkey, Tunisia, Gaza and Egypt. CLIMB employs a combination of novel geophysical field monitoring concepts, remote sensing techniques and integrated hydrologic modeling to improve process descriptions and understanding and to quantify existing uncertainties in climate change impact analysis. The Rio Mannu Basin, located in Sardinia; Italy, is one test site of the CLIMB project. The catchment has a size of 472.5 km2, it ranges from 62 to 946 meters in elevation, at mean annual temperatures of 16°C and precipitation of about 700 mm, the annual runoff volume is about 200 mm. The physically based Water Simulation Model WaSiM Vers. 2 (Schulla & Jasper (1999)) was setup to model current and projected future hydrological conditions. The availability of measured meteorological and hydrological data is poor as common to many Mediterranean catchments. The lack of available measured input data hampers the calibration of the model setup and the validation of model outputs. State of the art remote sensing techniques and field measuring techniques were applied to improve the quality of hydrological input parameters. In a field campaign about 250 soil samples were collected and lab-analyzed. Different geostatistical regionalization methods were tested to improve the model setup. The soil parameterization of the model was tested against publically available soil data. Results show a significant improvement of modeled soil moisture outputs. To validate WaSiMs evapotranspiration (ETact) outputs, Landsat TM images were used to calculate the actual monthly mean ETact rates using the triangle method (Jiang and Islam, 1999). Simulated spatial ETact patterns and those derived from remote sensing show a good fit especially for the growing season. WaSiM was driven with the meteorological forcing taken from 4 different ENSEMBLES climate projections for a reference (1971-2000) and a future (2041-2070) times series. Output results were analyzed for climate induced changes on selected hydrological variables. While the climate projections reveal increased precipitation rates in the spring season, first simulation results show an earlier onset and an increased duration of the dry season, imposing an increased irrigation demand and higher vulnerability of agricultural productivity.

Climate variations and salmonellosis transmission in Adelaide, South Australia: a comparison between regression models

NASA Astrophysics Data System (ADS)

Zhang, Ying; Bi, Peng; Hiller, Janet

2008-01-01

This is the first study to identify appropriate regression models for the association between climate variation and salmonellosis transmission. A comparison between different regression models was conducted using surveillance data in Adelaide, South Australia. By using notified salmonellosis cases and climatic variables from the Adelaide metropolitan area over the period 1990-2003, four regression methods were examined: standard Poisson regression, autoregressive adjusted Poisson regression, multiple linear regression, and a seasonal autoregressive integrated moving average (SARIMA) model. Notified salmonellosis cases in 2004 were used to test the forecasting ability of the four models. Parameter estimation, goodness-of-fit and forecasting ability of the four regression models were compared. Temperatures occurring 2 weeks prior to cases were positively associated with cases of salmonellosis. Rainfall was also inversely related to the number of cases. The comparison of the goodness-of-fit and forecasting ability suggest that the SARIMA model is better than the other three regression models. Temperature and rainfall may be used as climatic predictors of salmonellosis cases in regions with climatic characteristics similar to those of Adelaide. The SARIMA model could, thus, be adopted to quantify the relationship between climate variations and salmonellosis transmission.

The Relationship Between the Learning and Patient Safety Climates of Clinical Departments and Residents' Patient Safety Behaviors.

PubMed

Silkens, Milou E W M; Arah, Onyebuchi A; Wagner, Cordula; Scherpbier, Albert J J A; Heineman, Maas Jan; Lombarts, Kiki M J M H

2018-05-15

Improving residents' patient safety behavior should be a priority in graduate medical education to ensure the safety of current and future patients. Supportive learning and patient safety climates may foster this behavior. This study examined the extent to which residents' self-reported patient safety behavior can be explained by the learning climate and patient safety climate of their clinical departments. The authors collected learning climate data from clinical departments in the Netherlands that used the web-based Dutch Residency Educational Climate Test between September 2015 and October 2016. They also gathered data on those departments' patient safety climate and on residents' self-reported patient safety behavior. They used generalized linear mixed models and multivariate general linear models to test for associations in the data. In total, 1,006 residents evaluated 143 departments in 31 teaching hospitals. Departments' patient safety climate was associated with residents' overall self-reported patient safety behavior (regression coefficient (b) = 0.33; 95% confidence interval (CI) = 0.14 - 0.52). Departments' learning climate was not associated with residents' patient safety behavior (b = 0.01; 95% CI = -0.17 - 0.19), although it was with their patient safety climate (b = 0.73; 95% CI = 0.69 - 0.77). Departments should focus on establishing a supportive patient safety climate to improve residents' patient safety behavior. Building a supportive learning climate might help to improve the patient safety climate and, in turn, residents' patient safety behavior.

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

An Ice-and-Snow Hypothesis for Early Mars, and the Runoff-Production Test

NASA Astrophysics Data System (ADS)

Kite, E. S.

2017-10-01

Cold (snowmelt) models for Early Mars climate can be tested by measuring paleochannel widths and meander wavelengths for Early Mars watersheds with well-defined drainage area. I will review snowmelt models, and report results of these tests.

Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe

PubMed Central

Guis, Helene; Caminade, Cyril; Calvete, Carlos; Morse, Andrew P.; Tran, Annelise; Baylis, Matthew

2012-01-01

Vector-borne diseases are among those most sensitive to climate because the ecology of vectors and the development rate of pathogens within them are highly dependent on environmental conditions. Bluetongue (BT), a recently emerged arboviral disease of ruminants in Europe, is often cited as an illustration of climate's impact on disease emergence, although no study has yet tested this association. Here, we develop a framework to quantitatively evaluate the effects of climate on BT's emergence in Europe by integrating high-resolution climate observations and model simulations within a mechanistic model of BT transmission risk. We demonstrate that a climate-driven model explains, in both space and time, many aspects of BT's recent emergence and spread, including the 2006 BT outbreak in northwest Europe which occurred in the year of highest projected risk since at least 1960. Furthermore, the model provides mechanistic insight into BT's emergence, suggesting that the drivers of emergence across Europe differ between the South and the North. Driven by simulated future climate from an ensemble of 11 regional climate models, the model projects increase in the future risk of BT emergence across most of Europe with uncertainty in rate but not in trend. The framework described here is adaptable and applicable to other diseases, where the link between climate and disease transmission risk can be quantified, permitting the evaluation of scale and uncertainty in climate change's impact on the future of such diseases. PMID:21697167

The Dynamical Core Model Intercomparison Project (DCMIP-2016): Results of the Supercell Test Case

NASA Astrophysics Data System (ADS)

Zarzycki, C. M.; Reed, K. A.; Jablonowski, C.; Ullrich, P. A.; Kent, J.; Lauritzen, P. H.; Nair, R. D.

2016-12-01

The 2016 Dynamical Core Model Intercomparison Project (DCMIP-2016) assesses the modeling techniques for global climate and weather models and was recently held at the National Center for Atmospheric Research (NCAR) in conjunction with a two-week summer school. Over 12 different international modeling groups participated in DCMIP-2016 and focused on the evaluation of the newest non-hydrostatic dynamical core designs for future high-resolution weather and climate models. The paper highlights the results of the third DCMIP-2016 test case, which is an idealized supercell storm on a reduced-radius Earth. The supercell storm test permits the study of a non-hydrostatic moist flow field with strong vertical velocities and associated precipitation. This test assesses the behavior of global modeling systems at extremely high spatial resolution and is used in the development of next-generation numerical weather prediction capabilities. In this regime the effective grid spacing is very similar to the horizontal scale of convective plumes, emphasizing resolved non-hydrostatic dynamics. The supercell test case sheds light on the physics-dynamics interplay and highlights the impact of diffusion on model solutions.

The epistemological status of general circulation models

NASA Astrophysics Data System (ADS)

Loehle, Craig

2018-03-01

Forecasts of both likely anthropogenic effects on climate and consequent effects on nature and society are based on large, complex software tools called general circulation models (GCMs). Forecasts generated by GCMs have been used extensively in policy decisions related to climate change. However, the relation between underlying physical theories and results produced by GCMs is unclear. In the case of GCMs, many discretizations and approximations are made, and simulating Earth system processes is far from simple and currently leads to some results with unknown energy balance implications. Statistical testing of GCM forecasts for degree of agreement with data would facilitate assessment of fitness for use. If model results need to be put on an anomaly basis due to model bias, then both visual and quantitative measures of model fit depend strongly on the reference period used for normalization, making testing problematic. Epistemology is here applied to problems of statistical inference during testing, the relationship between the underlying physics and the models, the epistemic meaning of ensemble statistics, problems of spatial and temporal scale, the existence or not of an unforced null for climate fluctuations, the meaning of existing uncertainty estimates, and other issues. Rigorous reasoning entails carefully quantifying levels of uncertainty.

Informing Public Perceptions About Climate Change: A 'Mental Models' Approach.

PubMed

Wong-Parodi, Gabrielle; Bruine de Bruin, Wändi

2017-10-01

As the specter of climate change looms on the horizon, people will face complex decisions about whether to support climate change policies and how to cope with climate change impacts on their lives. Without some grasp of the relevant science, they may find it hard to make informed decisions. Climate experts therefore face the ethical need to effectively communicate to non-expert audiences. Unfortunately, climate experts may inadvertently violate the maxims of effective communication, which require sharing communications that are truthful, brief, relevant, clear, and tested for effectiveness. Here, we discuss the 'mental models' approach towards developing communications, which aims to help experts to meet the maxims of effective communications, and to better inform the judgments and decisions of non-expert audiences.

An integrated land change model for projecting future climate and land change scenarios

USGS Publications Warehouse

Wimberly, Michael; Sohl, Terry L.; Lamsal, Aashis; Liu, Zhihua; Hawbaker, Todd J.

2013-01-01

Climate change will have myriad effects on ecosystems worldwide, and natural and anthropogenic disturbances will be key drivers of these dynamics. In addition to climatic effects, continual expansion of human settlement into fire-prone forests will alter fire regimes, increase human vulnerability, and constrain future forest management options. There is a need for modeling tools to support the simulation and assessment of new management strategies over large regions in the context of changing climate, shifting development patterns, and an expanding wildland-urban interface. To address this need, we developed a prototype land change simulator that combines human-driven land use change (derived from the FORE-SCE model) with natural disturbances and vegetation dynamics (derived from the LADS model) and incorporates novel feedbacks between human land use and disturbance regimes. The prototype model was implemented in a test region encompassing the Denver metropolitan area along with its surrounding forested and agricultural landscapes. Initial results document the feasibility of integrated land change modeling at a regional scale but also highlighted conceptual and technical challenges for this type of model integration. Ongoing development will focus on improving climate sensitivities and modeling constraints imposed by climate change and human population growth on forest management activities.

Alternating high and low climate variability: The context of natural selection and speciation in Plio-Pleistocene hominin evolution.

PubMed

Potts, Richard; Faith, J Tyler

2015-10-01

Interaction of orbital insolation cycles defines a predictive model of alternating phases of high- and low-climate variability for tropical East Africa over the past 5 million years. This model, which is described in terms of climate variability stages, implies repeated increases in landscape/resource instability and intervening periods of stability in East Africa. It predicts eight prolonged (>192 kyr) eras of intensified habitat instability (high variability stages) in which hominin evolutionary innovations are likely to have occurred, potentially by variability selection. The prediction that repeated shifts toward high climate variability affected paleoenvironments and evolution is tested in three ways. In the first test, deep-sea records of northeast African terrigenous dust flux (Sites 721/722) and eastern Mediterranean sapropels (Site 967A) show increased and decreased variability in concert with predicted shifts in climate variability. These regional measurements of climate dynamics are complemented by stratigraphic observations in five basins with lengthy stratigraphic and paleoenvironmental records: the mid-Pleistocene Olorgesailie Basin, the Plio-Pleistocene Turkana and Olduvai Basins, and the Pliocene Tugen Hills sequence and Hadar Basin--all of which show that highly variable landscapes inhabited by hominin populations were indeed concentrated in predicted stages of prolonged high climate variability. Second, stringent null-model tests demonstrate a significant association of currently known first and last appearance datums (FADs and LADs) of the major hominin lineages, suites of technological behaviors, and dispersal events with the predicted intervals of prolonged high climate variability. Palynological study in the Nihewan Basin, China, provides a third test, which shows the occupation of highly diverse habitats in eastern Asia, consistent with the predicted increase in adaptability in dispersing Oldowan hominins. Integration of fossil, archeological, sedimentary, and paleolandscape evidence illustrates the potential influence of prolonged high variability on the origin and spread of critical adaptations and lineages in the evolution of Homo. The growing body of data concerning environmental dynamics supports the idea that the evolution of adaptability in response to climate and overall ecological instability represents a unifying theme in hominin evolutionary history. Published by Elsevier Ltd.

Analysis of Solar Chimneys in Different Climate Zones - Case of Social Housing in Ecuador

NASA Astrophysics Data System (ADS)

Godoy-Vaca, Luis; Almaguer, Manuel; Martínez-Gómez, Javier; Lobato, Andrea; Palme, Massimo

2017-10-01

The aim of this research is to simulate the performance of a solar chimney located in different macro-zones in Ecuador. The proposed solar chimney model was simulated using a python script in order to predict the temperature distribution and the mass flow over time. The results obtained were firstly compared with experimental data for dry-warm climate. Then, the model was evaluated and tested in real weather conditions: dry-warm, moist-warm and rainy-cold. In addition, the assumed chimney dimensions were chosen according to the literature for the studied conditions. In spite of evaluating the best nightly ventilation, different chimney wall materials were tested: solid brick, common brick and reinforced concrete. The results showed that concrete in a dry-warm climate, a metallic layer on the gap with solid brick in a moist-warm climate and reinforced concrete in a rainy cold climate used for the absorbent wall improve the thermal inertia of the social housing.

Climate Models

NASA Technical Reports Server (NTRS)

Druyan, Leonard M.

2012-01-01

Climate models is a very broad topic, so a single volume can only offer a small sampling of relevant research activities. This volume of 14 chapters includes descriptions of a variety of modeling studies for a variety of geographic regions by an international roster of authors. The climate research community generally uses the rubric climate models to refer to organized sets of computer instructions that produce simulations of climate evolution. The code is based on physical relationships that describe the shared variability of meteorological parameters such as temperature, humidity, precipitation rate, circulation, radiation fluxes, etc. Three-dimensional climate models are integrated over time in order to compute the temporal and spatial variations of these parameters. Model domains can be global or regional and the horizontal and vertical resolutions of the computational grid vary from model to model. Considering the entire climate system requires accounting for interactions between solar insolation, atmospheric, oceanic and continental processes, the latter including land hydrology and vegetation. Model simulations may concentrate on one or more of these components, but the most sophisticated models will estimate the mutual interactions of all of these environments. Advances in computer technology have prompted investments in more complex model configurations that consider more phenomena interactions than were possible with yesterday s computers. However, not every attempt to add to the computational layers is rewarded by better model performance. Extensive research is required to test and document any advantages gained by greater sophistication in model formulation. One purpose for publishing climate model research results is to present purported advances for evaluation by the scientific community.

Investigation of biogeophysical feedback on the African climate using a two-dimensional model

NASA Technical Reports Server (NTRS)

Xue, Yongkang; Liou, Kuo-Nan; Kasahara, Akira

1990-01-01

A numerical scheme is specifically designed to develop a time-dependent climate model to ensure the conservation of mass, momentum, energy, and water vapor, in order to study the biogeophysical feedback for the climate of Africa. A vegetation layer is incorporated in the present two-dimensional climate model. Using the coupled climate-vegetation model, two tests were performed involving the removal and expansion of the Sahara Desert. Results show that variations in the surface conditions produce a significant feedback to the climate system. It is noted that the simulation responses to the temperature and zonal wind in the case of an expanded desert agree with the climatological data for African dry years. Perturbed simulations have also been performed by changing the albedo only, without allowing the variation in the vegetation layer. It is shown that the variation in latent heat release is significant and is related to changes in the vegetation cover. As a result, precipitation and cloud cover are reduced.

Glaciological reconstruction of Holocene ice margins in northwestern Greenland

NASA Astrophysics Data System (ADS)

Birkel, S. D.; Osterberg, E. C.; Kelly, M. A.; Axford, Y.

2014-12-01

The past few decades of climate warming have brought overall margin retreat to the Greenland Ice Sheet. In order to place recent and projected changes in context, we are undertaking a collaborative field-modeling study that aims to reconstruct the Holocene history of ice-margin fluctuation near Thule (~76.5°N, 68.7°W), and also along the North Ice Cap (NIC) in the Nunatarssuaq region (~76.7°N, 67.4°W). Fieldwork reported by Kelly et al. (2013) reveals that ice in the study areas was less extensive than at present ca. 4700 (GIS) and ca. 880 (NIC) cal. years BP, presumably in response to a warmer climate. We are now exploring Holocene ice-climate coupling using the University of Maine Ice Sheet Model (UMISM). Our approach is to first test what imposed climate anomalies can afford steady state ice margins in accord with field data. A second test encompasses transient simulation of the Holocene, with climate boundary conditions supplied by existing paleo runs of the Community Climate System Model version 4 (CCSM4), and a climate forcing signal derived from Greenland ice cores. In both cases, the full ice sheet is simulated at 10 km resolution with nested domains at 0.5 km for the study areas. UMISM experiments are underway, and results will be reported at the meeting.

Decomposing the uncertainty in climate impact projections of Dynamic Vegetation Models: a test with the forest models LANDCLIM and FORCLIM

NASA Astrophysics Data System (ADS)

Cailleret, Maxime; Snell, Rebecca; von Waldow, Harald; Kotlarski, Sven; Bugmann, Harald

2015-04-01

Different levels of uncertainty should be considered in climate impact projections by Dynamic Vegetation Models (DVMs), particularly when it comes to managing climate risks. Such information is useful to detect the key processes and uncertainties in the climate model - impact model chain and may be used to support recommendations for future improvements in the simulation of both climate and biological systems. In addition, determining which uncertainty source is dominant is an important aspect to recognize the limitations of climate impact projections by a multi-model ensemble mean approach. However, to date, few studies have clarified how each uncertainty source (baseline climate data, greenhouse gas emission scenario, climate model, and DVM) affects the projection of ecosystem properties. Focusing on one greenhouse gas emission scenario, we assessed the uncertainty in the projections of a forest landscape model (LANDCLIM) and a stand-scale forest gap model (FORCLIM) that is caused by linking climate data with an impact model. LANDCLIM was used to assess the uncertainty in future landscape properties of the Visp valley in Switzerland that is due to (i) the use of different 'baseline' climate data (gridded data vs. data from weather stations), and (ii) differences in climate projections among 10 GCM-RCM chains. This latter point was also considered for the projections of future forest properties by FORCLIM at several sites along an environmental gradient in Switzerland (14 GCM-RCM chains), for which we also quantified the uncertainty caused by (iii) the model chain specific statistical properties of the climate time-series, and (iv) the stochasticity of the demographic processes included in the model, e.g., the annual number of saplings that establish, or tree mortality. Using methods of variance decomposition analysis, we found that (i) The use of different baseline climate data strongly impacts the prediction of forest properties at the lowest and highest, but not so much at medium elevations. (ii) Considering climate change, the variability that is due to the GCM-RCM chains is much greater than the variability induced by the uncertainty in the initial climatic conditions. (iii) The uncertainties caused by the intrinsic stochasticity in the DVMs and by the random generation of the climate time-series are negligible. Overall, our results indicate that DVMs are quite sensitive to the climate data, highlighting particularly (1) the limitations of using one single multi-model average climate change scenario in climate impact studies and (2) the need to better consider the uncertainty in climate model outputs for projecting future vegetation changes.

Organizational and media stress among professional football players: testing an achievement goal theory model.

PubMed

Kristiansen, E; Halvari, H; Roberts, G C

2012-08-01

The purpose of this study was to investigate media and coach-athlete stress experienced by professional football players and their relationship to motivational variables by testing an achievement goal theory (AGT) stress model. In order to do so, we developed scales specifically designed to assess media and coach-athlete stress. Eighty-two elite football players (M(age) =25.17 years, SD=5.19) completed a series of questionnaires. Correlations and bootstrapping were used as primary statistical analyses, supplemented by LISREL, to test the hypotheses. Results revealed that a mastery climate was directly and negatively associated with coach-athlete stress, while a performance climate was directly and positively associated with coach-athlete stress. In addition, an indirect positive path between the performance climate and media stress was revealed through ego orientation. These findings support some of the key postulates of AGT; a mastery climate reduces the perception of stress among athletes, and the converse is true for a performance climate. Coaches of elite footballers are advised to try to reduce the emphasis on performance criteria because of its stress-reducing effects. © 2011 John Wiley & Sons A/S.

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and Pilot Studies

NASA Technical Reports Server (NTRS)

Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.; Ruane, A. C.; Boote, K. J.; Thorburn, P.; Antle, J. M.; Nelson, G. C.; Porter, C.; Janssen, S.;

The Impact of School Climate and School Identification on Academic Achievement: Multilevel Modeling with Student and Teacher Data

PubMed Central

Maxwell, Sophie; Reynolds, Katherine J.; Lee, Eunro; Subasic, Emina; Bromhead, David

2017-01-01

School climate is a leading factor in explaining student learning and achievement. Less work has explored the impact of both staff and student perceptions of school climate raising interesting questions about whether staff school climate experiences can add “value” to students' achievement. In the current research, multiple sources were integrated into a multilevel model, including staff self-reports, student self-reports, objective school records of academic achievement, and socio-economic demographics. Achievement was assessed using a national literacy and numeracy tests (N = 760 staff and 2,257 students from 17 secondary schools). In addition, guided by the “social identity approach,” school identification is investigated as a possible psychological mechanism to explain the relationship between school climate and achievement. In line with predictions, results show that students' perceptions of school climate significantly explain writing and numeracy achievement and this effect is mediated by students' psychological identification with the school. Furthermore, staff perceptions of school climate explain students' achievement on numeracy, writing and reading tests (while accounting for students' responses). However, staff's school identification did not play a significant role. Implications of these findings for organizational, social, and educational research are discussed. PMID:29259564

The Impact of School Climate and School Identification on Academic Achievement: Multilevel Modeling with Student and Teacher Data.

PubMed

Maxwell, Sophie; Reynolds, Katherine J; Lee, Eunro; Subasic, Emina; Bromhead, David

2017-01-01

School climate is a leading factor in explaining student learning and achievement. Less work has explored the impact of both staff and student perceptions of school climate raising interesting questions about whether staff school climate experiences can add "value" to students' achievement. In the current research, multiple sources were integrated into a multilevel model, including staff self-reports, student self-reports, objective school records of academic achievement, and socio-economic demographics. Achievement was assessed using a national literacy and numeracy tests ( N = 760 staff and 2,257 students from 17 secondary schools). In addition, guided by the "social identity approach," school identification is investigated as a possible psychological mechanism to explain the relationship between school climate and achievement. In line with predictions, results show that students' perceptions of school climate significantly explain writing and numeracy achievement and this effect is mediated by students' psychological identification with the school. Furthermore, staff perceptions of school climate explain students' achievement on numeracy, writing and reading tests (while accounting for students' responses). However, staff's school identification did not play a significant role. Implications of these findings for organizational, social, and educational research are discussed.

Test of High-resolution Global and Regional Climate Model Projections

NASA Astrophysics Data System (ADS)

Stenchikov, Georgiy; Nikulin, Grigory; Hansson, Ulf; Kjellström, Erik; Raj, Jerry; Bangalath, Hamza; Osipov, Sergey

2014-05-01

In scope of CORDEX project we have simulated the past (1975-2005) and future (2006-2050) climates using the GFDL global high-resolution atmospheric model (HIRAM) and the Rossby Center nested regional model RCA4 for the Middle East and North Africa (MENA) region. Both global and nested runs were performed with roughly the same spatial resolution of 25 km in latitude and longitude, and were driven by the 2°x2.5°-resolution fields from GFDL ESM2M IPCC AR5 runs. The global HIRAM simulations could naturally account for interaction of regional processes with the larger-scale circulation features like Indian Summer Monsoon, which is lacking from regional model setup. Therefore in this study we specifically address the consistency of "global" and "regional" downscalings. The performance of RCA4, HIRAM, and ESM2M is tested based on mean, extreme, trends, seasonal and inter-annual variability of surface temperature, precipitation, and winds. The impact of climate change on dust storm activity, extreme precipitation and water resources is specifically addressed. We found that the global and regional climate projections appear to be quite consistent for the modeled period and differ more significantly from ESM2M than between each other.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Stress testing hydrologic models using bottom-up climate change assessment

NASA Astrophysics Data System (ADS)

Stephens, C.; Johnson, F.; Marshall, L. A.

2017-12-01

Bottom-up climate change assessment is a promising approach for understanding the vulnerability of a system to potential future changes. The technique has been utilised successfully in risk-based assessments of future flood severity and infrastructure vulnerability. We find that it is also an ideal tool for assessing hydrologic model performance in a changing climate. In this study, we applied bottom-up climate change to compare the performance of two different hydrologic models (an event-based and a continuous model) under increasingly severe climate change scenarios. This allowed us to diagnose likely sources of future prediction error in the two models. The climate change scenarios were based on projections for southern Australia, which indicate drier average conditions with increased extreme rainfall intensities. We found that the key weakness in using the event-based model to simulate drier future scenarios was the model's inability to dynamically account for changing antecedent conditions. This led to increased variability in model performance relative to the continuous model, which automatically accounts for the wetness of a catchment through dynamic simulation of water storages. When considering more intense future rainfall events, representation of antecedent conditions became less important than assumptions around (non)linearity in catchment response. The linear continuous model we applied may underestimate flood risk in a future climate with greater extreme rainfall intensity. In contrast with the recommendations of previous studies, this indicates that continuous simulation is not necessarily the key to robust flood modelling under climate change. By applying bottom-up climate change assessment, we were able to understand systematic changes in relative model performance under changing conditions and deduce likely sources of prediction error in the two models.

Improved hydrological model parametrization for climate change impact assessment under data scarcity - The potential of field monitoring techniques and geostatistics.

PubMed

Meyer, Swen; Blaschek, Michael; Duttmann, Rainer; Ludwig, Ralf

2016-02-01

According to current climate projections, Mediterranean countries are at high risk for an even pronounced susceptibility to changes in the hydrological budget and extremes. These changes are expected to have severe direct impacts on the management of water resources, agricultural productivity and drinking water supply. Current projections of future hydrological change, based on regional climate model results and subsequent hydrological modeling schemes, are very uncertain and poorly validated. The Rio Mannu di San Sperate Basin, located in Sardinia, Italy, is one test site of the CLIMB project. The Water Simulation Model (WaSiM) was set up to model current and future hydrological conditions. The availability of measured meteorological and hydrological data is poor as it is common for many Mediterranean catchments. In this study we conducted a soil sampling campaign in the Rio Mannu catchment. We tested different deterministic and hybrid geostatistical interpolation methods on soil textures and tested the performance of the applied models. We calculated a new soil texture map based on the best prediction method. The soil model in WaSiM was set up with the improved new soil information. The simulation results were compared to standard soil parametrization. WaSiMs was validated with spatial evapotranspiration rates using the triangle method (Jiang and Islam, 1999). WaSiM was driven with the meteorological forcing taken from 4 different ENSEMBLES climate projections for a reference (1971-2000) and a future (2041-2070) times series. The climate change impact was assessed based on differences between reference and future time series. The simulated results show a reduction of all hydrological quantities in the future in the spring season. Furthermore simulation results reveal an earlier onset of dry conditions in the catchment. We show that a solid soil model setup based on short-term field measurements can improve long-term modeling results, which is especially important in ungauged catchments. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Past climate variability and change in the Arctic and at high latitudes

USGS Publications Warehouse

Alley, Richard B.; Brigham-Grette, Julie; Miller, Gifford H.; Polyak, Leonid; ,; ,; ,

2009-01-01

Paleoclimate records play a key role in our understanding of Earth's past and present climate system and in our confidence in predicting future climate changes. Paleoclimate data help to elucidate past and present active mechanisms of climate change by placing the short instrumental record into a longer term context and by permitting models to be tested beyond the limited time that instrumental measurements have been available.

The impact of justice climate and justice orientation on work outcomes: a cross-level multifoci framework.

PubMed

Liao, Hui; Rupp, Deborah E

2005-03-01

In this article, which takes a person-situation approach, the authors propose and test a cross-level multifoci model of workplace justice. They crossed 3 types of justice (procedural, informational, and interpersonal) with 2 foci (organization and supervisor) and aggregated to the group level to create 6 distinct justice climate variables. They then tested for the effects of these variables on either organization-directed or supervisor-directed commitment, satisfaction, and citizenship behavior. The authors also tested justice orientation as a moderator of these relationships. The results, based on 231 employees constituting 44 work groups representing multiple organizations and occupations, revealed that 4 forms of justice climate (organization-focused procedural and informational justice climate and supervisor-focused procedural and interpersonal justice climate) were significantly related to various work outcomes after controlling for corresponding individual-level justice perceptions. In addition, some moderation effects were found. Implications for organizations and future research are discussed.

The economics (or lack thereof) of aerosol geoengineering

NASA Astrophysics Data System (ADS)

Goes, M.; Keller, K.; Tuana, N.

2009-04-01

Anthropogenic greenhouse gas emissions are changing the Earth's climate and impose substantial risks for current and future generations. What are scientifically sound, economically viable, and ethically defendable strategies to manage these climate risks? Ratified international agreements call for a reduction of greenhouse gas emissions to avoid dangerous anthropogenic interference with the climate system. Recent proposals, however, call for the deployment of a different approach: to geoengineer climate by injecting aerosol precursors into the stratosphere. Published economic studies typically suggest that substituting aerosol geoengineering for abatement of carbon dioxide emissions results in large net monetary benefits. However, these studies neglect the risks of aerosol geoengineering due to (i) the potential for future geoengineering failures and (ii) the negative impacts associated with the aerosol forcing. Here we use a simple integrated assessment model of climate change to analyze potential economic impacts of aerosol geoengineering strategies over a wide range of uncertain parameters such as climate sensitivity, the economic damages due to climate change, and the economic damages due to aerosol geoengineering forcing. The simplicity of the model provides the advantages of parsimony and transparency, but it also imposes severe caveats on the interpretation of the results. For example, the analysis is based on a globally aggregated model and is hence silent on the question of intragenerational distribution of costs and benefits. In addition, the analysis neglects the effects of endogenous learning about the climate system. We show that the risks associated with a future geoengineering failure and negative impacts of aerosol forcings can cause geoenginering strategies to fail an economic cost-benefit test. One key to this finding is that a geoengineering failure would lead to dramatic and abrupt climatic changes. The monetary damages due to this failure can dominate the cost-benefit analysis because the monetary damages of climate change are expected to increase with the rate of change. Substituting aerosol geoengineering for greenhouse gas emission abatement might fail not only an economic cost-benefit test but also an ethical test of distributional justice. Substituting aerosol geoengineering for greenhouse gas emissions abatements constitutes a conscious risk transfer to future generations. Intergenerational justice demands distributional justice, namely that present generations may not create benefits for themselves in exchange for burdens on future generations. We use the economic model to quantify this risk transfer to better inform the judgment of whether substituting aerosol geoengineering for carbon dioxide emission abatement fails this ethical test.

Cross-validation of an employee safety climate model in Malaysia.

PubMed

Bahari, Siti Fatimah; Clarke, Sharon

2013-06-01

Whilst substantial research has investigated the nature of safety climate, and its importance as a leading indicator of organisational safety, much of this research has been conducted with Western industrial samples. The current study focuses on the cross-validation of a safety climate model in the non-Western industrial context of Malaysian manufacturing. The first-order factorial validity of Cheyne et al.'s (1998) [Cheyne, A., Cox, S., Oliver, A., Tomas, J.M., 1998. Modelling safety climate in the prediction of levels of safety activity. Work and Stress, 12(3), 255-271] model was tested, using confirmatory factor analysis, in a Malaysian sample. Results showed that the model fit indices were below accepted levels, indicating that the original Cheyne et al. (1998) safety climate model was not supported. An alternative three-factor model was developed using exploratory factor analysis. Although these findings are not consistent with previously reported cross-validation studies, we argue that previous studies have focused on validation across Western samples, and that the current study demonstrates the need to take account of cultural factors in the development of safety climate models intended for use in non-Western contexts. The results have important implications for the transferability of existing safety climate models across cultures (for example, in global organisations) and highlight the need for future research to examine cross-cultural issues in relation to safety climate. Copyright © 2013 National Safety Council and Elsevier Ltd. All rights reserved.

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.

Collaborative Project: Development of an Isotope-Enabled CESM for Testing Abrupt Climate Changes

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

Liu, Zhengyu

One of the most important validations for a state-of-art Earth System Model (ESM) with respect to climate changes is the simulation of the climate evolution and abrupt climate change events in the Earth’s history of the last 21,000 years. However, one great challenge for model validation is that ESMs usually do not directly simulate geochemical variables that can be compared directly with past proxy records. In this proposal, we have met this challenge by developing the simulation capability of major isotopes in a state-of-art ESM, the Community Earth System Model (CESM), enabling us to make direct model-data comparison by comparingmore » the model directly against proxy climate records. Our isotope-enabled ESM incorporates the capability of simulating key isotopes and geotracers, notably δ 18O, δD, δ 14C, and δ 13C, Nd and Pa/Th. The isotope-enabled ESM have been used to perform some simulations for the last 21000 years. The direct comparison of these simulations with proxy records has shed light on the mechanisms of important climate change events.« less

Modeling glacial climates

NASA Technical Reports Server (NTRS)

North, G. R.; Crowley, T. J.

1984-01-01

Mathematical climate modelling has matured as a discipline to the point that it is useful in paleoclimatology. As an example a new two dimensional energy balance model is described and applied to several problems of current interest. The model includes the seasonal cycle and the detailed land-sea geographical distribution. By examining the changes in the seasonal cycle when external perturbations are forced upon the climate system it is possible to construct hypotheses about the origin of midlatitude ice sheets and polar ice caps. In particular the model predicts a rather sudden potential for glaciation over large areas when the Earth's orbital elements are only slightly altered. Similarly, the drift of continents or the change of atmospheric carbon dioxide over geological time induces radical changes in continental ice cover. With the advance of computer technology and improved understanding of the individual components of the climate system, these ideas will be tested in far more realistic models in the near future.

DOE unveils climate model in advance of global test

NASA Astrophysics Data System (ADS)

Popkin, Gabriel

2018-05-01

The world's growing collection of climate models has a high-profile new entry. Last week, after nearly 4 years of work, the U.S. Department of Energy (DOE) released computer code and initial results from an ambitious effort to simulate the Earth system. The new model is tailored to run on future supercomputers and designed to forecast not just how climate will change, but also how those changes might stress energy infrastructure. Results from an upcoming comparison of global models may show how well the new entrant works. But so far it is getting a mixed reception, with some questioning the need for another model and others saying the $80 million effort has yet to improve predictions of the future climate. Even the project's chief scientist, Ruby Leung of the Pacific Northwest National Laboratory in Richland, Washington, acknowledges that the model is not yet a leader.

Ecological networks are more sensitive to plant than to animal extinction under climate change

PubMed Central

Schleuning, Matthias; Fründ, Jochen; Schweiger, Oliver; Welk, Erik; Albrecht, Jörg; Albrecht, Matthias; Beil, Marion; Benadi, Gita; Blüthgen, Nico; Bruelheide, Helge; Böhning-Gaese, Katrin; Dehling, D. Matthias; Dormann, Carsten F.; Exeler, Nina; Farwig, Nina; Harpke, Alexander; Hickler, Thomas; Kratochwil, Anselm; Kuhlmann, Michael; Kühn, Ingolf; Michez, Denis; Mudri-Stojnić, Sonja; Plein, Michaela; Rasmont, Pierre; Schwabe, Angelika; Settele, Josef; Vujić, Ante; Weiner, Christiane N.; Wiemers, Martin; Hof, Christian

2016-01-01

Impacts of climate change on individual species are increasingly well documented, but we lack understanding of how these effects propagate through ecological communities. Here we combine species distribution models with ecological network analyses to test potential impacts of climate change on >700 plant and animal species in pollination and seed-dispersal networks from central Europe. We discover that animal species that interact with a low diversity of plant species have narrow climatic niches and are most vulnerable to climate change. In contrast, biotic specialization of plants is not related to climatic niche breadth and vulnerability. A simulation model incorporating different scenarios of species coextinction and capacities for partner switches shows that projected plant extinctions under climate change are more likely to trigger animal coextinctions than vice versa. This result demonstrates that impacts of climate change on biodiversity can be amplified via extinction cascades from plants to animals in ecological networks. PMID:28008919

Ecological networks are more sensitive to plant than to animal extinction under climate change.

PubMed

Schleuning, Matthias; Fründ, Jochen; Schweiger, Oliver; Welk, Erik; Albrecht, Jörg; Albrecht, Matthias; Beil, Marion; Benadi, Gita; Blüthgen, Nico; Bruelheide, Helge; Böhning-Gaese, Katrin; Dehling, D Matthias; Dormann, Carsten F; Exeler, Nina; Farwig, Nina; Harpke, Alexander; Hickler, Thomas; Kratochwil, Anselm; Kuhlmann, Michael; Kühn, Ingolf; Michez, Denis; Mudri-Stojnić, Sonja; Plein, Michaela; Rasmont, Pierre; Schwabe, Angelika; Settele, Josef; Vujić, Ante; Weiner, Christiane N; Wiemers, Martin; Hof, Christian

2016-12-23

Impacts of climate change on individual species are increasingly well documented, but we lack understanding of how these effects propagate through ecological communities. Here we combine species distribution models with ecological network analyses to test potential impacts of climate change on >700 plant and animal species in pollination and seed-dispersal networks from central Europe. We discover that animal species that interact with a low diversity of plant species have narrow climatic niches and are most vulnerable to climate change. In contrast, biotic specialization of plants is not related to climatic niche breadth and vulnerability. A simulation model incorporating different scenarios of species coextinction and capacities for partner switches shows that projected plant extinctions under climate change are more likely to trigger animal coextinctions than vice versa. This result demonstrates that impacts of climate change on biodiversity can be amplified via extinction cascades from plants to animals in ecological networks.

Transformational leadership and group interaction as climate antecedents: a social network analysis.

PubMed

Zohar, Dov; Tenne-Gazit, Orly

2008-07-01

In order to test the social mechanisms through which organizational climate emerges, this article introduces a model that combines transformational leadership and social interaction as antecedents of climate strength (i.e., the degree of within-unit agreement about climate perceptions). Despite their longstanding status as primary variables, both antecedents have received limited empirical research. The sample consisted of 45 platoons of infantry soldiers from 5 different brigades, using safety climate as the exemplar. Results indicate a partially mediated model between transformational leadership and climate strength, with density of group communication network as the mediating variable. In addition, the results showed independent effects for group centralization of the communication and friendship networks, which exerted incremental effects on climate strength over transformational leadership. Whereas centralization of the communication network was found to be negatively related to climate strength, centralization of the friendship network was positively related to it. Theoretical and practical implications are discussed.

A Multilevel Model of Team Cultural Diversity and Creativity: The Role of Climate for Inclusion

ERIC Educational Resources Information Center

Li, Ci-Rong; Lin, Chen-Ju; Tien, Yun-Hsiang; Chen, Chien-Ming

2017-01-01

We developed a multi-level model to test how team cultural diversity may relate to team- and individual-level creativity, integrating team diversity research and information-exchange perspective. We proposed that the team climate for inclusion would moderate both the relationship between cultural diversity and team information sharing and between…

DEVELOPMENT OF COLD CLIMATE HEAT PUMP USING TWO-STAGE COMPRESSION

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

Shen, Bo; Rice, C Keith; Abdelaziz, Omar

2015-01-01

This paper uses a well-regarded, hardware based heat pump system model to investigate a two-stage economizing cycle for cold climate heat pump applications. The two-stage compression cycle has two variable-speed compressors. The high stage compressor was modelled using a compressor map, and the low stage compressor was experimentally studied using calorimeter testing. A single-stage heat pump system was modelled as the baseline. The system performance predictions are compared between the two-stage and single-stage systems. Special considerations for designing a cold climate heat pump are addressed at both the system and component levels.

Climate-induced changes in forest disturbance and vegetation

NASA Technical Reports Server (NTRS)

Overpeck, Jonathan T.; Rind, David; Goldberg, Richard

1990-01-01

New and published climate-model results are discussed which indicate that global warming favors increased rates of forest disturbance as a result of weather more likely to cause forest fires, convective wind storms, coastal flooding, and hurricanes. New sensitivity tests carried out with a vegetation model indicate that climate-induced increases in disturbance could, in turn, significantly alter the total biomass and compositional response of forests to future warming. An increase in disturbance frequency is also likely to increase the rate at which natural vegetation responses to future climate change. The results reinforce the hypothesis that forests could be significantly altered by the first part of the next century. The modeling also confirms the potential utility of selected time series of fossil pollen data for investigating the poorly understood natural patterns of century-scale climate variability.

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.

Association of parameter, software, and hardware variation with large-scale behavior across 57,000 climate models

PubMed Central

Knight, Christopher G.; Knight, Sylvia H. E.; Massey, Neil; Aina, Tolu; Christensen, Carl; Frame, Dave J.; Kettleborough, Jamie A.; Martin, Andrew; Pascoe, Stephen; Sanderson, Ben; Stainforth, David A.; Allen, Myles R.

2007-01-01

In complex spatial models, as used to predict the climate response to greenhouse gas emissions, parameter variation within plausible bounds has major effects on model behavior of interest. Here, we present an unprecedentedly large ensemble of >57,000 climate model runs in which 10 parameters, initial conditions, hardware, and software used to run the model all have been varied. We relate information about the model runs to large-scale model behavior (equilibrium sensitivity of global mean temperature to a doubling of carbon dioxide). We demonstrate that effects of parameter, hardware, and software variation are detectable, complex, and interacting. However, we find most of the effects of parameter variation are caused by a small subset of parameters. Notably, the entrainment coefficient in clouds is associated with 30% of the variation seen in climate sensitivity, although both low and high values can give high climate sensitivity. We demonstrate that the effect of hardware and software is small relative to the effect of parameter variation and, over the wide range of systems tested, may be treated as equivalent to that caused by changes in initial conditions. We discuss the significance of these results in relation to the design and interpretation of climate modeling experiments and large-scale modeling more generally. PMID:17640921

Learning About Climate and Atmospheric Models Through Machine Learning

NASA Astrophysics Data System (ADS)

Lucas, D. D.

2017-12-01

From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Reconstructing Climate Change: The Model-Data Ping-Pong

NASA Astrophysics Data System (ADS)

Stocker, T. F.

2017-12-01

When Cesare Emiliani, the father of paleoceanography, made the first attempts at a quantitative reconstruction of Pleistocene climate change in the early 1950s, climate models were not yet conceived. The understanding of paleoceanographic records was therefore limited, and scientists had to resort to plausibility arguments to interpret their data. With the advent of coupled climate models in the early 1970s, for the first time hypotheses about climate processes and climate change could be tested in a dynamically consistent framework. However, only a model hierarchy can cope with the long time scales and the multi-component physical-biogeochemical Earth System. There are many examples how climate models have inspired the interpretation of paleoclimate data on the one hand, and conversely, how data have questioned long-held concepts and models. In this lecture I critically revisit a few examples of this model-data ping-pong, such as the bipolar seesaw, the mid-Holocene greenhouse gas increase, millennial and rapid CO2 changes reconstructed from polar ice cores, and the interpretation of novel paleoceanographic tracers. These examples also highlight many of the still unsolved questions and provide guidance for future research. The combination of high-resolution paleoceanographic data and modeling has never been more relevant than today. It will be the key for an appropriate risk assessment of impacts on the Earth System that are already underway in the Anthropocene.

Response of the global climate to changes in atmospheric chemical composition due to fossil fuel burning

NASA Technical Reports Server (NTRS)

Hameed, S.; Cess, R. D.; Hogan, J. S.

1980-01-01

Recent modeling of atmospheric chemical processes (Logan et al, 1978; Hameed et al, 1979) suggests that tropospheric ozone and methane might significantly increase in the future as the result of increasing anthropogenic emissions of CO, NO(x), and CH4 due to fossil fuel burning. Since O3 and CH4 are both greenhouse gases, increases in their concentrations could augment global warming due to larger future amounts of atmospheric CO2. To test the possible climatic impact of changes in tropospheric chemical composition, a zonal energy-balance climate model has been combined with a vertically averaged tropospheric chemical model. The latter model includes all relevant chemical reactions which affect species derived from H2O, O2, CH4, and NO(x). The climate model correspondingly incorporates changes in the infrared heating of the surface-troposphere system resulting from chemically induced changes in tropospheric ozone and methane. This coupled climate-chemical model indicates that global climate is sensitive to changes in emissions of CO, NO(x) and CH4, and that future increases in these emissions could augment global warming due to increasing atmospheric CO2.

Future Climate Impacts on Crop Water Demand and Groundwater Longevity in Agricultural Regions

NASA Astrophysics Data System (ADS)

Russo, T. A.; Sahoo, S.; Elliott, J. W.; Foster, I.

2016-12-01

Improving groundwater management practices under future drought conditions in agricultural regions requires three steps: 1) estimating the impacts of climate and drought on crop water demand, 2) projecting groundwater availability given climate and demand forcing, and 3) using this information to develop climate-smart policy and water use practices. We present an innovative combination of models to address the first two steps, and inform the third. Crop water demand was simulated using biophysical crop models forced by multiple climate models and climate scenarios, with one case simulating climate adaptation (e.g. modify planting or harvest time) and another without adaptation. These scenarios were intended to represent a range of drought projections and farm management responses. Nexty, we used projected climate conditions and simulated water demand across the United States as inputs to a novel machine learning-based groundwater model. The model was applied to major agricultural regions relying on the High Plains and Mississippi Alluvial aquifer systems in the US. The groundwater model integrates input data preprocessed using single spectrum analysis, mutual information, and a genetic algorithm, with an artificial neural network model. Model calibration and test results indicate low errors over the 33 year model run, and strong correlations to groundwater levels in hundreds of wells across each aquifer. Model results include a range of projected groundwater level changes from the present to 2050, and in some regions, identification and timeframe of aquifer depletion. These results quantify aquifer longevity under climate and crop scenarios, and provide decision makers with the data needed to compare scenarios of crop water demand, crop yield, and groundwater response, as they aim to balance water sustainability with food security.

Improving niche projections of plant species under climate change: Silene acaulis on the British Isles as a case study

NASA Astrophysics Data System (ADS)

Ferrarini, Alessandro; Alsafran, Mohammed H. S. A.; Dai, Junhu; Alatalo, Juha M.

2018-04-01

Empirical works to assist in choosing climatically relevant variables in the attempt to predict climate change impacts on plant species are limited. Further uncertainties arise in choice of an appropriate niche model. In this study we devised and tested a sharp methodological framework, based on stringent variable ranking and filtering and flexible model selection, to minimize uncertainty in both niche modelling and successive projection of plant species distributions. We used our approach to develop an accurate, parsimonious model of Silene acaulis (L.) presence/absence on the British Isles and to project its presence/absence under climate change. The approach suggests the importance of (a) defining a reduced set of climate variables, actually relevant to species presence/absence, from an extensive list of climate predictors, and (b) considering climate extremes instead of, or together with, climate averages in projections of plant species presence/absence under future climate scenarios. Our methodological approach reduced the number of relevant climate predictors by 95.23% (from 84 to only 4), while simultaneously achieving high cross-validated accuracy (97.84%) confirming enhanced model performance. Projections produced under different climate scenarios suggest that S. acaulis will likely face climate-driven fast decline in suitable areas on the British Isles, and that upward and northward shifts to occupy new climatically suitable areas are improbable in the future. Our results also imply that conservation measures for S. acaulis based upon assisted colonization are unlikely to succeed on the British Isles due to the absence of climatically suitable habitat, so different conservation actions (seed banks and/or botanical gardens) are needed.

Detection and Attribution of Regional Climate Change

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

Bala, G; Mirin, A

2007-01-19

We developed a high resolution global coupled modeling capability to perform breakthrough studies of the regional climate change. The atmospheric component in our simulation uses a 1{sup o} latitude x 1.25{sup o} longitude grid which is the finest resolution ever used for the NCAR coupled climate model CCSM3. Substantial testing and slight retuning was required to get an acceptable control simulation. The major accomplishment is the validation of this new high resolution configuration of CCSM3. There are major improvements in our simulation of the surface wind stress and sea ice thickness distribution in the Arctic. Surface wind stress and oceanmore » circulation in the Antarctic Circumpolar Current are also improved. Our results demonstrate that the FV version of the CCSM coupled model is a state of the art climate model whose simulation capabilities are in the class of those used for IPCC assessments. We have also provided 1000 years of model data to Scripps Institution of Oceanography to estimate the natural variability of stream flow in California. In the future, our global model simulations will provide boundary data to high-resolution mesoscale model that will be used at LLNL. The mesoscale model would dynamically downscale the GCM climate to regional scale on climate time scales.« less

Advancing Climate Change and Impacts Science Through Climate Informatics

NASA Astrophysics Data System (ADS)

Lenhardt, W.; Pouchard, L. C.; King, A. W.; Branstetter, M. L.; Kao, S.; Wang, D.

2010-12-01

This poster will outline the work to date on developing a climate informatics capability at Oak Ridge National Laboratory (ORNL). The central proposition of this effort is that the application of informatics and information science to the domain of climate change science is an essential means to bridge the realm of high performance computing (HPC) and domain science. The goal is to facilitate knowledge capture and the creation of new scientific insights. For example, a climate informatics capability will help with the understanding and use of model results in domain sciences that were not originally in the scope. From there, HPC can also benefit from feedback as the new approaches may lead to better parameterization in the models. In this poster we will summarize the challenges associated with climate change science that can benefit from the systematic application of informatics and we will highlight our work to date in creating the climate informatics capability to address these types of challenges. We have identified three areas that are particularly challenging in the context of climate change science: 1) integrating model and observational data across different spatial and temporal scales, 2) model linkages, i.e. climate models linked to other models such as hydrologic models, and 3) model diagnostics. Each of these has a methodological component and an informatics component. Our project under way at ORNL seeks to develop new approaches and tools in the context of linking climate change and water issues. We are basing our work on the following four use cases: 1) Evaluation/test of CCSM4 biases in hydrology (precipitation, soil water, runoff, river discharge) over the Rio Grande Basin. User: climate modeler. 2) Investigation of projected changes in hydrology of Rio Grande Basin using the VIC (Variable Infiltration Capacity Macroscale) Hydrologic Model. User: watershed hydrologist/modeler. 3) Impact of climate change on agricultural productivity of the Rio Grande Basin. User: climate impact scientist, agricultural economist. 4) Renegotiation of the 1944 “Treaty for the Utilization of Waters of the Colorado and Tijuana Rivers and of the Rio Grande”. User: A US State Department analyst or their counterpart in Mexico.

Radiation budget measurement/model interface

NASA Technical Reports Server (NTRS)

Vonderhaar, T. H.; Ciesielski, P.; Randel, D.; Stevens, D.

1983-01-01

This final report includes research results from the period February, 1981 through November, 1982. Two new results combine to form the final portion of this work. They are the work by Hanna (1982) and Stevens to successfully test and demonstrate a low-order spectral climate model and the work by Ciesielski et al. (1983) to combine and test the new radiation budget results from NIMBUS-7 with earlier satellite measurements. Together, the two related activities set the stage for future research on radiation budget measurement/model interfacing. Such combination of results will lead to new applications of satellite data to climate problems. The objectives of this research under the present contract are therefore satisfied. Additional research reported herein includes the compilation and documentation of the radiation budget data set a Colorado State University and the definition of climate-related experiments suggested after lengthy analysis of the satellite radiation budget experiments.

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.

Conclusions about Niche Expansion in Introduced Impatiens walleriana Populations Depend on Method of Analysis

PubMed Central

Mandle, Lisa; Warren, Dan L.; Hoffmann, Matthias H.; Peterson, A. Townsend; Schmitt, Johanna; von Wettberg, Eric J.

2010-01-01

Determining the degree to which climate niches are conserved across plant species' native and introduced ranges is valuable to developing successful strategies to limit the introduction and spread of invasive plants, and also has important ecological and evolutionary implications. Here, we test whether climate niches differ between native and introduced populations of Impatiens walleriana, globally one of the most popular horticultural species. We use approaches based on both raw climate data associated with occurrence points and ecological niche models (ENMs) developed with Maxent. We include comparisons of climate niche breadth in both geographic and environmental spaces, taking into account differences in available habitats between the distributional areas. We find significant differences in climate envelopes between native and introduced populations when comparing raw climate variables, with introduced populations appearing to expand into wetter and cooler climates. However, analyses controlling for differences in available habitat in each region do not indicate expansion of climate niches. We therefore cannot reject the hypothesis that observed differences in climate envelopes reflect only the limited environments available within the species' native range in East Africa. Our results suggest that models built from only native range occurrence data will not provide an accurate prediction of the potential for invasiveness if applied to areas containing a greater range of environmental combinations, and that tests of niche expansion may overestimate shifts in climate niches if they do not control carefully for environmental differences between distributional areas. PMID:21206912

Agricultural conservation practices can help mitigate the impact of climate change.

PubMed

Wagena, Moges B; Easton, Zachary M

2018-09-01

Agricultural conservation practices (CPs) are commonly implemented to reduce diffuse nutrient pollution. Climate change can complicate the development, implementation, and efficiency of agricultural CPs by altering hydrology, nutrient cycling, and erosion. This research quantifies the impact of climate change on hydrology, nutrient cycling, erosion, and the effectiveness of agricultural CP in the Susquehanna River Basin in the Chesapeake Bay Watershed, USA. We develop, calibrate, and test the Soil and Water Assessment Tool-Variable Source Area (SWAT-VSA) model and select four CPs; buffer strips, strip-cropping, no-till, and tile drainage, to test their effectiveness in reducing climate change impacts on water quality. We force the model with six downscaled global climate models (GCMs) for a historic period (1990-2014) and two future scenario periods (2041-2065 and 2075-2099) and quantify the impact of climate change on hydrology, nitrate-N (NO 3 -N), total N (TN), dissolved phosphorus (DP), total phosphorus (TP), and sediment export with and without CPs. We also test prioritizing CP installation on the 30% of agricultural lands that generate the most runoff (e.g., critical source areas-CSAs). Compared against the historical baseline and with no CPs, the ensemble model predictions indicate that climate change results in annual increases in flow (4.5±7.3%), surface runoff (3.5±6.1%), sediment export (28.5±18.2%) and TN export (9.5±5.1%), but decreases in NO 3 -N (12±12.8%), DP (14±11.5), and TP (2.5±7.4%) export. When agricultural CPs are simulated most do not appreciably change the water balance, however, tile drainage and strip-cropping decrease surface runoff, sediment export, and DP/TP, while buffer strips reduce N export. Installing CPs on CSAs results in nearly the same level of performance for most practices and most pollutants. These results suggest that climate change will influence the performance of agricultural CPs and that targeting agricultural CPs to CSAs can provide nearly the same level of water quality effects as more widespread adoption. Copyright © 2018 Elsevier B.V. All rights reserved.

Aerosol-Radiation-Cloud Interactions in the South-East Atlantic: Model-Relevant Observations and the Beneficiary Modeling Efforts in the Realm of the EVS-2 Project ORACLES

NASA Technical Reports Server (NTRS)

Redemann, Jens

2018-01-01

Globally, aerosols remain a major contributor to uncertainties in assessments of anthropogenically-induced changes to the Earth climate system, despite concerted efforts using satellite and suborbital observations and increasingly sophisticated models. The quantification of direct and indirect aerosol radiative effects, as well as cloud adjustments thereto, even at regional scales, continues to elude our capabilities. Some of our limitations are due to insufficient sampling and accuracy of the relevant observables, under an appropriate range of conditions to provide useful constraints for modeling efforts at various climate scales. In this talk, I will describe (1) the efforts of our group at NASA Ames to develop new airborne instrumentation to address some of the data insufficiencies mentioned above; (2) the efforts by the EVS-2 ORACLES project to address aerosol-cloud-climate interactions in the SE Atlantic and (3) time permitting, recent results from a synergistic use of A-Train aerosol data to test climate model simulations of present-day direct radiative effects in some of the AEROCOM phase II global climate models.

Child Centred Approach to Climate Change and Health Adaptation through Schools in Bangladesh: A Cluster Randomised Intervention Trial

PubMed Central

Kabir, Md Iqbal; Rahman, Md Bayzidur; Smith, Wayne; Lusha, Mirza Afreen Fatima; Milton, Abul Hasnat

2015-01-01

Background Bangladesh is one of the most vulnerable countries to climate change. People are getting educated at different levels on how to deal with potential impacts. One such educational mode was the preparation of a school manual, for high school students on climate change and health protection endorsed by the National Curriculum and Textbook Board, which is based on a 2008 World Health Organization manual. The objective of this study was to test the effectiveness of the manual in increasing the knowledge level of the school children about climate change and health adaptation. Methods This cluster randomized intervention trial involved 60 schools throughout Bangladesh, with 3293 secondary school students participating. School upazilas (sub-districts) were randomised into intervention and control groups, and two schools from each upazila were randomly selected. All year seven students from both groups of schools sat for a pre-test of 30 short questions of binary response. A total of 1515 students from 30 intervention schools received the intervention through classroom training based on the school manual and 1778 students of the 30 control schools did not get the manual but a leaflet on climate change and health issues. Six months later, a post-intervention test of the same questionnaire used in the pre-test was performed at both intervention and control schools. The pre and post test scores were analysed along with the demographic data by using random effects model. Results None of the various school level and student level variables were significantly different between the control and intervention group. However, the intervention group had a 17.42% (95% CI: 14.45 to 20.38, P = <0.001) higher score in the post-test after adjusting for pre-test score and other covariates in a multi-level linear regression model. Conclusions These results suggest that school-based intervention for climate change and health adaptation is effective for increasing the knowledge level of school children on this topic. PMID:26252381

Child Centred Approach to Climate Change and Health Adaptation through Schools in Bangladesh: A Cluster Randomised Intervention Trial.

PubMed

Kabir, Md Iqbal; Rahman, Md Bayzidur; Smith, Wayne; Lusha, Mirza Afreen Fatima; Milton, Abul Hasnat

2015-01-01

Bangladesh is one of the most vulnerable countries to climate change. People are getting educated at different levels on how to deal with potential impacts. One such educational mode was the preparation of a school manual, for high school students on climate change and health protection endorsed by the National Curriculum and Textbook Board, which is based on a 2008 World Health Organization manual. The objective of this study was to test the effectiveness of the manual in increasing the knowledge level of the school children about climate change and health adaptation. This cluster randomized intervention trial involved 60 schools throughout Bangladesh, with 3293 secondary school students participating. School upazilas (sub-districts) were randomised into intervention and control groups, and two schools from each upazila were randomly selected. All year seven students from both groups of schools sat for a pre-test of 30 short questions of binary response. A total of 1515 students from 30 intervention schools received the intervention through classroom training based on the school manual and 1778 students of the 30 control schools did not get the manual but a leaflet on climate change and health issues. Six months later, a post-intervention test of the same questionnaire used in the pre-test was performed at both intervention and control schools. The pre and post test scores were analysed along with the demographic data by using random effects model. None of the various school level and student level variables were significantly different between the control and intervention group. However, the intervention group had a 17.42% (95% CI: 14.45 to 20.38, P = <0.001) higher score in the post-test after adjusting for pre-test score and other covariates in a multi-level linear regression model. These results suggest that school-based intervention for climate change and health adaptation is effective for increasing the knowledge level of school children on this topic.

Connectivity planning to address climate change.

PubMed

Nuñez, Tristan A; Lawler, Joshua J; McRae, Brad H; Pierce, D John; Krosby, Meade B; Kavanagh, Darren M; Singleton, Peter H; Tewksbury, Joshua J

2013-04-01

As the climate changes, human land use may impede species from tracking areas with suitable climates. Maintaining connectivity between areas of different temperatures could allow organisms to move along temperature gradients and allow species to continue to occupy the same temperature space as the climate warms. We used a coarse-filter approach to identify broad corridors for movement between areas where human influence is low while simultaneously routing the corridors along present-day spatial gradients of temperature. We modified a cost-distance algorithm to model these corridors and tested the model with data on current land-use and climate patterns in the Pacific Northwest of the United States. The resulting maps identified a network of patches and corridors across which species may move as climates change. The corridors are likely to be robust to uncertainty in the magnitude and direction of future climate change because they are derived from gradients and land-use patterns. The assumptions we applied in our model simplified the stability of temperature gradients and species responses to climate change and land use, but the model is flexible enough to be tailored to specific regions by incorporating other climate variables or movement costs. When used at appropriate resolutions, our approach may be of value to local, regional, and continental conservation initiatives seeking to promote species movements in a changing climate. Planificación de Conectividad para Atender el Cambio Climático. © 2013 Society for Conservation Biology.

Palaeoclimatic insights into future climate challenges.

PubMed

Alley, Richard B

2003-09-15

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

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…

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.

UTCI-Fiala multi-node model of human heat transfer and temperature regulation

NASA Astrophysics Data System (ADS)

Fiala, Dusan; Havenith, George; Bröde, Peter; Kampmann, Bernhard; Jendritzky, Gerd

2012-05-01

The UTCI-Fiala mathematical model of human temperature regulation forms the basis of the new Universal Thermal Climate Index (UTC). Following extensive validation tests, adaptations and extensions, such as the inclusion of an adaptive clothing model, the model was used to predict human temperature and regulatory responses for combinations of the prevailing outdoor climate conditions. This paper provides an overview of the underlying algorithms and methods that constitute the multi-node dynamic UTCI-Fiala model of human thermal physiology and comfort. Treated topics include modelling heat and mass transfer within the body, numerical techniques, modelling environmental heat exchanges, thermoregulatory reactions of the central nervous system, and perceptual responses. Other contributions of this special issue describe the validation of the UTCI-Fiala model against measured data and the development of the adaptive clothing model for outdoor climates.

Do downscaled general circulation models reliably simulate historical climatic conditions?

USGS Publications Warehouse

Bock, Andrew R.; Hay, Lauren E.; McCabe, Gregory J.; Markstrom, Steven L.; Atkinson, R. Dwight

2018-01-01

The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated using CMIP5 generally are more reliable than those derived from CMIP3, likely due to improvements in PPT simulations. At most locations across the CONUS, the largest differences between GSD and SD PPT and RUN occurred in the lowest part of the distributions (i.e., low-flow RUN and low-magnitude PPT). Results indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate historical climatic conditions. But, in some geographic locations, none of the SD GCMs replicated historical conditions for two of the three variables (PPT and RUN) based on the KS test, with a significance level of 0.05. In these locations, improved GCM simulations of PPT are needed to more reliably estimate components of the hydrologic cycle. Simple metrics and statistical tests, such as those described here, can provide an initial set of criteria to help simplify GCM selection.

Collaborative Research: Reducing tropical precipitation biases in CESM — Tests of unified parameterizations with ARM observations

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

Larson, Vincent; Gettelman, Andrew; Morrison, Hugh

In state-of-the-art climate models, each cloud type is treated using its own separate cloud parameterization and its own separate microphysics parameterization. This use of separate schemes for separate cloud regimes is undesirable because it is theoretically unfounded, it hampers interpretation of results, and it leads to the temptation to overtune parameters. In this grant, we are creating a climate model that contains a unified cloud parameterization and a unified microphysics parameterization. This model will be used to address the problems of excessive frequency of drizzle in climate models and excessively early onset of deep convection in the Tropics over land.more » The resulting model will be compared with ARM observations.« less

A Practical Philosophy of Complex Climate Modelling

NASA Technical Reports Server (NTRS)

Schmidt, Gavin A.; Sherwood, Steven

2014-01-01

We give an overview of the practice of developing and using complex climate models, as seen from experiences in a major climate modelling center and through participation in the Coupled Model Intercomparison Project (CMIP).We discuss the construction and calibration of models; their evaluation, especially through use of out-of-sample tests; and their exploitation in multi-model ensembles to identify biases and make predictions. We stress that adequacy or utility of climate models is best assessed via their skill against more naive predictions. The framework we use for making inferences about reality using simulations is naturally Bayesian (in an informal sense), and has many points of contact with more familiar examples of scientific epistemology. While the use of complex simulations in science is a development that changes much in how science is done in practice, we argue that the concepts being applied fit very much into traditional practices of the scientific method, albeit those more often associated with laboratory work.

New Martian climate constraints from radar reflectivity within the north polar layered deposits

NASA Astrophysics Data System (ADS)

Lalich, D. E.; Holt, J. W.

2017-01-01

The north polar layered deposits (NPLD) of Mars represent a global climate record reaching back millions of years, potentially recorded in visible layers and radar reflectors. However, little is known of the specific link between those layers, reflectors, and the global climate. To test the hypothesis that reflectors are caused by thick and indurated layers known as "marker beds," the reflectivity of three reflectors was measured, mapped, and compared to a reflectivity model. The measured reflectivities match the model and show a strong sensitivity to layer thickness, implying that radar reflectivity may be used as a proxy for short-term accumulation patterns and that regional climate plays a strong role in layer thickness variations. Comparisons to an orbitally forced NPLD accumulation model show a strong correlation with predicted marker bed formation, but dust content is higher than expected, implying a stronger role for dust in Mars polar climate than previously thought.

Evaluating the robustness of conceptual rainfall-runoff models under climate variability in northern Tunisia

NASA Astrophysics Data System (ADS)

Dakhlaoui, H.; Ruelland, D.; Tramblay, Y.; Bargaoui, Z.

2017-07-01

To evaluate the impact of climate change on water resources at the catchment scale, not only future projections of climate are necessary but also robust rainfall-runoff models that must be fairly reliable under changing climate conditions. The aim of this study was thus to assess the robustness of three conceptual rainfall-runoff models (GR4j, HBV and IHACRES) on five basins in northern Tunisia under long-term climate variability, in the light of available future climate scenarios for this region. The robustness of the models was evaluated using a differential split sample test based on a climate classification of the observation period that simultaneously accounted for precipitation and temperature conditions. The study catchments include the main hydrographical basins in northern Tunisia, which produce most of the surface water resources in the country. A 30-year period (1970-2000) was used to capture a wide range of hydro-climatic conditions. The calibration was based on the Kling-Gupta Efficiency (KGE) criterion, while model transferability was evaluated based on the Nash-Sutcliffe efficiency criterion and volume error. The three hydrological models were shown to behave similarly under climate variability. The models simulated the runoff pattern better when transferred to wetter and colder conditions than to drier and warmer ones. It was shown that their robustness became unacceptable when climate conditions involved a decrease of more than 25% in annual precipitation and an increase of more than +1.75 °C in annual mean temperatures. The reduction in model robustness may be partly due to the climate dependence of some parameters. When compared to precipitation and temperature projections in the region, the limits of transferability obtained in this study are generally respected for short and middle term. For long term projections under the most pessimistic emission gas scenarios, the limits of transferability are generally not respected, which may hamper the use of conceptual models for hydrological projections in northern Tunisia.

Reliability of regional climate simulations

NASA Astrophysics Data System (ADS)

Ahrens, W.; Block, A.; Böhm, U.; Hauffe, D.; Keuler, K.; Kücken, M.; Nocke, Th.

2003-04-01

Quantification of uncertainty becomes more and more a key issue for assessing the trustability of future climate scenarios. In addition to the mean conditions, climate impact modelers focus in particular on extremes. Before generating such scenarios using e.g. dynamic regional climate models, a careful validation of present-day simulations should be performed to determine the range of errors for the quantities of interest under recent conditions as a raw estimate of their uncertainty in the future. Often, multiple aspects shall be covered together, and the required simulation accuracy depends on the user's demand. In our approach, a massive parallel regional climate model shall be used on the one hand to generate "long-term" high-resolution climate scenarios for several decades, and on the other hand to provide very high-resolution ensemble simulations of future dry spells or heavy rainfall events. To diagnosis the model's performance for present-day simulations, we have recently developed and tested a first version of a validation and visualization chain for this model. It is, however, applicable in a much more general sense and could be used as a common test bed for any regional climate model aiming at this type of simulations. Depending on the user's interest, integrated quality measures can be derived for near-surface parameters using multivariate techniques and multidimensional distance measures in a first step. At this point, advanced visualization techniques have been developed and included to allow for visual data mining and to qualitatively identify dominating aspects and regularities. Univariate techniques that are especially designed to assess climatic aspects in terms of statistical properties can then be used to quantitatively diagnose the error contributions of the individual used parameters. Finally, a comprehensive in-depth diagnosis tool allows to investigate, why the model produces the obtained near-surface results to answer the question if the model performs well from the modeler's point of view. Examples will be presented for results obtained using this approach for assessing the risk of potential total agricultural yield loss under drought conditions in Northeast Brazil and for evaluating simulation results for a 10-year period for Europe. To support multi-run simulations and result evaluation, the model will be embedded into an already existing simulation environment that provides further postprocessing tools for sensitivity studies, behavioral analysis and Monte-Carlo simulations, but also for ensemble scenario analysis in one of the next steps.

Modelling coffee leaf rust risk in Colombia with climate reanalysis data.

PubMed

Bebber, Daniel P; Castillo, Ángela Delgado; Gurr, Sarah J

2016-12-05

Many fungal plant diseases are strongly controlled by weather, and global climate change is thus likely to have affected fungal pathogen distributions and impacts. Modelling the response of plant diseases to climate change is hampered by the difficulty of estimating pathogen-relevant microclimatic variables from standard meteorological data. The availability of increasingly sophisticated high-resolution climate reanalyses may help overcome this challenge. We illustrate the use of climate reanalyses by testing the hypothesis that climate change increased the likelihood of the 2008-2011 outbreak of Coffee Leaf Rust (CLR, Hemileia vastatrix) in Colombia. We develop a model of germination and infection risk, and drive this model using estimates of leaf wetness duration and canopy temperature from the Japanese 55-Year Reanalysis (JRA-55). We model germination and infection as Weibull functions with different temperature optima, based upon existing experimental data. We find no evidence for an overall trend in disease risk in coffee-growing regions of Colombia from 1990 to 2015, therefore, we reject the climate change hypothesis. There was a significant elevation in predicted CLR infection risk from 2008 to 2011 compared with other years. JRA-55 data suggest a decrease in canopy surface water after 2008, which may have helped terminate the outbreak. The spatial resolution and accuracy of climate reanalyses are continually improving, increasing their utility for biological modelling. Confronting disease models with data requires not only accurate climate data, but also disease observations at high spatio-temporal resolution. Investment in monitoring, storage and accessibility of plant disease observation data are needed to match the quality of the climate data now available.This article is part of the themed issue 'Tackling emerging fungal threats to animal health, food security and ecosystem resilience'. © 2016 The Authors.

A Field Guide to Extra-Tropical Cyclones: Comparing Models to Observations

NASA Astrophysics Data System (ADS)

Bauer, M.

2008-12-01

Climate it is said is the accumulation of weather. And weather is not the concern of climate models. Justification for this latter sentiment has long hidden behind coarse model resolutions and blunt validation tools based on climatological maps and the like. The spatial-temporal resolutions of today's models and observations are converging onto meteorological scales however, which means that with the correct tools we can test the largely unproven assumption that climate model weather is correct enough, or at least lacks perverting biases, such that its accumulation does in fact result in a robust climate prediction. Towards this effort we introduce a new tool for extracting detailed cyclone statistics from climate model output. These include the usual cyclone distribution statistics (maps, histograms), but also adaptive cyclone- centric composites. We have also created a complementary dataset, The MAP Climatology of Mid-latitude Storminess (MCMS), which provides a detailed 6 hourly assessment of the areas under the influence of mid- latitude cyclones based on Reanalysis products. Using this we then extract complimentary composites from sources such as ISCCP and GPCP to create a large comparative dataset for climate model validation. A demonstration of the potential usefulness of these tools will be shown. dime.giss.nasa.gov/mcms/mcms.html

An eco-hydrologic model of malaria outbreaks

NASA Astrophysics Data System (ADS)

Montosi, E.; Manzoni, S.; Porporato, A.; Montanari, A.

2012-03-01

Malaria is a geographically widespread infectious disease that is well known to be affected by climate variability at both seasonal and interannual timescales. In an effort to identify climatic factors that impact malaria dynamics, there has been considerable research focused on the development of appropriate disease models for malaria transmission and their consideration alongside climatic datasets. These analyses have focused largely on variation in temperature and rainfall as direct climatic drivers of malaria dynamics. Here, we further these efforts by considering additionally the role that soil water content may play in driving malaria incidence. Specifically, we hypothesize that hydro-climatic variability should be an important factor in controlling the availability of mosquito habitats, thereby governing mosquito growth rates. To test this hypothesis, we reduce a nonlinear eco-hydrologic model to a simple linear model through a series of consecutive assumptions and apply this model to malaria incidence data from three South African provinces. Despite the assumptions made in the reduction of the model, we show that soil water content can account for a significant portion of malaria's case variability beyond its seasonal patterns, whereas neither temperature nor rainfall alone can do so. Future work should therefore consider soil water content as a simple and computable variable for incorporation into climate-driven disease models of malaria and other vector-borne infectious diseases.

An ecohydrological model of malaria outbreaks

NASA Astrophysics Data System (ADS)

Montosi, E.; Manzoni, S.; Porporato, A.; Montanari, A.

2012-08-01

Malaria is a geographically widespread infectious disease that is well known to be affected by climate variability at both seasonal and interannual timescales. In an effort to identify climatic factors that impact malaria dynamics, there has been considerable research focused on the development of appropriate disease models for malaria transmission driven by climatic time series. These analyses have focused largely on variation in temperature and rainfall as direct climatic drivers of malaria dynamics. Here, we further these efforts by considering additionally the role that soil water content may play in driving malaria incidence. Specifically, we hypothesize that hydro-climatic variability should be an important factor in controlling the availability of mosquito habitats, thereby governing mosquito growth rates. To test this hypothesis, we reduce a nonlinear ecohydrological model to a simple linear model through a series of consecutive assumptions and apply this model to malaria incidence data from three South African provinces. Despite the assumptions made in the reduction of the model, we show that soil water content can account for a significant portion of malaria's case variability beyond its seasonal patterns, whereas neither temperature nor rainfall alone can do so. Future work should therefore consider soil water content as a simple and computable variable for incorporation into climate-driven disease models of malaria and other vector-borne infectious diseases.

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.

A test of the hierarchical model of litter decomposition.

PubMed

Bradford, Mark A; Veen, G F Ciska; Bonis, Anne; Bradford, Ella M; Classen, Aimee T; Cornelissen, J Hans C; Crowther, Thomas W; De Long, Jonathan R; Freschet, Gregoire T; Kardol, Paul; Manrubia-Freixa, Marta; Maynard, Daniel S; Newman, Gregory S; Logtestijn, Richard S P; Viketoft, Maria; Wardle, David A; Wieder, William R; Wood, Stephen A; van der Putten, Wim H

2017-12-01

Our basic understanding of plant litter decomposition informs the assumptions underlying widely applied soil biogeochemical models, including those embedded in Earth system models. Confidence in projected carbon cycle-climate feedbacks therefore depends on accurate knowledge about the controls regulating the rate at which plant biomass is decomposed into products such as CO 2 . Here we test underlying assumptions of the dominant conceptual model of litter decomposition. The model posits that a primary control on the rate of decomposition at regional to global scales is climate (temperature and moisture), with the controlling effects of decomposers negligible at such broad spatial scales. Using a regional-scale litter decomposition experiment at six sites spanning from northern Sweden to southern France-and capturing both within and among site variation in putative controls-we find that contrary to predictions from the hierarchical model, decomposer (microbial) biomass strongly regulates decomposition at regional scales. Furthermore, the size of the microbial biomass dictates the absolute change in decomposition rates with changing climate variables. Our findings suggest the need for revision of the hierarchical model, with decomposers acting as both local- and broad-scale controls on litter decomposition rates, necessitating their explicit consideration in global biogeochemical models.

Identifying trends in climate: an application to the cenozoic

NASA Astrophysics Data System (ADS)

Richards, Gordon R.

1998-05-01

The recent literature on trending in climate has raised several issues, whether trends should be modeled as deterministic or stochastic, whether trends are nonlinear, and the relative merits of statistical models versus models based on physics. This article models trending since the late Cretaceous. This 68 million-year interval is selected because the reliability of tests for trending is critically dependent on the length of time spanned by the data. Two main hypotheses are tested, that the trend has been caused primarily by CO2 forcing, and that it reflects a variety of forcing factors which can be approximated by statistical methods. The CO2 data is obtained from model simulations. Several widely-used statistical models are found to be inadequate. ARIMA methods parameterize too much of the short-term variation, and do not identify low frequency movements. Further, the unit root in the ARIMA process does not predict the long-term path of temperature. Spectral methods also have little ability to predict temperature at long horizons. Instead, the statistical trend is estimated using a nonlinear smoothing filter. Both of these paradigms make it possible to model climate as a cointegrated process, in which temperature can wander quite far from the trend path in the intermediate term, but converges back over longer horizons. Comparing the forecasting properties of the two trend models demonstrates that the optimal forecasting model includes CO2 forcing and a parametric representation of the nonlinear variability in climate.

Introduction. Progress in Earth science and climate studies.

PubMed

Thompson, J Michael T

2008-12-28

In this introductory paper, I review the 'visions of the future' articles prepared by top young scientists for the second of the two Christmas 2008 Triennial Issues of Phil. Trans. R. Soc.A, devoted respectively to astronomy and Earth science. Topics covered in the Earth science issue include: trace gases in the atmosphere; dynamics of the Antarctic circumpolar current; a study of the boundary between the Earth's rocky mantle and its iron core; and two studies of volcanoes and their plumes. A final section devoted to ecology and climate covers: the mathematical modelling of plant-soil interactions; the effects of the boreal forests on the Earth's climate; the role of the past palaeoclimate in testing and calibrating today's numerical climate models; and the evaluation of these models including the quantification of their uncertainties.

Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers.

PubMed

Axelsen, Jacob Bock; Yaari, Rami; Grenfell, Bryan T; Stone, Lewi

2014-07-01

Human influenza occurs annually in most temperate climatic zones of the world, with epidemics peaking in the cold winter months. Considerable debate surrounds the relative role of epidemic dynamics, viral evolution, and climatic drivers in driving year-to-year variability of outbreaks. The ultimate test of understanding is prediction; however, existing influenza models rarely forecast beyond a single year at best. Here, we use a simple epidemiological model to reveal multiannual predictability based on high-quality influenza surveillance data for Israel; the model fit is corroborated by simple metapopulation comparisons within Israel. Successful forecasts are driven by temperature, humidity, antigenic drift, and immunity loss. Essentially, influenza dynamics are a balance between large perturbations following significant antigenic jumps, interspersed with nonlinear epidemic dynamics tuned by climatic forcing.

Hydrological changes in the tropics: an Holocene perspective

NASA Astrophysics Data System (ADS)

Braconnot, Pascale

2015-04-01

Past climates offer a large set of natural experiences that can be used to better understand the relative role of different climate feedbacks arising from changes in the Earth's global energetics, Earth's hydrological cycle or from the coupling between climate and biogeochemical cycles. In addition, the numerous climate reconstructions from different and independent ice, marine and terrestrial climate archives allow to test how climate models reproduce past changes and to assess their credibility when used for future climate projections. The presentation will review some of the mechanisms affecting the long term trend in the location of the intertropical convergence zone and the Afro-Asian monsoon. Using simulations of the PMIP project, as well as sensitivity experiments with the IPSL model, I'll discuss the role of monsoon changes in the global Earth's energetics and the different feedbacks from ocean and land-surface. The presentation will contrast the conditions in the Early, the mid and late Holocene and show how robust features of monsoon changes can be used to better assess future changes in regions where model results are uncertain, such as West Africa.

A mediation model linking dispatcher leadership and work ownership with safety climate as predictors of truck driver safety performance.

PubMed

Zohar, Dov; Huang, Yueng-hsiang; Lee, Jin; Robertson, Michelle

2014-01-01

The study was designed to test the effect of safety climate on safety behavior among lone employees whose work environment promotes individual rather than consensual or shared climate perceptions. The paper presents a mediation path model linking psychological (individual-level) safety climate antecedents and consequences as predictors of driving safety of long-haul truck drivers. Climate antecedents included dispatcher (distant) leadership and driver work ownership, two contextual attributes of lone work, whereas its proximal consequence included driving safety. Using a prospective design, safety outcomes, consisting of hard-braking frequency (i.e. traffic near-miss events) were collected six months after survey completion, using GPS-based truck deceleration data. Results supported the hypothesized model, indicating that distant leadership style and work ownership promote psychological safety climate perceptions, with subsequent prediction of hard-braking events mediated by driving safety. Theoretical and practical implications for studying safety climate among lone workers in general and professional drivers in particular are discussed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Integrating ecophysiology and forest landscape models to improve projections of drought effects under climate change.

PubMed

Gustafson, Eric J; De Bruijn, Arjan M G; Pangle, Robert E; Limousin, Jean-Marc; McDowell, Nate G; Pockman, William T; Sturtevant, Brian R; Muss, Jordan D; Kubiske, Mark E

2015-02-01

Fundamental drivers of ecosystem processes such as temperature and precipitation are rapidly changing and creating novel environmental conditions. Forest landscape models (FLM) are used by managers and policy-makers to make projections of future ecosystem dynamics under alternative management or policy options, but the links between the fundamental drivers and projected responses are weak and indirect, limiting their reliability for projecting the impacts of climate change. We developed and tested a relatively mechanistic method to simulate the effects of changing precipitation on species competition within the LANDIS-II FLM. Using data from a field precipitation manipulation experiment in a piñon pine (Pinus edulis) and juniper (Juniperus monosperma) ecosystem in New Mexico (USA), we calibrated our model to measurements from ambient control plots and tested predictions under the drought and irrigation treatments against empirical measurements. The model successfully predicted behavior of physiological variables under the treatments. Discrepancies between model output and empirical data occurred when the monthly time step of the model failed to capture the short-term dynamics of the ecosystem as recorded by instantaneous field measurements. We applied the model to heuristically assess the effect of alternative climate scenarios on the piñon-juniper ecosystem and found that warmer and drier climate reduced productivity and increased the risk of drought-induced mortality, especially for piñon. We concluded that the direct links between fundamental drivers and growth rates in our model hold great promise to improve our understanding of ecosystem processes under climate change and improve management decisions because of its greater reliance on first principles. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Large-Scale Features of Pliocene Climate: Results from the Pliocene Model Intercomparison Project

NASA Technical Reports Server (NTRS)

Haywood, A. M.; Hill, D.J.; Dolan, A. M.; Otto-Bliesner, B. L.; Bragg, F.; Chan, W.-L.; Chandler, M. A.; Contoux, C.; Dowsett, H. J.; Jost, A.;

The Solar Constant, Climate, and Some Tests of the Storage Hypothesis

NASA Technical Reports Server (NTRS)

Eddy, J. A.

1984-01-01

Activity related modulation of the solar constant can have practical consequences for climate only if storage is involved, as opposed to a detailed balance between sunspot blocking and facular reemission. Four empirical tests are considered that might distinguish between these opposing interpretations: monochromatic measurements of positive and negative flux; comparison of modelled and measured irradiance variations; the interpretation of secular trends in irradiance data; and the direct test of an anticipated signal in climate records of surface air temperature. The yet unanswered question of the role of faculae as possible reemitters of blocked radiation precludes a definitive answer, although other tests suggest their role to be minor, and that storage and an 11 year modulation is implicated. A crucial test is the behavior of the secular trend in irradiance in the declining years of the present activity cycle.

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.

A global food demand model for the assessment of complex human-earth systems

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

EDMONDS, JAMES A.; LINK, ROBERT; WALDHOFF, STEPHANIE T.

Demand for agricultural products is an important problem in climate change economics. Food consumption will shape and shaped by climate change and emissions mitigation policies through interactions with bioenergy and afforestation, two critical issues in meeting international climate goals such as two-degrees. We develop a model of food demand for staple and nonstaple commodities that evolves with changing incomes and prices. The model addresses a long-standing issue in estimating food demands, the evolution of demand relationships across large changes in income and prices. We discuss the model, some of its properties and limitations. We estimate parameter values using pooled cross-sectional-time-seriesmore » observations and the Metropolis Monte Carlo method and cross-validate the model by estimating parameters using a subset of the observations and test its ability to project into the unused observations. Finally, we apply bias correction techniques borrowed from the climate-modeling community and report results.« less

Equilibrium and Effective Climate Sensitivity

NASA Astrophysics Data System (ADS)

Rugenstein, M.; Bloch-Johnson, J.

2016-12-01

Atmosphere-ocean general circulation models, as well as the real world, take thousands of years to equilibrate to CO2 induced radiative perturbations. Equilibrium climate sensitivity - a fully equilibrated 2xCO2 perturbation - has been used for decades as a benchmark in model intercomparisons, as a test of our understanding of the climate system and paleo proxies, and to predict or project future climate change. Computational costs and limited time lead to the widespread practice of extrapolating equilibrium conditions from just a few decades of coupled simulations. The most common workaround is the "effective climate sensitivity" - defined through an extrapolation of a 150 year abrupt2xCO2 simulation, including the assumption of linear climate feedbacks. The definitions of effective and equilibrium climate sensitivity are often mixed up and used equivalently, and it is argued that "transient climate sensitivity" is the more relevant measure for predicting the next decades. We present an ongoing model intercomparison, the "LongRunMIP", to study century and millennia time scales of AOGCM equilibration and the linearity assumptions around feedback analysis. As a true ensemble of opportunity, there is no protocol and the only condition to participate is a coupled model simulation of any stabilizing scenario simulating more than 1000 years. Many of the submitted simulations took several years to conduct. As of July 2016 the contribution comprises 27 scenario simulations of 13 different models originating from 7 modeling centers, each between 1000 and 6000 years. To contribute, please contact the authors as soon as possible We present preliminary results, discussing differences between effective and equilibrium climate sensitivity, the usefulness of transient climate sensitivity, extrapolation methods, and the state of the coupled climate system close to equilibrium. Caption for the Figure below: Evolution of temperature anomaly and radiative imbalance of 22 simulations with 12 models (color indicates the model). 20 year moving average.

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

Assessment of the Effect of Climate Change on Grain Yields in China

NASA Astrophysics Data System (ADS)

Chou, J.

2006-12-01

The paper elaborates the social background and research background; makes clear what the key scientific issues need to be resolved and where the difficulties are. In the research area of parasailing the grain yield change caused by climate change, massive works have been done both in the domestic and in the foreign. It is our upcoming work to evaluate how our countrywide climate change information provided by this pattern influence our economic and social development; and how to make related policies and countermeasures. the main idea in this paper is that the grain yield change is by no means the linear composition of social economy function effect and the climatic change function effect. This paper identifies the economic evaluation object, proposes one new concept - climate change output. The grain yields change affected by the social factors and the climatic change working together. Climate change influences the grain yields by the non ¨C linear function from both climate change and social factor changes, not only by climate change itself. Therefore, in my paper, the appraisal object is defined as: The social factors change based on actual social changing situations; under the two kinds of climate change situation, the invariable climate change situation and variable climate change situation; the difference of grain yield outputs is called " climate change output ", In order to solve this problem, we propose a method to analyze and imitate on the historical materials. Giving the condition that the climate is invariable, the social economic factor changes cause the grain yield change. However, this grain yield change is a tentative quantity index, not an actual quantity number. So we use the existing historical materials to exam the climate change output, based on the characteristic that social factor changes greater in year than in age, but the climate factor changes greater in age than in year. The paper proposes and establishes one economy - climate model (C-D-C model) to appraise the grain yield change caused by the climatic change. Also the preliminary test on this model has been done. In selection of the appraisal methods, we take the C-D production function model, which has been proved more mature in the economic research, as our fundamental model. Then, we introduce climate index (arid index) to the C-D model to develop one new model. This new model utilizes the climatic change factor in the economical model to appraise how the climatic change influence the grain yield change. The new way of appraise should have the better application prospect. The economy - climate model (The C-D-C model) has been applied on the eight Chinese regions that we divide; it has been proved satisfactory in its feasibility, rationality and the application prospect. So we can provide the theoretical fundamentals for policy-making under the more complex and uncertain climate change. Therefore, we open a new possible channel for the global climate change research moving toward the actual social, economic life.

Validation of catchment models for predicting land-use and climate change impacts. 2. Case study for a Mediterranean catchment

NASA Astrophysics Data System (ADS)

Parkin, G.; O'Donnell, G.; Ewen, J.; Bathurst, J. C.; O'Connell, P. E.; Lavabre, J.

1996-02-01

Validation methods commonly used to test catchment models are not capable of demonstrating a model's fitness for making predictions for catchments where the catchment response is not known (including hypothetical catchments, and future conditions of existing catchments which are subject to land-use or climate change). This paper describes the first use of a new method of validation (Ewen and Parkin, 1996. J. Hydrol., 175: 583-594) designed to address these types of application; the method involves making 'blind' predictions of selected hydrological responses which are considered important for a particular application. SHETRAN (a physically based, distributed catchment modelling system) is tested on a small Mediterranean catchment. The test involves quantification of the uncertainty in four predicted features of the catchment response (continuous hydrograph, peak discharge rates, monthly runoff, and total runoff), and comparison of observations with the predicted ranges for these features. The results of this test are considered encouraging.

An evaluation of the variable-resolution CESM for modeling California's climate: Evaluation of VR-CESM for Modeling California's Climate

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

Huang, Xingying; Rhoades, Alan M.; Ullrich, Paul A.

In this paper, the recently developed variable-resolution option within the Community Earth System Model (VR-CESM) is assessed for long-term regional climate modeling of California at 0.25° (~ 28 km) and 0.125° (~ 14 km) horizontal resolutions. The mean climatology of near-surface temperature and precipitation is analyzed and contrasted with reanalysis, gridded observational data sets, and a traditional regional climate model (RCM)—the Weather Research and Forecasting (WRF) model. Statistical metrics for model evaluation and tests for differential significance have been extensively applied. VR-CESM tended to produce a warmer summer (by about 1–3°C) and overestimated overall winter precipitation (about 25%–35%) compared tomore » reference data sets when sea surface temperatures were prescribed. Increasing resolution from 0.25° to 0.125° did not produce a statistically significant improvement in the model results. By comparison, the analogous WRF climatology (constrained laterally and at the sea surface by ERA-Interim reanalysis) was ~1–3°C colder than the reference data sets, underestimated precipitation by ~20%–30% at 27 km resolution, and overestimated precipitation by ~ 65–85% at 9 km. Overall, VR-CESM produced comparable statistical biases to WRF in key climatological quantities. Moreover, this assessment highlights the value of variable-resolution global climate models (VRGCMs) in capturing fine-scale atmospheric processes, projecting future regional climate, and addressing the computational expense of uniform-resolution global climate models.« less

An evaluation of the variable-resolution CESM for modeling California's climate: Evaluation of VR-CESM for Modeling California's Climate

DOE PAGES

Huang, Xingying; Rhoades, Alan M.; Ullrich, Paul A.; ...

2016-03-01

In this paper, the recently developed variable-resolution option within the Community Earth System Model (VR-CESM) is assessed for long-term regional climate modeling of California at 0.25° (~ 28 km) and 0.125° (~ 14 km) horizontal resolutions. The mean climatology of near-surface temperature and precipitation is analyzed and contrasted with reanalysis, gridded observational data sets, and a traditional regional climate model (RCM)—the Weather Research and Forecasting (WRF) model. Statistical metrics for model evaluation and tests for differential significance have been extensively applied. VR-CESM tended to produce a warmer summer (by about 1–3°C) and overestimated overall winter precipitation (about 25%–35%) compared tomore » reference data sets when sea surface temperatures were prescribed. Increasing resolution from 0.25° to 0.125° did not produce a statistically significant improvement in the model results. By comparison, the analogous WRF climatology (constrained laterally and at the sea surface by ERA-Interim reanalysis) was ~1–3°C colder than the reference data sets, underestimated precipitation by ~20%–30% at 27 km resolution, and overestimated precipitation by ~ 65–85% at 9 km. Overall, VR-CESM produced comparable statistical biases to WRF in key climatological quantities. Moreover, this assessment highlights the value of variable-resolution global climate models (VRGCMs) in capturing fine-scale atmospheric processes, projecting future regional climate, and addressing the computational expense of uniform-resolution global climate models.« less

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.

Annual Corn Yield Estimation through Multi-temporal MODIS Data

NASA Astrophysics Data System (ADS)

Shao, Y.; Zheng, B.; Campbell, J. B.

2013-12-01

This research employed 13 years of the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate annual corn yield for the Midwest of the United States. The overall objective of this study was to examine if annual corn yield could be accurately predicted using MODIS time-series NDVI (Normalized Difference Vegetation Index) and ancillary data such monthly precipitation and temperature. MODIS-NDVI 16-Day composite images were acquired from the USGS EROS Data Center for calendar years 2000 to 2012. For the same time-period, county level corn yield statistics were obtained from the National Agricultural Statistics Service (NASS). The monthly precipitation and temperature measures were derived from Precipitation-Elevation Regressions on Independent Slopes Model (PRISM) climate data. A cropland mask was derived using 2006 National Land Cover Database. For each county and within the cropland mask, the MODIS-NDVI time-series data and PRISM climate data were spatially averaged, at their respective time steps. We developed a random forest predictive model with the MODIS-NDVI and climate data as predictors and corn yield as response. To assess the model accuracy, we used twelve years of data as training and the remaining year as hold-out testing set. The training and testing procedures were repeated 13 times. The R2 ranged from 0.72 to 0.83 for testing years. It was also found that the inclusion of climate data did not improve the model predictive performance. MODIS-NDVI time-series data alone might provide sufficient information for county level corn yield prediction.

Bullying among adolescents in North Cyprus and Turkey: testing a multifactor model.

PubMed

Bayraktar, Fatih

2012-04-01

Peer bullying has been studied since the 1970s. Therefore, a vast literature has accumulated about the various predictors of bullying. However, to date there has been no study which has combined individual-, peer-, parental-, teacher-, and school-related predictors of bullying within a model. In this sense, the main aim of this study was to test a multifactor model of bullying among adolescents in North Cyprus and Turkey. A total of 1,052 adolescents (554 girls, 498 boys) aged between 13 and 18 (M = 14.7, SD = 1.17) were recruited from North Cyprus and Turkey. Before testing the multifactor models, the measurement models were tested according to structural equation modeling propositions. Both models indicated that the psychological climate of the school, teacher attitudes within classroom, peer relationships, parental acceptance-rejection, and individual social competence factors had significant direct effects on bullying behaviors. Goodness-of-fit indexes indicated that the proposed multifactor model fitted both data well. The strongest predictors of bullying were the psychological climate of the school following individual social competence factors and teacher attitudes within classroom in both samples. All of the latent variables explained 44% and 51% of the variance in bullying in North Cyprus and Turkey, respectively.

Using Four Downscaling Techniques to Characterize Uncertainty in Updating Intensity-Duration-Frequency Curves Under Climate Change

NASA Astrophysics Data System (ADS)

Cook, L. M.; Samaras, C.; McGinnis, S. A.

2017-12-01

Intensity-duration-frequency (IDF) curves are a common input to urban drainage design, and are used to represent extreme rainfall in a region. As rainfall patterns shift into a non-stationary regime as a result of climate change, these curves will need to be updated with future projections of extreme precipitation. Many regions have begun to update these curves to reflect the trends from downscaled climate models; however, few studies have compared the methods for doing so, as well as the uncertainty that results from the selection of the native grid scale and temporal resolution of the climate model. This study examines the variability in updated IDF curves for Pittsburgh using four different methods for adjusting gridded regional climate model (RCM) outputs into station scale precipitation extremes: (1) a simple change factor applied to observed return levels, (2) a naïve adjustment of stationary and non-stationary Generalized Extreme Value (GEV) distribution parameters, (3) a transfer function of the GEV parameters from the annual maximum series, and (4) kernel density distribution mapping bias correction of the RCM time series. Return level estimates (rainfall intensities) and confidence intervals from these methods for the 1-hour to 48-hour duration are tested for sensitivity to the underlying spatial and temporal resolution of the climate ensemble from the NA-CORDEX project, as well as, the future time period for updating. The first goal is to determine if uncertainty is highest for: (i) the downscaling method, (ii) the climate model resolution, (iii) the climate model simulation, (iv) the GEV parameters, or (v) the future time period examined. Initial results of the 6-hour, 10-year return level adjusted with the simple change factor method using four climate model simulations of two different spatial resolutions show that uncertainty is highest in the estimation of the GEV parameters. The second goal is to determine if complex downscaling methods and high-resolution climate models are necessary for updating, or if simpler methods and lower resolution climate models will suffice. The final results can be used to inform the most appropriate method and climate model resolutions to use for updating IDF curves for urban drainage design.

The regional climate model REMO (v2015) coupled with the 1-D freshwater lake model FLake (v1): Fenno-Scandinavian climate and lakes

NASA Astrophysics Data System (ADS)

Pietikäinen, Joni-Pekka; Markkanen, Tiina; Sieck, Kevin; Jacob, Daniela; Korhonen, Johanna; Räisänen, Petri; Gao, Yao; Ahola, Jaakko; Korhonen, Hannele; Laaksonen, Ari; Kaurola, Jussi

2018-04-01

The regional climate model REMO was coupled with the FLake lake model to include an interactive treatment of lakes. Using this new version, the Fenno-Scandinavian climate and lake characteristics were studied in a set of 35-year hindcast simulations. Additionally, sensitivity tests related to the parameterization of snow albedo were conducted. Our results show that overall the new model version improves the representation of the Fenno-Scandinavian climate in terms of 2 m temperature and precipitation, but the downside is that an existing wintertime cold bias in the model is enhanced. The lake surface water temperature, ice depth and ice season length were analyzed in detail for 10 Finnish, 4 Swedish and 2 Russian lakes and 1 Estonian lake. The results show that the model can reproduce these characteristics with reasonably high accuracy. The cold bias during winter causes overestimation of ice layer thickness, for example, at several of the studied lakes, but overall the values from the model are realistic and represent the lake physics well in a long-term simulation. We also analyzed the snow depth on ice from 10 Finnish lakes and vertical temperature profiles from 5 Finnish lakes and the model results are realistic.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico.

PubMed

Johansson, Michael A; Reich, Nicholas G; Hota, Aditi; Brownstein, John S; Santillana, Mauricio

2016-09-26

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

PubMed Central

Johansson, Michael A.; Reich, Nicholas G.; Hota, Aditi; Brownstein, John S.; Santillana, Mauricio

2016-01-01

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model. PMID:27665707

Neutral biogeography and the evolution of climatic niches.

PubMed

Boucher, Florian C; Thuiller, Wilfried; Davies, T Jonathan; Lavergne, Sébastien

2014-05-01

Recent debate on whether climatic niches are conserved through time has focused on how phylogenetic niche conservatism can be measured by deviations from a Brownian motion model of evolutionary change. However, there has been no evaluation of this methodological approach. In particular, the fact that climatic niches are usually obtained from distribution data and are thus heavily influenced by biogeographic factors has largely been overlooked. Our main objective here was to test whether patterns of climatic niche evolution that are frequently observed might arise from neutral dynamics rather than from adaptive scenarios. We developed a model inspired by neutral biodiversity theory, where individuals disperse, compete, and undergo speciation independently of climate. We then sampled the climatic niches of species according to their geographic position and showed that even when species evolve independently of climate, their niches can nonetheless exhibit evolutionary patterns strongly differing from Brownian motion. Indeed, climatic niche evolution is better captured by a model of punctuated evolution with constraints due to landscape boundaries, two features that are traditionally interpreted as evidence for selective processes acting on the niche. We therefore suggest that deviation from Brownian motion alone should not be used as evidence for phylogenetic niche conservatism but that information on phenotypic traits directly linked to physiology is required to demonstrate that climatic niches have been conserved through time.

Neutral biogeography and the evolution of climatic niches

PubMed Central

Boucher, Florian C.; Thuiller, Wilfried; Davies, T. Jonathan; Lavergne, Sébastien

2014-01-01

Recent debate on whether climatic niches are conserved through time has focused on how phylogenetic niche conservatism can be measured by deviations from a Brownian motion model of evolutionary change. However, there has been no evaluation of this methodological approach. In particular, the fact that climatic niches are usually obtained from distribution data and are thus heavily influenced by biogeographic factors has largely been overlooked. Our main objective here was to test whether patterns of climatic niche evolution that are frequently observed might arise from neutral dynamics rather than adaptive scenarios. We develop a model inspired by Neutral Biodiversity Theory, where individuals disperse, compete, and undergo speciation independently of climate. We then sample the climatic niches of species according to their geographic position and show that even when species evolved independently of climate, their niches can nonetheless exhibit evolutionary patterns strongly differing from Brownian motion. Indeed, climatic niche evolution is better captured by a model of punctuated evolution with constraints due to landscape boundaries, two features that are traditionally interpreted as evidence for selective processes acting on the niche. We therefore suggest that deviation from Brownian motion alone should not be used as evidence for phylogenetic niche conservatism, but that information on phenotypic traits directly linked to physiology is required to demonstrate that climatic niches have been conserved through time. PMID:24739191

Adaptation of water resource systems to an uncertain future

NASA Astrophysics Data System (ADS)

Walsh, C. L.; Blenkinsop, S.; Fowler, H. J.; Burton, A.; Dawson, R. J.; Glenis, V.; Manning, L. J.; Kilsby, C. G.

2015-09-01

Globally, water resources management faces significant challenges from changing climate and growing populations. At local scales, the information provided by climate models is insufficient to support the water sector in making future adaptation decisions. Furthermore, projections of change in local water resources are wrought with uncertainties surrounding natural variability, future greenhouse gas emissions, model structure, population growth and water consumption habits. To analyse the magnitude of these uncertainties, and their implications for local scale water resource planning, we present a top-down approach for testing climate change adaptation options using probabilistic climate scenarios and demand projections. An integrated modelling framework is developed which implements a new, gridded spatial weather generator, coupled with a rainfall-runoff model and water resource management simulation model. We use this to provide projections of the number of days, and associated uncertainty that will require implementation of demand saving measures such as hose pipe bans and drought orders. Results, which are demonstrated for the Thames basin, UK, indicate existing water supplies are sensitive to a changing climate and an increasing population, and that the frequency of severe demand saving measures are projected to increase. Considering both climate projections and population growth the median number of drought order occurrences may increase five-fold. The effectiveness of a range of demand management and supply options have been tested and shown to provide significant benefits in terms of reducing the number of demand saving days. We found that increased supply arising from various adaptation options may compensate for increasingly variable flows; however, without reductions in overall demand for water resources such options will be insufficient on their own to adapt to uncertainties in the projected changes in climate and population. For example, a 30 % reduction in overall demand by 2050 has a greater impact on reducing the frequency of drought orders than any of the individual or combinations of supply options; hence a portfolio of measures are required.

Assessing Confidence in Pliocene Sea Surface Temperatures to Evaluate Predictive Models

NASA Technical Reports Server (NTRS)

Dowsett, Harry J.; Robinson, Marci M.; Haywood, Alan M.; Hill, Daniel J.; Dolan, Aisling. M.; Chan, Wing-Le; Abe-Ouchi, Ayako; Chandler, Mark A.; Rosenbloom, Nan A.; Otto-Bliesner, Bette L.;

Assessing confidence in Pliocene sea surface temperatures to evaluate predictive models

USGS Publications Warehouse

Dowsett, Harry J.; Robinson, Marci M.; Haywood, Alan M.; Hill, Daniel J.; Dolan, Aisling M.; Stoll, Danielle K.; Chan, Wing-Le; Abe-Ouchi, Ayako; Chandler, Mark A.; Rosenbloom, Nan A.; Otto-Bliesner, Bette L.; Bragg, Fran J.; Lunt, Daniel J.; Foley, Kevin M.; Riesselman, Christina R.

2012-01-01

In light of mounting empirical evidence that planetary warming is well underway, the climate research community looks to palaeoclimate research for a ground-truthing measure with which to test the accuracy of future climate simulations. Model experiments that attempt to simulate climates of the past serve to identify both similarities and differences between two climate states and, when compared with simulations run by other models and with geological data, to identify model-specific biases. Uncertainties associated with both the data and the models must be considered in such an exercise. The most recent period of sustained global warmth similar to what is projected for the near future occurred about 3.3–3.0 million years ago, during the Pliocene epoch. Here, we present Pliocene sea surface temperature data, newly characterized in terms of level of confidence, along with initial experimental results from four climate models. We conclude that, in terms of sea surface temperature, models are in good agreement with estimates of Pliocene sea surface temperature in most regions except the North Atlantic. Our analysis indicates that the discrepancy between the Pliocene proxy data and model simulations in the mid-latitudes of the North Atlantic, where models underestimate warming shown by our highest-confidence data, may provide a new perspective and insight into the predictive abilities of these models in simulating a past warm interval in Earth history. This is important because the Pliocene has a number of parallels to present predictions of late twenty-first century climate.

Impacts of Soil-aquifer Heat and Water Fluxes on Simulated Global Climate

NASA Technical Reports Server (NTRS)

Krakauer, N.Y.; Puma, Michael J.; Cook, B. I.

2013-01-01

Climate models have traditionally only represented heat and water fluxes within relatively shallow soil layers, but there is increasing interest in the possible role of heat and water exchanges with the deeper subsurface. Here, we integrate an idealized 50m deep aquifer into the land surface module of the GISS ModelE general circulation model to test the influence of aquifer-soil moisture and heat exchanges on climate variables. We evaluate the impact on the modeled climate of aquifer-soil heat and water fluxes separately, as well as in combination. The addition of the aquifer to ModelE has limited impact on annual-mean climate, with little change in global mean land temperature, precipitation, or evaporation. The seasonal amplitude of deep soil temperature is strongly damped by the soil-aquifer heat flux. This not only improves the model representation of permafrost area but propagates to the surface, resulting in an increase in the seasonal amplitude of surface air temperature of >1K in the Arctic. The soil-aquifer water and heat fluxes both slightly decrease interannual variability in soil moisture and in landsurface temperature, and decrease the soil moisture memory of the land surface on seasonal to annual timescales. The results of this experiment suggest that deepening the modeled land surface, compared to modeling only a shallower soil column with a no-flux bottom boundary condition, has limited impact on mean climate but does affect seasonality and interannual persistence.

Exploring the sensitivity of soil carbon dynamics to climate change, fire disturbance and permafrost thaw in a black spruce ecosystem

Treesearch

J.A. O' Donnell; J.W. Harden; A.D. McGuire; V.E. Romanovsky

2011-01-01

In the boreal region, soil organic carbon (OC) dynamics are strongly governed by the interaction between wildfire and permafrost. Using a combination of field measurements, numerical modeling of soil thermal dynamics, and mass-balance modeling of OC dynamics, we tested the sensitivity of soil OC storage to a suite of individual climate factors (air temperature, soil...

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...

Testing the effects of safety climate and disruptive children behavior on school bus drivers performance: A multilevel model.

PubMed

Zohar, Dov; Lee, Jin

2016-10-01

The study was designed to test a multilevel path model whose variables exert opposing effects on school bus drivers' performance. Whereas departmental safety climate was expected to improve driving safety, the opposite was true for in-vehicle disruptive children behavior. The driving safety path in this model consists of increasing risk-taking practices starting with safety shortcuts leading to rule violations and to near-miss events. The study used a sample of 474 school bus drivers in rural areas, driving children to school and school-related activities. Newly developed scales for measuring predictor, mediator and outcome variables were validated with video data taken from inner and outer cameras, which were installed in 29 buses. Results partially supported the model by indicating that group-level safety climate and individual-level children distraction exerted opposite effects on the driving safety path. Furthermore, as hypothesized, children disruption moderated the strength of the safety rule violation-near miss relationship, resulting in greater strength under high disruptiveness. At the same time, the hypothesized interaction between the two predictor variables was not supported. Theoretical and practical implications for studying safety climate in general and distracted driving in particular for professional drivers are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

2014-07-01

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 the solar radiation absorbed by individual plants in order to understand and predict 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 that 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 results of random distribution 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 can be included in vegetation models to simulate long-term transient responses of plant communities to climate change. The code and a user's manual are provided as Supplement of the paper.

Description and evaluation of the Earth System Regional Climate Model (RegCM-ES)

NASA Astrophysics Data System (ADS)

Farneti, Riccardo; Sitz, Lina; Di Sante, Fabio; Fuentes-Franco, Ramon; Coppola, Erika; Mariotti, Laura; Reale, Marco; Sannino, Gianmaria; Barreiro, Marcelo; Nogherotto, Rita; Giuliani, Graziano; Graffino, Giorgio; Solidoro, Cosimo; Giorgi, Filippo

2017-04-01

The increasing availability of satellite remote sensing data, of high temporal frequency and spatial resolution, has provided a new and enhanced view of the global ocean and atmosphere, revealing strong air-sea coupling processes throughout the ocean basins. In order to obtain an accurate representation and better understanding of the climate system, its variability and change, the inclusion of all mechanisms of interaction among the different sub-components, at high temporal and spatial resolution, becomes ever more desirable. Recently, global coupled models have been able to progressively refine their horizontal resolution to attempt to resolve smaller-scale processes. However, regional coupled ocean-atmosphere models can achieve even finer resolutions and provide additional information on the mechanisms of air-sea interactions and feedbacks. Here we describe a new, state-of-the-art, Earth System Regional Climate Model (RegCM-ES). RegCM-ES presently includes the coupling between atmosphere, ocean, land surface and sea-ice components, as well as an hydrological and ocean biogeochemistry model. The regional coupled model has been implemented and tested over some of the COordinated Regional climate Downscaling Experiment (CORDEX) domains. RegCM-ES has shown improvements in the representation of precipitation and SST fields over the tested domains, as well as realistic representations of coupled air-sea processes and interactions. The RegCM-ES model, which can be easily implemented over any regional domain of interest, is open source making it suitable for usage by the large scientific community.

A Numerical Climate Observing Network Design Study

NASA Technical Reports Server (NTRS)

Stammer, Detlef

2003-01-01

This project was concerned with three related questions of an optimal design of a climate observing system: 1. The spatial sampling characteristics required from an ARGO system. 2. The degree to which surface observations from ARGO can be used to calibrate and test satellite remote sensing observations of sea surface salinity (SSS) as it is anticipated now. 3. The more general design of an climate observing system as it is required in the near future for CLIVAR in the Atlantic. An important question in implementing an observing system is that of the sampling density required to observe climate-related variations in the ocean. For that purpose this project was concerned with the sampling requirements for the ARGO float system, but investigated also other elements of a climate observing system. As part of this project we studied the horizontal and vertical sampling characteristics of a global ARGO system which is required to make it fully complementary to altimeter data with the goal to capture climate related variations on large spatial scales (less thanAttachment: 1000 km). We addressed this question in the framework of a numerical model study in the North Atlantic with an 1/6 horizontal resolution. The advantage of a numerical design study is the knowledge of the full model state. Sampled by a synthetic float array, model results will therefore allow to test and improve existing deployment strategies with the goal to make the system as optimal and cost-efficient as possible. Attachment: "Optimal observations for variational data assimilation".

A modern pollen-climate dataset from the Darjeeling area, eastern Himalaya: Assessing its potential for past climate reconstruction

NASA Astrophysics Data System (ADS)

Ghosh, Ruby; Bruch, Angela A.; Portmann, Felix; Bera, Subir; Paruya, Dipak Kumar; Morthekai, P.; Ali, Sheikh Nawaz

2017-10-01

Relying on the ability of pollen assemblages to differentiate among elevationally stratified vegetation zones, we assess the potential of a modern pollen-climate dataset from the Darjeeling area, eastern Himalaya, in past climate reconstructions. The dataset includes 73 surface samples from 25 sites collected from a c. 130-3600 m a.s.l. elevation gradient along a horizontal distance of c. 150 km and 124 terrestrial pollen taxa, which are analysed with respect to various climatic and environmental variables such as mean annual temperature (MAT), mean annual precipitation (MAP), mean temperature of coldest quarter (MTCQ), mean temperature of warmest quarter (MTWQ), mean precipitation of driest quarter (MPDQ), mean precipitation of wettest quarter (MPWQ), AET (actual evapotranspiration) and MI (moisture index). To check the reliability of the modern pollen-climate relationships different ordination methods are employed and subsequently tested with Huisman-Olff-Fresco (HOF) models. A series of pollen-climate parameter transfer functions using weighted-averaging regression and calibration partial least squares (WA-PLS) models are developed to reconstruct past climate changes from modern pollen data, and have been cross-validated. Results indicate that three of the environmental variables i.e., MTCQ, MPDQ and MI have strong potential for past climate reconstruction based on the available surface pollen dataset. The potential of the present modern pollen-climate relationship for regional quantitative paleoclimate reconstruction is further tested on a Late Quaternary fossil pollen profile from the Darjeeling foothill region with previously reconstructed and quantified climate. The good agreement with existing data allows for new insights in the hydroclimatic conditions during the Last glacial maxima (LGM) with (winter) temperature being the dominant controlling factor for glacial changes during the LGM in the eastern Himalaya.

EdGCM: Research Tools for Training the Climate Change Generation

NASA Astrophysics Data System (ADS)

Chandler, M. A.; Sohl, L. E.; Zhou, J.; Sieber, R.

2011-12-01

Climate scientists employ complex computer simulations of the Earth's physical systems to prepare climate change forecasts, study the physical mechanisms of climate, and to test scientific hypotheses and computer parameterizations. The Intergovernmental Panel on Climate Change 4th Assessment Report (2007) demonstrates unequivocally that policy makers rely heavily on such Global Climate Models (GCMs) to assess the impacts of potential economic and emissions scenarios. However, true climate modeling capabilities are not disseminated to the majority of world governments or U.S. researchers - let alone to the educators who will be training the students who are about to be presented with a world full of climate change stakeholders. The goal is not entirely quixotic; in fact, by the mid-1990's prominent climate scientists were predicting with certainty that schools and politicians would "soon" be running GCMs on laptops [Randall, 1996]. For a variety of reasons this goal was never achieved (nor even really attempted). However, around the same time NASA and the National Science Foundation supported a small pilot project at Columbia University to show the potential of putting sophisticated computer climate models - not just "demos" or "toy models" - into the hands of non-specialists. The Educational Global Climate Modeling Project (EdGCM) gave users access to a real global climate model and provided them with the opportunity to experience the details of climate model setup, model operation, post-processing and scientific visualization. EdGCM was designed for use in both research and education - it is a full-blown research GCM, but the ultimate goal is to develop a capability to embed these crucial technologies across disciplines, networks, platforms, and even across academia and industry. With this capability in place we can begin training the skilled workforce that is necessary to deal with the multitude of climate impacts that will occur over the coming decades. To further increase the educational potential of climate models, the EdGCM project has also created "EZgcm". Through a joint venture of NASA, Columbia University and McGill University EZgcm moves the focus toward a greater use of Web 1.0 and Web 2.0-based technologies. It shifts the educational objectives towards a greater emphasis on teaching students how science is conducted and what role science plays in assessing climate change. That is, students learn about the steps of the scientific process as conveyed by climate modeling research: constructing a hypothesis, designing an experiment, running a computer model, using scientific visualization to support analysis, communicating the results of that analysis, and role playing the scientific peer review process. This is in stark contrast to what they learn from the political debate over climate change, which they often confuse with a scientific debate.

Reproducing multi-model ensemble average with Ensemble-averaged Reconstructed Forcings (ERF) in regional climate modeling

NASA Astrophysics Data System (ADS)

Erfanian, A.; Fomenko, L.; Wang, G.

2016-12-01

Multi-model ensemble (MME) average is considered the most reliable for simulating both present-day and future climates. It has been a primary reference for making conclusions in major coordinated studies i.e. IPCC Assessment Reports and CORDEX. The biases of individual models cancel out each other in MME average, enabling the ensemble mean to outperform individual members in simulating the mean climate. This enhancement however comes with tremendous computational cost, which is especially inhibiting for regional climate modeling as model uncertainties can originate from both RCMs and the driving GCMs. Here we propose the Ensemble-based Reconstructed Forcings (ERF) approach to regional climate modeling that achieves a similar level of bias reduction at a fraction of cost compared with the conventional MME approach. The new method constructs a single set of initial and boundary conditions (IBCs) by averaging the IBCs of multiple GCMs, and drives the RCM with this ensemble average of IBCs to conduct a single run. Using a regional climate model (RegCM4.3.4-CLM4.5), we tested the method over West Africa for multiple combination of (up to six) GCMs. Our results indicate that the performance of the ERF method is comparable to that of the MME average in simulating the mean climate. The bias reduction seen in ERF simulations is achieved by using more realistic IBCs in solving the system of equations underlying the RCM physics and dynamics. This endows the new method with a theoretical advantage in addition to reducing computational cost. The ERF output is an unaltered solution of the RCM as opposed to a climate state that might not be physically plausible due to the averaging of multiple solutions with the conventional MME approach. The ERF approach should be considered for use in major international efforts such as CORDEX. Key words: Multi-model ensemble, ensemble analysis, ERF, regional climate modeling

Advance strategy for climate change adaptation and mitigation in cities

NASA Astrophysics Data System (ADS)

Varquez, A. C. G.; Kanda, M.; Darmanto, N. S.; Sueishi, T.; Kawano, N.

2017-12-01

An on-going 5-yr project financially supported by the Ministry of Environment, Japan, has been carried out to specifically address the issue of prescribing appropriate adaptation and mitigation measures to climate change in cities. Entitled "Case Study on Mitigation and Local Adaptation to Climate Change in an Asian Megacity, Jakarta", the project's relevant objectives is to develop a research framework that can consider both urbanization and climate change with the main advantage of being readily implementable for all cities around the world. The test location is the benchmark city, Jakarta, Indonesia, with the end focus of evaluating the benefits of various mitigation and adaptation strategies in Jakarta and other megacities. The framework was designed to improve representation of urban areas when conducting climate change investigations in cities; and to be able to quantify separately the impacts of urbanization and climate change to all cities globally. It is comprised of a sophisticated, top-down, multi-downscaling approach utilizing a regional model (numerical weather model) and a microscale model (energy balance model and CFD model), with global circulation models (GCM) as input. The models, except the GCM, were configured to reasonably consider land cover, urban morphology, and anthropogenic heating (AH). Equally as important, methodologies that can collect and estimate global distribution of urban parametric and AH datasets are continually being developed. Urban growth models, climate scenario matrices that match representative concentration pathways with shared socio-economic pathways, present distribution of socio-demographic indicators such as population and GDP, existing GIS datasets of urban parameters, are utilized. From these tools, future urbanization (urban morphological parameters and AH) can be introduced into the models. Sensitivity using various combinations of GCM and urbanization can be conducted. Furthermore, since the models utilize parameters that can be readily modified to suit certain countermeasures, adaptation and mitigation strategies can be evaluated using thermal comfort and other social indicators. With the approaches introduced through this project, a deeper understanding of urban-climate interactions in the changing global climate can be achieved.

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.

Linking Climate Risk, Policy Networks and Adaptation Planning in Public Lands

NASA Astrophysics Data System (ADS)

Lubell, M.; Schwartz, M.; Peters, C.

2014-12-01

Federal public land management agencies in the United States have engaged a variety of planning efforts to address climate adaptation. A major goal of these efforts is to build policy networks that enable land managers to access information and expertise needed for responding to local climate risks. This paper investigates whether the perceived and modeled climate risk faced by different land managers is leading to larger networks or more participating in climate adaptation. In theory, the benefits of climate planning networks are larger when land managers are facing more potential changes. The basic hypothesis is tested with a survey of public land managers from hundreds of local and regional public lands management units in the Southwestern United States, as well as other stakeholders involved with climate adaptation planning. All survey respondents report their perceptions of climate risk along a variety of dimensions, as well as their participation in climate adaptation planning and information sharing networks. For a subset of respondents, we have spatially explicity GIS data about their location, which will be linked with downscaled climate model data. With the focus on climate change, the analysis is a subset of the overall idea of linking social and ecological systems.

A method to preserve trends in quantile mapping bias correction of climate modeled temperature

NASA Astrophysics Data System (ADS)

Grillakis, Manolis G.; Koutroulis, Aristeidis G.; Daliakopoulos, Ioannis N.; Tsanis, Ioannis K.

2017-09-01

Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

The greenhouse theory of climate change - A test by an inadvertent global experiment

NASA Technical Reports Server (NTRS)

Ramanathan, V.

1988-01-01

The greenhouse theory of climate change has reached the crucial stage of verification. Surface warming as large as that predicted by models would be unprecedented during an interglacial period such as the present. The theory, its scope for verification, and the emerging complexities of the climate feedback mechanisms are discussed in this paper. The evidence for change is described and competing nonclimatic forcings are discussed.

The link between leadership and safety outcomes in hospitals.

PubMed

Squires, Mae; Tourangeau, Ann; Spence Laschinger, Heather K; Doran, Diane

2010-11-01

To test and refine a model examining relationships among leadership, interactional justice, quality of the nursing work environment, safety climate and patient and nurse safety outcomes. The quality of nursing work environments may pose serious threats to patient and nurse safety. Justice is an important element in work environments that support safety initiatives yet little research has been done that looks at how leader interactional justice influences safety outcomes. A cross-sectional survey was conducted with 600 acute care registered nurses (RNs) to test and refine a model linking interactional justice, the quality of nurse leader-nurse relationships, work environment and safety climate with patient and nurse outcomes. In general the hypothesized model was supported. Resonant leadership and interactional justice influenced the quality of the leader-nurse relationship which in turn affected the quality of the work environment and safety climate. This ultimately was associated with decreased reported medication errors, intentions to leave and emotional exhaustion. Quality relationships based on fairness and empathy play a pivotal role in creating positive safety climates and work environments. To advocate for safe work environments, managers must strive to develop high-quality relationships through just leadership practices. © 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Ltd.

Mass and energy budgets of animals: Behavioral and ecological implications

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

Porter, W.P.

1991-11-01

The two major aims of our lab are as follows: First, to develop and field-test general mechanistic models that predict animal life history characteristics as influenced by climate and the physical, physiological behavioral characteristics of species. This involves: understanding how animal time and energy budgets are affected by climate and animal properties; predicting growth and reproductive potential from time and energy budgets; predicting mortality based on climate and time and energy budgets; and linking these individual based models to population dynamics. Second to conduct empirical studies of animal physiological ecology, particularly the effects of temperature on time and energy budgets.more » The physiological ecology of individual animals is the key link between the physical environment and population-level phenomena. We address the macroclimate to microclimate linkage on a broad spatial scale; address the links between individuals and population dynamics for lizard species; test the endotherm energetics and behavior model using beaver; address the spatial variation in climate and its effects on individual energetics, growth and reproduction; and address patchiness in the environment and constraints they may impose on individual energetics, growth and reproduction. These projects are described individually in the following section. 24 refs., 9 figs.« less

Short-term Time Step Convergence in a Climate Model

DOE PAGES

Wan, Hui; Rasch, Philip J.; Taylor, Mark; ...

2015-02-11

A testing procedure is designed to assess the convergence property of a global climate model with respect to time step size, based on evaluation of the root-mean-square temperature difference at the end of very short (1 h) simulations with time step sizes ranging from 1 s to 1800 s. A set of validation tests conducted without sub-grid scale parameterizations confirmed that the method was able to correctly assess the convergence rate of the dynamical core under various configurations. The testing procedure was then applied to the full model, and revealed a slow convergence of order 0.4 in contrast to themore » expected first-order convergence. Sensitivity experiments showed without ambiguity that the time stepping errors in the model were dominated by those from the stratiform cloud parameterizations, in particular the cloud microphysics. This provides a clear guidance for future work on the design of more accurate numerical methods for time stepping and process coupling in the model.« less

Nonhydrostatic nested climate modeling: A case study of the 2010 summer season over the western United States

NASA Astrophysics Data System (ADS)

Lebassi-Habtezion, Bereket; Diffenbaugh, Noah S.

2013-10-01

potential importance of local-scale climate phenomena motivates development of approaches to enable computationally feasible nonhydrostatic climate simulations. To that end, we evaluate the potential viability of nested nonhydrostatic model approaches, using the summer climate of the western United States (WUSA) as a case study. We use the Weather Research and Forecast (WRF) model to carry out five simulations of summer 2010. This suite allows us to test differences between nonhydrostatic and hydrostatic resolutions, single and multiple nesting approaches, and high- and low-resolution reanalysis boundary conditions. WRF simulations were evaluated against station observations, gridded observations, and reanalysis data over domains that cover the 11 WUSA states at nonhydrostatic grid spacing of 4 km and hydrostatic grid spacing of 25 km and 50 km. Results show that the nonhydrostatic simulations more accurately resolve the heterogeneity of surface temperature, precipitation, and wind speed features associated with the topography and orography of the WUSA region. In addition, we find that the simulation in which the nonhydrostatic grid is nested directly within the regional reanalysis exhibits the greatest overall agreement with observational data. Results therefore indicate that further development of nonhydrostatic nesting approaches is likely to yield important insights into the response of local-scale climate phenomena to increases in global greenhouse gas concentrations. However, the biases in regional precipitation, atmospheric circulation, and moisture flux identified in a subset of the nonhydrostatic simulations suggest that alternative nonhydrostatic modeling approaches such as superparameterization and variable-resolution global nonhydrostatic modeling will provide important complements to the nested approaches tested here.

Workplace injuries, safety climate and behaviors: application of an artificial neural network.

PubMed

Abubakar, A Mohammed; Karadal, Himmet; Bayighomog, Steven W; Merdan, Ethem

2018-05-09

This article proposes and tests a model for the interaction effect of the organizational safety climate and behaviors on workplace injuries. Using artificial neural network and survey data from 306 metal casting industry employees in central Anatolia, we found that an organizational safety climate mitigates workplace injuries, and safety behaviors enforce the strength of the negative impact of the safety climate on workplace injuries. The results suggest a complex relationship between the organizational safety climate, safety behavior and workplace injuries. Theoretical and practical implications are discussed in light of decreasing workplace injuries in the Anatolian metal casting industry.

Evaluation of Chemistry-Climate Model Results using Long-Term Satellite and Ground-Based Data

NASA Technical Reports Server (NTRS)

Stolarski, Richard S.

2005-01-01

Chemistry-climate models attempt to bring together our best knowledge of the key processes that govern the composition of the atmosphere and its response to changes in forcing. We test these models on a process by process basis by comparing model results to data from many sources. A more difficult task is testing the model response to changes. One way to do this is to use the natural and anthropogenic experiments that have been done on the atmosphere and are continuing to be done. These include the volcanic eruptions of El Chichon and Pinatubo, the solar cycle, and the injection of chlorine and bromine from CFCs and methyl bromide. The test of the model's response to these experiments is their ability to produce the long-term variations in ozone and the trace gases that affect ozone. We now have more than 25 years of satellite ozone data. We have more than 15 years of satellite and ground-based data of HC1, HN03, and many other gases. I will discuss the testing of models using long-term satellite data sets, long-term measurements from the Network for Detection of Stratospheric Change (NDSC) , long-term ground-based measurements of ozone.

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.

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

Can the combined use of an ensemble based modelling approach and the analysis of measured meteorological trends lead to increased confidence in climate change impact assessments?

NASA Astrophysics Data System (ADS)

Gädeke, Anne; Koch, Hagen; Pohle, Ina; Grünewald, Uwe

2014-05-01

In anthropogenically heavily impacted river catchments, such as the Lusatian river catchments of Spree and Schwarze Elster (Germany), the robust assessment of possible impacts of climate change on the regional water resources is of high relevance for the development and implementation of suitable climate change adaptation strategies. Large uncertainties inherent in future climate projections may, however, reduce the willingness of regional stakeholder to develop and implement suitable adaptation strategies to climate change. This study provides an overview of different possibilities to consider uncertainties in climate change impact assessments by means of (1) an ensemble based modelling approach and (2) the incorporation of measured and simulated meteorological trends. The ensemble based modelling approach consists of the meteorological output of four climate downscaling approaches (DAs) (two dynamical and two statistical DAs (113 realisations in total)), which drive different model configurations of two conceptually different hydrological models (HBV-light and WaSiM-ETH). As study area serve three near natural subcatchments of the Spree and Schwarze Elster river catchments. The objective of incorporating measured meteorological trends into the analysis was twofold: measured trends can (i) serve as a mean to validate the results of the DAs and (ii) be regarded as harbinger for the future direction of change. Moreover, regional stakeholders seem to have more trust in measurements than in modelling results. In order to evaluate the nature of the trends, both gradual (Mann-Kendall test) and step changes (Pettitt test) are considered as well as both temporal and spatial correlations in the data. The results of the ensemble based modelling chain show that depending on the type (dynamical or statistical) of DA used, opposing trends in precipitation, actual evapotranspiration and discharge are simulated in the scenario period (2031-2060). While the statistical DAs simulate a strong decrease in future long term annual precipitation, the dynamical DAs simulate a tendency towards increasing precipitation. The trend analysis suggests that precipitation has not changed significantly during the period 1961-2006. Therefore, the decrease simulated by the statistical DAs should be interpreted as a rather dry future projection. Concerning air temperature, measured and simulated trends agree on a positive trend. Also the uncertainty related to the hydrological model within the climate change modelling chain is comparably low when long-term averages are considered but increases significantly during extreme events. This proposed framework of combining an ensemble based modelling approach with measured trend analysis is a promising approach for regional stakeholders to gain more confidence into the final results of climate change impact assessments. However, climate change impact assessments will remain highly uncertain. Thus, flexible adaptation strategies need to be developed which should not only consider climate but also other aspects of global change.

Intensified Indian Ocean climate variability during the Last Glacial Maximum

NASA Astrophysics Data System (ADS)

Thirumalai, K.; DiNezro, P.; Tierney, J. E.; Puy, M.; Mohtadi, M.

2017-12-01

Climate models project increased year-to-year climate variability in the equatorial Indian Ocean in response to greenhouse gas warming. This response has been attributed to changes in the mean climate of the Indian Ocean associated with the zonal sea-surface temperature (SST) gradient. According to these studies, air-sea coupling is enhanced due to a stronger SST gradient driving anomalous easterlies that shoal the thermocline in the eastern Indian Ocean. We propose that this relationship between the variability and the zonal SST gradient is consistent across different mean climate states. We test this hypothesis using simulations of past and future climate performed with the Community Earth System Model Version 1 (CESM1). We constrain the realism of the model for the Last Glacial Maximum (LGM) where CESM1 simulates a mean climate consistent with a stronger SST gradient, agreeing with proxy reconstructions. CESM1 also simulates a pronounced increase in seasonal and interannual variability. We develop new estimates of climate variability on these timescales during the LGM using δ18O analysis of individual foraminifera (IFA). IFA data generated from four different cores located in the eastern Indian Ocean indicate a marked increase in δ18O-variance during the LGM as compared to the late Holocene. Such a significant increase in the IFA-δ18O variance strongly supports the modeling simulations. This agreement further supports the dynamics linking year-to-year variability and an altered SST gradient, increasing our confidence in model projections.

Contribution of physical modelling to climate-driven landslide hazard mapping: an alpine test site

NASA Astrophysics Data System (ADS)

Vandromme, R.; Desramaut, N.; Baills, A.; Hohmann, A.; Grandjean, G.; Sedan, O.; Mallet, J. P.

2012-04-01

The aim of this work is to develop a methodology for integrating climate change scenarios into quantitative hazard assessment and especially their precipitation component. The effects of climate change will be different depending on both the location of the site and the type of landslide considered. Indeed, mass movements can be triggered by different factors. This paper describes a methodology to address this issue and shows an application on an alpine test site. Mechanical approaches represent a solution for quantitative landslide susceptibility and hazard modeling. However, as the quantity and the quality of data are generally very heterogeneous at a regional scale, it is necessary to take into account the uncertainty in the analysis. In this perspective, a new hazard modeling method is developed and integrated in a program named ALICE. This program integrates mechanical stability analysis through a GIS software taking into account data uncertainty. This method proposes a quantitative classification of landslide hazard and offers a useful tool to gain time and efficiency in hazard mapping. However, an expertise approach is still necessary to finalize the maps. Indeed it is the only way to take into account some influent factors in slope stability such as heterogeneity of the geological formations or effects of anthropic interventions. To go further, the alpine test site (Barcelonnette area, France) is being used to integrate climate change scenarios into ALICE program, and especially their precipitation component with the help of a hydrological model (GARDENIA) and the regional climate model REMO (Jacob, 2001). From a DEM, land-cover map, geology, geotechnical data and so forth the program classifies hazard zones depending on geotechnics and different hydrological contexts varying in time. This communication, realized within the framework of Safeland project, is supported by the European Commission under the 7th Framework Programme for Research and Technological Development, Area "Environment", Activity 1.3.3.1 "Prediction of triggering and risk assessment for landslides".

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.

Simulations of the effect of a warmer climate on atmospheric humidity

NASA Technical Reports Server (NTRS)

Del Genio, Anthony D.; Lacis, Andrew A.; Ruedy, Reto A.

1991-01-01

Increases in the concentration of water vapor constitute the single largest positive feedback in models of global climate warming caused by greenhouse gases. It has been suggested that sinking air in the regions surrounding deep cumulus clouds will dry the upper troposphere and eliminate or reverse the direction of water vapor feedback. This hypothesis has been tested by performing an idealized simulation of climate change with two different versions of a climate model which both incorporate drying due to subsidence of clear air but differ in their parameterization of moist convection and stratiform clouds. Despite increased drying of the upper troposphere by cumulus clouds, upper-level humidity increases in the warmer climate because of enhanced upward moisture transport by the general circulation and increased accumulation of water vapor and ice at cumulus cloud tops.

Data-model synthesis of grassland carbon metabolism. Quantifying direct, indirect & interactive effects of warming & elevated CO 2

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

Pendall, Elise; Ogle, Kiona; Parton, William

2016-02-29

This research project improved understanding of how climate change (elevated atmospheric CO 2, warming and altered precipitation) can affect grassland ecosystem productivity and nutrient availability. Our advanced experimental and modeling methods allowed us to test 21 specific hypotheses. We found that ecosystem changes over years of exposure to climate change can shift the plant communities and potentially make them more resilient to future climate changes. These changes in plant communities may be related to increased growth of belowground roots and enhanced nutrient uptake by some species. We also found that climate change can increase the spread of invasive and noxiousmore » weeds. These findings are important for land managers to make adaptive planning decisions for domestic livestock production in response to climate variability in semi-arid grasslands.« less

Simulating seasonal tropical cyclone intensities at landfall along the South China coast

NASA Astrophysics Data System (ADS)

Lok, Charlie C. F.; Chan, Johnny C. L.

2018-04-01

A numerical method is developed using a regional climate model (RegCM3) and the Weather Forecast and Research (WRF) model to predict seasonal tropical cyclone (TC) intensities at landfall for the South China region. In designing the model system, three sensitivity tests have been performed to identify the optimal choice of the RegCM3 model domain, WRF horizontal resolution and WRF physics packages. Driven from the National Centers for Environmental Prediction Climate Forecast System Reanalysis dataset, the model system can produce a reasonable distribution of TC intensities at landfall on a seasonal scale. Analyses of the model output suggest that the strength and extent of the subtropical ridge in the East China Sea are crucial to simulating TC landfalls in the Guangdong and Hainan provinces. This study demonstrates the potential for predicting TC intensities at landfall on a seasonal basis as well as projecting future climate changes using numerical models.

Application of statistical downscaling technique for the production of wine grapes (Vitis vinifera L.) in Spain

NASA Astrophysics Data System (ADS)

Gaitán Fernández, E.; García Moreno, R.; Pino Otín, M. R.; Ribalaygua Batalla, J.

2012-04-01

Climate and soil are two of the most important limiting factors for agricultural production. Nowadays climate change has been documented in many geographical locations affecting different cropping systems. The General Circulation Models (GCM) has become important tools to simulate the more relevant aspects of the climate expected for the XXI century in the frame of climatic change. These models are able to reproduce the general features of the atmospheric dynamic but their low resolution (about 200 Km) avoids a proper simulation of lower scale meteorological effects. Downscaling techniques allow overcoming this problem by adapting the model outcomes to local scale. In this context, FIC (Fundación para la Investigación del Clima) has developed a statistical downscaling technique based on a two step analogue methods. This methodology has been broadly tested on national and international environments leading to excellent results on future climate models. In a collaboration project, this statistical downscaling technique was applied to predict future scenarios for the grape growing systems in Spain. The application of such model is very important to predict expected climate for the different growing crops, mainly for grape, where the success of different varieties are highly related to climate and soil. The model allowed the implementation of agricultural conservation practices in the crop production, detecting highly sensible areas to negative impacts produced by any modification of climate in the different regions, mainly those protected with protected designation of origin, and the definition of new production areas with optimal edaphoclimatic conditions for the different varieties.

Model Diagnostics for the Department of Energy's Accelerated Climate Modeling for Energy (ACME) Project

NASA Astrophysics Data System (ADS)

Smith, B.

2015-12-01

In 2014, eight Department of Energy (DOE) national laboratories, four academic institutions, one company, and the National Centre for Atmospheric Research combined forces in a project called Accelerated Climate Modeling for Energy (ACME) with the goal to speed Earth system model development for climate and energy. Over the planned 10-year span, the project will conduct simulations and modeling on DOE's most powerful high-performance computing systems at Oak Ridge, Argonne, and Lawrence Berkeley Leadership Compute Facilities. A key component of the ACME project is the development of an interactive test bed for the advanced Earth system model. Its execution infrastructure will accelerate model development and testing cycles. The ACME Workflow Group is leading the efforts to automate labor-intensive tasks, provide intelligent support for complex tasks and reduce duplication of effort through collaboration support. As part of this new workflow environment, we have created a diagnostic, metric, and intercomparison Python framework, called UVCMetrics, to aid in the testing-to-production execution of the ACME model. The framework exploits similarities among different diagnostics to compactly support diagnosis of new models. It presently focuses on atmosphere and land but is designed to support ocean and sea ice model components as well. This framework is built on top of the existing open-source software framework known as the Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT). Because of its flexible framework design, scientists and modelers now can generate thousands of possible diagnostic outputs. These diagnostics can compare model runs, compare model vs. observation, or simply verify a model is physically realistic. Additional diagnostics are easily integrated into the framework, and our users have already added several. Diagnostics can be generated, viewed, and manipulated from the UV-CDAT graphical user interface, Python command line scripts and programs, and web browsers. The framework is designed to be scalable to large datasets, yet easy to use and familiar to scientists using previous tools. Integration in the ACME overall user interface facilitates data publication, further analysis, and quick feedback to model developers and scientists making component or coupled model runs.

A Spectral Evaluation of Models Performances in Mediterranean Oak Woodlands

NASA Astrophysics Data System (ADS)

Vargas, R.; Baldocchi, D. D.; Abramowitz, G.; Carrara, A.; Correia, A.; Kobayashi, H.; Papale, D.; Pearson, D.; Pereira, J.; Piao, S.; Rambal, S.; Sonnentag, O.

2009-12-01

Ecosystem processes are influenced by climatic trends at multiple temporal scales including diel patterns and other mid-term climatic modes, such as interannual and seasonal variability. Because interactions between biophysical components of ecosystem processes are complex, it is important to test how models perform in frequency (e.g. hours, days, weeks, months, years) and time (i.e. day of the year) domains in addition to traditional tests of annual or monthly sums. Here we present a spectral evaluation using wavelet time series analysis of model performance in seven Mediterranean Oak Woodlands that encompass three deciduous and four evergreen sites. We tested the performance of five models (CABLE, ORCHIDEE, BEPS, Biome-BGC, and JULES) on measured variables of gross primary production (GPP) and evapotranspiration (ET). In general, model performance fails at intermediate periods (e.g. weeks to months) likely because these models do not represent the water pulse dynamics that influence GPP and ET at these Mediterranean systems. To improve the performance of a model it is critical to identify first where and when the model fails. Only by identifying where a model fails we can improve the model performance and use them as prognostic tools and to generate further hypotheses that can be tested by new experiments and measurements.

Measurement and structural relations of an authoritative school climate model: A multi-level latent variable investigation.

PubMed

Konold, Timothy R; Cornell, Dewey

2015-12-01

This study tested a conceptual model of school climate in which two key elements of an authoritative school, structure and support variables, are associated with student engagement in school and lower levels of peer aggression. Multilevel multivariate structural modeling was conducted in a statewide sample of 48,027 students in 323 public high schools who completed the Authoritative School Climate Survey. As hypothesized, two measures of structure (Disciplinary Structure and Academic Expectations) and two measures of support (Respect for Students and Willingness to Seek Help) were associated with higher student engagement (Affective Engagement and Cognitive Engagement) and lower peer aggression (Prevalence of Teasing and Bullying) on both student and school levels of analysis, controlling for the effects of school demographics (school size, percentage of minority students, and percentage of low income students). These results support the extension of authoritative school climate model to high school and guide further research on the conditions for a positive school climate. Copyright © 2015 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Development of flood regressions and climate change scenarios to explore estimates of future peak flows

USGS Publications Warehouse

Burns, Douglas A.; Smith, Martyn J.; Freehafer, Douglas A.

2015-12-31

The application uses predictions of future annual precipitation from five climate models and two future greenhouse gas emissions scenarios and provides results that are averaged over three future periods—2025 to 2049, 2050 to 2074, and 2075 to 2099. Results are presented in ensemble form as the mean, median, maximum, and minimum values among the five climate models for each greenhouse gas emissions scenario and period. These predictions of future annual precipitation are substituted into either the precipitation variable or a water balance equation for runoff to calculate potential future peak flows. This application is intended to be used only as an exploratory tool because (1) the regression equations on which the application is based have not been adequately tested outside the range of the current climate and (2) forecasting future precipitation with climate models and downscaling these results to a fine spatial resolution have a high degree of uncertainty. This report includes a discussion of the assumptions, uncertainties, and appropriate use of this exploratory application.

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.

Environmental Testing Campaign and Verification of Satellite Deimos-2 at INTA

NASA Astrophysics Data System (ADS)

Hernandez, Daniel; Vazquez, Mercedes; Anon, Manuel; Olivo, Esperanza; Gallego, Pablo; Morillo, Pablo; Parra, Javier; Capraro; Luengo, Mar; Garcia, Beatriz; Villacorta, Pablo

2014-06-01

In this paper the environmental test campaign and verification of the DEIMOS-2 (DM2) satellite will be presented and described. DM2 will be ready for launch in 2014.Firstly, a short description of the satellite is presented, including its physical characteristics and intended optical performances. DEIMOS-2 is a LEO satellite for earth observation that will provide high resolution imaging services for agriculture, civil protection, environmental issues, disasters monitoring, climate change, urban planning, cartography, security and intelligence.Then, the verification and test campaign carried out on the SM and FM models at INTA is described; including Mechanical test for the SM and Climatic, Mechanical and Electromagnetic Compatibility tests for the FM. In addition, this paper includes Centre of Gravity and Moment of Inertia measurements for both models, and other verification activities carried out in order to ensure satellite's health during launch and its in orbit performance.

Extreme Rainfall Events Over Southern Africa: Assessment of a Climate Model to Reproduce Daily Extremes

NASA Astrophysics Data System (ADS)

Williams, C.; Kniveton, D.; Layberry, R.

2007-12-01

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable extreme events, due to a number of factors including extensive poverty, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of a state-of-the-art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. Once the model's ability to reproduce extremes has been assessed, idealised regions of SST anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, results from sensitivity testing of the UK Meteorological Office Hadley Centre's climate model's domain size are firstly presented. Then simulations of current climate from the model, operating in both regional and global mode, are compared to the MIRA dataset at daily timescales. Thirdly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset. Finally, the results from the idealised SST experiments are briefly presented, suggesting associations between rainfall extremes and both local and remote SST anomalies.

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

Collins, William D.; Craig, Anthony P.; Truesdale, John E.

The integrated Earth System Model (iESM) has been developed as a new tool for pro- jecting the joint human/climate system. The iESM is based upon coupling an Integrated Assessment Model (IAM) and an Earth System Model (ESM) into a common modeling in- frastructure. IAMs are the primary tool for describing the human–Earth system, including the sources of global greenhouse gases (GHGs) and short-lived species, land use and land cover change, and other resource-related drivers of anthropogenic climate change. ESMs are the primary scientific tools for examining the physical, chemical, and biogeochemical impacts of human-induced changes to the climate system. Themore » iESM project integrates the economic and human dimension modeling of an IAM and a fully coupled ESM within a sin- gle simulation system while maintaining the separability of each model if needed. Both IAM and ESM codes are developed and used by large communities and have been extensively applied in recent national and international climate assessments. By introducing heretofore- omitted feedbacks between natural and societal drivers, we can improve scientific under- standing of the human–Earth system dynamics. Potential applications include studies of the interactions and feedbacks leading to the timing, scale, and geographic distribution of emissions trajectories and other human influences, corresponding climate effects, and the subsequent impacts of a changing climate on human and natural systems. This paper de- scribes the formulation, requirements, implementation, testing, and resulting functionality of the first version of the iESM released to the global climate community.« less

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

Collins, W. D.; Craig, A. P.; Truesdale, J. E.

The integrated Earth system model (iESM) has been developed as a new tool for projecting the joint human/climate system. The iESM is based upon coupling an integrated assessment model (IAM) and an Earth system model (ESM) into a common modeling infrastructure. IAMs are the primary tool for describing the human–Earth system, including the sources of global greenhouse gases (GHGs) and short-lived species (SLS), land use and land cover change (LULCC), and other resource-related drivers of anthropogenic climate change. ESMs are the primary scientific tools for examining the physical, chemical, and biogeochemical impacts of human-induced changes to the climate system. Themore » iESM project integrates the economic and human-dimension modeling of an IAM and a fully coupled ESM within a single simulation system while maintaining the separability of each model if needed. Both IAM and ESM codes are developed and used by large communities and have been extensively applied in recent national and international climate assessments. By introducing heretofore-omitted feedbacks between natural and societal drivers, we can improve scientific understanding of the human–Earth system dynamics. Potential applications include studies of the interactions and feedbacks leading to the timing, scale, and geographic distribution of emissions trajectories and other human influences, corresponding climate effects, and the subsequent impacts of a changing climate on human and natural systems. This paper describes the formulation, requirements, implementation, testing, and resulting functionality of the first version of the iESM released to the global climate community.« less

Does Climate Care about Land?

NASA Astrophysics Data System (ADS)

Dawson, E.; Lague, M. M.; Swann, A. L. S.

2017-12-01

Everyone knows that plants are influenced by the climate they live in. However, the reverse is also true: plants can influence climate both locally and globally by changing atmospheric circulation. Uncovering the role that plants play in climate has been challenging—the interactions are complex and vary greatly in different regions of the world. We lack a systematic understanding of the role of vegetation in the climate system. Using a new simplified land model coupled to a modern Earth System Model (ESM), we are able to separate the individual influences of the land system in the context of modern ESMs. For example, with our model we are able to test how the capacity of the land to hold water influences the atmosphere. If less water is able to evaporate, this could lead to substantial warming, and could even influence clouds. Understanding specifically where and how the atmosphere is influenced by the land surface improves our understanding of how future changes in the land surface will in turn feedback on climate, and how that will impact people. This improved understanding also advances our knowledge of the key role biology plays in driving the global climate system.

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.

Contributions to Future Stratospheric Climate Change: An Idealized Chemistry-Climate Model Sensitivity Study

NASA Technical Reports Server (NTRS)

Hurwitz, M. M.; Braesicke, P.; Pyle, J. A.

2010-01-01

Within the framework of an idealized model sensitivity study, three of the main contributors to future stratospheric climate change are evaluated: increases in greenhouse gas concentrations, ozone recovery, and changing sea surface temperatures (SSTs). These three contributors are explored in combination and separately, to test the interactions between ozone and climate; the linearity of their contributions to stratospheric climate change is also assessed. In a simplified chemistry-climate model, stratospheric global mean temperature is most sensitive to CO2 doubling, followed by ozone depletion, then by increased SSTs. At polar latitudes, the Northern Hemisphere (NH) stratosphere is more sensitive to changes in CO2, SSTs and O3 than is the Southern Hemisphere (SH); the opposing responses to ozone depletion under low or high background CO2 concentrations, as seen with present-day SSTs, are much weaker and are not statistically significant under enhanced SSTs. Consistent with previous studies, the strength of the Brewer-Dobson circulation is found to increase in an idealized future climate; SSTs contribute most to this increase in the upper troposphere/lower stratosphere (UT/LS) region, while CO2 and ozone changes contribute most in the stratosphere and mesosphere.

Final Report for High Latitude Climate Modeling: ARM Takes Us Beyond Case Studies

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

Russell, Lynn M; Lubin, Dan

2013-06-18

The main thrust of this project was to devise a method by which the majority of North Slope of Alaska (NSA) meteorological and radiometric data, collected on a daily basis, could be used to evaluate and improve global climate model (GCM) simulations and their parameterizations, particularly for cloud microphysics. Although the standard ARM Program sensors for a less complete suite of instruments for cloud and aerosol studies than the instruments on an intensive field program such as the 2008 Indirect and Semi-Direct Aerosol Campaign (ISDAC), the advantage they offer lies in the long time base and large volume of datamore » that covers a wide range of meteorological and climatological conditions. The challenge has been devising a method to interpret the NSA data in a practical way, so that a wide variety of meteorological conditions in all seasons can be examined with climate models. If successful, climate modelers would have a robust alternative to the usual “case study” approach (i.e., from intensive field programs only) for testing and evaluating their parameterizations’ performance. Understanding climate change on regional scales requires a broad scientific consideration of anthropogenic influences that goes beyond greenhouse gas emissions to also include aerosol-induced changes in cloud properties. For instance, it is now clear that on small scales, human-induced aerosol plumes can exert microclimatic radiative and hydrologic forcing that rivals that of greenhouse gas–forced warming. This project has made significant scientific progress by investigating what causes successive versions of climate models continue to exhibit errors in cloud amount, cloud microphysical and radiative properties, precipitation, and radiation balance, as compared with observations and, in particular, in Arctic regions. To find out what is going wrong, we have tested the models' cloud representation over the full range of meteorological conditions found in the Arctic using the ARM North Slope of Alaska (NSA) data.« less

Evaluating Options for Improving California's Drought Resilience

NASA Astrophysics Data System (ADS)

Ray, P. A.; Schwarz, A.; Wi, S.; Correa, M.; Brown, C.

2015-12-01

Through a unique collaborative arrangement, the University of Massachusetts (UMass) and the California Department of Water Resources (DWR) have together performed a baseline climate change analysis of the California state (State Water Project) and federal (Central Valley Project) water systems. The first step in the baseline analysis was development of an improved basinwide hydrologic model covering a large area of California including all major tributaries to the state and federal water systems. The CalLite modeling system used by DWR for planning purposes allowed simulation of the system of reservoirs, rivers, control points, and deliveries which are then used to create performance metrics that quantify a wide range of system characteristics including water deliveries, water quality, and environmental/ecological factors. A baseline climate stress test was conducted to identify current vulnerabilities to climate change through the linking of the modeling chain with Decision Scaling concepts through the UMass bottom-up climate stress-testing algorithm. This procedure allowed the first comprehensive climate stress analysis of the California state and federal water systems not constrained by observed historical variability and wet-dry year sequences. A forward-looking drought vulnerability and adaptation assessment of the water systems based on this workflow is ongoing and preliminary results will be presented. Presentation of results will include discussion of the collaborative arrangement between DWR and UMass, which is instrumental to both the success of the research and the education of policy makers.

Low-warming Scenarios and their Approximation: Testing Emulation Performance for Average and Extreme Variables

NASA Astrophysics Data System (ADS)

Tebaldi, C.; Knutti, R.; Armbruster, A.

2017-12-01

Taking advantage of the availability of ensemble simulations under low-warming scenarios performed with NCAR-DOE CESM, we test the performance of established methods for climate model output emulation. The goal is to provide a green, yellow or red light to the large impact research community that may be interested in performing impact analysis using climate model output other than, or in conjunction with, CESM's, especially as the IPCC Special Report on the 1.5 target urgently calls for scientific contributions exploring the costs and benefits of attaining these ambitious goals. We test the performance of emulators of average temperature and precipitation - and their interannual variability - and we also explore the possibility of emulating indices of extremes (ETCCDI indices), devised to offer impact relevant information from daily output of temperature and precipitation. Different degrees of departure from the linearity assumed in these traditional emulation approaches are found across the various quantities considered, and across regions, highlighting different degrees of quality in the approximations, and therefore some challenges in the provision of climate change information for impact analysis under these new scenarios that not many models have thus far targeted through their simulations.

Analysis of reference evapotranspiration (ET0) trends under climate change in Bangladesh using observed and CMIP5 data sets

NASA Astrophysics Data System (ADS)

Rahman, Mohammad Atiqur; Yunsheng, Lou; Sultana, Nahid; Ongoma, Victor

2018-03-01

ET0 is an important hydro-meteorological phenomenon, which is influenced by changing climate like other climatic parameters. This study investigates the present and future trends of ET0 in Bangladesh using 39 years' historical and downscaled CMIP5 daily climatic data for the twenty-first century. Statistical Downscaling Model (SDSM) was used to downscale the climate data required to calculate ET0. Penman-Monteith formula was applied in ET0 calculation for both the historical and modelled data. To analyse ET0 trends and trend changing patterns, modified Mann-Kendall and Sequential Mann-Kendall tests were, respectively, done. Spatial variations of ET0 trends are presented by inverse distance weighting interpolation using ArcGIS 10.2.2. Results show that RCP8.5 (2061-2099) will experience the highest amount of ET0 totals in comparison to the historical and all other scenarios in the same time span of 39 years. Though significant positive trends were observed in the mid and last months of year from month-wise trend analysis of representative concentration pathways, significant negative trends were also found for some months using historical data in similar analysis. From long-term annual trend analysis, it was found that major part of the country represents decreasing trends using historical data, but increasing trends were observed for modelled data. Theil-Sen estimations of ET0 trends in the study depict a good consistency with the Mann-Kendall test results. The findings of the study would contribute in irrigation water management and planning of the country and also in furthering the climate change study using modelled data in the context of Bangladesh.

Testing competing forms of the Milankovitch hypothesis: A multivariate approach

NASA Astrophysics Data System (ADS)

Kaufmann, Robert K.; Juselius, Katarina

2016-02-01

We test competing forms of the Milankovitch hypothesis by estimating the coefficients and diagnostic statistics for a cointegrated vector autoregressive model that includes 10 climate variables and four exogenous variables for solar insolation. The estimates are consistent with the physical mechanisms postulated to drive glacial cycles. They show that the climate variables are driven partly by solar insolation, determining the timing and magnitude of glaciations and terminations, and partly by internal feedback dynamics, pushing the climate variables away from equilibrium. We argue that the latter is consistent with a weak form of the Milankovitch hypothesis and that it should be restated as follows: internal climate dynamics impose perturbations on glacial cycles that are driven by solar insolation. Our results show that these perturbations are likely caused by slow adjustment between land ice volume and solar insolation. The estimated adjustment dynamics show that solar insolation affects an array of climate variables other than ice volume, each at a unique rate. This implies that previous efforts to test the strong form of the Milankovitch hypothesis by examining the relationship between solar insolation and a single climate variable are likely to suffer from omitted variable bias.

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

Duane, Greg; Tsonis, Anastasios; Kocarev, Ljupco

This collaborative reserach has several components but the main idea is that when imperfect copies of a given nonlinear dynamical system are coupled, they may synchronize for some set of coupling parameters. This idea is to be tested for several IPCC-like models each one with its own formulation and representing an “imperfect” copy of the true climate system. By computing the coupling parameters, which will lead the models to a synchronized state, a consensus on climate change simulations may be achieved.

Development and testing of transfer functions for generating quantitative climatic estimates from Australian pollen data

NASA Astrophysics Data System (ADS)

Cook, Ellyn J.; van der Kaars, Sander

2006-10-01

We review attempts to derive quantitative climatic estimates from Australian pollen data, including the climatic envelope, climatic indicator and modern analogue approaches, and outline the need to pursue alternatives for use as input to, or validation of, simulations by models of past, present and future climate patterns. To this end, we have constructed and tested modern pollen-climate transfer functions for mainland southeastern Australia and Tasmania using the existing southeastern Australian pollen database and for northern Australia using a new pollen database we are developing. After testing for statistical significance, 11 parameters were selected for mainland southeastern Australia, seven for Tasmania and six for northern Australia. The functions are based on weighted-averaging partial least squares regression and their predictive ability evaluated against modern observational climate data using leave-one-out cross-validation. Functions for summer, annual and winter rainfall and temperatures are most robust for southeastern Australia, while in Tasmania functions for minimum temperature of the coldest period, mean winter and mean annual temperature are the most reliable. In northern Australia, annual and summer rainfall and annual and summer moisture indexes are the strongest. The validation of all functions means all can be applied to Quaternary pollen records from these three areas with confidence. Copyright

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 joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models.

PubMed

Lovejoy, S; de Lima, M I P

2015-07-01

Over the range of time scales from about 10 days to 30-100 years, in addition to the familiar weather and climate regimes, there is an intermediate "macroweather" regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spite of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be "homogenized" by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time.

Comparison of Grid Nudging and Spectral Nudging Techniques for Dynamical Climate Downscaling within the WRF Model

NASA Astrophysics Data System (ADS)

Fan, X.; Chen, L.; Ma, Z.

2010-12-01

Climate downscaling has been an active research and application area in the past several decades focusing on regional climate studies. Dynamical downscaling, in addition to statistical methods, has been widely used in downscaling as the advanced modern numerical weather and regional climate models emerge. The utilization of numerical models enables that a full set of climate variables are generated in the process of downscaling, which are dynamically consistent due to the constraints of physical laws. While we are generating high resolution regional climate, the large scale climate patterns should be retained. To serve this purpose, nudging techniques, including grid analysis nudging and spectral nudging, have been used in different models. There are studies demonstrating the benefit and advantages of each nudging technique; however, the results are sensitive to many factors such as nudging coefficients and the amount of information to nudge to, and thus the conclusions are controversy. While in a companion work of developing approaches for quantitative assessment of the downscaled climate, in this study, the two nudging techniques are under extensive experiments in the Weather Research and Forecasting (WRF) model. Using the same model provides fair comparability. Applying the quantitative assessments provides objectiveness of comparison. Three types of downscaling experiments were performed for one month of choice. The first type is serving as a base whereas the large scale information is communicated through lateral boundary conditions only; the second is using the grid analysis nudging; and the third is using spectral nudging. Emphases are given to the experiments of different nudging coefficients and nudging to different variables in the grid analysis nudging; while in spectral nudging, we focus on testing the nudging coefficients, different wave numbers on different model levels to nudge.

The Iowa K-12 Climate Science Education Initiative: a comprehensive approach to meeting in-service teachers' stated needs for teaching climate literacy with NGSS

NASA Astrophysics Data System (ADS)

Stanier, C. O.; Spak, S.; Neal, T. A.; Herder, S.; Malek, A.; Miller, Z.

2017-12-01

The Iowa Board of Education voted unanimously in 2015 to adopt NGSS performance standards. The CGRER - College of Education Iowa K-12 Climate Science Education Initiative was established in 2016 to work directly with Iowa inservice teachers to provide what teachers need most to teach climate literacy and climate science content through investigational learning aligned with NGSS. Here we present teachers' requests for teaching climate with NGSS, and an approach to provide resources for place-based authentic inquiry on climate, developed, tested, and refined in partnership with inservice and preservice teachers. A survey of inservice middle school and high school science teachers was conducted at the 2016 Iowa Council of Teachers of Mathematics/Iowa Academy of Sciences - Iowa Science Teaching Section Fall Conference and online in fall 2016. Participants (n=383) were asked about their prior experience and education, the resources they use and need, their level of comfort in teaching climate science, perceived barriers, and how they address potential controversy. Teachers indicated preference for professional development on climate content and complete curricula packaged with lessons and interactive models aligned to Iowa standards, as well as training on instructional strategies to enhance students' ability to interpret scientific evidence. We identify trends in responses by teaching experience, climate content knowledge and its source, grade level, and urban and rural districts. Less than 20% of respondents reported controversy or negativity in teaching climate to date, and a majority were comfortable teaching climate science and climate change, with equal confidence in teaching climate and other STEM content through investigational activities. We present an approach and materials to meet these stated needs, created and tested in collaboration with Iowa teachers. We combine professional development and modular curricula with bundled standards, concepts, models, data, field activities, and sequences of individual and group investigational and student-driven inquiry prompts on climate science, climate change, and climate impacts. We identify key resource availability needed to teach place-based climate literacy aligned with NGSS as a standalone curriculum and through local impacts.

A Biome map for Modelling Global Mid-Pliocene Climate Change

NASA Astrophysics Data System (ADS)

Salzmann, U.; Haywood, A. M.

2006-12-01

The importance of vegetation-climate feedbacks was highlighted by several paleo-climate modelling exercises but their role as a boundary condition in Tertiary modelling has not been fully recognised or explored. Several paleo-vegetation datasets and maps have been produced for specific time slabs or regions for the Tertiary, but the vegetation classifications that have been used differ, thus making meaningful comparisons difficult. In order to facilitate further investigations into Tertiary climate and environmental change we are presently implementing the comprehensive GIS database TEVIS (Tertiary Environment and Vegetation Information System). TEVIS integrates marine and terrestrial vegetation data, taken from fossil pollen, leaf or wood, into an internally consistent classification scheme to produce for different time slabs global Tertiary Biome and Mega- Biome maps (Harrison & Prentice, 2003). In the frame of our ongoing 5-year programme we present a first global vegetation map for the mid-Pliocene time slab, a period of sustained global warmth. Data were synthesised from the PRISM data set (Thompson and Fleming 1996) after translating them to the Biome classification scheme and from new literature. The outcomes of the Biome map are compared with modelling results using an advanced numerical general circulation model (HadAM3) and the BIOME 4 vegetation model. Our combined proxy data and modelling approach will provide new palaeoclimate datasets to test models that are used to predict future climate change, and provide a more rigorous picture of climate and environmental changes during the Neogene.

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

Implications of between-isolate variation for climate change impact modelling of Haemonchus contortus populations.

PubMed

Rose Vineer, H; Steiner, J; Knapp-Lawitzke, F; Bull, K; von Son-de Fernex, E; Bosco, A; Hertzberg, H; Demeler, J; Rinaldi, L; Morrison, A A; Skuce, P; Bartley, D J; Morgan, E R

2016-10-15

The impact of climate change on parasites and parasitic diseases is a growing concern and numerous empirical and mechanistic models have been developed to predict climate-driven spatial and temporal changes in the distribution of parasites and disease risk. Variation in parasite phenotype and life-history traits between isolates could undermine the application of such models at broad spatial scales. Seasonal variation in the transmission of the haematophagous gastrointestinal nematode Haemonchus contortus, one of the most pathogenic helminth species infecting sheep and goats worldwide, is primarily determined by the impact of environmental conditions on the free-living stages. To evaluate variability in the development success and mortality of the free-living stages of H. contortus and the impact of this variability on future climate impact modelling, three isolates of diverse origin were cultured at a range of temperatures between 15°C and 37°C to determine their development success compared with simulations using the GLOWORM-FL H. contortus model. No significant difference was observed in the developmental success of the three isolates of H. contortus tested, nor between isolates and model simulations. However, development success of all isolates at 37°C was lower than predicted by the model, suggesting the potential for overestimation of transmission risk at higher temperatures, such as those predicted under some scenarios of climate change. Recommendations are made for future climate impact modelling of gastrointestinal nematodes. Copyright © 2016 Elsevier B.V. All rights reserved.

Incorporating abundance information and guiding variable selection for climate-based ensemble forecasting of species' distributional shifts.

PubMed

Tanner, Evan P; Papeş, Monica; Elmore, R Dwayne; Fuhlendorf, Samuel D; Davis, Craig A

2017-01-01

Ecological niche models (ENMs) have increasingly been used to estimate the potential effects of climate change on species' distributions worldwide. Recently, predictions of species abundance have also been obtained with such models, though knowledge about the climatic variables affecting species abundance is often lacking. To address this, we used a well-studied guild (temperate North American quail) and the Maxent modeling algorithm to compare model performance of three variable selection approaches: correlation/variable contribution (CVC), biological (i.e., variables known to affect species abundance), and random. We then applied the best approach to forecast potential distributions, under future climatic conditions, and analyze future potential distributions in light of available abundance data and presence-only occurrence data. To estimate species' distributional shifts we generated ensemble forecasts using four global circulation models, four representative concentration pathways, and two time periods (2050 and 2070). Furthermore, we present distributional shifts where 75%, 90%, and 100% of our ensemble models agreed. The CVC variable selection approach outperformed our biological approach for four of the six species. Model projections indicated species-specific effects of climate change on future distributions of temperate North American quail. The Gambel's quail (Callipepla gambelii) was the only species predicted to gain area in climatic suitability across all three scenarios of ensemble model agreement. Conversely, the scaled quail (Callipepla squamata) was the only species predicted to lose area in climatic suitability across all three scenarios of ensemble model agreement. Our models projected future loss of areas for the northern bobwhite (Colinus virginianus) and scaled quail in portions of their distributions which are currently areas of high abundance. Climatic variables that influence local abundance may not always scale up to influence species' distributions. Special attention should be given to selecting variables for ENMs, and tests of model performance should be used to validate the choice of variables.

Implications of Climate Change on the Heat Budget of Lentic Systems Used for Power Station Cooling: Case Study Clinton Lake, Illinois.

PubMed

Quijano, Juan C; Jackson, P Ryan; Santacruz, Santiago; Morales, Viviana M; García, Marcelo H

2016-01-05

We use a numerical model to analyze the impact of climate change-in particular higher air temperatures-on a nuclear power station that recirculates the water from a reservoir for cooling. The model solves the hydrodynamics, the transfer of heat in the reservoir, and the energy balance at the surface. We use the numerical model to (i) quantify the heat budget in the reservoir and determine how this budget is affected by the combined effect of the power station and climate change and (ii) quantify the impact of climate change on both the downstream thermal pollution and the power station capacity. We consider four different scenarios of climate change. Results of simulations show that climate change will reduce the ability to dissipate heat to the atmosphere and therefore the cooling capacity of the reservoir. We observed an increase of 25% in the thermal load downstream of the reservoir, and a reduction in the capacity of the power station of 18% during the summer months for the worst-case climate change scenario tested. These results suggest that climate change is an important threat for both the downstream thermal pollution and the generation of electricity by power stations that use lentic systems for cooling.

Implications of climate change on the heat budget of lentic systems used for power station cooling: Case study Clinton Lake, Illinois

USGS Publications Warehouse

Quijano, Juan C; Jackson, P. Ryan; Santacruz, Santiago; Morales, Viviana M; Garcia, Marcelo H.

2016-01-01

We use a numerical model to analyze the impact of climate change--in particular higher air temperatures--on a nuclear power station that recirculates the water from a reservoir for cooling. The model solves the hydrodynamics, the transfer of heat in the reservoir, and the energy balance at the surface. We use the numerical model to (i) quantify the heat budget in the reservoir and determine how this budget is affected by the combined effect of the power station and climate change and (ii) quantify the impact of climate change on both the downstream thermal pollution and the power station capacity. We consider four different scenarios of climate change. Results of simulations show that climate change will reduce the ability to dissipate heat to the atmosphere and therefore the cooling capacity of the reservoir. We observed an increase of 25% in the thermal load downstream of the reservoir, and a reduction in the capacity of the power station of 18% during the summer months for the worst-case climate change scenario tested. These results suggest that climate change is an important threat for both the downstream thermal pollution and the generation of electricity by power stations that use lentic systems for cooling.

Modeling hydrology and in-stream transport on drained forested lands in coastal Carolinas, U.S.A.

Treesearch

Devendra Amatya

2005-01-01

This study summarizes the successional development and testing of forest hydrologic models based on DRAINMOD that predicts the hydrology of low-gradient poorly drained watersheds as affected by land management and climatic variation. The field scale (DRAINLOB) and watershed-scale in-stream routing (DRAINWAT) models were successfully tested with water table and outflow...

A global model of malaria climate sensitivity: comparing malaria response to historic climate data based on simulation and officially reported malaria incidence.

PubMed

Edlund, Stefan; Davis, Matthew; Douglas, Judith V; Kershenbaum, Arik; Waraporn, Narongrit; Lessler, Justin; Kaufman, James H

2012-09-18

The role of the Anopheles vector in malaria transmission and the effect of climate on Anopheles populations are well established. Models of the impact of climate change on the global malaria burden now have access to high-resolution climate data, but malaria surveillance data tends to be less precise, making model calibration problematic. Measurement of malaria response to fluctuations in climate variables offers a way to address these difficulties. Given the demonstrated sensitivity of malaria transmission to vector capacity, this work tests response functions to fluctuations in land surface temperature and precipitation. This study of regional sensitivity of malaria incidence to year-to-year climate variations used an extended Macdonald Ross compartmental disease model (to compute malaria incidence) built on top of a global Anopheles vector capacity model (based on 10 years of satellite climate data). The predicted incidence was compared with estimates from the World Health Organization and the Malaria Atlas. The models and denominator data used are freely available through the Eclipse Foundation's Spatiotemporal Epidemiological Modeller (STEM). Although the absolute scale factor relating reported malaria to absolute incidence is uncertain, there is a positive correlation between predicted and reported year-to-year variation in malaria burden with an averaged root mean square (RMS) error of 25% comparing normalized incidence across 86 countries. Based on this, the proposed measure of sensitivity of malaria to variations in climate variables indicates locations where malaria is most likely to increase or decrease in response to specific climate factors. Bootstrapping measures the increased uncertainty in predicting malaria sensitivity when reporting is restricted to national level and an annual basis. Results indicate a potential 20x improvement in accuracy if data were available at the level ISO 3166-2 national subdivisions and with monthly time sampling. The high spatial resolution possible with state-of-the-art numerical models can identify regions most likely to require intervention due to climate changes. Higher-resolution surveillance data can provide a better understanding of how climate fluctuations affect malaria incidence and improve predictions. An open-source modelling framework, such as STEM, can be a valuable tool for the scientific community and provide a collaborative platform for developing such models.

Modelling uncertainties and possible future trends of precipitation and temperature for 10 sub-basins in Columbia River Basin (CRB)

NASA Astrophysics Data System (ADS)

Ahmadalipour, A.; Rana, A.; Qin, Y.; Moradkhani, H.

2014-12-01

Trends and changes in future climatic parameters, such as, precipitation and temperature have been a central part of climate change studies. In the present work, we have analyzed the seasonal and yearly trends and uncertainties of prediction in all the 10 sub-basins of Columbia River Basin (CRB) for future time period of 2010-2099. The work is carried out using 2 different sets of statistically downscaled Global Climate Model (GCMs) projection datasets i.e. Bias correction and statistical downscaling (BCSD) generated at Portland State University and The Multivariate Adaptive Constructed Analogs (MACA) generated at University of Idaho. The analysis is done for with 10 GCM downscaled products each from CMIP5 daily dataset totaling to 40 different downscaled products for robust analysis. Summer, winter and yearly trend analysis is performed for all the 10 sub-basins using linear regression (significance tested by student t test) and Mann Kendall test (0.05 percent significance level), for precipitation (P), temperature maximum (Tmax) and temperature minimum (Tmin). Thereafter, all the parameters are modelled for uncertainty, across all models, in all the 10 sub-basins and across the CRB for future scenario periods. Results have indicated in varied degree of trends for all the sub-basins, mostly pointing towards a significant increase in all three climatic parameters, for all the seasons and yearly considerations. Uncertainty analysis have reveled very high change in all the parameters across models and sub-basins under consideration. Basin wide uncertainty analysis is performed to corroborate results from smaller, sub-basin scale. Similar trends and uncertainties are reported on the larger scale as well. Interestingly, both trends and uncertainties are higher during winter period than during summer, contributing to large part of the yearly change.

Storylines of socio-economic and climatic drivers for land use and their hydrological impacts in alpine catchments - the STELLA project example

NASA Astrophysics Data System (ADS)

Strasser, Ulrich; Formayer, Herbert; Förster, Kristian; Marke, Thomas; Meißl, Gertraud; Schermer, Markus; Stotten, Friederike; Themessl, Matthias

2016-04-01

Future land use in Alpine catchments is controlled by the evolution of socio-economy and climate. Estimates of their coupled development should hence fulfill the principles of plausibility (be convincing) and consistency (be unambiguous). In the project STELLA, coupled future climate and land use scenarios are used as input in a hydrological modelling exercise with the physically-based, distributed water balance model WaSiM. The aim of the project is to quantify the effects of these two framing components on the future water cycle. The test site for the simulations is the catchment of the Brixentaler Ache in Tyrol/Austria (47.5°N, 322 km2). The so-called „storylines" of future coupled climate and forest/land use management, policy, social cooperation, tourism and economy have jointly been developed in an inter- and transdisciplinary assessment with local actors. The climate background is given by simulations for the A1B (temperature conditions like today in Merano/Italy, 46.7°N) and RCP 8.5 (temperature conditions like today in Bologna/Italy, 44.5°N) emission scenarios. These two climate scenarios were combined with three potential socio-economic developments („local"/„glocal"/ „superglobal"), each in a positive and in a negative specification. From these twelve storylines of coupled climate/land use future, a set of four storylines was selected to be used in transient hydrological modelling experiments. Historical simulations of the water balance for the test site reveal the pattern of land use being the most prominent factor for the spatial distribution of its components. A new prototype for a snow-canopy interaction simulation module provides explicit rates of intercepted and sublimated snow from the trees and stems of the different forest stands in the catchment. This new canopy module will be used to model the coupled climate/land use future storylines for the Brixental. The aim is to quantify the effects of climate change and land use on the water balance and streamflow, both separately and in their respective combination.

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).

Modeling non-linear growth responses to temperature and hydrology in wetland trees

NASA Astrophysics Data System (ADS)

Keim, R.; Allen, S. T.

2016-12-01

Growth responses of wetland trees to flooding and climate variations are difficult to model because they depend on multiple, apparently interacting factors, but are a critical link in hydrological control of wetland carbon budgets. To more generally understand tree growth to hydrological forcing, we modeled non-linear responses of tree ring growth to flooding and climate at sub-annual time steps, using Vaganov-Shashkin response functions. We calibrated the model to six baldcypress tree-ring chronologies from two hydrologically distinct sites in southern Louisiana, and tested several hypotheses of plasticity in wetlands tree responses to interacting environmental variables. The model outperformed traditional multiple linear regression. More importantly, optimized response parameters were generally similar among sites with varying hydrological conditions, suggesting generality to the functions. Model forms that included interacting responses to multiple forcing factors were more effective than were single response functions, indicating the principle of a single limiting factor is not correct in wetlands and both climatic and hydrological variables must be considered in predicting responses to hydrological or climate change.

The integrated Earth system model version 1: formulation and functionality

DOE PAGES

Collins, W. D.; Craig, A. P.; Truesdale, J. E.; ...

2015-07-23

The integrated Earth system model (iESM) has been developed as a new tool for projecting the joint human/climate system. The iESM is based upon coupling an integrated assessment model (IAM) and an Earth system model (ESM) into a common modeling infrastructure. IAMs are the primary tool for describing the human–Earth system, including the sources of global greenhouse gases (GHGs) and short-lived species (SLS), land use and land cover change (LULCC), and other resource-related drivers of anthropogenic climate change. ESMs are the primary scientific tools for examining the physical, chemical, and biogeochemical impacts of human-induced changes to the climate system. Themore » iESM project integrates the economic and human-dimension modeling of an IAM and a fully coupled ESM within a single simulation system while maintaining the separability of each model if needed. Both IAM and ESM codes are developed and used by large communities and have been extensively applied in recent national and international climate assessments. By introducing heretofore-omitted feedbacks between natural and societal drivers, we can improve scientific understanding of the human–Earth system dynamics. Potential applications include studies of the interactions and feedbacks leading to the timing, scale, and geographic distribution of emissions trajectories and other human influences, corresponding climate effects, and the subsequent impacts of a changing climate on human and natural systems. This paper describes the formulation, requirements, implementation, testing, and resulting functionality of the first version of the iESM released to the global climate community.« less

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.

Symbiont diversity may help coral reefs survive moderate climate change.

PubMed

Baskett, Marissa L; Gaines, Steven D; Nisbet, Roger M

2009-01-01

Given climate change, thermal stress-related mass coral-bleaching events present one of the greatest anthropogenic threats to coral reefs. While corals and their symbiotic algae may respond to future temperatures through genetic adaptation and shifts in community compositions, the climate may change too rapidly for coral response. To test this potential for response, here we develop a model of coral and symbiont ecological dynamics and symbiont evolutionary dynamics. Model results without variation in symbiont thermal tolerance predict coral reef collapse within decades under multiple future climate scenarios, consistent with previous threshold-based predictions. However, model results with genetic or community-level variation in symbiont thermal tolerance can predict coral reef persistence into the next century, provided low enough greenhouse gas emissions occur. Therefore, the level of greenhouse gas emissions will have a significant effect on the future of coral reefs, and accounting for biodiversity and biological dynamics is vital to estimating the size of this effect.

Dam operations may improve aquatic habitat and offset negative effects of climate change.

PubMed

Benjankar, Rohan; Tonina, Daniele; McKean, James A; Sohrabi, Mohammad M; Chen, Quiwen; Vidergar, Dmitri

2018-05-01

Dam operation impacts on stream hydraulics and ecological processes are well documented, but their effect depends on geographical regions and varies spatially and temporally. Many studies have quantified their effects on aquatic ecosystem based mostly on flow hydraulics overlooking stream water temperature and climatic conditions. Here, we used an integrated modeling framework, an ecohydraulics virtual watershed, that links catchment hydrology, hydraulics, stream water temperature and aquatic habitat models to test the hypothesis that reservoir management may help to mitigate some impacts caused by climate change on downstream flows and temperature. To address this hypothesis we applied the model to analyze the impact of reservoir operation (regulated flows) on Bull Trout, a cold water obligate salmonid, habitat, against unregulated flows for dry, average, and wet climatic conditions in the South Fork Boise River (SFBR), Idaho, USA. Copyright © 2018 Elsevier Ltd. All rights reserved.

Modelling the enigmatic Late Pliocene Glacial Event - Marine Isotope Stage M2

USGS Publications Warehouse

Dolan, Aisling M.; Haywood, Alan M.; Hunter, Stephen J.; Tindall, Julia C.; Dowsett, Harry J.; Hill, Daniel J.; Pickering, Steven J.

2015-01-01

The Pliocene Epoch (5.2 to 2.58 Ma) has often been targeted to investigate the nature of warm climates. However, climate records for the Pliocene exhibit significant variability and show intervals that apparently experienced a cooler than modern climate. Marine Isotope Stage (MIS) M2 (~ 3.3 Ma) is a globally recognisable cooling event that disturbs an otherwise relatively (compared to present-day) warm background climate state. It remains unclear whether this event corresponds to significant ice sheet build-up in the Northern and Southern Hemisphere. Estimates of sea level for this interval vary, and range from modern values to estimates of 65 m sea level fall with respect to present day. Here we implement plausible M2 ice sheet configurations into a coupled atmosphere–ocean climate model to test the hypothesis that larger-than-modern ice sheet configurations may have existed at M2. Climate model results are compared with proxy climate data available for M2 to assess the plausibility of each ice sheet configuration. Whilst the outcomes of our data/model comparisons are not in all cases straight forward to interpret, there is little indication that results from model simulations in which significant ice masses have been prescribed in the Northern Hemisphere are incompatible with proxy data from the North Atlantic, Northeast Arctic Russia, North Africa and the Southern Ocean. Therefore, our model results do not preclude the possibility of the existence of larger ice masses during M2 in the Northern or Southern Hemisphere. Specifically they are not able to discount the possibility of significant ice masses in the Northern Hemisphere during the M2 event, consistent with a global sea-level fall of between 40 m and 60 m. This study highlights the general need for more focused and coordinated data generation in the future to improve the coverage and consistency in proxy records for M2, which will allow these and future M2 sensitivity tests to be interrogated further.

Future Climate Impacts on Harmful Algal Blooms in an Agriculturally Dominated Ecosystem

NASA Astrophysics Data System (ADS)

Aloysius, N. R.; Martin, J.; Ludsin, S.; Stumpf, R. P.

2015-12-01

Cyanobacteria blooms have become a major problem worldwide in aquatic ecosystems that receive excessive runoff of limiting nutrients from terrestrial drainage. Such blooms often are considered harmful because they degrade ecosystem services, threaten public health, and burden local economies. Owing to changing agricultural land-use practices, Lake Erie, the most biologically productive of the North American Great Lakes, has begun to undergo a re-eutrophication in which the frequency and extent of harmful algal blooms (HABs) has increased. Continued climate change has been hypothesized to magnify the HAB problem in Lake Erie in the absence of new agricultural management practices, although this hypothesis has yet to be formally tested empirically. Herein, we tested this hypothesis by predicting how the frequency and extent of potentially harmful cyanobacteria blooms will change in Lake Erie during the 21st century under the Intergovernmental Panel on Climate Change Fifth Assessment climate projections in the region. To do so, we used 80 ensembles of climate projections from 20 Global Climate Models (GCMs) and two greenhouse gas emission scenarios (moderate reduction, RCP4.5; business-as-usual, RCP8.5) to drive a spatiotemporally explicit watershed-hydrology model that was linked to several statistical predictive models of annual cyanobacteria blooms in Lake Erie. Owing to anticipated increases in precipitation during spring and warmer temperatures during summer, our ensemble of predictions revealed that, if current land-management practices continue, the frequency of severe HABs in Lake Erie will increase during the 21st century. These findings identify a real need to consider future climate projections when developing nutrient reduction strategies in the short term, with adaptation also needing to be encouraged under both greenhouse gas emissions scenarios in the absence of effective nutrient mitigation strategies.

Assessing performance and seasonal bias of pollen-based climate reconstructions in a perfect model world

NASA Astrophysics Data System (ADS)

Trachsel, M.; Rehfeld, K.; Telford, R.; Laepple, T.

2017-12-01

Reconstructions of summer, winter or annual mean temperatures based on the species composition of bio-indicators such as pollen are routinely used in climate model-proxy data comparison studies. Most reconstruction algorithms exploit the joint distribution of modern spatial climate and species distribution for the development of the reconstructions. They rely on the space-for-time substitution and the specific assumption that environmental variables other than those reconstructed are not important or that their relationship with the reconstructed variable(s) should be the same in the past as in the modern spatial calibration dataset. Here we test the implications of this "correlative uniformitarianism" assumption on climate reconstructions in an ideal model world, in which climate and vegetation are known at all times. The alternate reality is a climate simulation of the last 6000 years with dynamic vegetation. Transient changes of plant functional types are considered as surrogate pollen counts and allow us to establish, apply and evaluate transfer functions in the modeled world. We find that the transfer function cross validation r2 is of limited use to identify reconstructible climate variables, as it only relies on the modern spatial climate-vegetation relationship. However, ordination approaches that assess the amount of fossil vegetation variance explained by the reconstructions are promising. We show that correlations between climate variables in the modern climate-vegetation relationship are systematically extended into the reconstructions. Summer temperatures, the most prominent driving variable for modeled vegetation change in the Northern Hemisphere, are accurately reconstructed. However, the amplitude of the model winter and mean annual temperature cooling between the mid-Holocene and present day is overestimated and similar to the summer trend in magnitude. This effect occurs because temporal changes of a dominant climate variable are imprinted on a less important variable, leading to reconstructions biased towards the dominant variable's trends. Our results, although based on a model vegetation that is inevitably simpler than reality, indicate that reconstructions of multiple climate variables based on modern spatial bio-indicator datasets should be treated with caution.

Climate change risks and conservation implications for a threatened small-range mammal species.

PubMed

Morueta-Holme, Naia; Fløjgaard, Camilla; Svenning, Jens-Christian

2010-04-29

Climate change is already affecting the distributions of many species and may lead to numerous extinctions over the next century. Small-range species are likely to be a special concern, but the extent to which they are sensitive to climate is currently unclear. Species distribution modeling, if carefully implemented, can be used to assess climate sensitivity and potential climate change impacts, even for rare and cryptic species. We used species distribution modeling to assess the climate sensitivity, climate change risks and conservation implications for a threatened small-range mammal species, the Iberian desman (Galemys pyrenaicus), which is a phylogenetically isolated insectivore endemic to south-western Europe. Atlas data on the distribution of G. pyrenaicus was linked to data on climate, topography and human impact using two species distribution modeling algorithms to test hypotheses on the factors that determine the range for this species. Predictive models were developed and projected onto climate scenarios for 2070-2099 to assess climate change risks and conservation possibilities. Mean summer temperature and water balance appeared to be the main factors influencing the distribution of G. pyrenaicus. Climate change was predicted to result in significant reductions of the species' range. However, the severity of these reductions was highly dependent on which predictor was the most important limiting factor. Notably, if mean summer temperature is the main range determinant, G. pyrenaicus is at risk of near total extinction in Spain under the most severe climate change scenario. The range projections for Europe indicate that assisted migration may be a possible long-term conservation strategy for G. pyrenaicus in the face of global warming. Climate change clearly poses a severe threat to this illustrative endemic species. Our findings confirm that endemic species can be highly vulnerable to a warming climate and highlight the fact that assisted migration has potential as a conservation strategy for species threatened by climate change.

Climate Change Risks and Conservation Implications for a Threatened Small-Range Mammal Species

PubMed Central

Morueta-Holme, Naia; Fløjgaard, Camilla; Svenning, Jens-Christian

2010-01-01

Background Climate change is already affecting the distributions of many species and may lead to numerous extinctions over the next century. Small-range species are likely to be a special concern, but the extent to which they are sensitive to climate is currently unclear. Species distribution modeling, if carefully implemented, can be used to assess climate sensitivity and potential climate change impacts, even for rare and cryptic species. Methodology/Principal Findings We used species distribution modeling to assess the climate sensitivity, climate change risks and conservation implications for a threatened small-range mammal species, the Iberian desman (Galemys pyrenaicus), which is a phylogenetically isolated insectivore endemic to south-western Europe. Atlas data on the distribution of G. pyrenaicus was linked to data on climate, topography and human impact using two species distribution modeling algorithms to test hypotheses on the factors that determine the range for this species. Predictive models were developed and projected onto climate scenarios for 2070–2099 to assess climate change risks and conservation possibilities. Mean summer temperature and water balance appeared to be the main factors influencing the distribution of G. pyrenaicus. Climate change was predicted to result in significant reductions of the species' range. However, the severity of these reductions was highly dependent on which predictor was the most important limiting factor. Notably, if mean summer temperature is the main range determinant, G. pyrenaicus is at risk of near total extinction in Spain under the most severe climate change scenario. The range projections for Europe indicate that assisted migration may be a possible long-term conservation strategy for G. pyrenaicus in the face of global warming. Conclusions/Significance Climate change clearly poses a severe threat to this illustrative endemic species. Our findings confirm that endemic species can be highly vulnerable to a warming climate and highlight the fact that assisted migration has potential as a conservation strategy for species threatened by climate change. PMID:20454451

A multi-dimensional environment-health risk analysis system for the English regions

NASA Astrophysics Data System (ADS)

Vitolo, Claudia; Scutari, Marco; Ghalaieny, Mohamed; Tucker, Allan; Russell, Andrew

2017-04-01

There is an overwhelming body of evidence that environmental pollution, and air pollution in particular, is a significant threat to health worldwide. While in developed countries the introduction of environmental legislation and sustainable technologies aims to mitigate adverse effects, developing countries are at higher risk. Within the scope of the British Council funded KEHRA project, work is on-going to develop a reproducible and reliable system to assess health risks due to exposure to pollution under climate change and across countries. Our approach is based on the use of Bayesian Networks. We used these graphical models to explore and model the statistical dependence structure of the intricate environment-health nexus. We developed a robust modelling workflow in the R programming language to facilitate reproducibility and tested it on the English regions in the United Kingdom. Preliminary results are encouraging, showing that the model tests generally well in sample (training data spans the period 1981-2005) and has good predictive power when tested out of sample (testing data spans the period 2006-2014). We plan to show the results of this preliminary analysis as well as test the model under future climate change scenarios. Future work will also investigate the transferability of the model from a data-rich (England) to a data-poor environment (Kazakhstan).

The economics and ethics of aerosol geoengineering strategies

NASA Astrophysics Data System (ADS)

Goes, Marlos; Keller, Klaus; Tuana, Nancy

2010-05-01

Anthropogenic greenhouse gas emissions are changing the Earth's climate and impose substantial risks for current and future generations. What are scientifically sound, economically viable, and ethically defendable strategies to manage these climate risks? Ratified international agreements call for a reduction of greenhouse gas emissions to avoid dangerous anthropogenic interference with the climate system. Recent proposals, however, call for a different approach: geoengineering climate by injecting aerosol precursors into the stratosphere. Published economic studies typically neglect the risks of aerosol geoengineering due to (i) a potential failure to sustain the aerosol forcing and (ii) due to potential negative impacts associated with aerosol forcings. Here we use a simple integrated assessment model of climate change to analyze potential economic impacts of aerosol geoengineering strategies over a wide range of uncertain parameters such as climate sensitivity, the economic damages due to climate change, and the economic damages due to aerosol geoengineering forcings. The simplicity of the model provides the advantages of parsimony and transparency, but it also imposes considerable caveats. For example, the analysis is based on a globally aggregated model and is hence silent on intragenerational distribution of costs and benefits. In addition, the analysis neglects the effects of future learning and is based on a simple representation of climate change impacts. We use this integrated assessment model to show three main points. First, substituting aerosol geoengineering for the reduction of greenhouse gas emissions can fail the test of economic efficiency. One key to this finding is that a failure to sustain the aerosol forcing can lead to sizeable and abrupt climatic changes. The monetary damages due to such a discontinuous aerosol geoengineering can dominate the cost-benefit analysis because the monetary damages of climate change are expected to increase with the rate of change. Second, the relative contribution of aerosol geoengineering to an economically optimal portfolio hinges critically on deeply uncertain estimates of the damages due to aerosol forcing. Even if we assume that aerosol forcing could be deployed continuously, the aerosol geoengineering does not considerably displace the reduction of greenhouse gas emissions in the simple economic optimal growth model until the damages due to the aerosol forcing are rather low. Third, deploying aerosol geoengineering may also fail an ethical test regarding issues of intergenerational justice. Substituting aerosol geoengineering for reducing greenhouse gas emissions constitutes a conscious risk transfer to future generations, for example due to the increased risk of future abrupt climate change. This risk transfer is in tension with the requirement of intergenerational justice that present generations should not create benefits for themselves in exchange for burdens on future generations.

Linking climate projections to performance: A yield-based decision scaling assessment of a large urban water resources system

NASA Astrophysics Data System (ADS)

Turner, Sean W. D.; Marlow, David; Ekström, Marie; Rhodes, Bruce G.; Kularathna, Udaya; Jeffrey, Paul J.

2014-04-01

Despite a decade of research into climate change impacts on water resources, the scientific community has delivered relatively few practical methodological developments for integrating uncertainty into water resources system design. This paper presents an application of the "decision scaling" methodology for assessing climate change impacts on water resources system performance and asks how such an approach might inform planning decisions. The decision scaling method reverses the conventional ethos of climate impact assessment by first establishing the climate conditions that would compel planners to intervene. Climate model projections are introduced at the end of the process to characterize climate risk in such a way that avoids the process of propagating those projections through hydrological models. Here we simulated 1000 multisite synthetic monthly streamflow traces in a model of the Melbourne bulk supply system to test the sensitivity of system performance to variations in streamflow statistics. An empirical relation was derived to convert decision-critical flow statistics to climatic units, against which 138 alternative climate projections were plotted and compared. We defined the decision threshold in terms of a system yield metric constrained by multiple performance criteria. Our approach allows for fast and simple incorporation of demand forecast uncertainty and demonstrates the reach of the decision scaling method through successful execution in a large and complex water resources system. Scope for wider application in urban water resources planning is discussed.

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…

Assessing forest vulnerability and the potential distribution of pine beetles under current and future climate scenarios in the Interior West of the US

USGS Publications Warehouse

Evangelista, P.H.; Kumar, S.; Stohlgren, T.J.; Young, N.E.

2011-01-01

The aim of our study was to estimate forest vulnerability and potential distribution of three bark beetles (Curculionidae: Scolytinae) under current and projected climate conditions for 2020 and 2050. Our study focused on the mountain pine beetle (Dendroctonus ponderosae), western pine beetle (Dendroctonus brevicomis), and pine engraver (Ips pini). This study was conducted across eight states in the Interior West of the US covering approximately 2.2millionkm2 and encompassing about 95% of the Rocky Mountains in the contiguous US. Our analyses relied on aerial surveys of bark beetle outbreaks that occurred between 1991 and 2008. Occurrence points for each species were generated within polygons created from the aerial surveys. Current and projected climate scenarios were acquired from the WorldClim database and represented by 19 bioclimatic variables. We used Maxent modeling technique fit with occurrence points and current climate data to model potential beetle distributions and forest vulnerability. Three available climate models, each having two emission scenarios, were modeled independently and results averaged to produce two predictions for 2020 and two predictions for 2050 for each analysis. Environmental parameters defined by current climate models were then used to predict conditions under future climate scenarios, and changes in different species' ranges were calculated. Our results suggested that the potential distribution for bark beetles under current climate conditions is extensive, which coincides with infestation trends observed in the last decade. Our results predicted that suitable habitats for the mountain pine beetle and pine engraver beetle will stabilize or decrease under future climate conditions, while habitat for the western pine beetle will continue to increase over time. The greatest increase in habitat area was for the western pine beetle, where one climate model predicted a 27% increase by 2050. In contrast, the predicted habitat of the mountain pine beetle from another climate model suggested a decrease in habitat areas as great as 46% by 2050. Generally, 2020 and 2050 models that tested the three climate scenarios independently had similar trends, though one climate scenario for the western pine beetle produced contrasting results. Ranges for all three species of bark beetles shifted considerably geographically suggesting that some host species may become more vulnerable to beetle attack in the future, while others may have a reduced risk over time. ?? 2011 Elsevier B.V.

Testing a new application for TOPSIS: monitoring drought and wet periods in Iran

NASA Astrophysics Data System (ADS)

Roshan, Gholamreza; Ghanghermeh, AbdolAzim; Grab, Stefan W.

2018-01-01

Globally, droughts are a recurring major natural disaster owing to below normal precipitation, and are occasionally associated with high temperatures, which together negatively impact upon human health and social, economic, and cultural activities. Drought early warning and monitoring is thus essential for reducing such potential impacts on society. To this end, several experimental methods have previously been proposed for calculating drought, yet these are based almost entirely on precipitation alone. Here, for the first time, and in contrast to previous studies, we use seven climate parameters to establish drought/wet periods; these include: T min, T max, sunshine hours, relative humidity, average rainfall, number of rain days greater than 1 mm, and the ratio of total precipitation to number of days with precipitation, using the technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. To test the TOPSIS method for different climate zones, six sample stations representing a variety of different climate conditions were used by assigning weight changes to climate parameters, which are then applied to the model, together with multivariate regression analysis. For the six stations tested, model results indicate the lowest errors for Zabol station and maximum errors for Kermanshah. The validation techniques strongly support our proposed new method for calculating and rating drought/wet events using TOPSIS.

AGU Climate Scientists Offer Question-and-Answer Service for Media

NASA Astrophysics Data System (ADS)

Jackson, Stacy

2010-03-01

In fall 2009, AGU launched a member-driven pilot project to improve the accuracy of climate science coverage in the media and to improve public understanding of climate science. The project's goal was to increase the accessibility of climate science experts to journalists across the full spectrum of media outlets. As a supplement to the traditional one-to-one journalist-expert relationship model, the project tested the novel approach of providing a question-and-answer (Q&A) service with a pool of expert scientists and a Web-based interface with journalists. Questions were explicitly limited to climate science to maintain a nonadvocacy, nonpartisan perspective.

Assessing performance and seasonal bias of pollen-based climate reconstructions in a perfect model world

NASA Astrophysics Data System (ADS)

Rehfeld, Kira; Trachsel, Mathias; Telford, Richard J.; Laepple, Thomas

2016-12-01

Reconstructions of summer, winter or annual mean temperatures based on the species composition of bio-indicators such as pollen, foraminifera or chironomids are routinely used in climate model-proxy data comparison studies. Most reconstruction algorithms exploit the joint distribution of modern spatial climate and species distribution for the development of the reconstructions. They rely on the space-for-time substitution and the specific assumption that environmental variables other than those reconstructed are not important or that their relationship with the reconstructed variable(s) should be the same in the past as in the modern spatial calibration dataset. Here we test the implications of this "correlative uniformitarianism" assumption on climate reconstructions in an ideal model world, in which climate and vegetation are known at all times. The alternate reality is a climate simulation of the last 6000 years with dynamic vegetation. Transient changes of plant functional types are considered as surrogate pollen counts and allow us to establish, apply and evaluate transfer functions in the modeled world. We find that in our model experiments the transfer function cross validation r2 is of limited use to identify reconstructible climate variables, as it only relies on the modern spatial climate-vegetation relationship. However, ordination approaches that assess the amount of fossil vegetation variance explained by the reconstructions are promising. We furthermore show that correlations between climate variables in the modern climate-vegetation relationship are systematically extended into the reconstructions. Summer temperatures, the most prominent driving variable for modeled vegetation change in the Northern Hemisphere, are accurately reconstructed. However, the amplitude of the model winter and mean annual temperature cooling between the mid-Holocene and present day is overestimated and similar to the summer trend in magnitude. This effect occurs because temporal changes of a dominant climate variable, such as summer temperatures in the model's Arctic, are imprinted on a less important variable, leading to reconstructions biased towards the dominant variable's trends. Our results, although based on a model vegetation that is inevitably simpler than reality, indicate that reconstructions of multiple climate variables based on modern spatial bio-indicator datasets should be treated with caution. Expert knowledge on the ecophysiological drivers of the proxies, as well as statistical methods that go beyond the cross validation on modern calibration datasets, are crucial to avoid misinterpretation.

The Martian climate: Energy balance models with CO2/H2O atmospheres

NASA Technical Reports Server (NTRS)

Hoffert, M. I.

1984-01-01

Progress in the development of a multi-reservoir, time dependent energy balance climate model for Mars driven by prescribed insolation at the top of the atmosphere is reported. The first approximately half-year of the program was devoted to assembling and testing components of the full model. Specific accomplishments were made on a longwave radiation code, coupling seasonal solar input to a ground temperature simulation, and conceptualizing an approach to modeling the seasonal pressure waves that develop in the Martian atmosphere as a result of sublimation and condensation of CO2 in polar regions.

Linking molar organizational climate and strategic implementation climate to clinicians' use of evidence-based psychotherapy techniques: cross-sectional and lagged analyses from a 2-year observational study.

PubMed

Williams, Nathaniel J; Ehrhart, Mark G; Aarons, Gregory A; Marcus, Steven C; Beidas, Rinad S

2018-06-25

Behavioral health organizations are characterized by multiple organizational climates, including molar climate, which encompasses clinicians' shared perceptions of how the work environment impacts their personal well-being, and strategic implementation climate, which includes clinicians' shared perceptions of the extent to which evidence-based practice implementation is expected, supported, and rewarded by the organization. Theory suggests these climates have joint, cross-level effects on clinicians' implementation of evidence-based practice and that these effects may be long term (i.e., up to 2 years); however, no empirical studies have tested these relationships. We hypothesize that molar climate moderates implementation climate's concurrent and long-term relationships with clinicians' use of evidence-based practice such that strategic implementation climate will have its most positive effects when it is accompanied by a positive molar climate. Hypotheses were tested using data collected from 235 clinicians in 20 behavioral health organizations. At baseline, clinicians reported on molar climate and implementation climate. At baseline and at a 2-year follow-up, all clinicians who were present in the organizations reported on their use of cognitive-behavioral psychotherapy techniques, an evidence-based practice for youth psychiatric disorders. Two-level mixed-effects regression models tested whether baseline molar climate and implementation climate interacted in predicting clinicians' evidence-based practice use at baseline and at 2-year follow-up. In organizations with more positive molar climates at baseline, higher levels of implementation climate predicted increased evidence-based practice use among clinicians who were present at baseline and among clinicians who were present in the organizations at 2-year follow-up; however, in organizations with less positive molar climates, implementation climate was not related to clinicians' use of evidence-based practice at either time point. Optimizing clinicians' implementation of evidence-based practice in behavioral health requires attention to both molar climate and strategic implementation climate. Strategies that focus exclusively on implementation climate may not be effective levers for behavior change if the organization does not also engender a positive molar climate. These findings have implications for the development of implementation theory and effective implementation strategies.

Tree-ring growth of Scots pine, Common beech and Pedunculate oak under future climate in northeastern Germany

NASA Astrophysics Data System (ADS)

Jurasinski, Gerald; Scharnweber, Tobias; Schröder, Christian; Lennartz, Bernd; Bauwe, Andreas

2017-04-01

Tree growth depends, among other factors, largely on the prevailing climatic conditions. Therefore, tree growth patterns are to be expected under climate change. Here, we analyze the tree-ring growth response of three major European tree species to projected future climate across a climatic (mostly precipitation) gradient in northeastern Germany. We used monthly data for temperature, precipitation, and the standardized precipitation evapotranspiration index (SPEI) over multiple time scales (1, 3, 6, 12, and 24 months) to construct models of tree-ring growth for Scots pine (Pinus syl- vestris L.) at three pure stands, and for Common beech (Fagus sylvatica L.) and Pedunculate oak (Quercus robur L.) at three mature mixed stands. The regression models were derived using a two-step approach based on partial least squares regression (PLSR) to extract potentially well explaining variables followed by ordinary least squares regression (OLSR) to consolidate the models to the least number of variables while retaining high explanatory power. The stability of the models was tested with a comprehensive calibration-verification scheme. All models were successfully verified with R2s ranging from 0.21 for the western pine stand to 0.62 for the beech stand in the east. For growth prediction, climate data forecasted until 2100 by the regional climate model WETTREG2010 based on the A1B Intergovernmental Panel on Climate Change (IPCC) emission scenario was used. For beech and oak, growth rates will likely decrease until the end of the 21st century. For pine, modeled growth trends vary and range from a slight growth increase to a weak decrease in growth rates depending on the position along the climatic gradient. The climatic gradient across the study area will possibly affect the future growth of oak with larger growth reductions towards the drier east. For beech, site-specific adaptations seem to override the influence of the climatic gradient. We conclude that in Northeastern Germany Scots pine has great potential to remain resilient to projected climate change without any greater impairment, whereas Common beech and Pedunculate oak will likely face lesser growth under the expected warmer and dryer climate conditions. The results call for an adaptation of forest management to mitigate the negative effects of climate change for beech and oak in the region.

Inter-model variability in hydrological extremes projections for Amazonian sub-basins

NASA Astrophysics Data System (ADS)

Andres Rodriguez, Daniel; Garofolo, Lucas; Lázaro de Siqueira Júnior, José; Samprogna Mohor, Guilherme; Tomasella, Javier

2014-05-01

Irreducible uncertainties due to knowledge's limitations, chaotic nature of climate system and human decision-making process drive uncertainties in Climate Change projections. Such uncertainties affect the impact studies, mainly when associated to extreme events, and difficult the decision-making process aimed at mitigation and adaptation. However, these uncertainties allow the possibility to develop exploratory analyses on system's vulnerability to different sceneries. The use of different climate model's projections allows to aboard uncertainties issues allowing the use of multiple runs to explore a wide range of potential impacts and its implications for potential vulnerabilities. Statistical approaches for analyses of extreme values are usually based on stationarity assumptions. However, nonstationarity is relevant at the time scales considered for extreme value analyses and could have great implications in dynamic complex systems, mainly under climate change transformations. Because this, it is required to consider the nonstationarity in the statistical distribution parameters. We carried out a study of the dispersion in hydrological extremes projections using climate change projections from several climate models to feed the Distributed Hydrological Model of the National Institute for Spatial Research, MHD-INPE, applied in Amazonian sub-basins. This model is a large-scale hydrological model that uses a TopModel approach to solve runoff generation processes at the grid-cell scale. MHD-INPE model was calibrated for 1970-1990 using observed meteorological data and comparing observed and simulated discharges by using several performance coeficients. Hydrological Model integrations were performed for present historical time (1970-1990) and for future period (2010-2100). Because climate models simulate the variability of the climate system in statistical terms rather than reproduce the historical behavior of climate variables, the performances of the model's runs during the historical period, when feed with climate model data, were tested using descriptors of the Flow Duration Curves. The analyses of projected extreme values were carried out considering the nonstationarity of the GEV distribution parameters and compared with extremes events in present time. Results show inter-model variability in a broad dispersion on projected extreme's values. Such dispersion implies different degrees of socio-economic impacts associated to extreme hydrological events. Despite the no existence of one optimum result, this variability allows the analyses of adaptation strategies and its potential vulnerabilities.

Testing the Hydrological Coherence of High-Resolution Gridded Precipitation and Temperature Data Sets

NASA Astrophysics Data System (ADS)

Laiti, L.; Mallucci, S.; Piccolroaz, S.; Bellin, A.; Zardi, D.; Fiori, A.; Nikulin, G.; Majone, B.

2018-03-01

Assessing the accuracy of gridded climate data sets is highly relevant to climate change impact studies, since evaluation, bias correction, and statistical downscaling of climate models commonly use these products as reference. Among all impact studies those addressing hydrological fluxes are the most affected by errors and biases plaguing these data. This paper introduces a framework, coined Hydrological Coherence Test (HyCoT), for assessing the hydrological coherence of gridded data sets with hydrological observations. HyCoT provides a framework for excluding meteorological forcing data sets not complying with observations, as function of the particular goal at hand. The proposed methodology allows falsifying the hypothesis that a given data set is coherent with hydrological observations on the basis of the performance of hydrological modeling measured by a metric selected by the modeler. HyCoT is demonstrated in the Adige catchment (southeastern Alps, Italy) for streamflow analysis, using a distributed hydrological model. The comparison covers the period 1989-2008 and includes five gridded daily meteorological data sets: E-OBS, MSWEP, MESAN, APGD, and ADIGE. The analysis highlights that APGD and ADIGE, the data sets with highest effective resolution, display similar spatiotemporal precipitation patterns and produce the largest hydrological efficiency indices. Lower performances are observed for E-OBS, MESAN, and MSWEP, especially in small catchments. HyCoT reveals deficiencies in the representation of spatiotemporal patterns of gridded climate data sets, which cannot be corrected by simply rescaling the meteorological forcing fields, as often done in bias correction of climate model outputs. We recommend this framework to assess the hydrological coherence of gridded data sets to be used in large-scale hydroclimatic studies.

Testing a theory of organizational culture, climate and youth outcomes in child welfare systems: a United States national study.

PubMed

Williams, Nathaniel J; Glisson, Charles

2014-04-01

Theories of organizational culture and climate (OCC) applied to child welfare systems hypothesize that strategic dimensions of organizational culture influence organizational climate and that OCC explains system variance in youth outcomes. This study provides the first structural test of the direct and indirect effects of culture and climate on youth outcomes in a national sample of child welfare systems and isolates specific culture and climate dimensions most associated with youth outcomes. The study applies multilevel path analysis (ML-PA) to a U.S. nationwide sample of 2,380 youth in 73 child welfare systems participating in the second National Survey of Child and Adolescent Well-being. Youths were selected in a national, two-stage, stratified random sample design. Youths' psychosocial functioning was assessed by caregivers' responses to the Child Behavior Checklist at intake and at 18-month follow-up. OCC was assessed by front-line caseworkers' (N=1,740) aggregated responses to the Organizational Social Context measure. Comparison of the a priori and subsequent trimmed models confirmed a reduced model that excluded rigid organizational culture and explained 70% of the system variance in youth outcomes. Controlling for youth- and system-level covariates, systems with more proficient and less resistant organizational cultures exhibited more functional, more engaged, and less stressful climates. Systems with more proficient cultures and more engaged, more functional, and more stressful climates exhibited superior youth outcomes. Findings suggest child welfare administrators can support service effectiveness with interventions that improve specific dimensions of culture and climate. Copyright © 2013 Elsevier Ltd. All rights reserved.

Testing a theory of organizational culture, climate and youth outcomes in child welfare systems: A United States national study

PubMed Central

Williams, Nathaniel J.; Glisson, Charles

2013-01-01

Theories of organizational culture and climate (OCC) applied to child welfare systems hypothesize that strategic dimensions of organizational culture influence organizational climate and that OCC explains system variance in youth outcomes. This study provides the first structural test of the direct and indirect effects of culture and climate on youth outcomes in a national sample of child welfare systems and isolates specific culture and climate dimensions most associated with youth outcomes. The study applies multilevel path analysis (ML-PA) to a U.S. nationwide sample of 2,380 youth in 73 child welfare systems participating in the second National Survey of Child and Adolescent Well-being. Youths were selected in a national, two-stage, stratified random sample design. Youths’ psychosocial functioning was assessed by caregivers’ responses to the Child Behavior Checklist at intake and at 18-month follow-up. OCC was assessed by front-line caseworkers’ (N=1,740) aggregated responses to the Organizational Social Context measure. Comparison of the a priori and subsequent trimmed models confirmed a reduced model that excluded rigid organizational culture and explained 70% of the system variance in youth outcomes. Controlling for youth- and system-level covariates, systems with more proficient and less resistant organizational cultures exhibited more functional, more engaged, and less stressful climates. Systems with more proficient cultures and more engaged, more functional, and more stressful climates exhibited superior youth outcomes. Findings suggest child welfare administrators can support service effectiveness with interventions that improve specific dimensions of culture and climate. PMID:24094999

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.

Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

NASA Astrophysics Data System (ADS)

Williams, Paul; Howe, Nicola; Gregory, Jonathan; Smith, Robin; Joshi, Manoj

2016-04-01

In climate simulations, the impacts of the sub-grid scales on the resolved scales are conventionally represented using deterministic closure schemes, which assume that the impacts are uniquely determined by the resolved scales. Stochastic parameterization relaxes this assumption, by sampling the sub-grid variability in a computationally inexpensive manner. This presentation shows that the simulated climatological state of the ocean is improved in many respects by implementing a simple stochastic parameterization of ocean eddies into a coupled atmosphere-ocean general circulation model. Simulations from a high-resolution, eddy-permitting ocean model are used to calculate the eddy statistics needed to inject realistic stochastic noise into a low-resolution, non-eddy-permitting version of the same model. A suite of four stochastic experiments is then run to test the sensitivity of the simulated climate to the noise definition, by varying the noise amplitude and decorrelation time within reasonable limits. The addition of zero-mean noise to the ocean temperature tendency is found to have a non-zero effect on the mean climate. Specifically, in terms of the ocean temperature and salinity fields both at the surface and at depth, the noise reduces many of the biases in the low-resolution model and causes it to more closely resemble the high-resolution model. The variability of the strength of the global ocean thermohaline circulation is also improved. It is concluded that stochastic ocean perturbations can yield reductions in climate model error that are comparable to those obtained by refining the resolution, but without the increased computational cost. Therefore, stochastic parameterizations of ocean eddies have the potential to significantly improve climate simulations. Reference PD Williams, NJ Howe, JM Gregory, RS Smith, and MM Joshi (2016) Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies. Journal of Climate, under revision.

Collaborative Research: Robust Climate Projections and Stochastic Stability of Dynamical Systems

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

Ghil, Michael; McWilliams, James; Neelin, J. David

The project was completed along the lines of the original proposal, with additional elements arising as new results were obtained. The originally proposed three thrusts were expanded to include an additional, fourth one. (i) The e ffects of stochastic perturbations on climate models have been examined at the fundamental level by using the theory of deterministic and random dynamical systems, in both nite and in nite dimensions. (ii) The theoretical results have been implemented first on a delay-diff erential equation (DDE) model of the El-Nino/Southern-Oscillation (ENSO) phenomenon. (iii) More detailed, physical aspects of model robustness have been considered, as proposed,more » within the stripped-down ICTP-AGCM (formerly SPEEDY) climate model. This aspect of the research has been complemented by both observational and intermediate-model aspects of mid-latitude and tropical climate. (iv) An additional thrust of the research relied on new and unexpected results of (i) and involved reduced-modeling strategies and associated prediction aspects have been tested within the team's empirical model reduction (EMR) framework. Finally, more detailed, physical aspects have been considered within the stripped-down SPEEDY climate model. The results of each of these four complementary e fforts are presented in the next four sections, organized by topic and by the team members concentrating on the topic under discussion.« less

Benefits of Long-term Catchment/Observatory Research: Reynolds Creek Case

NASA Astrophysics Data System (ADS)

Seyfried, M. S.; Marks, D. G.; Pierson, F. B.; Lohse, K. A.; Flerchinger, G. N.

2017-12-01

Long-term catchments/observatories fill an important role in the larger spectrum of ecohydrologic research. We use three examples of roles the Reynolds Creek Experimental Watershed (RCEW) has played in advancing research to illustrate the benefits of these observatories. Two characteristics of the RCEW are critical: longevity and scale. Longevity provides continuity of effort and historical context, scale provides environmental gradients, replication and management options. First, the RCEW is a laboratory for ecohydrologic model testing and development. The extensive RCEW data have been used for testing a variety models. The RCEW is also the site of several "home grown" models. Today Isnobal, a process-based snow model, is being used to inform reservoir management for power supply and irrigation of major catchments in the western US. This model is the result of many years of directed field research and model testing in the "outdoor laboratory" of the RCEW, which provided a range of topography, vegetation cover, a climatic gradient spanning the rain-snow transition elevation and many years of climate data to evaluate inter-annual variations. Second, the RCEW provides scientific and physical support for multi-institutional, interdisciplinary research. By providing preexisting instrumentation, on-site support, and historical context for research, the RCEW has been host to research from a variety of institutions. This is most evident today in the collaborative research with the co-located Reynolds Creek Critical Zone Observatory. We have built upon traditional hydrologic research to incorporate the linkages between water availability, soil development and vegetative productivity that are critical to natural resource management. Third, the RCEW provides documentation of climate change impacts on ecohydrology. The observatories are in the unique position of providing direct linkages between climate change and ecohydrologic change. Thus, we have measured temperature increases at the RCEW and have been able to directly link those increases to changes in snow accumulation and melt at different elevations, soil water trends, and streamflow amount and timing. This kind of linkage facilitates a process-level understanding of how climate change impacts the landscape.

Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling.

PubMed

Arenas-Castro, Salvador; Gonçalves, João; Alves, Paulo; Alcaraz-Segura, Domingo; Honrado, João P

2018-01-01

Global environmental changes are rapidly affecting species' distributions and habitat suitability worldwide, requiring a continuous update of biodiversity status to support effective decisions on conservation policy and management. In this regard, satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and quicker evaluation of ecosystem responses to environmental drivers and changes than climate and structural or compositional landscape attributes. Thus, EFAs may hold advantages as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. Here we describe a modelling framework to assess the predictive ability of EFAs as Essential Biodiversity Variables (EBVs) against traditional datasets (climate, land-cover) at several scales. We test the framework with a multi-scale assessment of habitat suitability for two plant species of conservation concern, both protected under the EU Habitats Directive, differing in terms of life history, range and distribution pattern (Iris boissieri and Taxus baccata). We fitted four sets of SDMs for the two test species, calibrated with: interpolated climate variables; landscape variables; EFAs; and a combination of climate and landscape variables. EFA-based models performed very well at the several scales (AUCmedian from 0.881±0.072 to 0.983±0.125), and similarly to traditional climate-based models, individually or in combination with land-cover predictors (AUCmedian from 0.882±0.059 to 0.995±0.083). Moreover, EFA-based models identified additional suitable areas and provided valuable information on functional features of habitat suitability for both test species (narrowly vs. widely distributed), for both coarse and fine scales. Our results suggest a relatively small scale-dependence of the predictive ability of satellite-derived EFAs, supporting their use as meaningful EBVs in SDMs from regional and broader scales to more local and finer scales. Since the evaluation of species' conservation status and habitat quality should as far as possible be performed based on scalable indicators linking to meaningful processes, our framework may guide conservation managers in decision-making related to biodiversity monitoring and reporting schemes.

Range position and climate sensitivity: The structure of among-population demographic responses to climatic variation

USGS Publications Warehouse

Amburgey, Staci M.; Miller, David A. W.; Grant, Evan H. Campbell; Rittenhouse, Tracy A. G.; Benard, Michael F.; Richardson, Jonathan L.; Urban, Mark C.; Hughson, Ward; Brand, Adrianne B,; Davis, Christopher J.; Hardin, Carmen R.; Paton, Peter W. C.; Raithel, Christopher J.; Relyea, Rick A.; Scott, A. Floyd; Skelly, David K.; Skidds, Dennis E.; Smith, Charles K.; Werner, Earl E.

2018-01-01

Species’ distributions will respond to climate change based on the relationship between local demographic processes and climate and how this relationship varies based on range position. A rarely tested demographic prediction is that populations at the extremes of a species’ climate envelope (e.g., populations in areas with the highest mean annual temperature) will be most sensitive to local shifts in climate (i.e., warming). We tested this prediction using a dynamic species distribution model linking demographic rates to variation in temperature and precipitation for wood frogs (Lithobates sylvaticus) in North America. Using long-term monitoring data from 746 populations in 27 study areas, we determined how climatic variation affected population growth rates and how these relationships varied with respect to long-term climate. Some models supported the predicted pattern, with negative effects of extreme summer temperatures in hotter areas and positive effects on recruitment for summer water availability in drier areas. We also found evidence of interacting temperature and precipitation influencing population size, such as extreme heat having less of a negative effect in wetter areas. Other results were contrary to predictions, such as positive effects of summer water availability in wetter parts of the range and positive responses to winter warming especially in milder areas. In general, we found wood frogs were more sensitive to changes in temperature or temperature interacting with precipitation than to changes in precipitation alone. Our results suggest that sensitivity to changes in climate cannot be predicted simply by knowing locations within the species’ climate envelope. Many climate processes did not affect population growth rates in the predicted direction based on range position. Processes such as species-interactions, local adaptation, and interactions with the physical landscape likely affect the responses we observed. Our work highlights the need to measure demographic responses to changing climate.

Expertly validated models and phylogenetically-controlled analysis suggests responses to climate change are related to species traits in the order lagomorpha.

PubMed

Leach, Katie; Kelly, Ruth; Cameron, Alison; Montgomery, W Ian; Reid, Neil

2015-01-01

Climate change during the past five decades has impacted significantly on natural ecosystems, and the rate of current climate change is of great concern among conservation biologists. Species Distribution Models (SDMs) have been used widely to project changes in species' bioclimatic envelopes under future climate scenarios. Here, we aimed to advance this technique by assessing future changes in the bioclimatic envelopes of an entire mammalian order, the Lagomorpha, using a novel framework for model validation based jointly on subjective expert evaluation and objective model evaluation statistics. SDMs were built using climatic, topographical, and habitat variables for all 87 lagomorph species under past and current climate scenarios. Expert evaluation and Kappa values were used to validate past and current models and only those deemed 'modellable' within our framework were projected under future climate scenarios (58 species). Phylogenetically-controlled regressions were used to test whether species traits correlated with predicted responses to climate change. Climate change is likely to impact more than two-thirds of lagomorph species, with leporids (rabbits, hares, and jackrabbits) likely to undertake poleward shifts with little overall change in range extent, whilst pikas are likely to show extreme shifts to higher altitudes associated with marked range declines, including the likely extinction of Kozlov's Pika (Ochotona koslowi). Smaller-bodied species were more likely to exhibit range contractions and elevational increases, but showing little poleward movement, and fecund species were more likely to shift latitudinally and elevationally. Our results suggest that species traits may be important indicators of future climate change and we believe multi-species approaches, as demonstrated here, are likely to lead to more effective mitigation measures and conservation management. We strongly advocate studies minimising data gaps in our knowledge of the Order, specifically collecting more specimens for biodiversity archives and targeting data deficient geographic regions.

Comparing the Global Charcoal Database with Burned Area Trends from an Offline Fire Model Driven by the NCAR Last Millennium Ensemble

NASA Astrophysics Data System (ADS)

Schaefer, A.; Magi, B. I.; Marlon, J. R.; Bartlein, P. J.

2017-12-01

This study uses an offline fire model driven by output from the NCAR Community Earth System Model Last Millennium Ensemble (LME) to evaluate how climate, ecological, and human factors contributed to burned area over the past millennium, and uses the Global Charcoal Database (GCD) record of fire activity as a constraint. The offline fire model is similar to the fire module within the NCAR Community Land Model. The LME experiment includes 13 simulations of the Earth system from 850 CE through 2005 CE, and the fire model simulates burned area using LME climate and vegetation with imposed land use and land cover change. The fire model trends are compared to GCD records of charcoal accumulation rates derived from sediment cores. The comparisons are a way to assess the skill of the fire model, but also set up a methodology to directly test hypotheses of the main drivers of fire patterns over the past millennium. The focus is on regions selected from the GCD with high data density, and that have lake sediment cores that best capture the last millennium. Preliminary results are based on a fire model which excludes burning cropland and pasture land cover types, but this allows some assessment of how climate variability is captured by the fire model. Generally, there is good agreement between modeled burned area trends and fire trends from GCD for many regions of interest, suggesting the strength of climate variability as a control. At the global scale, trends and features are similar from 850 to 1700, which includes the Medieval Climate Anomaly and the Little Ice Age. After 1700, the trends significantly deviate, which may be due to non-cultivated land being converted to cultivated. In key regions of high data density in the GCD such as the Western USA, the trends agree from 850 to 1200 but diverge from 1200 to 1300. From 1300 to 1800, the trends show good agreement again. Implementing processes to include burning cultivated land within the fire model is anticipated to improve the agreement, but also to test the sensitivity of models to different drivers of fire.

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

PubMed Central

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

2009-01-01

Background Plague, caused by the bacterium Yersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plague foci. Maxent models were constructed using geocoded seroprevalence data from surveillance of California ground squirrels (Spermophilus beecheyi) as case points and Worldclim bioclimatic data as predictor variables, and compared and validated using area under the receiver operating curve (AUC) statistics. Additionally, model results were compared to locations of positive and negative coyote (Canis latrans) samples, in order to determine the correlation between Maxent model predictions and areas of plague risk as determined via wild carnivore surveillance. Results Models of plague activity in California ground squirrels, based on recent climate conditions, accurately identified case locations (AUC of 0.913 to 0.948) and were significantly correlated with coyote samples. The final models were used to identify potential plague risk areas based on an ensemble of six future climate scenarios. These models suggest that by 2050, climate conditions may reduce plague risk in the southern parts of California and increase risk along the northern coast and Sierras. Conclusion Because different modeling approaches can yield substantially different results, care should be taken when interpreting future model predictions. Nonetheless, niche modeling can be a useful tool for exploring and mapping the potential response of plague activity to climate change. The final models in this study were used to identify potential plague risk areas based on an ensemble of six future climate scenarios, which can help public managers decide where to allocate surveillance resources. In addition, Maxent model results were significantly correlated with coyote samples, indicating that carnivore surveillance programs will continue to be important for tracking the response of plague to future climate conditions. PMID:19558717

The continuum of hydroclimate variability in western North America during the last millennium

USGS Publications Warehouse

Ault, Toby R.; Cole, Julia E.; Overpeck, Jonathan T.; Pederson, Gregory T.; St. George, Scott; Otto-Bliesner, Bette; Woodhouse, Connie A.; Deser, Clara

2013-01-01

The distribution of climatic variance across the frequency spectrum has substantial importance for anticipating how climate will evolve in the future. Here we estimate power spectra and power laws (ß) from instrumental, proxy, and climate model data to characterize the hydroclimate continuum in western North America (WNA). We test the significance of our estimates of spectral densities and ß against the null hypothesis that they reflect solely the effects of local (non-climate) sources of autocorrelation at the monthly timescale. Although tree-ring based hydroclimate reconstructions are generally consistent with this null hypothesis, values of ß calculated from long-moisture sensitive chronologies (as opposed to reconstructions), and other types of hydroclimate proxies, exceed null expectations. We therefore argue that there is more low-frequency variability in hydroclimate than monthly autocorrelation alone can generate. Coupled model results archived as part of the Climate Model Intercomparison Project 5 (CMIP5) are consistent with the null hypothesis and appear unable to generate variance in hydroclimate commensurate with paleoclimate records. Consequently, at decadal to multidecadal timescales there is more variability in instrumental and proxy data than in the models, suggesting that the risk of prolonged droughts under climate change may be underestimated by CMIP5 simulations of the future.

Impact of climate change on crop yield and role of model for achieving food security.

PubMed

Kumar, Manoj

2016-08-01

In recent times, several studies around the globe indicate that climatic changes are likely to impact the food production and poses serious challenge to food security. In the face of climate change, agricultural systems need to adapt measures for not only increasing food supply catering to the growing population worldwide with changing dietary patterns but also to negate the negative environmental impacts on the earth. Crop simulation models are the primary tools available to assess the potential consequences of climate change on crop production and informative adaptive strategies in agriculture risk management. In consideration with the important issue, this is an attempt to provide a review on the relationship between climate change impacts and crop production. It also emphasizes the role of crop simulation models in achieving food security. Significant progress has been made in understanding the potential consequences of environment-related temperature and precipitation effect on agricultural production during the last half century. Increased CO2 fertilization has enhanced the potential impacts of climate change, but its feasibility is still in doubt and debates among researchers. To assess the potential consequences of climate change on agriculture, different crop simulation models have been developed, to provide informative strategies to avoid risks and understand the physical and biological processes. Furthermore, they can help in crop improvement programmes by identifying appropriate future crop management practises and recognizing the traits having the greatest impact on yield. Nonetheless, climate change assessment through model is subjected to a range of uncertainties. The prediction uncertainty can be reduced by using multimodel, incorporating crop modelling with plant physiology, biochemistry and gene-based modelling. For devloping new model, there is a need to generate and compile high-quality field data for model testing. Therefore, assessment of agricultural productivity to sustain food security for generations is essential to maintain a collective knowledge and resources for preventing negative impact as well as managing crop practises.

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.

Assessment of a model of forest dynamics under contrasting climate and disturbance regimes in the Pacific Northwest [FORCLIM

USGS Publications Warehouse

Busing, Richard T.; Solomon, Allen M.

2005-01-01

An individual-based model of forest dynamics (FORCLIM) was tested for its ability to simulate forest composition and structure in the Pacific Northwest region of North America. Simulation results across gradients of climate and disturbance were compared to forest survey data from several vegetation zones in western Oregon. Modelled patterns of tree species composition, total basal area and stand height across climate gradients matched those in the forest survey data. However, the density of small stems (<50 cm DBH) was underestimated by the model. Thus actual size-class structure and other density-based parameters of stand structure were not simulated with high accuracy. The addition of partial-stand disturbances at moderate frequencies (<0.01 yr-1) often improved agreement between simulated and actual results. Strengths and weaknesses of the FORCLIM model in simulating forest dynamics and structure in the Pacific Northwest are discussed.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department.

PubMed

Calegari, Rafael; Fogliatto, Flavio S; Lucini, Filipe R; Neyeloff, Jeruza; Kuchenbecker, Ricardo S; Schaan, Beatriz D

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department

PubMed Central

Fogliatto, Flavio S.; Neyeloff, Jeruza; Kuchenbecker, Ricardo S.; Schaan, Beatriz D.

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification. PMID:27725842

The effects of variable biome distribution on global climate

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

Noever, D.A.; Brittain, A.; Matsos, H.C.

1996-12-31

In projecting climatic adjustments to anthropogenically elevated atmospheric carbon dioxide, most global climate models fix biome distribution to current geographic conditions. The authors develop a model that examines the albedo-related effects of biome distribution on global temperature. The model was tested on historical biome changes since 1860 and the results fit both the observed trend and order of magnitude change in global temperature. Once backtested in this way on historical data, the model is then used to generate an optimized future biome distribution which minimizes projected greenhouse effects on global temperature. Because of the complexity of this combinatorial search anmore » artificial intelligence method, the genetic algorithm, was employed. The genetic algorithm assigns various biome distributions to the planet, then adjusts their percentage area and albedo effects to regulate or moderate temperature changes.« less

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

USGS Publications Warehouse

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

2015-01-01

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

Decision-relevant evaluation of climate models: A case study of chill hours in California

NASA Astrophysics Data System (ADS)

Jagannathan, K. A.; Jones, A. D.; Kerr, A. C.

2017-12-01

The past decade has seen a proliferation of different climate datasets with over 60 climate models currently in use. Comparative evaluation and validation of models can assist practitioners chose the most appropriate models for adaptation planning. However, such assessments are usually conducted for `climate metrics' such as seasonal temperature, while sectoral decisions are often based on `decision-relevant outcome metrics' such as growing degree days or chill hours. Since climate models predict different metrics with varying skill, the goal of this research is to conduct a bottom-up evaluation of model skill for `outcome-based' metrics. Using chill hours (number of hours in winter months where temperature is lesser than 45 deg F) in Fresno, CA as a case, we assess how well different GCMs predict the historical mean and slope of chill hours, and whether and to what extent projections differ based on model selection. We then compare our results with other climate-based evaluations of the region, to identify similarities and differences. For the model skill evaluation, historically observed chill hours were compared with simulations from 27 GCMs (and multiple ensembles). Model skill scores were generated based on a statistical hypothesis test of the comparative assessment. Future projections from RCP 8.5 runs were evaluated, and a simple bias correction was also conducted. Our analysis indicates that model skill in predicting chill hour slope is dependent on its skill in predicting mean chill hours, which results from the non-linear nature of the chill metric. However, there was no clear relationship between the models that performed well for the chill hour metric and those that performed well in other temperature-based evaluations (such winter minimum temperature or diurnal temperature range). Further, contrary to conclusions from other studies, we also found that the multi-model mean or large ensemble mean results may not always be most appropriate for this outcome metric. Our assessment sheds light on key differences between global versus local skill, and broad versus specific skill of climate models, highlighting that decision-relevant model evaluation may be crucial for providing practitioners with the best available climate information for their specific needs.

Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

NASA Astrophysics Data System (ADS)

Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

2013-10-01

In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

Simulating the carbon, water, energy budgets and greenhouse gas emissions of arctic soils with the ISBA land surface model

NASA Astrophysics Data System (ADS)

Morel, Xavier; Decharme, Bertrand; Delire, Christine

2017-04-01

Permafrost soils and boreal wetlands represent an important challenge for future climate simulations. Our aim is to be able to correctly represent the most important thermal, hydrologic and carbon cycle related processes in boreal areas with our land surface model ISBA (Masson et al, 2013). This is particularly important since ISBA is part of the CNRM-CM Climate Model (Voldoire et al, 2012), that is used for projections of future climate changes. To achieve this goal, we replaced the one layer original soil carbon module based on the CENTURY model (Parton et al, 1987) by a multi-layer soil carbon module that represents C pools and fluxes (CO2 and CH4), organic matter decomposition, gas diffusion (Khvorostyanov et al., 2008), CH4 ebullition and plant-mediated transport, and cryoturbation (Koven et al., 2009). The carbon budget of the new model is closed. The soil carbon module is tightly coupled to the ISBA energy and water budget module that solves the one-dimensional Fourier law and the mixed-form of the Richards equation explicitly to calculate the time evolution of the soil energy and water budgets (Boone et al., 2000; Decharme et al. 2011). The carbon, energy and water modules are solved using the same vertical discretization. Snowpack processes are represented by a multi-layer snow model (Decharme et al, 2016). We test this new model on a pair of monitoring sites in Greenland, one in a permafrost area (Zackenberg Ecological Research Operations, Jensen et al, 2014) and the other in a region without permafrost (Nuuk Ecological Research Operations, Jensen et al, 2013); both sites are established within the GeoBasis part of the Greenland Ecosystem Monitoring (GEM) program. The site of Chokurdakh, in a permafrost area of Siberia is is our third studied site. We test the model's ability to represent the physical variables (soil temperature and water profiles, snow height), the energy and water fluxes as well as the carbon dioxyde and methane fluxes. We also test the model behaviour in the case of a flooded fen, hence giving a first insight of the sensitivity of greenhouse gas emissions with respect to surface hydrology. Comparing the model results on these three climatically distinct sites also gives a first insight on the model sensitivity to the forcing climate variables, and show that the model is generic enough to reasonably model methane and carbon dioxyde emission behaviour from different types of boreal ecosystems.

Inhomogeneities detection in annual precipitation time series in Portugal using direct sequential simulation

NASA Astrophysics Data System (ADS)

Caineta, Júlio; Ribeiro, Sara; Costa, Ana Cristina; Henriques, Roberto; Soares, Amílcar

2014-05-01

Climate data homogenisation is of major importance in monitoring climate change, the validation of weather forecasting, general circulation and regional atmospheric models, modelling of erosion, drought monitoring, among other studies of hydrological and environmental impacts. This happens because non-climate factors can cause time series discontinuities which may hide the true climatic signal and patterns, thus potentially bias the conclusions of those studies. In the last two decades, many methods have been developed to identify and remove these inhomogeneities. One of those is based on geostatistical simulation (DSS - direct sequential simulation), where local probability density functions (pdf) are calculated at candidate monitoring stations, using spatial and temporal neighbouring observations, and then are used for detection of inhomogeneities. This approach has been previously applied to detect inhomogeneities in four precipitation series (wet day count) from a network with 66 monitoring stations located in the southern region of Portugal (1980-2001). This study revealed promising results and the potential advantages of geostatistical techniques for inhomogeneities detection in climate time series. This work extends the case study presented before and investigates the application of the geostatistical stochastic approach to ten precipitation series that were previously classified as inhomogeneous by one of six absolute homogeneity tests (Mann-Kendall test, Wald-Wolfowitz runs test, Von Neumann ratio test, Standard normal homogeneity test (SNHT) for a single break, Pettit test, and Buishand range test). Moreover, a sensibility analysis is implemented to investigate the number of simulated realisations that should be used to accurately infer the local pdfs. Accordingly, the number of simulations per iteration is increased from 50 to 500, which resulted in a more representative local pdf. A set of default and recommended settings is provided, which will help other users to implement this method. The need of user intervention is reduced to a minimum through the usage of a cross-platform script. Finally, as in the previous study, the results are compared with those from the SNHT, Pettit and Buishand range tests, which were applied to composite (ratio) reference series. Acknowledgements: The authors gratefully acknowledge the financial support of "Fundação para a Ciência e Tecnologia" (FCT), Portugal, through the research project PTDC/GEO-MET/4026/2012 ("GSIMCLI - Geostatistical simulation with local distributions for the homogenization and interpolation of climate data").

How Hot was Africa during the Mid-Holocene? Reexamining Africa's Thermal History via integrated Climate and Proxy System Modeling

NASA Astrophysics Data System (ADS)

Dee, S.; Russell, J. M.; Morrill, C.

2017-12-01

Climate models predict Africa will warm by up to 5°C in the coming century. Reconstructions of African temperature since the Last Glacial Maximum (LGM) have made fundamental contributions to our understanding of past, present, and future climate and can help constrain predictions from general circulation models (GCMs). However, many of these reconstructions are based on proxies of lake temperature, so the confounding influences of lacustrine processes may complicate our interpretations of past changes in tropical climate. These proxy-specific uncertainties require robust methodology for data-model comparison. We develop a new proxy system model (PSM) for paleolimnology to facilitate data-model comparison and to fully characterize uncertainties in climate reconstructions. Output from GCMs are used to force the PSM to simulate lake temperature, hydrology, and associated proxy uncertainties. We compare reconstructed East African lake and air temperatures in individual records and in a stack of 9 lake records to those predicted by our PSM forced with Paleoclimate Model Intercomparison Project (PMIP3) simulations, focusing on the mid-Holocene (6 kyr BP). We additionally employ single-forcing transient climate simulations from TraCE (10 kyr to 4 kyr B.P. and historical), as well as 200-yr time slice simulations from CESM1.0 to run the lake PSM. We test the sensitivity of African climate change during the mid-Holocene to orbital, greenhouse gas, and ice-sheet forcing in single-forcing simulations, and investigate dynamical hypotheses for these changes. Reconstructions of tropical African temperature indicate 1-2ºC warming during the mid-Holocene relative to the present, similar to changes predicted in the coming decades. However, most climate models underestimate the warming observed in these paleoclimate data (Fig. 1, 6kyr B.P.). We investigate this discrepancy using the new lake PSM and climate model simulations, with attention to the (potentially non-stationary) relationship between lake surface temperature and air temperature. The data-model comparison helps partition the impacts of lake-specific processes such as energy balance, mixing, sedimentation and bioturbation. We provide new insight into the patterns, amplitudes, sensitivity, and mechanisms of African temperature change.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models

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

Lovejoy, S., E-mail: lovejoy@physics.mcgill.ca; Lima, M. I. P. de; Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra

2015-07-15

Over the range of time scales from about 10 days to 30–100 years, in addition to the familiar weather and climate regimes, there is an intermediate “macroweather” regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spitemore » of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be “homogenized” by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time.« less

How model and input uncertainty impact maize yield simulations in West Africa

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter; Wang, Enli

2015-02-01

Crop models are common tools for simulating crop yields and crop production in studies on food security and global change. Various uncertainties however exist, not only in the model design and model parameters, but also and maybe even more important in soil, climate and management input data. We analyze the performance of the point-scale crop model APSIM and the global scale crop model LPJmL with different climate and soil conditions under different agricultural management in the low-input maize-growing areas of Burkina Faso, West Africa. We test the models’ response to different levels of input information from little to detailed information on soil, climate (1961-2000) and agricultural management and compare the models’ ability to represent the observed spatial (between locations) and temporal variability (between years) in crop yields. We found that the resolution of different soil, climate and management information influences the simulated crop yields in both models. However, the difference between models is larger than between input data and larger between simulations with different climate and management information than between simulations with different soil information. The observed spatial variability can be represented well from both models even with little information on soils and management but APSIM simulates a higher variation between single locations than LPJmL. The agreement of simulated and observed temporal variability is lower due to non-climatic factors e.g. investment in agricultural research and development between 1987 and 1991 in Burkina Faso which resulted in a doubling of maize yields. The findings of our study highlight the importance of scale and model choice and show that the most detailed input data does not necessarily improve model performance.

First Evaluation of the CCAM Aerosol Simulation over Africa: Implications for Regional Climate Modeling

NASA Astrophysics Data System (ADS)

Horowitz, H.; Garland, R. M.; Thatcher, M. J.; Naidoo, M.; van der Merwe, J.; Landman, W.; Engelbrecht, F.

2015-12-01

An accurate representation of African aerosols in climate models is needed to understand the regional and global radiative forcing and climate impacts of aerosols, at present and under future climate change. However, aerosol simulations in regional climate models for Africa have not been well-tested. Africa contains the largest single source of biomass-burning smoke aerosols and dust globally. Although aerosols are short-lived relative to greenhouse gases, black carbon in particular is estimated to be second only to carbon dioxide in contributing to warming on a global scale. Moreover, Saharan dust is exported great distances over the Atlantic Ocean, affecting nutrient transport to regions like the Amazon rainforest, which can further impact climate. Biomass burning aerosols are also exported from Africa, westward from Angola over the Atlantic Ocean and off the southeastern coast of South Africa to the Indian Ocean. Here, we perform the first extensive quantitative evaluation of the Conformal-Cubic Atmospheric Model (CCAM) aerosol simulation against monitored data, focusing on aerosol optical depth (AOD) observations over Africa. We analyze historical regional simulations for 1999 - 2012 from CCAM consistent with the experimental design of CORDEX at 50 km global horizontal resolution, through the dynamical downscaling of ERA-Interim data reanalysis data, with the CMIP5 emissions inventory (RCP8.5 scenario). CCAM has a prognostic aerosol scheme for organic carbon, black carbon, sulfate, and dust, and non-prognostic sea salt. The CCAM AOD at 550nm was compared to AOD (observed at 440nm, adjusted to 550nm with the Ångström exponent) from long-term AERONET stations across Africa. Sites strongly impacted by dust and biomass burning and with long continuous records were prioritized. In general, the model captures the monthly trends of the AERONET data. This presentation provides a basis for understanding how well aerosol particles are represented over Africa in regional climate modeling and the potential impact on climate predictions, and is the first large scale climate model-measurement verification of aerosols over Africa that we are aware of. CCAM is widely used for regional climate modeling applications, and we also discuss further improvements to the aerosol parameterizations based on our results.

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

USGS Publications Warehouse

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

2005-01-01

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

Simulated Tree Growth across the Northern Hemisphere and the Seasonality of Climate Signals Encoded within Tree-ring Widths

NASA Astrophysics Data System (ADS)

Li, X.; St George, S.

2013-12-01

Both dendrochronological theory and regional and global networks of tree-ring width measurements indicate that trees can respond to climate variations quite differently from one location to another. To explain these geographical differences at hemispheric scale, we used a process-based model of tree-ring formation (the Vaganov-Shashkin model) to simulate tree growth at over 6000 locations across the Northern Hemisphere. We compared the seasonality and strength of climate signals in the simulated tree-ring records against parallel analysis conducted on a hemispheric network of real tree-ring observations, tested the ability of the model to reproduce behaviors that emerge from large networks of tree-ring widths and used the model outputs to explain why the network exhibits these behaviors. The simulated tree-ring records are consistent with observations with respect to the seasonality and relative strength of the encoded climate signals, and time-related changes in these climate signals can be predicted using the modeled relative growth rate due to temperature or soil moisture. The positive imprint of winter (DJF) precipitation is strongest in simulations from the American Southwest and northern Mexico as well as selected locations in the Mediterranean and central Asia. Summer (JJA) precipitation has higher positive correlations with simulations in the mid-latitudes, but some high-latitude coastal sites exhibit a negative association. The influence of summer temperature is mainly positive at high-latitude or high-altitude sites and negative in the mid-latitudes. The absolute magnitude of climate correlations are generally higher in simulations than in observations, but the pattern and geographical differences remain the same, demonstrating that the model has skill in reproducing tree-ring growth response to climate variability in the Northern Hemisphere. Because the model uses only temperature, precipitation and latitude as input and is not adjusted for species or other biological factors, the fact that the climate response of the simulations largely agrees with the observations may imply that climate, rather than biology, is the main factor that influences large-scale patterns of the climate information recorded by tree rings. Our results also suggest that the Vaganov-Shashkin model could be used to estimate the likely climate response of trees in ';frontier' areas that have not been sampled extensively. Seasonal Climate Correlations of Simulated Tree-ring Records

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

The Use of Climatic Niches in Screening Procedures for Introduced Species to Evaluate Risk of Spread: A Case with the American Eastern Grey Squirrel

PubMed Central

Di Febbraro, Mirko; Lurz, Peter W. W.; Genovesi, Piero; Maiorano, Luigi; Girardello, Marco; Bertolino, Sandro

2013-01-01

Species introduction represents one of the most serious threats for biodiversity. The realized climatic niche of an invasive species can be used to predict its potential distribution in new areas, providing a basis for screening procedures in the compilation of black and white lists to prevent new introductions. We tested this assertion by modeling the realized climatic niche of the Eastern grey squirrel Sciurus carolinensis. Maxent was used to develop three models: one considering only records from the native range (NRM), a second including records from native and invasive range (NIRM), a third calibrated with invasive occurrences and projected in the native range (RCM). Niche conservatism was tested considering both a niche equivalency and a niche similarity test. NRM failed to predict suitable parts of the currently invaded range in Europe, while RCM underestimated the suitability in the native range. NIRM accurately predicted both the native and invasive range. The niche equivalency hypothesis was rejected due to a significant difference between the grey squirrel’s niche in native and invasive ranges. The niche similarity test yielded no significant results. Our analyses support the hypothesis of a shift in the species’ climatic niche in the area of introductions. Species Distribution Models (SDMs) appear to be a useful tool in the compilation of black lists, allowing identifying areas vulnerable to invasions. We advise caution in the use of SDMs based only on the native range of a species for the compilation of white lists for other geographic areas, due to the significant risk of underestimating its potential invasive range. PMID:23843957

Impacts of climate change on large forest wildfire of Washington and Oregon

NASA Astrophysics Data System (ADS)

Yang, Z.; Davis, R. J.; Yost, A.; Cohen, W. B.

2014-12-01

Climate changes in the 21st century were projected to have major impact on wildfire. The state of Washington and Oregon contains a tightly coupled forest ecosystem and fire regime. The objective of this study was to examine the impact of future climate changes for large wildfire in the two states. MAXENT algorithm was used to develop a large forest wildfire suitability model using historical fire for the 1971-2000 time period and validated for 1981-2010 time period . Input variables include climate (e.g. July-August temperature) and topographic variables (e.g. elevation). The model test AUC of 0.77±0.1. Using the predicted versus expected curve and methods described by Hirzel and others (Hirzel et al. 2006), we reclassified the model into four classes; low suitability (0-0.36), moderate suitability 0.36-0.5), high suitability (0.5-0.75), and very high suitability (0.75-1.0). To examine the future climate change impact, climate scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) from 33 different climate models were used to predict the large wildfire suitability from 1971-2100 using the NASA Earth Exchange (NEX) Downscaled Climate Projections (NEX-DCP30) dataset. Results from ensembles of all the climate scenarios showed that the area with high and very high suitability for large wildfire increased under all 4 climate scenarios from 1971 to 2100. However, under RCP 2.6, the area start to decline from 2080 while the other three scenarios keep increasing. On the extreme case of RCP 8.5, very high suitable area increases from less than 1% during 1971-2000 to 14.9% during 2070-2100. Details about temporal patterns for the study area and changes by ecoregions will be presented.

How does bias correction of RCM precipitation affect modelled runoff?

NASA Astrophysics Data System (ADS)

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

2014-09-01

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

Vegetation sensitivity to global anthropogenic carbon dioxide emissions in a topographically complex region

USGS Publications Warehouse

Diffenbaugh, N.S.; Sloan, L.C.; Snyder, M.A.; Bell, J.L.; Kaplan, J.; Shafer, S.L.; Bartlein, P.J.

2003-01-01

Anthropogenic increases in atmospheric carbon dioxide (CO2) concentrations may affect vegetation distribution both directly through changes in photosynthesis and water-use efficiency, and indirectly through CO2-induced climate change. Using an equilibrium vegetation model (BIOME4) driven by a regional climate model (RegCM2.5), we tested the sensitivity of vegetation in the western United States, a topographically complex region, to the direct, indirect, and combined effects of doubled preindustrial atmospheric CO2 concentrations. Those sensitivities were quantified using the kappa statistic. Simulated vegetation in the western United States was sensitive to changes in atmospheric CO2 concentrations, with woody biome types replacing less woody types throughout the domain. The simulated vegetation was also sensitive to climatic effects, particularly at high elevations, due to both warming throughout the domain and decreased precipitation in key mountain regions such as the Sierra Nevada of California and the Cascade and Blue Mountains of Oregon. Significantly, when the direct effects of CO2 on vegetation were tested in combination with the indirect effects of CO2-induced climate change, new vegetation patterns were created that were not seen in either of the individual cases. This result indicates that climatic and nonclimatic effects must be considered in tandem when assessing the potential impacts of elevated CO2 levels.

Agent Model Development for Assessing Climate-Induced Geopolitical Instability.

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

Boslough, Mark B.; Backus, George A.

2005-12-01

We present the initial stages of development of new agent-based computational methods to generate and test hypotheses about linkages between environmental change and international instability. This report summarizes the first year's effort of an originally proposed three-year Laboratory Directed Research and Development (LDRD) project. The preliminary work focused on a set of simple agent-based models and benefited from lessons learned in previous related projects and case studies of human response to climate change and environmental scarcity. Our approach was to define a qualitative model using extremely simple cellular agent models akin to Lovelock's Daisyworld and Schelling's segregation model. Such modelsmore » do not require significant computing resources, and users can modify behavior rules to gain insights. One of the difficulties in agent-based modeling is finding the right balance between model simplicity and real-world representation. Our approach was to keep agent behaviors as simple as possible during the development stage (described herein) and to ground them with a realistic geospatial Earth system model in subsequent years. This work is directed toward incorporating projected climate data--including various C02 scenarios from the Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report--and ultimately toward coupling a useful agent-based model to a general circulation model.3« less

Combined influence of multiple climatic factors on the incidence of bacterial foodborne diseases.

PubMed

Park, Myoung Su; Park, Ki Hwan; Bahk, Gyung Jin

2018-01-01

Information regarding the relationship between the incidence of foodborne diseases (FBD) and climatic factors is useful in designing preventive strategies for FBD based on anticipated future climate change. To better predict the effect of climate change on foodborne pathogens, the present study investigated the combined influence of multiple climatic factors on bacterial FBD incidence in South Korea. During 2011-2015, the relationships between 8 climatic factors and the incidences of 13 bacterial FBD, were determined based on inpatient stays, on a monthly basis using the Pearson correlation analyses, multicollinearity tests, principal component analysis (PCA), and the seasonal autoregressive integrated moving average (SARIMA) modeling. Of the 8 climatic variables, the combination of temperature, relative humidity, precipitation, insolation, and cloudiness was significantly associated with salmonellosis (P<0.01), vibriosis (P<0.05), and enterohemorrhagic Escherichia coli O157:H7 infection (P<0.01). The combined effects of snowfall, wind speed, duration of sunshine, and cloudiness were not significant for these 3 FBD. Other FBD, including campylobacteriosis, were not significantly associated with any combination of climatic factors. These findings indicate that the relationships between multiple climatic factors and bacterial FBD incidence can be valuable for the development of prediction models for future patterns of diseases in response to changes in climate. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessment of climate change downscaling and non-stationarity on the spatial pattern of a mangrove ecosystem in an arid coastal region of southern Iran

NASA Astrophysics Data System (ADS)

Etemadi, Halimeh; Samadi, S. Zahra; Sharifikia, Mohammad; Smoak, Joseph M.

2016-10-01

Mangrove wetlands exist in the transition zone between terrestrial and marine environments and have remarkable ecological and socio-economic value. This study uses climate change downscaling to address the question of non-stationarity influences on mangrove variations (expansion and contraction) within an arid coastal region. Our two-step approach includes downscaling models and uncertainty assessment, followed by a non-stationary and trend procedure using the Extreme Value Analysis (extRemes code). The Long Ashton Research Station Weather Generator (LARS-WG) model along with two different general circulation model (GCMs) (MIRH and HadCM3) were used to downscale climatic variables during current (1968-2011) and future (2011-2030, 2045-2065, and 2080-2099) periods. Parametric and non-parametric bootstrapping uncertainty tests demonstrated that the LARS-WGS model skillfully downscaled climatic variables at the 95 % significance level. Downscaling results using MIHR model show that minimum and maximum temperatures will increase in the future (2011-2030, 2045-2065, and 2080-2099) during winter and summer in a range of +4.21 and +4.7 °C, and +3.62 and +3.55 °C, respectively. HadCM3 analysis also revealed an increase in minimum (˜+3.03 °C) and maximum (˜+3.3 °C) temperatures during wet and dry seasons. In addition, we examined how much mangrove area has changed during the past decades and, thus, if climate change non-stationarity impacts mangrove ecosystems. Our results using remote sensing techniques and the non-parametric Mann-Whitney two-sample test indicated a sharp decline in mangrove area during 1972,1987, and 1997 periods ( p value = 0.002). Non-stationary assessment using the generalized extreme value (GEV) distributions by including mangrove area as a covariate further indicated that the null hypothesis of the stationary climate (no trend) should be rejected due to the very low p values for precipitation ( p value = 0.0027), minimum ( p value = 0.000000029) and maximum ( p value = 0.00016) temperatures. Based on non-stationary analysis and an upward trend in downscaled temperature extremes, climate change may control mangrove development in the future.

Adaptation of water resource systems to an uncertain future

NASA Astrophysics Data System (ADS)

Walsh, Claire L.; Blenkinsop, Stephen; Fowler, Hayley J.; Burton, Aidan; Dawson, Richard J.; Glenis, Vassilis; Manning, Lucy J.; Jahanshahi, Golnaz; Kilsby, Chris G.

2016-05-01

Globally, water resources management faces significant challenges from changing climate and growing populations. At local scales, the information provided by climate models is insufficient to support the water sector in making future adaptation decisions. Furthermore, projections of change in local water resources are wrought with uncertainties surrounding natural variability, future greenhouse gas emissions, model structure, population growth, and water consumption habits. To analyse the magnitude of these uncertainties, and their implications for local-scale water resource planning, we present a top-down approach for testing climate change adaptation options using probabilistic climate scenarios and demand projections. An integrated modelling framework is developed which implements a new, gridded spatial weather generator, coupled with a rainfall-runoff model and water resource management simulation model. We use this to provide projections of the number of days and associated uncertainty that will require implementation of demand saving measures such as hose pipe bans and drought orders. Results, which are demonstrated for the Thames Basin, UK, indicate existing water supplies are sensitive to a changing climate and an increasing population, and that the frequency of severe demand saving measures are projected to increase. Considering both climate projections and population growth, the median number of drought order occurrences may increase 5-fold by the 2050s. The effectiveness of a range of demand management and supply options have been tested and shown to provide significant benefits in terms of reducing the number of demand saving days. A decrease in per capita demand of 3.75 % reduces the median frequency of drought order measures by 50 % by the 2020s. We found that increased supply arising from various adaptation options may compensate for increasingly variable flows; however, without reductions in overall demand for water resources such options will be insufficient on their own to adapt to uncertainties in the projected changes in climate and population. For example, a 30 % reduction in overall demand by 2050 has a greater impact on reducing the frequency of drought orders than any of the individual or combinations of supply options; hence, a portfolio of measures is required.

Climate Central World Weather Attribution (WWA) project: Real-time extreme weather event attribution analysis

NASA Astrophysics Data System (ADS)

Haustein, Karsten; Otto, Friederike; Uhe, Peter; Allen, Myles; Cullen, Heidi

2015-04-01

Extreme weather detection and attribution analysis has emerged as a core theme in climate science over the last decade or so. By using a combination of observational data and climate models it is possible to identify the role of climate change in certain types of extreme weather events such as sea level rise and its contribution to storm surges, extreme heat events and droughts or heavy rainfall and flood events. These analyses are usually carried out after an extreme event has occurred when reanalysis and observational data become available. The Climate Central WWA project will exploit the increasing forecast skill of seasonal forecast prediction systems such as the UK MetOffice GloSea5 (Global seasonal forecasting system) ensemble forecasting method. This way, the current weather can be fed into climate models to simulate large ensembles of possible weather scenarios before an event has fully emerged yet. This effort runs along parallel and intersecting tracks of science and communications that involve research, message development and testing, staged socialization of attribution science with key audiences, and dissemination. The method we employ uses a very large ensemble of simulations of regional climate models to run two different analyses: one to represent the current climate as it was observed, and one to represent the same events in the world that might have been without human-induced climate change. For the weather "as observed" experiment, the atmospheric model uses observed sea surface temperature (SST) data from GloSea5 (currently) and present-day atmospheric gas concentrations to simulate weather events that are possible given the observed climate conditions. The weather in the "world that might have been" experiments is obtained by removing the anthropogenic forcing from the observed SSTs, thereby simulating a counterfactual world without human activity. The anthropogenic forcing is obtained by comparing the CMIP5 historical and natural simulations from a variety of CMIP5 model ensembles. Here, we present results for the UK 2013/14 winter floods as proof of concept and we show validation and testing results that demonstrate the robustness of our method. We also revisit the record temperatures over Europe in 2014 and present a detailed analysis of this attribution exercise as it is one of the events to demonstrate that we can make a sensible statement of how the odds for such a year to occur have changed while it still unfolds.

Impact of high-performance work systems on individual- and branch-level performance: test of a multilevel model of intermediate linkages.

PubMed

Aryee, Samuel; Walumbwa, Fred O; Seidu, Emmanuel Y M; Otaye, Lilian E

2012-03-01

We proposed and tested a multilevel model, underpinned by empowerment theory, that examines the processes linking high-performance work systems (HPWS) and performance outcomes at the individual and organizational levels of analyses. Data were obtained from 37 branches of 2 banking institutions in Ghana. Results of hierarchical regression analysis revealed that branch-level HPWS relates to empowerment climate. Additionally, results of hierarchical linear modeling that examined the hypothesized cross-level relationships revealed 3 salient findings. First, experienced HPWS and empowerment climate partially mediate the influence of branch-level HPWS on psychological empowerment. Second, psychological empowerment partially mediates the influence of empowerment climate and experienced HPWS on service performance. Third, service orientation moderates the psychological empowerment-service performance relationship such that the relationship is stronger for those high rather than low in service orientation. Last, ordinary least squares regression results revealed that branch-level HPWS influences branch-level market performance through cross-level and individual-level influences on service performance that emerges at the branch level as aggregated service performance.

Modeling Climate and Societal Resilience in the Mediterranean During the Last Millennium

NASA Astrophysics Data System (ADS)

Wagner, S.; Xoplaki, E.; Luterbacher, J.; Zorita, E.; Fleitmann, D.; Preiser-Kapeller, J.; Toreti, A., , Dr; Sargent, A. M.; Bozkurt, D.; White, S.; Haldon, J. F.; Akçer-Ön, S.; Izdebski, A.

2017-12-01

Past civilisations were influenced by complex external and internal forces, including changes in the environment, climate, politics and economy. A geographical hotspot of the interplay between those agents is the Mediterranean, a cradle of cultural and scientific development. We analyse a novel compilation of high-quality hydroclimate proxy records and spatial reconstructions from the Mediterranean and compare them with two Earth System Model simulations (CCSM4, MPI-ESM-P) for three historical time intervals - the Crusaders, 1095-1290 CE; the Mamluk regime in Transjordan, 1260-1516 CE; and the Ottoman crisis and Celâlî Rebellion, 1580-1610 CE - when environmental and climatic stress tested the resilience of complex societies. ESMs provide important information on the dynamical mechanisms and underlying processes that led to anomalous hydroclimatic conditions of the past. We find that the multidecadal precipitation and drought variations in the Central and Eastern Mediterranean during the three periods cannot be explained by external forcings (solar variations, tropical volcanism); rather they were driven by internal climate dynamics. The integrated analysis of palaeoclimate proxies, climate reconstructions and model simulations sheds light on our understanding of past climate change and its societal impact. Finally, our research emphasises the need to further study the societal dimension of environmental and climate change in the past, in order to properly understand the role that climate has played in human history.

Using statistical models to explore ensemble uncertainty in climate impact studies: the example of air pollution in Europe

NASA Astrophysics Data System (ADS)

Lemaire, Vincent E. P.; Colette, Augustin; Menut, Laurent

2016-03-01

Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to climate change so that, in the future, a climate penalty could jeopardize the expected efficiency of air pollution mitigation measures. A common method to assess the impact of climate on air quality consists in implementing chemistry-transport models forced by climate projections. However, the computing cost of such methods requires optimizing ensemble exploration techniques. By using a training data set from a deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for eight regions in Europe and developed statistical models that could be used to predict air pollutant concentrations. The evolution of the key climate variables driving either particulate or gaseous pollution allows selecting the members of the EuroCordex ensemble of regional climate projections that should be used in priority for future air quality projections (CanESM2/RCA4; CNRM-CM5-LR/RCA4 and CSIRO-Mk3-6-0/RCA4 and MPI-ESM-LR/CCLM following the EuroCordex terminology). After having tested the validity of the statistical model in predictive mode, we can provide ranges of uncertainty attributed to the spread of the regional climate projection ensemble by the end of the century (2071-2100) for the RCP8.5. In the three regions where the statistical model of the impact of climate change on PM2.5 offers satisfactory performances, we find a climate benefit (a decrease of PM2.5 concentrations under future climate) of -1.08 (±0.21), -1.03 (±0.32), -0.83 (±0.14) µg m-3, for respectively Eastern Europe, Mid-Europe and Northern Italy. In the British-Irish Isles, Scandinavia, France, the Iberian Peninsula and the Mediterranean, the statistical model is not considered skillful enough to draw any conclusion for PM2.5. In Eastern Europe, France, the Iberian Peninsula, Mid-Europe and Northern Italy, the statistical model of the impact of climate change on ozone was considered satisfactory and it confirms the climate penalty bearing upon ozone of 10.51 (±3.06), 11.70 (±3.63), 11.53 (±1.55), 9.86 (±4.41), 4.82 (±1.79) µg m-3, respectively. In the British-Irish Isles, Scandinavia and the Mediterranean, the skill of the statistical model was not considered robust enough to draw any conclusion for ozone pollution.

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

Smart licensing and environmental flows: Modeling framework and sensitivity testing

NASA Astrophysics Data System (ADS)

Wilby, R. L.; Fenn, C. R.; Wood, P. J.; Timlett, R.; Lequesne, T.

2011-12-01

Adapting to climate change is just one among many challenges facing river managers. The response will involve balancing the long-term water demands of society with the changing needs of the environment in sustainable and cost effective ways. This paper describes a modeling framework for evaluating the sensitivity of low river flows to different configurations of abstraction licensing under both historical climate variability and expected climate change. A rainfall-runoff model is used to quantify trade-offs among environmental flow (e-flow) requirements, potential surface and groundwater abstraction volumes, and the frequency of harmful low-flow conditions. Using the River Itchen in southern England as a case study it is shown that the abstraction volume is more sensitive to uncertainty in the regional climate change projection than to the e-flow target. It is also found that "smarter" licensing arrangements (involving a mix of hands off flows and "rising block" abstraction rules) could achieve e-flow targets more frequently than conventional seasonal abstraction limits, with only modest reductions in average annual yield, even under a hotter, drier climate change scenario.

Optical properties of light absorbing carbon aggregates mixed with sulfate: assessment of different model geometries for climate forcing calculations.

PubMed

Kahnert, Michael; Nousiainen, Timo; Lindqvist, Hannakaisa; Ebert, Martin

2012-04-23

Light scattering by light absorbing carbon (LAC) aggregates encapsulated into sulfate shells is computed by use of the discrete dipole method. Computations are performed for a UV, visible, and IR wavelength, different particle sizes, and volume fractions. Reference computations are compared to three classes of simplified model particles that have been proposed for climate modeling purposes. Neither model matches the reference results sufficiently well. Remarkably, more realistic core-shell geometries fall behind homogeneous mixture models. An extended model based on a core-shell-shell geometry is proposed and tested. Good agreement is found for total optical cross sections and the asymmetry parameter. © 2012 Optical Society of America

Integrating World Views, Knowledge and Venues in Climate Science Education

NASA Astrophysics Data System (ADS)

Sparrow, E. B.; Chase, M. J.; Demientieff, S.; Brunacini, J.; Pfirman, S. L.

2015-12-01

The Reaching Arctic Communities Facing Climate Change Project integrates traditional and western knowledge and observations in climate science to facilitate dialog and learning among Alaska Native adults about climate change and its impacts on the environment and on Alaskan communities. In one of the models we have tested, the informal education took place at a 4-day camp by the Tanana River in Fairbanks, Alaska. Participants included Alaska Native elders, leaders, educators and natural resource managers, community members and university scientists. Results of pre/post camp surveys showed increased awareness of scientific and technical language used in climate science, improved ability to locate resources, tools, and strategies for learning about climate change, enhanced capacity to communicate climate change in a relevant way to their audiences and communities, confirmed the value of elders in helping them understand, respond and adapt to climate change, and that the camp setting facilitated an in-depth discussion and sharing of knowledge. The camp also enhanced the awareness about weather, climate and the environment of the camp facilitators who also noticed a shift in their own thinking and behavior. After the camp one participant who is an educator shared some of the hands-on tools developed by Polar Learning and Responding Climate Change Education Partnership project and used at the camp, with her 6th grade students, with the other teachers in her school and also at a state conference. Another shared what she learned with her family and friends as well as at a conference sponsored by her faith community where she was an invited speaker. Another camp was scheduled for this past summer but was cancelled due to some unforeseen weather/climate related events. A camp is planned for early summer in 2016; however other models of reaching the adult Native populations in a similar culturally responsive manner as the camps will also be explored and tested.

Climatic niche conservatism and biogeographical non-equilibrium in Eschscholzia californica (Papaveraceae), an invasive plant in the Chilean Mediterranean region.

PubMed

Peña-Gómez, Francisco T; Guerrero, Pablo C; Bizama, Gustavo; Duarte, Milén; Bustamante, Ramiro O

2014-01-01

Species climate requirements are useful for predicting their geographic distribution. It is often assumed that the niche requirements for invasive plants are conserved during invasion, especially when the invaded regions share similar climate conditions. California and central Chile have a remarkable degree of convergence in their vegetation structure, and a similar Mediterranean climate. Such similarities make these geographic areas an interesting natural experiment for testing climatic niche dynamics and the equilibrium of invasive species in a new environment. We tested to see if the climatic niche of Eschscholzia californica is conserved in the invaded range (central Chile), and we assessed whether the invasion process has reached a biogeographical equilibrium, i.e., occupy all the suitable geographic locations that have suitable conditions under native niche requirements. We compared the climatic niche in the native and invaded ranges as well as the projected potential geographic distribution in the invaded range. In order to compare climatic niches, we conducted a Principal Component Analysis (PCA) and Species Distribution Models (SDMs), to estimate E. californica's potential geographic distribution. We also used SDMs to predict altitudinal distribution limits in central Chile. Our results indicated that the climatic niche occupied by E. californica in the invaded range is firmly conserved, occupying a subset of the native climatic niche but leaving a substantial fraction of it unfilled. Comparisons of projected SDMs for central Chile indicate a similarity, yet the projection from native range predicted a larger geographic distribution in central Chile compared to the prediction of the model constructed for central Chile. The projected niche occupancy profile from California predicted a higher mean elevation than that projected from central Chile. We concluded that the invasion process of E. californica in central Chile is consistent with climatic niche conservatism but there is potential for further expansion in Chile.

Implementation and evaluation of a monthly water balance model over the US on an 800 m grid

USGS Publications Warehouse

Hostetler, Steven W.; Alder, Jay R.

2016-01-01

We simulate the 1950–2010 water balance for the conterminous U.S. (CONUS) with a monthly water balance model (MWBM) using the 800 m Parameter-elevation Regression on Independent Slopes Model (PRISM) data set as model input. We employed observed snow and streamflow data sets to guide modification of the snow and potential evapotranspiration components in the default model and to evaluate model performance. Based on various metrics and sensitivity tests, the modified model yields reasonably good simulations of seasonal snowpack in the West (range of bias of ±50 mm at 68% of 713 SNOTEL sites), the gradients and magnitudes of actual evapotranspiration, and runoff (median correlation of 0.83 and median Nash-Sutcliff efficiency of 0.6 between simulated and observed annual time series at 1427 USGS gage sites). The model generally performs well along the Pacific Coast, the high elevations of the Basin and Range and over the Midwest and East, but not as well over the dry areas of the Southwest and upper Plains regions due, in part, to the apportioning of direct versus delayed runoff. Sensitivity testing and application of the MWBM to simulate the future water balance at four National Parks when driven by 30 climate models from the Climate Model Intercomparison Program Phase 5 (CMIP5) demonstrate that the model is useful for evaluating first-order, climate driven hydrologic change on monthly and annual time scales.

Test Driven Development of a Parameterized Ice Sheet Component

NASA Astrophysics Data System (ADS)

Clune, T.

2011-12-01

Test driven development (TDD) is a software development methodology that offers many advantages over traditional approaches including reduced development and maintenance costs, improved reliability, and superior design quality. Although TDD is widely accepted in many software communities, the suitability to scientific software is largely undemonstrated and warrants a degree of skepticism. Indeed, numerical algorithms pose several challenges to unit testing in general, and TDD in particular. Among these challenges are the need to have simple, non-redundant closed-form expressions to compare against the results obtained from the implementation as well as realistic error estimates. The necessity for serial and parallel performance raises additional concerns for many scientific applicaitons. In previous work I demonstrated that TDD performed well for the development of a relatively simple numerical model that simulates the growth of snowflakes, but the results were anecdotal and of limited relevance to far more complex software components typical of climate models. This investigation has now been extended by successfully applying TDD to the implementation of a substantial portion of a new parameterized ice sheet component within a full climate model. After a brief introduction to TDD, I will present techniques that address some of the obstacles encountered with numerical algorithms. I will conclude with some quantitative and qualitative comparisons against climate components developed in a more traditional manner.

The PMIP4 contribution to CMIP6 - Part 1: Overview and over-arching analysis plan

NASA Astrophysics Data System (ADS)

Kageyama, Masa; Braconnot, Pascale; Harrison, Sandy P.; Haywood, Alan M.; Jungclaus, Johann H.; Otto-Bliesner, Bette L.; Peterschmitt, Jean-Yves; Abe-Ouchi, Ayako; Albani, Samuel; Bartlein, Patrick J.; Brierley, Chris; Crucifix, Michel; Dolan, Aisling; Fernandez-Donado, Laura; Fischer, Hubertus; Hopcroft, Peter O.; Ivanovic, Ruza F.; Lambert, Fabrice; Lunt, Daniel J.; Mahowald, Natalie M.; Peltier, W. Richard; Phipps, Steven J.; Roche, Didier M.; Schmidt, Gavin A.; Tarasov, Lev; Valdes, Paul J.; Zhang, Qiong; Zhou, Tianjun

2018-03-01

This paper is the first of a series of four GMD papers on the PMIP4-CMIP6 experiments. Part 2 (Otto-Bliesner et al., 2017) gives details about the two PMIP4-CMIP6 interglacial experiments, Part 3 (Jungclaus et al., 2017) about the last millennium experiment, and Part 4 (Kageyama et al., 2017) about the Last Glacial Maximum experiment. The mid-Pliocene Warm Period experiment is part of the Pliocene Model Intercomparison Project (PlioMIP) - Phase 2, detailed in Haywood et al. (2016).The goal of the Paleoclimate Modelling Intercomparison Project (PMIP) is to understand the response of the climate system to different climate forcings for documented climatic states very different from the present and historical climates. Through comparison with observations of the environmental impact of these climate changes, or with climate reconstructions based on physical, chemical, or biological records, PMIP also addresses the issue of how well state-of-the-art numerical models simulate climate change. Climate models are usually developed using the present and historical climates as references, but climate projections show that future climates will lie well outside these conditions. Palaeoclimates very different from these reference states therefore provide stringent tests for state-of-the-art models and a way to assess whether their sensitivity to forcings is compatible with palaeoclimatic evidence. Simulations of five different periods have been designed to address the objectives of the sixth phase of the Coupled Model Intercomparison Project (CMIP6): the millennium prior to the industrial epoch (CMIP6 name: past1000); the mid-Holocene, 6000 years ago (midHolocene); the Last Glacial Maximum, 21 000 years ago (lgm); the Last Interglacial, 127 000 years ago (lig127k); and the mid-Pliocene Warm Period, 3.2 million years ago (midPliocene-eoi400). These climatic periods are well documented by palaeoclimatic and palaeoenvironmental records, with climate and environmental changes relevant for the study and projection of future climate changes. This paper describes the motivation for the choice of these periods and the design of the numerical experiments and database requests, with a focus on their novel features compared to the experiments performed in previous phases of PMIP and CMIP. It also outlines the analysis plan that takes advantage of the comparisons of the results across periods and across CMIP6 in collaboration with other MIPs.

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.

An evaluation of applying the 'Critical thinking model' to teaching global warming to junior high school students

NASA Astrophysics Data System (ADS)

Huang, J.; Hong, C.; Hsu, Y.

2013-12-01

Climate change is a consequence of interaction among the biosphere, atmosphere, hydrosphere and geosphere. The causes of climate change are extremely complicated for scientists to explain. The fact that the global climate has kept warming in the past few decades is one example. It remains controversial for scientists whether this warming is the result of human activity or natural causes. This research aims to lead students to discuss the causes of global warming from distinct and controversial viewpoints to help the students realize the uncertainty and complicated characteristics of the global warming issue. The context of applying the critical thinking model to teaching the scientific concepts of climate change and global warming is designed for use in junior high schools. The videos of the upside concept 'An Inconvenient Truth' (a 2006 documentary film directed by Davis Guggenheim) and the reverse-side concept 'The Great Global Warming Swindle' (a 2007 documentary film made by British television producer/director Martin Durkin) about the global warming crisis are incorporated into lessons in order to guide students to make their own decisions appropriately when discussing the earth climate change crisis. A questionnaire, individual teacher interviews and observations in class were conducted to evaluate the curriculum. The pre-test and post-test questionnaires showed differences in the students' knowledge, attitudes and behavior towards the global warming phenomenon before and after attending the lessons. The results show that those students who attended the whole curriculum had a significant increase in their knowledge and behavior factors of global climate (P value <0.001*). However, there was no significant improvement in their attitudes between the pre-test and post-test questionnaires (P value=0.329). From the individual interviews, the teachers who gave the lessons indicated that this project could increase the interaction with their students during class and improve the efficiency of learning.

A MULTILAYER BIOCHEMICAL DRY DEPOSITION MODEL 2. MODEL EVALUATION

EPA Science Inventory

The multilayer biochemical dry deposition model (MLBC) described in the accompanying paper was tested against half-hourly eddy correlation data from six field sites under a wide range of climate conditions with various plant types. Modeled CO₂, O₃, SO_2<...

Agricultural response functions to changes in carbon, temperature, and water based on the C3MP data set

NASA Astrophysics Data System (ADS)

Snyder, A.; Ruane, A. C.; Phillips, M.; Calvin, K. V.; Clarke, L.

2017-12-01

Agricultural yields vary depending on temperature, precipitation/irrigation conditions, fertilizer application, and CO2 concentration. The Coordinated Climate-Crop Modeling Project (C3MP), conducted as a component of the Agricultural Model Intercomparison and Improvement Project (AgMIP), organized a sensitivity experiments across carbon-temperature-water (CTW) space across 1100 management conditions in 50+ countries, sampling 15 crop species and 20 crop models. Such coordinated sensitivity tests allow for the building of emulators of yield response to changes in CTW values, allowing rapid estimation of yield changes from the types of climate changes projected by the climate modeling community. The resulting emulator may be used to supply agricultural responses to climate change in any user-defined scenario, rather than the restriction to the RCPs in many past works. We present the resulting emulators built from the C3MP output data set for use in the Global Change Assessment Model (GCAM) integrated assessment model that allows for the co-evolution of socioeconomic development, greenhouse gas emissions, climate change, and agricultural sector ramifications. C3MP-based emulators may be of use in designing agricultural impact studies in other IAMs, and we place them in the context of past crop modeling efforts, including the Challinor et al. Meta-analysis, the AgMIP Wheat team results, the AgMIP Global Gridded Crop Model Intercomparison (GGCMI) fast-track modeling results, and the MACSUR impact response surface results.

Echoes of a Forgotten Past: Eugenics, Testing, and Education Reform.

ERIC Educational Resources Information Center

Stoskopf, Alan

2002-01-01

Review of the work of Goddard, Terman, and Thorndike and the role of eugenics and the intelligence quotient in testing points out dangers to be avoided in the current testing climate, such as use of the business model, single-number scores, and tracking. (Contains 42 references.) (SK)

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

NASA Astrophysics Data System (ADS)

Kislov, Alexander

2015-04-01

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

Simulating soil organic carbon stock as affected by land cover change and climate change, Hyrcanian forests (northern Iran).

PubMed

Soleimani, Azam; Hosseini, Seyed Mohsen; Massah Bavani, Ali Reza; Jafari, Mostafa; Francaviglia, Rosa

2017-12-01

Soil organic carbon (SOC) contains a considerable portion of the world's terrestrial carbon stock, and is affected by changes in land cover and climate. SOC modeling is a useful approach to assess the impact of land use, land use change and climate change on carbon (C) sequestration. This study aimed to: (i) test the performance of RothC model using data measured from different land covers in Hyrcanian forests (northern Iran); and (ii) predict changes in SOC under different climate change scenarios that may occur in the future. The following land covers were considered: Quercus castaneifolia (QC), Acer velutinum (AV), Alnus subcordata (AS), Cupressus sempervirens (CS) plantations and a natural forest (NF). For assessment of future climate change projections the Fifth Assessment IPCC report was used. These projections were generated with nine Global Climate Models (GCMs), for two Representative Concentration Pathways (RCPs) leading to very low and high greenhouse gases concentration levels (RCP 2.6 and RCP 8.5 respectively), and for four 20year-periods up to 2099 (2030s, 2050s, 2070s and 2090s). Simulated values of SOC correlated well with measured data (R 2 =0.64 to 0.91) indicating a good efficiency of the RothC model. Our results showed an overall decrease in SOC stocks by 2099 under all land covers and climate change scenarios, but the extent of the decrease varied with the climate models, the emissions scenarios, time periods and land covers. Acer velutinum plantation was the most sensitive land cover to future climate change (range of decrease 8.34-21.83tCha -1 ). Results suggest that modeling techniques can be effectively applied for evaluating SOC stocks, allowing the identification of current patterns in the soil and the prediction of future conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post missed NDAS cycles since 1 Apr 1995 Log of NAM model code changes Log of NAM model test runs Problems and Prediction (NCWCP) 5830 University Research Court College Park, MD 20740 Page Author: EMC Webmaster Page

Satellite Remote Sensing and Mesoscale Modeling of Biomass Burning Aerosols over the Southeast Asian Maritime Continent: Climatic Implications of Smokes on Regional Energy Balance, Cloud Formations and Precipitations

NASA Astrophysics Data System (ADS)

Feng, N.

2015-12-01

The influences of anthropogenic aerosols have been suggested as an important reason for climate changes over Southeast Asia (SE Asia, 10°S~20°N and 90°E~135°E). Accurate observations and modelling of aerosols effects on the weather and climate patterns is crucial for a better understanding and mitigation of anthropogenic climate change. This study uses NASA satellite observations along with online-coupled Weather Research and Forecasting model with Chemistry (WRF-Chem) to evaluate aerosols impacts on climate over SE Asia. We assess the direct and semi-direct radiative effects of smoke particles over this region during September, 2009 when a significant El Niño event caused the highest biomass burning activity during the last 15 years. Quantification efforts are made to assess how changes of radiative and non radiative parameters (sensible and latent heat) due to smoke aerosols would affect regional climate process such as precipitations, clouds and planetary boundary layer process. Comparison of model simulations for the current land cover conditions against surface meteorological observations and satellite observations of precipitations and cloudiness show satisfactory performance of the model over our study area. In order to quantitatively validate the model results, several experiments will be performed to test the aerosols radiative feedback under different radiation schemes and with/without considering aerosol effects explicitly in the model. Relevant ground-based data (e.g. AERONET), along with aerosol vertical profile data from CALIPSO, will also be applied.

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.

Future climate stimulates population out-breaks by relaxing constraints on reproduction.

PubMed

Heldt, Katherine A; Connell, Sean D; Anderson, Kathryn; Russell, Bayden D; Munguia, Pablo

2016-09-14

When conditions are stressful, reproduction and population growth are reduced, but when favourable, reproduction and population size can boom. Theory suggests climate change is an increasingly stressful environment, predicting extinctions or decreased abundances. However, if favourable conditions align, such as an increase in resources or release from competition and predation, future climate can fuel population growth. Tests of such population growth models and the mechanisms by which they are enabled are rare. We tested whether intergenerational increases in population size might be facilitated by adjustments in reproductive success to favourable environmental conditions in a large-scale mesocosm experiment. Herbivorous amphipod populations responded to future climate by increasing 20 fold, suggesting that future climate might relax environmental constraints on fecundity. We then assessed whether future climate reduces variation in mating success, boosting population fecundity and size. The proportion of gravid females doubled, and variance in phenotypic variation of male secondary sexual characters (i.e. gnathopods) was significantly reduced. While future climate can enhance individual growth and survival, it may also reduce constraints on mechanisms of reproduction such that enhanced intra-generational productivity and reproductive success transfers to subsequent generations. Where both intra and intergenerational production is enhanced, population sizes might boom.

GFDL's unified regional-global weather-climate modeling system with variable resolution capability for severe weather predictions and regional climate simulations

NASA Astrophysics Data System (ADS)

Lin, S. J.

2015-12-01

The NOAA/Geophysical Fluid Dynamics Laboratory has been developing a unified regional-global modeling system with variable resolution capabilities that can be used for severe weather predictions (e.g., tornado outbreak events and cat-5 hurricanes) and ultra-high-resolution (1-km) regional climate simulations within a consistent global modeling framework. The fundation of this flexible regional-global modeling system is the non-hydrostatic extension of the vertically Lagrangian dynamical core (Lin 2004, Monthly Weather Review) known in the community as FV3 (finite-volume on the cubed-sphere). Because of its flexability and computational efficiency, the FV3 is one of the final candidates of NOAA's Next Generation Global Prediction System (NGGPS). We have built into the modeling system a stretched (single) grid capability, a two-way (regional-global) multiple nested grid capability, and the combination of the stretched and two-way nests, so as to make convection-resolving regional climate simulation within a consistent global modeling system feasible using today's High Performance Computing System. One of our main scientific goals is to enable simulations of high impact weather phenomena (such as tornadoes, thunderstorms, category-5 hurricanes) within an IPCC-class climate modeling system previously regarded as impossible. In this presentation I will demonstrate that it is computationally feasible to simulate not only super-cell thunderstorms, but also the subsequent genesis of tornadoes using a global model that was originally designed for century long climate simulations. As a unified weather-climate modeling system, we evaluated the performance of the model with horizontal resolution ranging from 1 km to as low as 200 km. In particular, for downscaling studies, we have developed various tests to ensure that the large-scale circulation within the global varaible resolution system is well simulated while at the same time the small-scale can be accurately captured within the targeted high resolution region.

Calculating distributed glacier mass balance for the Swiss Alps from RCM output: Development and testing of downscaling and validation methods

NASA Astrophysics Data System (ADS)

Machguth, H.; Paul, F.; Kotlarski, S.; Hoelzle, M.

2009-04-01

Climate model output has been applied in several studies on glacier mass balance calculation. Hereby, computation of mass balance has mostly been performed at the native resolution of the climate model output or data from individual cells were selected and statistically downscaled. Little attention has been given to the issue of downscaling entire fields of climate model output to a resolution fine enough to compute glacier mass balance in rugged high-mountain terrain. In this study we explore the use of gridded output from a regional climate model (RCM) to drive a distributed mass balance model for the perimeter of the Swiss Alps and the time frame 1979-2003. Our focus lies on the development and testing of downscaling and validation methods. The mass balance model runs at daily steps and 100 m spatial resolution while the RCM REMO provides daily grids (approx. 18 km resolution) of dynamically downscaled re-analysis data. Interpolation techniques and sub-grid parametrizations are combined to bridge the gap in spatial resolution and to obtain daily input fields of air temperature, global radiation and precipitation. The meteorological input fields are compared to measurements at 14 high-elevation weather stations. Computed mass balances are compared to various sets of direct measurements, including stake readings and mass balances for entire glaciers. The validation procedure is performed separately for annual, winter and summer balances. Time series of mass balances for entire glaciers obtained from the model run agree well with observed time series. On the one hand, summer melt measured at stakes on several glaciers is well reproduced by the model, on the other hand, observed accumulation is either over- or underestimated. It is shown that these shifts are systematic and correlated to regional biases in the meteorological input fields. We conclude that the gap in spatial resolution is not a large drawback, while biases in RCM output are a major limitation to model performance. The development and testing of methods to reduce regionally variable biases in entire fields of RCM output should be a focus of pursuing studies.

Expertly Validated Models and Phylogenetically-Controlled Analysis Suggests Responses to Climate Change Are Related to Species Traits in the Order Lagomorpha

PubMed Central

Leach, Katie; Kelly, Ruth; Cameron, Alison; Montgomery, W. Ian; Reid, Neil

2015-01-01

Climate change during the past five decades has impacted significantly on natural ecosystems, and the rate of current climate change is of great concern among conservation biologists. Species Distribution Models (SDMs) have been used widely to project changes in species’ bioclimatic envelopes under future climate scenarios. Here, we aimed to advance this technique by assessing future changes in the bioclimatic envelopes of an entire mammalian order, the Lagomorpha, using a novel framework for model validation based jointly on subjective expert evaluation and objective model evaluation statistics. SDMs were built using climatic, topographical, and habitat variables for all 87 lagomorph species under past and current climate scenarios. Expert evaluation and Kappa values were used to validate past and current models and only those deemed ‘modellable’ within our framework were projected under future climate scenarios (58 species). Phylogenetically-controlled regressions were used to test whether species traits correlated with predicted responses to climate change. Climate change is likely to impact more than two-thirds of lagomorph species, with leporids (rabbits, hares, and jackrabbits) likely to undertake poleward shifts with little overall change in range extent, whilst pikas are likely to show extreme shifts to higher altitudes associated with marked range declines, including the likely extinction of Kozlov’s Pika (Ochotona koslowi). Smaller-bodied species were more likely to exhibit range contractions and elevational increases, but showing little poleward movement, and fecund species were more likely to shift latitudinally and elevationally. Our results suggest that species traits may be important indicators of future climate change and we believe multi-species approaches, as demonstrated here, are likely to lead to more effective mitigation measures and conservation management. We strongly advocate studies minimising data gaps in our knowledge of the Order, specifically collecting more specimens for biodiversity archives and targeting data deficient geographic regions. PMID:25874407

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.

PyMCT: A Very High Level Language Coupling Tool For Climate System Models

NASA Astrophysics Data System (ADS)

Tobis, M.; Pierrehumbert, R. T.; Steder, M.; Jacob, R. L.

2006-12-01

At the Climate Systems Center of the University of Chicago, we have been examining strategies for applying agile programming techniques to complex high-performance modeling experiments. While the "agile" development methodology differs from a conventional requirements process and its associated milestones, the process remain a formal one. It is distinguished by continuous improvement in functionality, large numbers of small releases, extensive and ongoing testing strategies, and a strong reliance on very high level languages (VHLL). Here we report on PyMCT, which we intend as a core element in a model ensemble control superstructure. PyMCT is a set of Python bindings for MCT, the Fortran-90 based Model Coupling Toolkit, which forms the infrastructure for the inter-component communication in the Community Climate System Model (CCSM). MCT provides a scalable model communication infrastructure. In order to take maximum advantage of agile software development methodologies, we exposed MCT functionality to Python, a prominent VHLL. We describe how the scalable architecture of MCT allows us to overcome the relatively weak runtime performance of Python, so that the performance of the combined system is not severely impacted. To demonstrate these advantages, we reimplemented the CCSM coupler in Python. While this alone offers no new functionality, it does provide a rigorous test of PyMCT functionality and performance. We reimplemented the CPL6 library, presenting an interesting case study of the comparison between conventional Fortran-90 programming and the higher abstraction level provided by a VHLL. The powerful abstractions provided by Python will allow much more complex experimental paradigms. In particular, we hope to build on the scriptability of our coupling strategy to enable systematic sensitivity tests. Our most ambitious objective is to combine our efforts with Bayesian inverse modeling techniques toward objective tuning at the highest level, across model architectures.

A new economic assessment index for the impact of climate change on grain yield

NASA Astrophysics Data System (ADS)

Dong, Wenjie; Chou, Jieming; Feng, Guolin

2007-03-01

The impact of climate change on agriculture has received wide attention by the scientific community. This paper studies how to assess the grain yield impact of climate change, according to the climate change over a long time period in the future as predicted by a climate system model. The application of the concept of a traditional “yield impact of meteorological factor (YIMF)” or “yield impact of weather factor” to the grain yield assessment of a decadal or even a longer timescale would be suffocated at the outset because the YIMF is for studying the phenomenon on an interannual timescale, and it is difficult to distinguish between the trend caused by climate change and the one resulting from changes in non-climatic factors. Therefore, the concept of the yield impact of climatic change (YICC), which is defined as the difference in the per unit area yields (PUAY) of a grain crop under a changing and an envisaged invariant climate conditions, is presented in this paper to assess the impact of global climate change on grain yields. The climatic factor has been introduced into the renowned economic Cobb-Douglas model, yielding a quantitative assessment method of YICC using real data. The method has been tested using the historical data of Northeast China, and the results show that it has an encouraging application outlook.

Climate change forecasting in a mountainous data scarce watershed using CMIP5 models under representative concentration pathways

NASA Astrophysics Data System (ADS)

Aghakhani Afshar, A.; Hasanzadeh, Y.; Besalatpour, A. A.; Pourreza-Bilondi, M.

2017-07-01

Hydrology cycle of river basins and available water resources in arid and semi-arid regions are highly affected by climate changes. In recent years, the increment of temperature due to excessive increased emission of greenhouse gases has led to an abnormality in the climate system of the earth. The main objective of this study is to survey the future climate changes in one of the biggest mountainous watersheds in northeast of Iran (i.e., Kashafrood). In this research, by considering the precipitation and temperature as two important climatic parameters in watersheds, 14 models evolved in the general circulation models (GCMs) of the newest generation in the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used to forecast the future climate changes in the study area. For the historical period of 1992-2005, four evaluation criteria including Nash-Sutcliffe (NS), percent of bias (PBIAS), coefficient of determination ( R 2) and the ratio of the root-mean-square-error to the standard deviation of measured data (RSR) were used to compare the simulated observed data for assessing goodness-of-fit of the models. In the primary results, four climate models namely GFDL-ESM2G, IPSL-CM5A-MR, MIROC-ESM, and NorESM1-M were selected among the abovementioned 14 models due to their more prediction accuracies to the investigated evaluation criteria. Thereafter, climate changes of the future periods (near-century, 2006-2037; mid-century, 2037-2070; and late-century, 2070-2100) were investigated and compared by four representative concentration pathways (RCPs) of new emission scenarios of RCP2.6, RCP4.5, RCP6.0, and RCP8.5. In order to assess the trend of annual and seasonal changes of climatic components, Mann-Kendall non-parametric test (MK) was also employed. The results of Mann-Kendall test revealed that the precipitation has significant variable trends of both positive and negative alterations. Furthermore, the mean, maximum, and minimum temperature values had significant positive trends at 90, 99, and 99.9 % confidence level. On the other hand, in all parts of the Kashafrood Watershed (KW), the average temperature of watershed will be increased up to 0.56-3.3 °C and the mean precipitation will be decreased up to 10.7 % by the end of the twenty-first century comparing to the historical baselines. Also, in seasonal scale, the maximum and minimum precipitations will occur in spring and summer, respectively, and the mean temperature is higher than the historical baseline in all seasons. The maximum and minimum values of the mean temperature will occur in summer and winter, respectively, and the amount of seasonal precipitation in these seasons will be reduced.

Evidence for Large Decadal Variability in the Tropical Mean Radiative Energy Budget

NASA Technical Reports Server (NTRS)

Wielicki, Bruce A.; Wong, Takmeng; Allan, Richard; Slingo, Anthony; Kiehl, Jeffrey T.; Soden, Brian J.; Gordon, C. T.; Miller, Alvin J.; Yang, Shi-Keng; Randall, David R.;

Microhabitat and Climatic Niche Change Explain Patterns of Diversification among Frog Families.

PubMed

Moen, Daniel S; Wiens, John J

2017-07-01

A major goal of ecology and evolutionary biology is to explain patterns of species richness among clades. Differences in rates of net diversification (speciation minus extinction over time) may often explain these patterns, but the factors that drive variation in diversification rates remain uncertain. Three important candidates are climatic niche position (e.g., whether clades are primarily temperate or tropical), rates of climatic niche change among species within clades, and microhabitat (e.g., aquatic, terrestrial, arboreal). The first two factors have been tested separately in several studies, but the relative importance of all three is largely unknown. Here we explore the correlates of diversification among families of frogs, which collectively represent ∼88% of amphibian species. We assemble and analyze data on phylogeny, climate, and microhabitat for thousands of species. We find that the best-fitting phylogenetic multiple regression model includes all three types of variables: microhabitat, rates of climatic niche change, and climatic niche position. This model explains 67% of the variation in diversification rates among frog families, with arboreal microhabitat explaining ∼31%, niche rates ∼25%, and climatic niche position ∼11%. Surprisingly, we show that microhabitat can have a much stronger influence on diversification than climatic niche position or rates of climatic niche change.

Paleoclimates: Understanding climate change past and present

USGS Publications Warehouse

Cronin, Thomas M.

2010-01-01

The field of paleoclimatology relies on physical, chemical, and biological proxies of past climate changes that have been preserved in natural archives such as glacial ice, tree rings, sediments, corals, and speleothems. Paleoclimate archives obtained through field investigations, ocean sediment coring expeditions, ice sheet coring programs, and other projects allow scientists to reconstruct climate change over much of earth's history. When combined with computer model simulations, paleoclimatic reconstructions are used to test hypotheses about the causes of climatic change, such as greenhouse gases, solar variability, earth's orbital variations, and hydrological, oceanic, and tectonic processes. This book is a comprehensive, state-of-the art synthesis of paleoclimate research covering all geological timescales, emphasizing topics that shed light on modern trends in the earth's climate. Thomas M. Cronin discusses recent discoveries about past periods of global warmth, changes in atmospheric greenhouse gas concentrations, abrupt climate and sea-level change, natural temperature variability, and other topics directly relevant to controversies over the causes and impacts of climate change. This text is geared toward advanced undergraduate and graduate students and researchers in geology, geography, biology, glaciology, oceanography, atmospheric sciences, and climate modeling, fields that contribute to paleoclimatology. This volume can also serve as a reference for those requiring a general background on natural climate variability.

Domestic and International Climate Migration from Rural Mexico

PubMed Central

Nawrotzki, Raphael J.; Runfola, Daniel M.; Hunter, Lori M.; Riosmena, Fernando

2016-01-01

Evidence is increasing that climate change and variability may influence human migration patterns. However, there is less agreement regarding the type of migration streams most strongly impacted. This study tests whether climate change more strongly impacted international compared to domestic migration from rural Mexico during 1986-99. We employ eight temperature and precipitation-based climate change indices linked to detailed migration histories obtained from the Mexican Migration Project. Results from multilevel discrete-time event-history models challenge the assumption that climate-related migration will be predominantly short distance and domestic, but instead show that climate change more strongly impacted international moves from rural Mexico. The stronger climate impact on international migration may be explained by the self-insurance function of international migration, the presence of strong migrant networks, and climate-related changes in wage difference. While a warming in temperature increased international outmigration, higher levels of precipitation declined the odds of an international move. PMID:28439146

Domestic and International Climate Migration from Rural Mexico.

PubMed

Nawrotzki, Raphael J; Runfola, Daniel M; Hunter, Lori M; Riosmena, Fernando

2016-12-01

Evidence is increasing that climate change and variability may influence human migration patterns. However, there is less agreement regarding the type of migration streams most strongly impacted. This study tests whether climate change more strongly impacted international compared to domestic migration from rural Mexico during 1986-99. We employ eight temperature and precipitation-based climate change indices linked to detailed migration histories obtained from the Mexican Migration Project. Results from multilevel discrete-time event-history models challenge the assumption that climate-related migration will be predominantly short distance and domestic, but instead show that climate change more strongly impacted international moves from rural Mexico. The stronger climate impact on international migration may be explained by the self-insurance function of international migration, the presence of strong migrant networks, and climate-related changes in wage difference. While a warming in temperature increased international outmigration, higher levels of precipitation declined the odds of an international move.

Multidisciplinary research in the space sciences

NASA Technical Reports Server (NTRS)

Broecker, W. S.; Flynn, G. W.

1983-01-01

Research activities were carried out in the following areas during this reporting period: (1) astrophysics; (2) climate and atmospheric modeling; and (3) climate applications of earth observations & geological studies. An ultra-low-noise 115 GHz receiver based upon a superconducting tunnel diode mixer has been designed and constructed. The first laboratory tests have yielded spectacular results: a single-sideband noise temperature of 75 K considerably more sensitive than any other receiver at this frequency. The receiver will replace that currently in use on the Columbia-GISS CO Sky Survey telescope. The 1.2 meter millimeter-wave telescope at Columbia University has been used to complete two large-scale surveys of molecular matter in the part of the inner galaxy which is visible from the Northern hemisphere (the first galactic quadrant); one of the distant galaxy and one of the solar neighborhood. The research conducted during the past year in the climate and atmospheric modeling programs has been focused on the development of appropriate atmospheric and upper ocean models, and preliminary applications of these models. Principal models are a one-dimensional radiative-convective model, a three-dimensional global climate model, and an upper ocean model. During the past year this project has focused on development of 2-channel satellite analysis methods and radiative transfer studies in support of multichannel analysis techniques.

Modelling the effectiveness of grass buffer strips in managing muddy floods under a changing climate

NASA Astrophysics Data System (ADS)

Mullan, Donal; Vandaele, Karel; Boardman, John; Meneely, John; Crossley, Laura H.

2016-10-01

Muddy floods occur when rainfall generates runoff on agricultural land, detaching and transporting sediment into the surrounding natural and built environment. In the Belgian Loess Belt, muddy floods occur regularly and lead to considerable economic costs associated with damage to property and infrastructure. Mitigation measures designed to manage the problem have been tested in a pilot area within Flanders and were found to be cost-effective within three years. This study assesses whether these mitigation measures will remain effective under a changing climate. To test this, the Water Erosion Prediction Project (WEPP) model was used to examine muddy flooding diagnostics (precipitation, runoff, soil loss and sediment yield) for a case study hillslope in Flanders where grass buffer strips are currently used as a mitigation measure. The model was run for present day conditions and then under 33 future site-specific climate scenarios. These future scenarios were generated from three earth system models driven by four representative concentration pathways and downscaled using quantile mapping and the weather generator CLIGEN. Results reveal that under the majority of future scenarios, muddy flooding diagnostics are projected to increase, mostly as a consequence of large scale precipitation events rather than mean changes. The magnitude of muddy flood events for a given return period is also generally projected to increase. These findings indicate that present day mitigation measures may have a reduced capacity to manage muddy flooding given the changes imposed by a warming climate with an enhanced hydrological cycle. Revisions to the design of existing mitigation measures within existing policy frameworks are considered the most effective way to account for the impacts of climate change in future mitigation planning.

Predictive Modeling of Rice Yellow Stem Borer Population Dynamics under Climate Change Scenarios in Indramayu

NASA Astrophysics Data System (ADS)

Nurhayati, E.; Koesmaryono, Y.; Impron

2017-03-01

Rice Yellow Stem Borer (YSB) is one of the major insect pests in rice plants that has high attack intensity in rice production center areas, especially in West Java. This pest is consider as holometabola insects that causes rice damage in the vegetative phase (deadheart) as well as generative phase (whitehead). Climatic factor is one of the environmental factors influence the pattern of dynamics population. The purpose of this study was to develop a predictive modeling of YSB pest dynamics population under climate change scenarios (2016-2035 period) using Dymex Model in Indramayu area, West Java. YSB modeling required two main components, namely climate parameters and YSB development lower threshold of temperature (To) to describe YSB life cycle in every phase. Calibration and validation test of models showed the coefficient of determination (R2) between the predicted results and observations of the study area were 0.74 and 0.88 respectively, which was able to illustrate the development, mortality, transfer of individuals from one stage to the next life also fecundity and YSB reproduction. On baseline climate condition, there was a tendency of population abundance peak (outbreak) occured when a change of rainfall intensity in the rainy season transition to dry season or the opposite conditions was happen. In both of application of climate change scenarios, the model outputs were generated well and able to predict the pattern of YSB population dynamics with a the increasing trend of specific population numbers, generation numbers per season and also shifting pattern of populations abundance peak in the future climatic conditions. These results can be adopted as a tool to predict outbreak and to give early warning to control YSB pest more effectively.

Solar Effects on Global Climate Due to Cosmic Rays and Solar Energetic Particles

NASA Technical Reports Server (NTRS)

Turco, R. P.; Raeder, J.; DAuria, R.

2005-01-01

Although the work reported here does not directly connect solar variability with global climate change, this research establishes a plausible quantitative causative link between observed solar activity and apparently correlated variations in terrestrial climate parameters. Specifically, we have demonstrated that ion-mediated nucleation of atmospheric particles is a likely, and likely widespread, phenomenon that relates solar variability to changes in the microphysical properties of clouds. To investigate this relationship, we have constructed and applied a new model describing the formation and evolution of ionic clusters under a range of atmospheric conditions throughout the lower atmosphere. The activation of large ionic clusters into cloud nuclei is predicted to be favorable in the upper troposphere and mesosphere, and possibly in the lower stratosphere. The model developed under this grant needs to be extended to include additional cluster families, and should be incorporated into microphysical models to further test the cause-and-effect linkages that may ultimately explain key aspects of the connections between solar variability and 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.

Perturbations and gradients as fundamental tests for modeling the soil carbon cycle

NASA Astrophysics Data System (ADS)

Bond-Lamberty, B. P.; Bailey, V. L.; Becker, K.; Fansler, S.; Hinkle, C.; Liu, C.

2013-12-01

An important step in matching process-level knowledge to larger-scale measurements and model results is to challenge those models with site-specific perturbations and/or changing environmental conditions. Here we subject modified versions of an ecosystem process model to two stringent tests: replicating a long-term climate change dryland experiment (Rattlesnake Mountain) and partitioning the carbon fluxes of a soil drainage gradient in the northern Everglades (Disney Wilderness Preserve). For both sites, on-site measurements were supplemented by laboratory incubations of soil columns. We used a parameter-space search algorithm to optimize, within observational limits, the model's influential inputs, so that the spun-up carbon stocks and fluxes matched observed values. Modeled carbon fluxes (net primary production and net ecosystem exchange) agreed with measured values, within observational error limits, but the model's partitioning of soil fluxes (autotrophic versus heterotrophic), did not match laboratory measurements from either site. Accounting for site heterogeneity at DWP, modeled carbon exchange was reasonably consistent with values from eddy covariance. We discuss the implications of this work for ecosystem- to global scale modeling of ecosystems in a changing climate.

An alternate approach to assessing climate risks

NASA Astrophysics Data System (ADS)

Brown, Casey; Wilby, Robert L.

2012-10-01

U.S. federal agencies are now required to review the potential impacts of climate change on their assets and missions. Similar arrangements are also in place in the United Kingdom under reporting powers for key infrastructure providers (http://www.defra.gov.uk/environment/climate/sectors/reporting-authorities/reporting-authorities-reports/). These requirements reflect growing concern about climate resilience and the management of long-lived assets. At one level, analyzing climate risks is a matter of due diligence, given mounting scientific evidence. However, there is no consensus about the means for doing so nor about whether climate models are even ft for the purpose; in addition, several important issues are often overlooked when incorporating climate information into adaptation decisions. An alternative to the scenarioled strategy, such as an approach based on a vulnerability analysis ("stress test"), may identify practical options for resource managers.

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.

High resolution climate scenarios for snowmelt modelling in small alpine catchments

NASA Astrophysics Data System (ADS)

Schirmer, M.; Peleg, N.; Burlando, P.; Jonas, T.

2017-12-01

Snow in the Alps is affected by climate change with regard to duration, timing and amount. This has implications with respect to important societal issues as drinking water supply or hydropower generation. In Switzerland, the latter received a lot of attention following the political decision to phase out of nuclear electricity production. An increasing number of authorization requests for small hydropower plants located in small alpine catchments was observed in the recent years. This situation generates ecological conflicts, while the expected climate change poses a threat to water availability thus putting at risk investments in such hydropower plants. Reliable high-resolution climate scenarios are thus required, which account for small-scale processes to achieve realistic predictions of snowmelt runoff and its variability in small alpine catchments. We therefore used a novel model chain by coupling a stochastic 2-dimensional weather generator (AWE-GEN-2d) with a state-of-the-art energy balance snow cover model (FSM). AWE-GEN-2d was applied to generate ensembles of climate variables at very fine temporal and spatial resolution, thus providing all climatic input variables required for the energy balance modelling. The land-surface model FSM was used to describe spatially variable snow cover accumulation and melt processes. The FSM was refined to allow applications at very high spatial resolution by specifically accounting for small-scale processes, such as a subgrid-parametrization of snow covered area or an improved representation of forest-snow processes. For the present study, the model chain was tested for current climate conditions using extensive observational dataset of different spatial and temporal coverage. Small-scale spatial processes such as elevation gradients or aspect differences in the snow distribution were evaluated using airborne LiDAR data. 40-year of monitoring data for snow water equivalent, snowmelt and snow-covered area for entire Switzerland was used to verify snow distribution patterns at coarser spatial and temporal scale. The ability of the model chain to reproduce current climate conditions in small alpine catchments makes this model combination an outstanding candidate to produce high resolution climate scenarios of snowmelt in small alpine catchments.

Martian climate - An empirical test of possible gross variations

NASA Technical Reports Server (NTRS)

Owen, T.

1974-01-01

There appears to be evidence for a cyclic behavior of the Martian climate in which the surface pressure periodically reaches values compatible with the flow of water in equatorial regions on the planet. A relatively simple test of such hypotheses is pointed out. The premise on which cyclic models are based is that a substantial reservoir of volatils exist in frozen form at one or both poles. The proposed test involves a determination of the relative abundances of neon and argon isotopes. The required measurements may be made after the soft landing next February of Soviet spacecraft presently en route to the planet.

Military Psychology: An Overview,

DTIC Science & Technology

1984-05-01

intelligence tests that were widely used in World War I and also served as the models for most group intelligence tests developed after the war for military and...in such areas as supervision, job satisfaction, organizational climate , and work-group effectiveness. For more information write: LMDC/AN, Maxwell Air...primate. Animal models and methods from the disciplines of behavioral toxicology, behavioral pharmacology, physiological psychology, and neurophysiology

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

NASA Astrophysics Data System (ADS)

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

2009-12-01

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

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post streamline the interaction of analysis, forecast, and post-processing systems within NCEP. The NEMS Force, and will eventually provide support to the community through the Developmental Test Center (DTC

Ecohydrologic separation of water between trees and streams in a Mediterranean climate

Treesearch

J. Renee Brooks; Holly R. Barnard; Rob Coulombe; Jeffrey J. McDonnell

2010-01-01

Here, we directly explore links between hydrology and transpiration at the small watershed scale in a seasonally dry climate. Our central questions were: to what extent do trees and streams return the same water pool to the hydrosphere and how does this vary spatially within a watershed? These questions are fundamental to testing watershed hydrology models and coupled...

Is carbon storage enough? Can plants adapt? New questions in climate change research.

Treesearch

Sally Duncan

2002-01-01

As it becomes increasingly apparent that human activities are partly responsible for global warming, the focus of climate change research is shifting from the churning out of assessments to the pursuit of science that can test the robustness of existing models. The questions now being addressed are becoming more challenging: Can water-use efficiency of plants keep up...

Forecasting European cold waves based on subsampling strategies of CMIP5 and Euro-CORDEX ensembles

NASA Astrophysics Data System (ADS)

Cordero-Llana, Laura; Braconnot, Pascale; Vautard, Robert; Vrac, Mathieu; Jezequel, Aglae

2016-04-01

Forecasting future extreme events under the present changing climate represents a difficult task. Currently there are a large number of ensembles of simulations for climate projections that take in account different models and scenarios. However, there is a need for reducing the size of the ensemble to make the interpretation of these simulations more manageable for impact studies or climate risk assessment. This can be achieved by developing subsampling strategies to identify a limited number of simulations that best represent the ensemble. In this study, cold waves are chosen to test different approaches for subsampling available simulations. The definition of cold waves depends on the criteria used, but they are generally defined using a minimum temperature threshold, the duration of the cold spell as well as their geographical extend. These climate indicators are not universal, highlighting the difficulty of directly comparing different studies. As part of the of the CLIPC European project, we use daily surface temperature data obtained from CMIP5 outputs as well as Euro-CORDEX simulations to predict future cold waves events in Europe. From these simulations a clustering method is applied to minimise the number of ensembles required. Furthermore, we analyse the different uncertainties that arise from the different model characteristics and definitions of climate indicators. Finally, we will test if the same subsampling strategy can be used for different climate indicators. This will facilitate the use of the subsampling results for a wide number of impact assessment studies.

Evaluation of Probable Maximum Precipitation and Flood under Climate Change in the 21st Century

NASA Astrophysics Data System (ADS)

Gangrade, S.; Kao, S. C.; Rastogi, D.; Ashfaq, M.; Naz, B. S.; Kabela, E.; Anantharaj, V. G.; Singh, N.; Preston, B. L.; Mei, R.

2016-12-01

Critical infrastructures are potentially vulnerable to extreme hydro-climatic events. Under a warming environment, the magnitude and frequency of extreme precipitation and flood are likely to increase enhancing the needs to more accurately quantify the risks due to climate change. In this study, we utilized an integrated modeling framework that includes the Weather Research Forecasting (WRF) model and a high resolution distributed hydrology soil vegetation model (DHSVM) to simulate probable maximum precipitation (PMP) and flood (PMF) events over Alabama-Coosa-Tallapoosa River Basin. A total of 120 storms were selected to simulate moisture maximized PMP under different meteorological forcings, including historical storms driven by Climate Forecast System Reanalysis (CFSR) and baseline (1981-2010), near term future (2021-2050) and long term future (2071-2100) storms driven by Community Climate System Model version 4 (CCSM4) under Representative Concentrations Pathway 8.5 emission scenario. We also analyzed the sensitivity of PMF to various antecedent hydrologic conditions such as initial soil moisture conditions and tested different compulsive approaches. Overall, a statistical significant increase is projected for future PMP and PMF, mainly attributed to the increase of background air temperature. The ensemble of simulated PMP and PMF along with their sensitivity allows us to better quantify the potential risks associated with hydro-climatic extreme events on critical energy-water infrastructures such as major hydropower dams and nuclear power plants.

Climate change likely to reduce orchid bee abundance even in climatic suitable sites.

PubMed

Faleiro, Frederico Valtuille; Nemésio, André; Loyola, Rafael

2018-06-01

Studies have tested whether model predictions based on species' occurrence can predict the spatial pattern of population abundance. The relationship between predicted environmental suitability and population abundance varies in shape, strength and predictive power. However, little attention has been paid to the congruence in predictions of different models fed with occurrence or abundance data, in particular when comparing metrics of climate change impact. Here, we used the ecological niche modeling fit with presence-absence and abundance data of orchid bees to predict the effect of climate change on species and assembly level distribution patterns. In addition, we assessed whether predictions of presence-absence models can be used as a proxy to abundance patterns. We obtained georeferenced abundance data of orchid bees (Hymenoptera: Apidae: Euglossina) in the Brazilian Atlantic Forest. Sampling method consisted in attracting male orchid bees to baits of at least five different aromatic compounds and collecting the individuals with entomological nets or bait traps. We limited abundance data to those obtained by similar standard sampling protocol to avoid bias in abundance estimation. We used boosted regression trees to model ecological niches and project them into six climate models and two Representative Concentration Pathways. We found that models based on species occurrences worked as a proxy for changes in population abundance when the output of the models were continuous; results were very different when outputs were discretized to binary predictions. We found an overall trend of diminishing abundance in the future, but a clear retention of climatically suitable sites too. Furthermore, geographic distance to gained climatic suitable areas can be very short, although it embraces great variation. Changes in species richness and turnover would be concentrated in western and southern Atlantic Forest. Our findings offer support to the ongoing debate of suitability-abundance models and can be used to support spatial conservation prioritization schemes and species triage in Atlantic Forest. © 2018 John Wiley & Sons Ltd.

Functional linear models to test for differences in prairie wetland hydraulic gradients

USGS Publications Warehouse

Greenwood, Mark C.; Sojda, Richard S.; Preston, Todd M.; Swayne, David A.; Yang, Wanhong; Voinov, A.A.; Rizzoli, A.; Filatova, T.

2010-01-01

Functional data analysis provides a framework for analyzing multiple time series measured frequently in time, treating each series as a continuous function of time. Functional linear models are used to test for effects on hydraulic gradient functional responses collected from three types of land use in Northeastern Montana at fourteen locations. Penalized regression-splines are used to estimate the underlying continuous functions based on the discretely recorded (over time) gradient measurements. Permutation methods are used to assess the statistical significance of effects. A method for accommodating missing observations in each time series is described. Hydraulic gradients may be an initial and fundamental ecosystem process that responds to climate change. We suggest other potential uses of these methods for detecting evidence of climate change.

Evaluating meteo marine climatic model inputs for the investigation of coastal hydrodynamics

NASA Astrophysics Data System (ADS)

Bellafiore, D.; Bucchignani, E.; Umgiesser, G.

2010-09-01

One of the major aspects discussed in the recent works on climate change is how to provide information from the global scale to the local one. In fact the influence of sea level rise and changes in the meteorological conditions due to climate change in strategic areas like the coastal zone is at the base of the well known mitigation and risk assessment plans. The investigation of the coastal zone hydrodynamics, from a modeling point of view, has been the field for the connection between hydraulic models and ocean models and, in terms of process studies, finite element models have demonstrated their suitability in the reproduction of complex coastal morphology and in the capability to reproduce different spatial scale hydrodynamic processes. In this work the connection between two different model families, the climate models and the hydrodynamic models usually implemented for process studies, is tested. Together, they can be the most suitable tool for the investigation of climate change on coastal systems. A finite element model, SHYFEM (Shallow water Hydrodynamic Finite Element Model), is implemented on the Adriatic Sea, to investigate the effect of wind forcing datasets produced by different downscaling from global climate models in terms of surge and its coastal effects. The wind datasets are produced by the regional climate model COSMO-CLM (CIRA), and by EBU-POM model (Belgrade University), both downscaling from ECHAM4. As a first step the downscaled wind datasets, that have different spatial resolutions, has been analyzed for the period 1960-1990 to compare what is their capability to reproduce the measured wind statistics in the coastal zone in front of the Venice Lagoon. The particularity of the Adriatic Sea meteo climate is connected with the influence of the orography in the strengthening of winds like Bora, from North-East. The increase in spatial resolution permits the more resolved wind dataset to better reproduce meteorology and to provide a more realistic forcing for hydrodynamic simulations. After this analysis, effects on water level variations, under different wind forcing, has been analyzed to define what is the local effect on sea level changes in the coastal area of the North Adriatic. Surge statistics produced from different climate model forcings for the IPCC A1B scenario have been studied to provide local information on climate change effects on coastal hydrodynamics due to meteorological effect. This typology of application has been considered a suitable tool for coastal management and can be considered a study field that will increase its importance in the more general investigation on scale interaction processes as the effects of global scale climate phenomena on local areas.

The critical role of fire in catchment coevolution in South Eastern Australia

NASA Astrophysics Data System (ADS)

Nyman, P.; Inbar, A.; Lane, P. N. J.; Sheridan, G. J.

2016-12-01

Temperate south east Australian forested uplands are characterised by complex spatial patterns in forest types, soils and fire regimes, even within areas with similar geologies and landscape position. Preliminary measurements and experiments suggest that positive and negative feedbacks between the vegetation, fuels, fire frequency and soil erosion may control the coevolution of these observed system states. Here we propose the hypotheses that in this landscape post-fire soil erosion has played a dominant role in the coevolved system-state combinations of standing biomass, fire frequency and soil depth. To test the hypothesis a 1D simulation model was developed that links together an ecohydrological model to drive the biomass production and water and energy partitioning, a stochastic fire model that is controlled by climate, fuel load and moisture conditions, and a geomorphic model that controls soil production and fluvial and diffusive sediment transport rates. The model was calibrated to the range of existing observed quasi-equalibrium system-states of soil depth, standing biomass, fuel loading and fire frequency using field measurements from 12 instrumented eco-hydrologic microclimate research sites. The long-term partitioning of rainfall into evaporation, transpiration, and streamflow was calibrated against field and literature values. Fuel moisture and micro-climate variables were calibrated to the field microclimate stations. The calibrated model was able to reasonably replicate the observed quasi-equilibrium system-states and hydrologic outputs using current climate forcings operating over a 10,000 year period, providing confidence in the model structure and performance. The model was then used to test the hypothesis stated above, by alternatively including or excluding the post fire erosion process. An alternate hypothesis, whereby the observed system states are dominated by climate related differences in soil production rates was also tested in this way. The results support the hypothesis that feedbacks between fire, ecology, hydrology and geomorphology have played a critical role in the coevolution of south east Australian forested uplands. Similar pyro-eco-hydrologic feedbacks may play a critical role in catchment coevolution in other forested systems globally.

Application of synthetic scenarios to address water resource concerns: A management-guided case study from the Upper Colorado River Basin

USGS Publications Warehouse

McAfee, Stephanie A.; Pederson, Gregory T.; Woodhouse, Connie A.; McCabe, Gregory

2017-01-01

Water managers are increasingly interested in better understanding and planning for projected resource impacts from climate change. In this management-guided study, we use a very large suite of synthetic climate scenarios in a statistical modeling framework to simultaneously evaluate how (1) average temperature and precipitation changes, (2) initial basin conditions, and (3) temporal characteristics of the input climate data influence water-year flow in the Upper Colorado River. The results here suggest that existing studies may underestimate the degree of uncertainty in future streamflow, particularly under moderate temperature and precipitation changes. However, we also find that the relative severity of future flow projections within a given climate scenario can be estimated with simple metrics that characterize the input climate data and basin conditions. These results suggest that simple testing, like the analyses presented in this paper, may be helpful in understanding differences between existing studies or in identifying specific conditions for physically based mechanistic modeling. Both options could reduce overall cost and improve the efficiency of conducting climate change impacts studies.

Potential impacts of climate change on the ecology of dengue and its mosquito vector the Asian tiger mosquito (Aedes albopictus)

NASA Astrophysics Data System (ADS)

Erickson, R. A.; Hayhoe, K.; Presley, S. M.; Allen, L. J. S.; Long, K. R.; Cox, S. B.

2012-09-01

Shifts in temperature and precipitation patterns caused by global climate change may have profound impacts on the ecology of certain infectious diseases. We examine the potential impacts of climate change on the transmission and maintenance dynamics of dengue, a resurging mosquito-vectored infectious disease. In particular, we project changes in dengue season length for three cities: Atlanta, GA; Chicago, IL and Lubbock, TX. These cities are located on the edges of the range of the Asian tiger mosquito within the United States of America and were chosen as test cases. We use a disease model that explicitly incorporates mosquito population dynamics and high-resolution climate projections. Based on projected changes under the Special Report on Emissions Scenarios (SRES) A1fi (higher) and B1 (lower) emission scenarios as simulated by four global climate models, we found that the projected warming shortened mosquito lifespan, which in turn decreased the potential dengue season. These results illustrate the difficulty in predicting how climate change may alter complex systems.

Regionalizing Africa: Patterns of Precipitation Variability in Observations and Global Climate Models

NASA Technical Reports Server (NTRS)

Badr, Hamada S.; Dezfuli, Amin K.; Zaitchik, Benjamin F.; Peters-Lidard, Christa D.

2016-01-01

Many studies have documented dramatic climatic and environmental changes that have affected Africa over different time scales. These studies often raise questions regarding the spatial extent and regional connectivity of changes inferred from observations and proxies and/or derived from climate models. Objective regionalization offers a tool for addressing these questions. To demonstrate this potential, applications of hierarchical climate regionalizations of Africa using observations and GCM historical simulations and future projections are presented. First, Africa is regionalized based on interannual precipitation variability using Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data for the period 19812014. A number of data processing techniques and clustering algorithms are tested to ensure a robust definition of climate regions. These regionalization results highlight the seasonal and even month-to-month specificity of regional climate associations across the continent, emphasizing the need to consider time of year as well as research question when defining a coherent region for climate analysis. CHIRPS regions are then compared to those of five GCMs for the historic period, with a focus on boreal summer. Results show that some GCMs capture the climatic coherence of the Sahel and associated teleconnections in a manner that is similar to observations, while other models break the Sahel into uncorrelated subregions or produce a Sahel-like region of variability that is spatially displaced from observations. Finally, shifts in climate regions under projected twenty-first-century climate change for different GCMs and emissions pathways are examined. A projected change is found in the coherence of the Sahel, in which the western and eastern Sahel become distinct regions with different teleconnections. This pattern is most pronounced in high-emissions scenarios.

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

NASA Technical Reports Server (NTRS)

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

2015-01-01

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

Thermal Testing and Model Correlation for Advanced Topographic Laser Altimeter Instrument (ATLAS)

NASA Technical Reports Server (NTRS)

Patel, Deepak

2016-01-01

The Advanced Topographic Laser Altimeter System (ATLAS) part of the Ice Cloud and Land Elevation Satellite 2 (ICESat-2) is an upcoming Earth Science mission focusing on the effects of climate change. The flight instrument passed all environmental testing at GSFC (Goddard Space Flight Center) and is now ready to be shipped to the spacecraft vendor for integration and testing. This topic covers the analysis leading up to the test setup for ATLAS thermal testing as well as model correlation to flight predictions. Test setup analysis section will include areas where ATLAS could not meet flight like conditions and what were the limitations. Model correlation section will walk through changes that had to be made to the thermal model in order to match test results. The correlated model will then be integrated with spacecraft model for on-orbit predictions.

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.

Effect of the learning climate of residency programs on faculty's teaching performance as evaluated by residents.

PubMed

Lombarts, Kiki M J M H; Heineman, Maas Jan; Scherpbier, Albert J J A; Arah, Onyebuchi A

2014-01-01

To understand teaching performance of individual faculty, the climate in which residents' learning takes place, the learning climate, may be important. There is emerging evidence that specific climates do predict specific outcomes. Until now, the effect of learning climate on the performance of the individual faculty who actually do the teaching was unknown. THIS STUDY: (i) tested the hypothesis that a positive learning climate was associated with better teaching performance of individual faculty as evaluated by residents, and (ii) explored which dimensions of learning climate were associated with faculty's teaching performance. We conducted two cross-sectional questionnaire surveys amongst residents from 45 residency training programs and multiple specialties in 17 hospitals in the Netherlands. Residents evaluated the teaching performance of individual faculty using the robust System for Evaluating Teaching Qualities (SETQ) and evaluated the learning climate of residency programs using the Dutch Residency Educational Climate Test (D-RECT). The validated D-RECT questionnaire consisted of 11 subscales of learning climate. Main outcome measure was faculty's overall teaching (SETQ) score. We used multivariable adjusted linear mixed models to estimate the separate associations of overall learning climate and each of its subscales with faculty's teaching performance. In total 451 residents completed 3569 SETQ evaluations of 502 faculty. Residents also evaluated the learning climate of 45 residency programs in 17 hospitals in the Netherlands. Overall learning climate was positively associated with faculty's teaching performance (regression coefficient 0.54, 95% confidence interval: 0.37 to 0.71; P<0.001). Three out of 11 learning climate subscales were substantially associated with better teaching performance: 'coaching and assessment', 'work is adapted to residents' competence', and 'formal education'. Individual faculty's teaching performance evaluations are positively affected by better learning climate of residency programs.

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

Single-Column Modeling, GCM Parameterizations and Atmospheric Radiation Measurement Data

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

Somerville, R.C.J.; Iacobellis, S.F.

2005-03-18

Our overall goal is identical to that of the Atmospheric Radiation Measurement (ARM) Program: the development of new and improved parameterizations of cloud-radiation effects and related processes, using ARM data at all three ARM sites, and the implementation and testing of these parameterizations in global and regional models. To test recently developed prognostic parameterizations based on detailed cloud microphysics, we have first compared single-column model (SCM) output with ARM observations at the Southern Great Plains (SGP), North Slope of Alaska (NSA) and Topical Western Pacific (TWP) sites. We focus on the predicted cloud amounts and on a suite of radiativemore » quantities strongly dependent on clouds, such as downwelling surface shortwave radiation. Our results demonstrate the superiority of parameterizations based on comprehensive treatments of cloud microphysics and cloud-radiative interactions. At the SGP and NSA sites, the SCM results simulate the ARM measurements well and are demonstrably more realistic than typical parameterizations found in conventional operational forecasting models. At the TWP site, the model performance depends strongly on details of the scheme, and the results of our diagnostic tests suggest ways to develop improved parameterizations better suited to simulating cloud-radiation interactions in the tropics generally. These advances have made it possible to take the next step and build on this progress, by incorporating our parameterization schemes in state-of-the-art 3D atmospheric models, and diagnosing and evaluating the results using independent data. Because the improved cloud-radiation results have been obtained largely via implementing detailed and physically comprehensive cloud microphysics, we anticipate that improved predictions of hydrologic cycle components, and hence of precipitation, may also be achievable. We are currently testing the performance of our ARM-based parameterizations in state-of-the--art global and regional models. One fruitful strategy for evaluating advances in parameterizations has turned out to be using short-range numerical weather prediction as a test-bed within which to implement and improve parameterizations for modeling and predicting climate variability. The global models we have used to date are the CAM atmospheric component of the National Center for Atmospheric Research (NCAR) CCSM climate model as well as the National Centers for Environmental Prediction (NCEP) numerical weather prediction model, thus allowing testing in both climate simulation and numerical weather prediction modes. We present detailed results of these tests, demonstrating the sensitivity of model performance to changes in parameterizations.« less

Current Analogues of Future Climate Indicate the Likely Response of a Sensitive Montane Tropical Avifauna to a Warming World

PubMed Central

Anderson, Alexander S.; Storlie, Collin J.; Shoo, Luke P.; Pearson, Richard G.; Williams, Stephen E.

2013-01-01

Among birds, tropical montane species are likely to be among the most vulnerable to climate change, yet little is known about how climate drives their distributions, nor how to predict their likely responses to temperature increases. Correlative models of species’ environmental niches have been widely used to predict changes in distribution, but direct tests of the relationship between key variables, such as temperature, and species’ actual distributions are few. In the absence of historical data with which to compare observations and detect shifts, space-for-time substitutions, where warmer locations are used as analogues of future conditions, offer an opportunity to test for species’ responses to climate. We collected density data for rainforest birds across elevational gradients in northern and southern subregions within the Australian Wet Tropics (AWT). Using environmental optima calculated from elevational density profiles, we detected a significant elevational difference between the two regions in ten of 26 species. More species showed a positive (19 spp.) than negative (7 spp.) displacement, with a median difference of ∼80.6 m across the species analysed that is concordant with that expected due to latitudinal temperature differences (∼75.5 m). Models of temperature gradients derived from broad-scale climate surfaces showed comparable performance to those based on in-situ measurements, suggesting the former is sufficient for modeling impacts. These findings not only confirm temperature as an important factor driving elevational distributions of these species, but also suggest species will shift upslope to track their preferred environmental conditions. Our approach uses optima calculated from elevational density profiles, offering a data-efficient alternative to distribution limits for gauging climate constraints, and is sensitive enough to detect distribution shifts in this avifauna in response to temperature changes of as little as 0.4 degrees. We foresee important applications in the urgent task of detecting and monitoring impacts of climate change on montane tropical biodiversity. PMID:23936005

Current analogues of future climate indicate the likely response of a sensitive montane tropical avifauna to a warming world.

PubMed

Anderson, Alexander S; Storlie, Collin J; Shoo, Luke P; Pearson, Richard G; Williams, Stephen E

2013-01-01

Among birds, tropical montane species are likely to be among the most vulnerable to climate change, yet little is known about how climate drives their distributions, nor how to predict their likely responses to temperature increases. Correlative models of species' environmental niches have been widely used to predict changes in distribution, but direct tests of the relationship between key variables, such as temperature, and species' actual distributions are few. In the absence of historical data with which to compare observations and detect shifts, space-for-time substitutions, where warmer locations are used as analogues of future conditions, offer an opportunity to test for species' responses to climate. We collected density data for rainforest birds across elevational gradients in northern and southern subregions within the Australian Wet Tropics (AWT). Using environmental optima calculated from elevational density profiles, we detected a significant elevational difference between the two regions in ten of 26 species. More species showed a positive (19 spp.) than negative (7 spp.) displacement, with a median difference of ∼80.6 m across the species analysed that is concordant with that expected due to latitudinal temperature differences (∼75.5 m). Models of temperature gradients derived from broad-scale climate surfaces showed comparable performance to those based on in-situ measurements, suggesting the former is sufficient for modeling impacts. These findings not only confirm temperature as an important factor driving elevational distributions of these species, but also suggest species will shift upslope to track their preferred environmental conditions. Our approach uses optima calculated from elevational density profiles, offering a data-efficient alternative to distribution limits for gauging climate constraints, and is sensitive enough to detect distribution shifts in this avifauna in response to temperature changes of as little as 0.4 degrees. We foresee important applications in the urgent task of detecting and monitoring impacts of climate change on montane tropical biodiversity.

Rising temperatures reduce global wheat production

USDA-ARS?s Scientific Manuscript database

Crop models are essential to assess the threat of climate change for food production but have not been systematically tested against temperature experiments, despite demonstrated uncertainty in temperature response. Herein, we compare 30 different wheat crop models against field experiments in which...

Military Potential Test of the Model PA23-250B Fixed-Wing Instrument Trainer

DTIC Science & Technology

1964-11-30

cabin heater was installed in the test airplane. Existing climatic conditions precluded actual tests to determine the capability of the heater to...housed within the engine contol pedestal under the engine conr- trol levers. r , aulic pressure is supplied to the control unit by an engine-driven

Background sampling and transferability of species distribution model ensembles under climate change

NASA Astrophysics Data System (ADS)

Iturbide, Maialen; Bedia, Joaquín; Gutiérrez, José Manuel

2018-07-01

Species Distribution Models (SDMs) constitute an important tool to assist decision-making in environmental conservation and planning. A popular application of these models is the projection of species distributions under climate change conditions. Yet there are still a range of methodological SDM factors which limit the transferability of these models, contributing significantly to the overall uncertainty of the resulting projections. An important source of uncertainty often neglected in climate change studies comes from the use of background data (a.k.a. pseudo-absences) for model calibration. Here, we study the sensitivity to pseudo-absence sampling as a determinant factor for SDM stability and transferability under climate change conditions, focusing on European wide projections of Quercus robur as an illustrative case study. We explore the uncertainty in future projections derived from ten pseudo-absence realizations and three popular SDMs (GLM, Random Forest and MARS). The contribution of the pseudo-absence realization to the uncertainty was higher in peripheral regions and clearly differed among the tested SDMs in the whole study domain, being MARS the most sensitive - with projections differing up to a 40% for different realizations - and GLM the most stable. As a result we conclude that parsimonious SDMs are preferable in this context, avoiding complex methods (such as MARS) which may exhibit poor model transferability. Accounting for this new source of SDM-dependent uncertainty is crucial when forming multi-model ensembles to undertake climate change projections.

Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

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. 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. Monthly temperature 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. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. 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.

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Chuang (POST) Fanglin Yang (VSDB) Perry Shafran (VERIFICATION) Ilya Rivin (HYCOM) David Behringer (MOM4 * Functional Equivalence test for MOM4p0 on GAEA - Dave Behringer * NCEP Gaea module - $NETCDF * Use a forum

Evaluation of methodology for detecting/predicting migration of forest species

Treesearch

Dale S. Solomon; William B. Leak

1996-01-01

Available methods for analyzing migration of forest species are evaluated, including simulation models, remeasured plots, resurveys, pollen/vegetation analysis, and age/distance trends. Simulation models have provided some of the most drastic estimates of species changes due to predicted changes in global climate. However, these models require additional testing...

Integrated surface/subsurface permafrost thermal hydrology: Model formulation and proof-of-concept simulations

DOE PAGES

Painter, Scott L.; Coon, Ethan T.; Atchley, Adam L.; ...

2016-08-11

The need to understand potential climate impacts and feedbacks in Arctic regions has prompted recent interest in modeling of permafrost dynamics in a warming climate. A new fine-scale integrated surface/subsurface thermal hydrology modeling capability is described and demonstrated in proof-of-concept simulations. The new modeling capability combines a surface energy balance model with recently developed three-dimensional subsurface thermal hydrology models and new models for nonisothermal surface water flows and snow distribution in the microtopography. Surface water flows are modeled using the diffusion wave equation extended to include energy transport and phase change of ponded water. Variation of snow depth in themore » microtopography, physically the result of wind scour, is also modeled heuristically with a diffusion wave equation. The multiple surface and subsurface processes are implemented by leveraging highly parallel community software. Fully integrated thermal hydrology simulations on the tilted open book catchment, an important test case for integrated surface/subsurface flow modeling, are presented. Fine-scale 100-year projections of the integrated permafrost thermal hydrological system on an ice wedge polygon at Barrow Alaska in a warming climate are also presented. Finally, these simulations demonstrate the feasibility of microtopography-resolving, process-rich simulations as a tool to help understand possible future evolution of the carbon-rich Arctic tundra in a warming climate.« less

An assessment of the distribution and spread of the tick Hyalomma marginatum in the western Palearctic under different climate scenarios.

PubMed

Estrada-Peña, Agustín; Sánchez, Nely; Estrada-Sánchez, Adrián

2012-09-01

We applied a process-driven model to evaluate the impact of climate scenarios for the years 2020, 2050, and 2080 on the life cycle of Hyalomma marginatum ticks in the western Palearctic. The net growth rate of the tick populations increased in every scenario tested compared to the current climate baseline. These results support the expectations of increased tick survival and increased population turnover in future climate scenarios. We included a basic evaluation of host movement based on rules connected to altitude, slope, size of the near patches, and inter-patch distances in the real landscape over the target area. Data on landscape were obtained from medium-resolution MODIS satellite imagery, which allowed us to test the potential spread of the populations. Such a model of host dispersal linked to the process-driven life cycle model demonstrated that eastern (Turkey, Russia, and Balkans) populations of H. marginatum currently are well separated and have little mixing with western (Italy, Spain, and northern Africa) populations. The northern limit is marked by the cold areas in the Balkans, Alps, and Pyrenees. Under the warmer conditions predicted by the climate scenarios, the exchange of ticks throughout new areas, previously free of the vector, is expected to increase, mainly in the Balkans and southern Russia, over the limit of the mountain ranges. Therefore, the northern limit of the tick range would increase. Additional studies are necessary to understand the implications of host changes in range and abundance for H. marginatum and Crimean-Congo hemorrhagic fever virus.

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.

Assessments of Future Maize Yield Potential Changes in the Korean Peninsula Using Multiple Crop Models

NASA Astrophysics Data System (ADS)

Kim, S. H.; Lim, C. H.; Kim, J.; Lee, W. K.; Kafatos, M.

2016-12-01

The Korean Peninsula has unique agricultural environment due to the differences of political and socio-economical system between Republic of Korea (SK, hereafter) and Democratic Peoples' Republic of Korea (NK, hereafter). NK has been suffering lack of food supplies caused by natural disasters, land degradation and political failure. The neighboring developed country SK has better agricultural system but very low food self-sufficiency rate. Maize is an important crop in both countries since it is staple food for NK and SK is No. 2 maize importing country in the world after Japan. Therefore, evaluating maize yield potential (Yp) in the two distinct regions is essential to assess food security under climate change and variability. In this study, we utilized multiple process-based crop models, having ability of regional scale assessment, to evaluate maize Yp and assess the model uncertainties -EPIC, GEPIC, DSSAT, and APSIM model that has capability of regional scale expansion (apsimRegions). First we evaluated each crop model for 3 years from 2012 to 2014 using reanalysis data (RDAPS; Regional Data Assimilation and Prediction System produced by Korea Meteorological Agency) and observed yield data. Each model performances were compared over the different regions in the Korean Peninsula having different local climate characteristics. To quantify of the major influence of at each climate variables, we also conducted sensitivity test using 20 years of climatology in historical period from 1981 to 2000. Lastly, the multi-crop model ensemble analysis was performed for future period from 2031 to 2050. The required weather variables projected for mid-century were employed from COordinated Regional climate Downscaling EXperiment (CORDEX) East Asia. The high-resolution climate data were obtained from multiple regional climate models (RCM) driven by multiple climate scenarios projected from multiple global climate models (GCMs) in conjunction with multiple greenhouse gas concentration pathways. The results indicate that the projected Yp in the Korean peninsula is significantly changed comparing to the historical period and proper adaptation strategies such as optimized planting dates can considerably alleviate Yp decrease.

Intercomparison of hydrological model structures and calibration approaches in climate scenario impact projections

NASA Astrophysics Data System (ADS)

Vansteenkiste, Thomas; Tavakoli, Mohsen; Ntegeka, Victor; De Smedt, Florimond; Batelaan, Okke; Pereira, Fernando; Willems, Patrick

2014-11-01

The objective of this paper is to investigate the effects of hydrological model structure and calibration on climate change impact results in hydrology. The uncertainty in the hydrological impact results is assessed by the relative change in runoff volumes and peak and low flow extremes from historical and future climate conditions. The effect of the hydrological model structure is examined through the use of five hydrological models with different spatial resolutions and process descriptions. These were applied to a medium sized catchment in Belgium. The models vary from the lumped conceptual NAM, PDM and VHM models over the intermediate detailed and distributed WetSpa model to the fully distributed MIKE SHE model. The latter model accounts for the 3D groundwater processes and interacts bi-directionally with a full hydrodynamic MIKE 11 river model. After careful and manual calibration of these models, accounting for the accuracy of the peak and low flow extremes and runoff subflows, and the changes in these extremes for changing rainfall conditions, the five models respond in a similar way to the climate scenarios over Belgium. Future projections on peak flows are highly uncertain with expected increases as well as decreases depending on the climate scenario. The projections on future low flows are more uniform; low flows decrease (up to 60%) for all models and for all climate scenarios. However, the uncertainties in the impact projections are high, mainly in the dry season. With respect to the model structural uncertainty, the PDM model simulates significantly higher runoff peak flows under future wet scenarios, which is explained by its specific model structure. For the low flow extremes, the MIKE SHE model projects significantly lower low flows in dry scenario conditions in comparison to the other models, probably due to its large difference in process descriptions for the groundwater component, the groundwater-river interactions. The effect of the model calibration was tested by comparing the manual calibration approach with automatic calibrations of the VHM model based on different objective functions. The calibration approach did not significantly alter the model results for peak flow, but the low flow projections were again highly influenced. Model choice as well as calibration strategy hence have a critical impact on low flows, more than on peak flows. These results highlight the high uncertainty in low flow modelling, especially in a climate change context.

Process-based modelling to evaluate simulated groundwater levels and frequencies in a Chalk catchment in south-western England

NASA Astrophysics Data System (ADS)

Brenner, Simon; Coxon, Gemma; Howden, Nicholas J. K.; Freer, Jim; Hartmann, Andreas

2018-02-01

Chalk aquifers are an important source of drinking water in the UK. Due to their properties, they are particularly vulnerable to groundwater-related hazards like floods and droughts. Understanding and predicting groundwater levels is therefore important for effective and safe water management. Chalk is known for its high porosity and, due to its dissolvability, exposed to karstification and strong subsurface heterogeneity. To cope with the karstic heterogeneity and limited data availability, specialised modelling approaches are required that balance model complexity and data availability. In this study, we present a novel approach to evaluate simulated groundwater level frequencies derived from a semi-distributed karst model that represents subsurface heterogeneity by distribution functions. Simulated groundwater storages are transferred into groundwater levels using evidence from different observations wells. Using a percentile approach we can assess the number of days exceeding or falling below selected groundwater level percentiles. Firstly, we evaluate the performance of the model when simulating groundwater level time series using a spilt sample test and parameter identifiability analysis. Secondly, we apply a split sample test to the simulated groundwater level percentiles to explore the performance in predicting groundwater level exceedances. We show that the model provides robust simulations of discharge and groundwater levels at three observation wells at a test site in a chalk-dominated catchment in south-western England. The second split sample test also indicates that the percentile approach is able to reliably predict groundwater level exceedances across all considered timescales up to their 75th percentile. However, when looking at the 90th percentile, it only provides acceptable predictions for long time periods and it fails when the 95th percentile of groundwater exceedance levels is considered. By modifying the historic forcings of our model according to expected future climate changes, we create simple climate scenarios and we show that the projected climate changes may lead to generally lower groundwater levels and a reduction of exceedances of high groundwater level percentiles.

Climate change impacts on rainfall extremes and urban drainage: state-of-the-art review

NASA Astrophysics Data System (ADS)

Willems, Patrick; Olsson, Jonas; Arnbjerg-Nielsen, Karsten; Beecham, Simon; Pathirana, Assela; Bülow Gregersen, Ida; Madsen, Henrik; Nguyen, Van-Thanh-Van

2013-04-01

Under the umbrella of the IWA/IAHR Joint Committee on Urban Drainage, the International Working Group on Urban Rainfall (IGUR) has reviewed existing methodologies for the analysis of long-term historical and future trends in urban rainfall extremes and their effects on urban drainage systems, due to anthropogenic climate change. Current practises have several limitations and pitfalls, which are important to be considered by trend or climate change impact modellers and users of trend/impact results. The review considers the following aspects: Analysis of long-term historical trends due to anthropogenic climate change: influence of data limitation, instrumental or environmental changes, interannual variations and longer term climate oscillations on trend testing results. Analysis of long-term future trends due to anthropogenic climate change: by complementing empirical historical data with the results from physically-based climate models, dynamic downscaling to the urban scale by means of Limited Area Models (LAMs) including explicitly small-scale cloud processes; validation of RCM/GCM results for local conditions accounting for natural variability, limited length of the available time series, difference in spatial scales, and influence of climate oscillations; statistical downscaling methods combined with bias correction; uncertainties associated with the climate forcing scenarios, the climate models, the initial states and the statistical downscaling step; uncertainties in the impact models (e.g. runoff peak flows, flood or surcharge frequencies, and CSO frequencies and volumes), including the impacts of more extreme conditions than considered during impact model calibration and validation. Implications for urban drainage infrastructure design and management: upgrading of the urban drainage system as part of a program of routine and scheduled replacement and renewal of aging infrastructure; how to account for the uncertainties; flexible and sustainable solutions; adaptive approach that provides inherent flexibility and reversibility and avoids closing off options; importance of active learning. References: Willems, P., Olsson, J., Arnbjerg-Nielsen, K., Beecham, S., Pathirana, A., Bülow Gregersen, I., Madsen, H., Nguyen, V-T-V. (2012). Impacts of climate change on rainfall extremes and urban drainage. IWA Publishing, 252 p., Paperback Print ISBN 9781780401256; Ebook ISBN 9781780401263 Willems, P., Arnbjerg-Nielsen, K., Olsson, J., Nguyen, V.T.V. (2012), 'Climate change impact assessment on urban rainfall extremes and urban drainage: methods and shortcomings', Atmospheric Research, 103, 106-118

What scaling means in wind engineering: Complementary role of the reduced scale approach in a BLWT and the full scale testing in a large climatic wind tunnel

NASA Astrophysics Data System (ADS)

Flamand, Olivier

2017-12-01

Wind engineering problems are commonly studied by wind tunnel experiments at a reduced scale. This introduces several limitations and calls for a careful planning of the tests and the interpretation of the experimental results. The talk first revisits the similitude laws and discusses how they are actually applied in wind engineering. It will also remind readers why different scaling laws govern in different wind engineering problems. Secondly, the paper focuses on the ways to simplify a detailed structure (bridge, building, platform) when fabricating the downscaled models for the tests. This will be illustrated by several examples from recent engineering projects. Finally, under the most severe weather conditions, manmade structures and equipment should remain operational. What “recreating the climate” means and aims to achieve will be illustrated through common practice in climatic wind tunnel modelling.

Climate envelope predictions indicate an enlarged suitable wintering distribution for Great Bustards (Otis tarda dybowskii) in China for the 21st century

PubMed Central

Mi, Chunrong; Falk, Huettmann

2016-01-01

The rapidly changing climate makes humans realize that there is a critical need to incorporate climate change adaptation into conservation planning. Whether the wintering habitats of Great Bustards (Otis tarda dybowskii), a globally endangered migratory subspecies whose population is approximately 1,500–2,200 individuals in China, would be still suitable in a changing climate environment, and where this could be found, is an important protection issue. In this study, we selected the most suitable species distribution model for bustards using climate envelopes from four machine learning models, combining two modelling approaches (TreeNet and Random Forest) with two sets of variables (correlated variables removed or not). We used common evaluation methods area under the receiver operating characteristic curves (AUC) and the True Skill Statistic (TSS) as well as independent test data to identify the most suitable model. As often found elsewhere, we found Random Forest with all environmental variables outperformed in all assessment methods. When we projected the best model to the latest IPCC-CMIP5 climate scenarios (Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 in three Global Circulation Models (GCMs)), and averaged the project results of the three models, we found that suitable wintering habitats in the current bustard distribution would increase during the 21st century. The Northeast Plain and the south of North China were projected to become two major wintering areas for bustards. However, the models suggest that some currently suitable habitats will experience a reduction, such as Dongting Lake and Poyang Lake in the Middle and Lower Yangtze River Basin. Although our results suggested that suitable habitats in China would widen with climate change, greater efforts should be undertaken to assess and mitigate unstudied human disturbance, such as pollution, hunting, agricultural development, infrastructure construction, habitat fragmentation, and oil and mine exploitation. All of these are negatively and intensely linked with global change. PMID:26855870

Nonlinear responses of southern African rainfall to forcing from Atlantic SST in a high-resolution regional climate model

NASA Astrophysics Data System (ADS)

Williams, C.; Kniveton, D.; Layberry, R.

2009-04-01

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. In this research, high resolution satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA) are used as a basis for undertaking model experiments using a state-of-the-art regional climate model. The MIRA dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. Once the model's ability to reproduce extremes has been assessed, idealised regions of sea surface temperature (SST) anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, results from sensitivity testing of the regional climate model's domain size are briefly presented, before a comparison of simulated daily rainfall from the model with the satellite-derived dataset. Secondly, simulations of current climate and rainfall extremes from the model are compared to the MIRA dataset at daily timescales. Finally, the results from the idealised SST experiments are presented, suggesting highly nonlinear associations between rainfall extremes remote SST anomalies.

Climate envelope predictions indicate an enlarged suitable wintering distribution for Great Bustards (Otis tarda dybowskii) in China for the 21st century.

PubMed

Mi, Chunrong; Falk, Huettmann; Guo, Yumin

2016-01-01

The rapidly changing climate makes humans realize that there is a critical need to incorporate climate change adaptation into conservation planning. Whether the wintering habitats of Great Bustards (Otis tarda dybowskii), a globally endangered migratory subspecies whose population is approximately 1,500-2,200 individuals in China, would be still suitable in a changing climate environment, and where this could be found, is an important protection issue. In this study, we selected the most suitable species distribution model for bustards using climate envelopes from four machine learning models, combining two modelling approaches (TreeNet and Random Forest) with two sets of variables (correlated variables removed or not). We used common evaluation methods area under the receiver operating characteristic curves (AUC) and the True Skill Statistic (TSS) as well as independent test data to identify the most suitable model. As often found elsewhere, we found Random Forest with all environmental variables outperformed in all assessment methods. When we projected the best model to the latest IPCC-CMIP5 climate scenarios (Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 in three Global Circulation Models (GCMs)), and averaged the project results of the three models, we found that suitable wintering habitats in the current bustard distribution would increase during the 21st century. The Northeast Plain and the south of North China were projected to become two major wintering areas for bustards. However, the models suggest that some currently suitable habitats will experience a reduction, such as Dongting Lake and Poyang Lake in the Middle and Lower Yangtze River Basin. Although our results suggested that suitable habitats in China would widen with climate change, greater efforts should be undertaken to assess and mitigate unstudied human disturbance, such as pollution, hunting, agricultural development, infrastructure construction, habitat fragmentation, and oil and mine exploitation. All of these are negatively and intensely linked with global change.

Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

PubMed

Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang

2016-09-01

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for different period of year ecologists might focus on.

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

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.

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

2016-01-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

Modeling soil respiration and variations of source components using a multi-factor global climate change experiment

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

Chen, Xiongwen; Post, Wilfred M; Norby, Richard J

2011-01-01

Soil respiration is an important component of the global carbon cycle and is highly responsive to changes in soil temperature and moisture. Accurate prediction of soil respiration and its changes under future climatic conditions requires a clear understanding of the processes involved. In spite of this, most current empirical soil respiration models incorporate just few of the underlying mechanisms that may influence its response. In this study, a new partial process-based component model built on source components of soil respiration was tested using data collected from a multi-factor climate change experiment that manipulates CO2 concentrations, temperature and precipitation. These resultsmore » were then compared to results generated using several other established models. The component model we tested performed well across different treatments of global climate change. In contrast, some other models, which worked well predicting ambient environmental conditions, were unable to predict the changes under different climate change treatments. Based on the component model, the relative proportions of heterotrophic respiration (Rh) in the total soil respiration at different treatments varied from 0.33 to 0.85. There is a significant increase in the proportion of Rh under the elevated atmospheric CO2 concentration in comparison ambient conditions. The dry treatment resulted in higher proportion of Rh at elevated CO2 and ambient T than under elevated CO2 and elevated T. Also, the ratios between root growth and root maintenance respiration varied across different treatments. Neither increased temperature nor elevated atmospheric CO2 changed Q10 values significantly, while the average Q10 value at wet sites was significantly higher than it at dry sites. There was a higher possibility of increased soil respiration under drying relative to wetting conditions across all treatments based on monthly data, indicating that soil respiration may also be related to soil moisture at previous time periods. Our results reveal that the extent, time delay and contribution of different source components need to be included into mechanistic/processes-based soil respiration models at corresponding scale.« less

Climate Process Team "Representing calving and iceberg dynamics in global climate models"

NASA Astrophysics Data System (ADS)

Sergienko, O. V.; Adcroft, A.; Amundson, J. M.; Bassis, J. N.; Hallberg, R.; Pollard, D.; Stearns, L. A.; Stern, A. A.

2016-12-01

Iceberg calving accounts for approximately 50% of the ice mass loss from the Greenland and Antarctic ice sheets. By changing a glacier's geometry, calving can also significantly perturb the glacier's stress-regime far upstream of the grounding line. This process can enhance discharge of ice across the grounding line. Once calved, icebergs drift into the open ocean where they melt, injecting freshwater to the ocean and affecting the large-scale ocean circulation. The spatial redistribution of the freshwater flux have strong impact on sea-ice formation and its spatial variability. A Climate Process Team "Representing calving and iceberg dynamics in global climate models" was established in the fall 2014. The major objectives of the CPT are: (1) develop parameterizations of calving processes that are suitable for continental-scale ice-sheet models that simulate the evolution of the Antarctic and Greenland ice sheets; (2) compile the data sets of the glaciological and oceanographic observations that are necessary to test, validate and constrain the developed parameterizations and models; (3) develop a physically based iceberg component for inclusion in the large-scale ocean circulation model. Several calving parameterizations based suitable for various glaciological settings have been developed and implemented in a continental-scale ice sheet model. Simulations of the present-day Antarctic and Greenland ice sheets show that the ice-sheet geometric configurations (thickness and extent) are sensitive to the calving process. In order to guide the development as well as to test calving parameterizations, available observations (of various kinds) have been compiled and organized into a database. Monthly estimates of iceberg distribution around the coast of Greenland have been produced with a goal of constructing iceberg size distribution and probability functions for iceberg occurrence in particular regions. A physically based iceberg model component was used in a GFDL global climate model. The simulation results show that the Antarctic iceberg calving-size distribution affects iceberg trajectories, determines where iceberg meltwater enters the ocean and the increased ice-berg freshwater transport leads to increased sea-ice growth around much of the East Antarctic coastline.

PICUS v1.6 - enhancing the water cycle within a hybrid ecosystem model to assess the provision of drinking water in a changing climate

NASA Astrophysics Data System (ADS)

Schimmel, A.; Rammer, W.; Lexer, M. J.

2012-04-01

The PICUS model is a hybrid ecosystem model which is based on a 3D patch model and a physiological stand level production model. The model includes, among others, a submodel of bark beetle disturbances in Norway spruce and a management module allowing any silvicultural treatment to be mimicked realistically. It has been tested intensively for its ability to realistically reproduce tree growth and stand dynamics in complex structured mixed and mono-species temperate forest ecosystems. In several applications the models capacity to generate relevant forest related attributes which were subsequently fed into indicator systems to assess sustainable forest management under current and future climatic conditions has been proven. However, the relatively coarse monthly temporal resolution of the driving climate data as well as the process resolution of the major water relations within the simulated ecosystem hampered the inclusion of more detailed physiologically based assessments of drought conditions and water provisioning ecosystem services. In this contribution we present the improved model version PICUS v1.6 focusing on the newly implemented logic for the water cycle calculations. Transpiration, evaporation from leave surfaces and the forest floor, snow cover and snow melt as well as soil water dynamics in several soil horizons are covered. In enhancing the model overarching goal was to retain the large-scale applicability by keeping the input requirements to a minimum while improving the physiological foundation of water related ecosystem processes. The new model version is tested against empirical time series data. Future model applications are outlined.

Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

NASA Astrophysics Data System (ADS)

Williams, Paul; Howe, Nicola; Gregory, Jonathan; Smith, Robin; Joshi, Manoj

2017-04-01

In climate simulations, the impacts of the subgrid scales on the resolved scales are conventionally represented using deterministic closure schemes, which assume that the impacts are uniquely determined by the resolved scales. Stochastic parameterization relaxes this assumption, by sampling the subgrid variability in a computationally inexpensive manner. This study shows that the simulated climatological state of the ocean is improved in many respects by implementing a simple stochastic parameterization of ocean eddies into a coupled atmosphere-ocean general circulation model. Simulations from a high-resolution, eddy-permitting ocean model are used to calculate the eddy statistics needed to inject realistic stochastic noise into a low-resolution, non-eddy-permitting version of the same model. A suite of four stochastic experiments is then run to test the sensitivity of the simulated climate to the noise definition by varying the noise amplitude and decorrelation time within reasonable limits. The addition of zero-mean noise to the ocean temperature tendency is found to have a nonzero effect on the mean climate. Specifically, in terms of the ocean temperature and salinity fields both at the surface and at depth, the noise reduces many of the biases in the low-resolution model and causes it to more closely resemble the high-resolution model. The variability of the strength of the global ocean thermohaline circulation is also improved. It is concluded that stochastic ocean perturbations can yield reductions in climate model error that are comparable to those obtained by refining the resolution, but without the increased computational cost. Therefore, stochastic parameterizations of ocean eddies have the potential to significantly improve climate simulations. Reference Williams PD, Howe NJ, Gregory JM, Smith RS, and Joshi MM (2016) Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies. Journal of Climate, 29, 8763-8781. http://dx.doi.org/10.1175/JCLI-D-15-0746.1

Incorporating changes in albedo in estimating the climate mitigation benefits of land use change projects

NASA Astrophysics Data System (ADS)

Bird, D. N.; Kunda, M.; Mayer, A.; Schlamadinger, B.; Canella, L.; Johnston, M.

2008-04-01

Some climate scientists are questioning whether the practice of converting of non-forest lands to forest land (afforestation or reforestation) is an effective climate change mitigation option. The discussion focuses particularly on areas where the new forest is primarily coniferous and there is significant amount of snow since the increased climate forcing due to the change in albedo may counteract the decreased climate forcing due to carbon dioxide removal. In this paper, we develop a stand-based model that combines changes in surface albedo, solar radiation, latitude, cloud cover and carbon sequestration. As well, we develop a procedure to convert carbon stock changes to equivalent climatic forcing or climatic forcing to equivalent carbon stock changes. Using the model, we investigate the sensitivity of combined affects of changes in surface albedo and carbon stock changes to model parameters. The model is sensitive to amount of cloud, atmospheric absorption, timing of canopy closure, carbon sequestration rate among other factors. The sensitivity of the model is investigated at one Canadian site, and then the model is tested at numerous sites across Canada. In general, we find that the change in albedo reduces the carbon sequestration benefits by approximately 30% over 100 years, but this is not drastic enough to suggest that one should not use afforestation or reforestation as a climate change mitigation option. This occurs because the forests grow in places where there is significant amount of cloud in winter. As well, variations in sequestration rate seem to be counterbalanced by the amount and timing of canopy closure. We close by speculating that the effects of albedo may also be significant in locations at lower latitudes, where there are less clouds, and where there are extended dry seasons. These conditions make grasses light coloured and when irrigated crops, dark forests or other vegetation such as biofuels replace the grasses, the change in carbon stocks may not compensate for the darkening of the surface.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

The seasonal-cycle climate model

NASA Technical Reports Server (NTRS)

Marx, L.; Randall, D. A.

1981-01-01

The seasonal cycle run which will become the control run for the comparison with runs utilizing codes and parameterizations developed by outside investigators is discussed. The climate model currently exists in two parallel versions: one running on the Amdahl and the other running on the CYBER 203. These two versions are as nearly identical as machine capability and the requirement for high speed performance will allow. Developmental changes are made on the Amdahl/CMS version for ease of testing and rapidity of turnaround. The changes are subsequently incorporated into the CYBER 203 version using vectorization techniques where speed improvement can be realized. The 400 day seasonal cycle run serves as a control run for both medium and long range climate forecasts alsensitivity studies.

Xanthos

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

2017-05-30

Xanthos is a Python package designed to quantify and analyze global water availability in history and in future at 0.5° × 0.5° spatial resolution and a monthly time step under a changing climate. Its performance was also tested through real applications. It is open-source, extendable and convenient to researchers who work on long-term climate data for studies of global water supply, and Global Change Assessment Model (GCAM). This package integrates inherent global gridded data maps, I/O modules, Water-Balance Model modules and diagnostics modules by user-defined configuration.

Assessing and Upgrading Ocean Mixing for the Study of Climate Change

NASA Astrophysics Data System (ADS)

Howard, A. M.; Fells, J.; Lindo, F.; Tulsee, V.; Canuto, V.; Cheng, Y.; Dubovikov, M. S.; Leboissetier, A.

2016-12-01

Climate is critical. Climate variability affects us all; Climate Change is a burning issue. Droughts, floods, other extreme events, and Global Warming's effects on these and problems such as sea-level rise and ecosystem disruption threaten lives. Citizens must be informed to make decisions concerning climate such as "business as usual" vs. mitigating emissions to keep warming within bounds. Medgar Evers undergraduates aid NASA research while learning climate science and developing computer&math skills. To make useful predictions we must realistically model each component of the climate system, including the ocean, whose critical role includes transporting&storing heat and dissolved CO2. We need physically based parameterizations of key ocean processes that can't be put explicitly in a global climate model, e.g. vertical&lateral mixing. The NASA-GISS turbulence group uses theory to model mixing including: 1) a comprehensive scheme for small scale vertical mixing, including convection&shear, internal waves & double-diffusion, and bottom tides 2) a new parameterization for the lateral&vertical mixing by mesoscale eddies. For better understanding we write our own programs. To assess the modelling MATLAB programs visualize and calculate statistics, including means, standard deviations and correlations, on NASA-GISS OGCM output with different mixing schemes and help us study drift from observations. We also try to upgrade the schemes, e.g. the bottom tidal mixing parameterizations' roughness, calculated from high resolution topographic data using Gaussian weighting functions with cut-offs. We study the effects of their parameters to improve them. A FORTRAN program extracts topography data subsets of manageable size for a MATLAB program, tested on idealized cases, to visualize&calculate roughness on. Students are introduced to modeling a complex system, gain a deeper appreciation of climate science, programming skills and familiarity with MATLAB, while furthering climate science by improving our mixing schemes. We are incorporating climate research into our college curriculum. The PI is both a member of the turbulence group at NASA-GISS and an associate professor at Medgar Evers College of CUNY, an urban minority serving institution in central Brooklyn. Supported by NSF Award AGS-1359293.

Sensitivity study of the UHI in the city of Szeged (Hungary) to different offline simulation set-ups using SURFEX/TEB

NASA Astrophysics Data System (ADS)

Zsebeházi, Gabriella; Hamdi, Rafiq; Szépszó, Gabriella

2015-04-01

Urbanised areas modify the local climate due to the physical properties of surface subjects and their morphology. The urban effect on local climate and regional climate change interact, resulting in more serious climate change impacts (e.g., more heatwave events) over cities. Majority of people are now living in cities and thus, affected by these enhanced changes. Therefore, targeted adaptation and mitigation strategies in cities are of high importance. Regional climate models (RCMs) are sufficient tools for estimating future climate change of an area in detail, although most of them cannot represent the urban climate characteristics, because their spatial resolution is too coarse (in general 10-50 km) and they do not use a specific urban parametrization over urbanized areas. To describe the interactions between the urban surface and atmosphere on few km spatial scale, we use the externalised SURFEX land surface scheme including the TEB urban canopy model in offline mode (i.e. the interaction is only one-way). The driving atmospheric conditions highly influence the impact results, thus the good quality of these data is particularly essential. The overall aim of our research is to understand the behaviour of the impact model and its interaction with the forcing coming from the atmospheric model in order to reduce the biases, which can lead to qualified impact studies of climate change over urban areas. As a preliminary test, several short (few-day) 1 km resolution simulations are carried out over a domain covering a Hungarian town, Szeged, which is located at the flat southern part of Hungary. The atmospheric forcing is provided by ALARO (a new version of the limited-area model of the ARPEGE-IFS system running at the Royal Meteorological Institute of Belgium) applied over Hungary. The focal point of our investigations is the ability of SURFEX to simulate the diurnal evolution and spatial pattern of urban heat island (UHI). Different offline simulation set-ups have been tested: 1. Atmospheric forcing at 4km and 10km resolutions; 2. Atmospheric forcing prepared with and without TEB; 3. Coupling of forcings on 3h and 1h temporal frequencies; 4. Different forcing levels on 50m, 40m, 30m, 20m, 10m; 5. Different computation method of 2m temperature using CANOPY, Paulson, and Geleyn schemes. Finally, some outcomes are also compared to the results obtained using ALADIN-Climate RCM (adapted and used at the Hungarian Meteorological Service on 10 km resolution) as driving atmospheric model. The presentation is dedicated to show the results and main conclusions of our studies.

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.

Climate fails to predict wood decomposition at regional scales

NASA Astrophysics Data System (ADS)

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

2014-07-01

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

Global warming not so harmful for all plants - response of holomycotrophic orchid species for the future climate change.

PubMed

Kolanowska, Marta; Kras, Marta; Lipińska, Monika; Mystkowska, Katarzyna; Szlachetko, Dariusz L; Naczk, Aleksandra M

2017-10-05

Current and expected changes in global climate are major threat for biological diversity affecting individuals, communities and ecosystems. However, there is no general trend in the plants response to the climate change. The aim of present study was to evaluate impact of the future climate changes on the distribution of holomycotrophic orchid species using ecological niche modeling approach. Three different scenarios of future climate changes were tested to obtain the most comprehensive insight in the possible habitat loss of 16 holomycotrophic orchids. The extinction of Cephalanthera austiniae was predicted in all analyses. The coverage of suitable niches of Pogoniopsis schenckii will decrease to 1-30% of its current extent. The reduction of at least 50% of climatic niche of Erythrorchis cassythoides and Limodorum abortivum will be observed. In turn, the coverage of suitable niches of Hexalectris spicata, Uleiorchis ulaei and Wullschlaegelia calcarata may be even 16-74 times larger than in the present time. The conducted niche modeling and analysis of the similarity of their climatic tolerance showed instead that the future modification of the coverage of their suitable niches will not be unified and the future climate changes may be not so harmful for holomycotrophic orchids as expected.

Climate@Home: Crowdsourcing Climate Change Research

NASA Astrophysics Data System (ADS)

Xu, C.; Yang, C.; Li, J.; Sun, M.; Bambacus, M.

2011-12-01

Climate change deeply impacts human wellbeing. Significant amounts of resources have been invested in building super-computers that are capable of running advanced climate models, which help scientists understand climate change mechanisms, and predict its trend. Although climate change influences all human beings, the general public is largely excluded from the research. On the other hand, scientists are eagerly seeking communication mediums for effectively enlightening the public on climate change and its consequences. The Climate@Home project is devoted to connect the two ends with an innovative solution: crowdsourcing climate computing to the general public by harvesting volunteered computing resources from the participants. A distributed web-based computing platform will be built to support climate computing, and the general public can 'plug-in' their personal computers to participate in the research. People contribute the spare computing power of their computers to run a computer model, which is used by scientists to predict climate change. Traditionally, only super-computers could handle such a large computing processing load. By orchestrating massive amounts of personal computers to perform atomized data processing tasks, investments on new super-computers, energy consumed by super-computers, and carbon release from super-computers are reduced. Meanwhile, the platform forms a social network of climate researchers and the general public, which may be leveraged to raise climate awareness among the participants. A portal is to be built as the gateway to the climate@home project. Three types of roles and the corresponding functionalities are designed and supported. The end users include the citizen participants, climate scientists, and project managers. Citizen participants connect their computing resources to the platform by downloading and installing a computing engine on their personal computers. Computer climate models are defined at the server side. Climate scientists configure computer model parameters through the portal user interface. After model configuration, scientists then launch the computing task. Next, data is atomized and distributed to computing engines that are running on citizen participants' computers. Scientists will receive notifications on the completion of computing tasks, and examine modeling results via visualization modules of the portal. Computing tasks, computing resources, and participants are managed by project managers via portal tools. A portal prototype has been built for proof of concept. Three forums have been setup for different groups of users to share information on science aspect, technology aspect, and educational outreach aspect. A facebook account has been setup to distribute messages via the most popular social networking platform. New treads are synchronized from the forums to facebook. A mapping tool displays geographic locations of the participants and the status of tasks on each client node. A group of users have been invited to test functions such as forums, blogs, and computing resource monitoring.

Modelling the climatic niche of turtles: a deep-time perspective

PubMed Central

Schmidt, Daniela N.; Valdes, Paul J.; Holroyd, Patricia A.; Farnsworth, Alexander

2016-01-01

Ectotherms have close physiological ties with the thermal environment; consequently, the impact of future climate change on their biogeographic distributions is of major interest. Here, we use the modern and deep-time fossil record of testudines (turtles, tortoises, and terrapins) to provide the first test of climate on the niche limits of both extant and extinct (Late Cretaceous, Maastrichtian) taxa. Ecological niche models are used to assess niche overlap in model projections for key testudine ecotypes and families. An ordination framework is applied to quantify metrics of niche change (stability, expansion, and unfilling) between the Maastrichtian and present day. Results indicate that niche stability over evolutionary timescales varies between testudine clades. Groups that originated in the Early Cretaceous show climatic niche stability, whereas those diversifying towards the end of the Cretaceous display larger niche expansion towards the modern. Temperature is the dominant driver of modern and past distributions, whereas precipitation is important for freshwater turtle ranges. Our findings demonstrate that testudines were able to occupy warmer climates than present day in the geological record. However, the projected rate and magnitude of future environmental change, in concert with other conservation threats, presents challenges for acclimation or adaptation. PMID:27655766

Physiological-based modelling of marine fish early life stages provides process knowledge on climate impacts

NASA Astrophysics Data System (ADS)

Peck, M. A.

2016-02-01

Gaining a cause-and-effect understanding of climate-driven changes in marine fish populations at appropriate spatial scales is important for providing robust advice for ecosystem-based fisheries management. Coupling long-term, retrospective analyses and 3-d biophysical, individual-based models (IBMs) shows great potential to reveal mechanism underlying historical changes and to project future changes in marine fishes. IBMs created for marine fish early life stages integrate organismal-level physiological responses and climate-driven changes in marine habitats (from ocean physics to lower trophic level productivity) to test and reveal processes affecting marine fish recruitment. Case studies are provided for hindcasts and future (A1 and B2 projection) simulations performed on some of the most ecologically- and commercially-important pelagic and demersal fishes in the North Sea including European anchovy, Atlantic herring, European sprat and Atlantic cod. We discuss the utility of coupling biophysical IBMs to size-spectrum models to better project indirect (trophodynamic) pathways of climate influence on the early life stages of these and other fishes. Opportunities and challenges are discussed regarding the ability of these physiological-based tools to capture climate-driven changes in living marine resources and food web dynamics of shelf seas.

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

DOE PAGES

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

2017-06-22

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

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

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

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

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

Comparative genetic responses to climate in the varieties of Pinus ponderosa and Pseudotsuga menziesii: clines in growth potential

Treesearch

Gerald E. Rehfeldt; Laura P. Leites; J. Bradley St Clair; Barry C. Jaquish; Cuauhtemoc Saenz-Romero; Javier Lopez-Upton; Dennis G. Joyce

2014-01-01

Height growth data were assembled from 10 Pinus ponderosa and 17 Pseudotsuga menziesii provenance tests. Data from the disparate studies were scaled according to climate similarities of the provenances to provide single datasets for 781 P. ponderosa and 1193 P. menziesii populations. Mixed effects models were used for two sub-specific varieties of each species to...

Stormwater runoff in watersheds: a system for prediciting impacts of development and climate change

Treesearch

Ann Blair; Denise Sanger; Susan Lovelace

2016-01-01

The Stormwater Runoff Modeling System (SWARM) enhances understanding of impacts of land-use and climate change on stormwater runoff in watersheds. We developed this singleevent system based on US Department of Agriculture, Natural Resources Conservation Service curve number and unit hydrograph methods. We tested SWARM using US Geological Survey discharge and rain data...

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.

Weather and seasonal climate prediction for South America using a multi-model superensemble

NASA Astrophysics Data System (ADS)

Chaves, Rosane R.; Ross, Robert S.; Krishnamurti, T. N.

2005-11-01

This work examines the feasibility of weather and seasonal climate predictions for South America using the multi-model synthetic superensemble approach for climate, and the multi-model conventional superensemble approach for numerical weather prediction, both developed at Florida State University (FSU). The effect on seasonal climate forecasts of the number of models used in the synthetic superensemble is investigated. It is shown that the synthetic superensemble approach for climate and the conventional superensemble approach for numerical weather prediction can reduce the errors over South America in seasonal climate prediction and numerical weather prediction.For climate prediction, a suite of 13 models is used. The forecast lead-time is 1 month for the climate forecasts, which consist of precipitation and surface temperature forecasts. The multi-model ensemble is comprised of four versions of the FSU-Coupled Ocean-Atmosphere Model, seven models from the Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction (DEMETER), a version of the Community Climate Model (CCM3), and a version of the predictive Ocean Atmosphere Model for Australia (POAMA). The results show that conditions over South America are appropriately simulated by the Florida State University Synthetic Superensemble (FSUSSE) in comparison to observations and that the skill of this approach increases with the use of additional models in the ensemble. When compared to observations, the forecasts are generally better than those from both a single climate model and the multi-model ensemble mean, for the variables tested in this study.For numerical weather prediction, the conventional Florida State University Superensemble (FSUSE) is used to predict the mass and motion fields over South America. Predictions of mean sea level pressure, 500 hPa geopotential height, and 850 hPa wind are made with a multi-model superensemble comprised of six global models for the period January, February, and December of 2000. The six global models are from the following forecast centers: FSU, Bureau of Meteorology Research Center (BMRC), Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP), Naval Research Laboratory (NRL), and Recherche en Prevision Numerique (RPN). Predictions of precipitation are made for the period January, February, and December of 2001 with a multi-analysis-multi-model superensemble where, in addition to the six forecast models just mentioned, five additional versions of the FSU model are used in the ensemble, each with a different initialization (analysis) based on different physical initialization procedures. On the basis of observations, the results show that the FSUSE provides the best forecasts of the mass and motion field variables to forecast day 5, when compared to both the models comprising the ensemble and the multi-model ensemble mean during the wet season of December-February over South America. Individual case studies show that the FSUSE provides excellent predictions of rainfall for particular synoptic events to forecast day 3. Copyright

Modeling temperature and humidity profiles within forest canopies

USDA-ARS?s Scientific Manuscript database

Physically-based models are a powerful tool to help understand interactions of vegetation, atmospheric dynamics, and hydrology, and to test hypotheses regarding the effects of land cover, management, hydrometeorology, and climate variability on ecosystem processes. The purpose of this paper is to f...

Predicting optimum crop designs using crop models and seasonal climate forecasts.

PubMed

Rodriguez, D; de Voil, P; Hudson, D; Brown, J N; Hayman, P; Marrou, H; Meinke, H

2018-02-02

Expected increases in food demand and the need to limit the incorporation of new lands into agriculture to curtail emissions, highlight the urgency to bridge productivity gaps, increase farmers profits and manage risks in dryland cropping. A way to bridge those gaps is to identify optimum combination of genetics (G), and agronomic managements (M) i.e. crop designs (GxM), for the prevailing and expected growing environment (E). Our understanding of crop stress physiology indicates that in hindsight, those optimum crop designs should be known, while the main problem is to predict relevant attributes of the E, at the time of sowing, so that optimum GxM combinations could be informed. Here we test our capacity to inform that "hindsight", by linking a tested crop model (APSIM) with a skillful seasonal climate forecasting system, to answer "What is the value of the skill in seasonal climate forecasting, to inform crop designs?" Results showed that the GCM POAMA-2 was reliable and skillful, and that when linked with APSIM, optimum crop designs could be informed. We conclude that reliable and skillful GCMs that are easily interfaced with crop simulation models, can be used to inform optimum crop designs, increase farmers profits and reduce risks.

Impact Assessment of Salinization Affected Soil on Greenhouse Crops using SALTMED

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Are there pre-Quaternary geological analogues for a future greenhouse warming?

USGS Publications Warehouse

Haywood, A.M.; Ridgwell, A.; Lunt, D.J.; Hill, D.J.; Pound, M.J.; Dowsett, H.J.; Dolan, A.M.; Francis, J.E.; Williams, M.

2011-01-01

Given the inherent uncertainties in predicting how climate and environments will respond to anthropogenic emissions of greenhouse gases, it would be beneficial to society if science could identify geological analogues to the human race's current grand climate experiment. This has been a focus of the geological and palaeoclimate communities over the last 30 years, with many scientific papers claiming that intervals in Earth history can be used as an analogue for future climate change. Using a coupled ocean-atmosphere modelling approach, we test this assertion for the most probable pre-Quaternary candidates of the last 100 million years: the Mid- and Late Cretaceous, the Palaeocene-Eocene Thermal Maximum (PETM), the Early Eocene, as well as warm intervals within the Miocene and Pliocene epochs. These intervals fail as true direct analogues since they either represent equilibrium climate states to a long-term CO2 forcing-whereas anthropogenic emissions of greenhouse gases provide a progressive (transient) forcing on climate-or the sensitivity of the climate system itself to CO2 was different. While no close geological analogue exists, past warm intervals in Earth history provide a unique opportunity to investigate processes that operated during warm (high CO2) climate states. Palaeoclimate and environmental reconstruction/modelling are facilitating the assessment and calculation of the response of global temperatures to increasing CO2 concentrations in the longer term (multiple centuries); this is now referred to as the Earth System Sensitivity, which is critical in identifying CO2 thresholds in the atmosphere that must not be crossed to avoid dangerous levels of climate change in the long term. Palaeoclimatology also provides a unique and independent way to evaluate the qualities of climate and Earth system models used to predict future climate. ?? 2011 The Royal Society.

Are there pre-Quaternary geological analogues for a future greenhouse warming?

PubMed

Haywood, Alan M; Ridgwell, Andy; Lunt, Daniel J; Hill, Daniel J; Pound, Matthew J; Dowsett, Harry J; Dolan, Aisling M; Francis, Jane E; Williams, Mark

2011-03-13

Given the inherent uncertainties in predicting how climate and environments will respond to anthropogenic emissions of greenhouse gases, it would be beneficial to society if science could identify geological analogues to the human race's current grand climate experiment. This has been a focus of the geological and palaeoclimate communities over the last 30 years, with many scientific papers claiming that intervals in Earth history can be used as an analogue for future climate change. Using a coupled ocean-atmosphere modelling approach, we test this assertion for the most probable pre-Quaternary candidates of the last 100 million years: the Mid- and Late Cretaceous, the Palaeocene-Eocene Thermal Maximum (PETM), the Early Eocene, as well as warm intervals within the Miocene and Pliocene epochs. These intervals fail as true direct analogues since they either represent equilibrium climate states to a long-term CO(2) forcing--whereas anthropogenic emissions of greenhouse gases provide a progressive (transient) forcing on climate--or the sensitivity of the climate system itself to CO(2) was different. While no close geological analogue exists, past warm intervals in Earth history provide a unique opportunity to investigate processes that operated during warm (high CO(2)) climate states. Palaeoclimate and environmental reconstruction/modelling are facilitating the assessment and calculation of the response of global temperatures to increasing CO(2) concentrations in the longer term (multiple centuries); this is now referred to as the Earth System Sensitivity, which is critical in identifying CO(2) thresholds in the atmosphere that must not be crossed to avoid dangerous levels of climate change in the long term. Palaeoclimatology also provides a unique and independent way to evaluate the qualities of climate and Earth system models used to predict future climate.

Association of genetic and phenotypic variability with geography and climate in three southern California oaks.

PubMed

Riordan, Erin C; Gugger, Paul F; Ortego, Joaquín; Smith, Carrie; Gaddis, Keith; Thompson, Pam; Sork, Victoria L

2016-01-01

Geography and climate shape the distribution of organisms, their genotypes, and their phenotypes. To understand historical and future evolutionary and ecological responses to climate, we compared the association of geography and climate of three oak species (Quercus engelmannii, Quercus berberidifolia, and Quercus cornelius-mulleri) in an environmentally heterogeneous region of southern California at three organizational levels: regional species distributions, genetic variation, and phenotypic variation. We identified climatic variables influencing regional distribution patterns using species distribution models (SDMs), and then tested whether those individual variables are important in shaping genetic (microsatellite) and phenotypic (leaf morphology) variation. We estimated the relative contributions of geography and climate using multivariate redundancy analyses (RDA) with variance partitioning. The modeled distribution of each species was influenced by climate differently. Our analysis of genetic variation using RDA identified small but significant associations between genetic variation with climate and geography in Q. engelmannii and Q. cornelius-mulleri, but not in Q. berberidifolia, and climate explained more of the variation. Our analysis of phenotypic variation in Q. engelmannii indicated that climate had more impact than geography, but not in Q. berberidifolia. Throughout our analyses, we did not find a consistent pattern in effects of individual climatic variables. Our comparative analysis illustrates that climate influences tree response at all organizational levels, but the important climate factors vary depending on the level and on the species. Because of these species-specific and level-specific responses, today's sympatric species are unlikely to have similar distributions in the future. © 2016 Botanical Society of America.

Perceptions of competence, implicit theory of ability, perception of motivational climate, and achievement goals: a test of the trichotomous conceptualization of endorsement of achievement motivation in the physical education setting.

PubMed

Cury, F; Da Fonséca, D; Rufo, M; Sarrazin, P

2002-08-01

To test and extend the conceptualization of the endorsement of achievement goals in the physical education setting Mastery, Performance-approach, and Performance-approach goals, Perception of the physical education competence, Implicit theory about sport ability, and Perception of the motivational climate were assessed among 682 boys attending five French schools. Analysis indicated that (1) Performance-approach goals were positively associated with perception of physical education Competence, Entity beliefs about sport ability, the Performance dimension of the motivational climate, and negatively associated with Incremental beliefs about sport ability. (2) Mastery goals were positively associated with perception of physical education Competence, Incremental beliefs about sport ability, the Mastery dimension of the motivational climate, and negatively associated with the Performance dimension of the motivational climate. Also, (3) Performance-avoidance goals were positively associated with Entity beliefs about sport ability and the Performance dimension of the motivational climate; these goals were negatively associated with Incremental beliefs about sport ability and perception of physical education Competence. These results clearly attested to the validity of the trichotomous model in the physical education setting.

The MIT IGSM-CAM framework for uncertainty studies in global and regional climate change

NASA Astrophysics Data System (ADS)

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

2011-12-01

The MIT Integrated Global System Model (IGSM) version 2.3 is an intermediate complexity fully coupled earth system model that allows simulation of critical feedbacks among its various components, including the atmosphere, ocean, land, urban processes and human activities. A fundamental feature of the IGSM2.3 is the ability to modify its climate parameters: climate sensitivity, net aerosol forcing and ocean heat uptake rate. As such, the IGSM2.3 provides an efficient tool for generating probabilistic distribution functions of climate parameters using optimal fingerprint diagnostics. A limitation of the IGSM2.3 is its zonal-mean atmosphere model that does not permit regional climate studies. For this reason, the MIT IGSM2.3 was linked to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM) version 3 and new modules were developed and implemented in CAM in order to modify its climate sensitivity and net aerosol forcing to match that of the IGSM. The IGSM-CAM provides an efficient and innovative framework to study regional climate change where climate parameters can be modified to span the range of uncertainty and various emissions scenarios can be tested. This paper presents results from the cloud radiative adjustment method used to modify CAM's climate sensitivity. We also show results from 21st century simulations based on two emissions scenarios (a median "business as usual" scenario where no policy is implemented after 2012 and a policy scenario where greenhouse-gas are stabilized at 660 ppm CO2-equivalent concentrations by 2100) and three sets of climate parameters. The three values of climate sensitivity chosen are median and the bounds of the 90% probability interval of the probability distribution obtained by comparing the observed 20th century climate change with simulations by the IGSM with a wide range of climate parameters values. The associated aerosol forcing values were chosen to ensure a good agreement of the simulations with the observed climate change over the 20th century. Because the concentrations of sulfate aerosols significantly decrease over the 21st century in both emissions scenarios, climate changes obtained in these six simulations provide a good approximation for the median, and the 5th and 95th percentiles of the probability distribution of 21st century climate change.

Simulations of forest mortality in Colorado River basin

NASA Astrophysics Data System (ADS)

Wei, L.; Xu, C.; Johnson, D. J.; Zhou, H.; McDowell, N.

2017-12-01

The Colorado River Basin (CRB) had experienced multiple severe forest mortality events under the recent changing climate. Such forest mortality events may have great impacts on ecosystem services and water budget of the watershed. It is hence important to estimate and predict the forest mortality in the CRB with climate change. We simulated forest mortality in the CRB with a model of plant hydraulics within the FATES (the Functionally Assembled Terrestrial Ecosystem Simulator) coupled to the DOE Earth System model (ACME: Accelerated Climate Model of Energy) at a 0.5 x 0.5 degree resolution. Moreover, we incorporated a stable carbon isotope (δ13C) module to ACME(FATE) and used it as a new predictor of forest mortality. The δ13C values of plants with C3 photosynthetic pathway (almost all trees are C3 plants) can indicate the water stress plants experiencing (the more intensive stress, the less negative δ13C value). We set a δ13C threshold in model simulation, above which forest mortality initiates. We validate the mortality simulations with field data based on Forest Inventory and Analysis (FIA) data, which were aggregated into the same spatial resolution as the model simulations. Different mortality schemes in the model (carbon starvation, hydraulic failure, and δ13C) were tested and compared. Each scheme demonstrated its strength and the plant hydraulics module provided more reliable simulations of forest mortality than the earlier ACME(FATE) version. Further testing is required for better forest mortality modelling.

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

Projecting species’ vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

USGS Publications Warehouse

Steen, Valerie; Sofaer, Helen R.; Skagen, Susan K.; Ray, Andrea J.; Noon, Barry R

2017-01-01

Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross-validation skill indicates extrapolative skill. We compared projected vulnerability for 29 wetland-dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross-validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross-validation results were correlated with extrapolation results, the use of cross-validation performance metrics to guide modeling choices where knowledge is limited was supported.

Projecting species' vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

PubMed

Steen, Valerie; Sofaer, Helen R; Skagen, Susan K; Ray, Andrea J; Noon, Barry R

2017-11-01

Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross-validation skill indicates extrapolative skill. We compared projected vulnerability for 29 wetland-dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross-validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross-validation results were correlated with extrapolation results, the use of cross-validation performance metrics to guide modeling choices where knowledge is limited was supported.

Simulation skill of APCC set of global climate models for Asian summer monsoon rainfall variability

NASA Astrophysics Data System (ADS)

Singh, U. K.; Singh, G. P.; Singh, Vikas

2015-04-01

The performance of 11 Asia-Pacific Economic Cooperation Climate Center (APCC) global climate models (coupled and uncoupled both) in simulating the seasonal summer (June-August) monsoon rainfall variability over Asia (especially over India and East Asia) has been evaluated in detail using hind-cast data (3 months advance) generated from APCC which provides the regional climate information product services based on multi-model ensemble dynamical seasonal prediction systems. The skill of each global climate model over Asia was tested separately in detail for the period of 21 years (1983-2003), and simulated Asian summer monsoon rainfall (ASMR) has been verified using various statistical measures for Indian and East Asian land masses separately. The analysis found a large variation in spatial ASMR simulated with uncoupled model compared to coupled models (like Predictive Ocean Atmosphere Model for Australia, National Centers for Environmental Prediction and Japan Meteorological Agency). The simulated ASMR in coupled model was closer to Climate Prediction Centre Merged Analysis of Precipitation (CMAP) compared to uncoupled models although the amount of ASMR was underestimated in both models. Analysis also found a high spread in simulated ASMR among the ensemble members (suggesting that the model's performance is highly dependent on its initial conditions). The correlation analysis between sea surface temperature (SST) and ASMR shows that that the coupled models are strongly associated with ASMR compared to the uncoupled models (suggesting that air-sea interaction is well cared in coupled models). The analysis of rainfall using various statistical measures suggests that the multi-model ensemble (MME) performed better compared to individual model and also separate study indicate that Indian and East Asian land masses are more useful compared to Asia monsoon rainfall as a whole. The results of various statistical measures like skill of multi-model ensemble, large spread among the ensemble members of individual model, strong teleconnection (correlation analysis) with SST, coefficient of variation, inter-annual variability, analysis of Taylor diagram, etc. suggest that there is a need to improve coupled model instead of uncoupled model for the development of a better dynamical seasonal forecast system.

Assessing ocean vertical mixing schemes for the study of climate change

NASA Astrophysics Data System (ADS)

Howard, A. M.; Lindo, F.; Fells, J.; Tulsee, V.; Cheng, Y.; Canuto, V.

2014-12-01

Climate change is a burning issue of our time. It is critical to know the consequences of choosing "business as usual" vs. mitigating our emissions for impacts e.g. ecosystem disruption, sea-level rise, floods and droughts. To make predictions we must model realistically each component of the climate system. The ocean must be modeled carefully as it plays a critical role, including transporting heat and storing heat and dissolved carbon dioxide. Modeling the ocean realistically in turn requires physically based parameterizations of key processes in it that cannot be explicitly represented in a global climate model. One such process is vertical mixing. The turbulence group at NASA-GISS has developed a comprehensive new vertical mixing scheme (GISSVM) based on turbulence theory, including surface convection and wind shear, interior waves and double-diffusion, and bottom tides. The GISSVM is tested in stand-alone ocean simulations before being used in coupled climate models. It is also being upgraded to more faithfully represent the physical processes. To help assess mixing schemes, students use data from NASA-GISS to create visualizations and calculate statistics including mean bias and rms differences and correlations of fields. These are created and programmed with MATLAB. Results with the commonly used KPP mixing scheme and the present GISSVM and candidate improved variants of GISSVM will be compared between stand-alone ocean models and coupled models and observations. This project introduces students to modeling of a complex system, an important theme in contemporary science and helps them gain a better appreciation of climate science and a new perspective on it. They also gain familiarity with MATLAB, a widely used tool, and develop skills in writing and understanding programs. Moreover they contribute to the advancement of science by providing information that will help guide the improvement of the GISSVM and hence of ocean and climate models and ultimately our understanding and prediction of climate. The PI is both a member of the turbulence group at NASA-GISS and an associate professor at Medgar Evers College of CUNY, a minority serving institution in an urban setting in central Brooklyn. This Project is supported by NSF award AGS-1359293 REU site: CUNY/GISS Center for Global Climate Research.

Longitudinal Relations Among Positivity, Perceived Positive School Climate, and Prosocial Behavior in Colombian Adolescents.

PubMed

Luengo Kanacri, Bernadette P; Eisenberg, Nancy; Thartori, Eriona; Pastorelli, Concetta; Uribe Tirado, Liliana M; Gerbino, Maria; Caprara, Gian V

2017-07-01

Bidirectional relations among adolescents' positivity, perceived positive school climate, and prosocial behavior were examined in Colombian youth. Also, the role of a positive school climate in mediating the relation of positivity to prosocial behaviors was tested. Adolescents (N = 151; M age of child in Wave 1 = 12.68, SD = 1.06; 58.9% male) and their parents (N = 127) provided data in two waves (9 months apart). A model of bidirectional relations between positivity and perceived positive school climate emerged. In addition, adolescents with higher levels of perceived positive school climate at age 12 showed higher levels of prosocial behaviors in the following year. Positive school climate related positivity to adolescents' prosocial behavior over time. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.

Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe

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

De Kauwe, M. G.; Zhou, S. -X.; Medlyn, B. E.

Future climate change has the potential to increase drought in many regions of the globe, making it essential that land surface models (LSMs) used in coupled climate models realistically capture the drought responses of vegetation. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art LSMs currently assume the same drought sensitivity for all vegetation. We tested whether variable drought sensitivities are needed to explain the observed large-scale patterns of drought impact on the carbon, water and energy fluxes. We implemented data-driven drought sensitivities in the Community Atmosphere Biosphere Land Exchange (CABLE) LSMmore » and evaluated alternative sensitivities across a latitudinal gradient in Europe during the 2003 heatwave. The model predicted an overly abrupt onset of drought unless average soil water potential was calculated with dynamic weighting across soil layers. We found that high drought sensitivity at the most mesic sites, and low drought sensitivity at the most xeric sites, was necessary to accurately model responses during drought. Furthermore, our results indicate that LSMs will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.« less

Coupled impacts of climate and land use change across a river-lake continuum: insights from an integrated assessment model of Lake Champlain’s Missisquoi Basin, 2000-2040

NASA Astrophysics Data System (ADS)

Zia, Asim; Bomblies, Arne; Schroth, Andrew W.; Koliba, Christopher; Isles, Peter D. F.; Tsai, Yushiou; Mohammed, Ibrahim N.; Bucini, Gabriela; Clemins, Patrick J.; Turnbull, Scott; Rodgers, Morgan; Hamed, Ahmed; Beckage, Brian; Winter, Jonathan; Adair, Carol; Galford, Gillian L.; Rizzo, Donna; Van Houten, Judith

2016-11-01

Global climate change (GCC) is projected to bring higher-intensity precipitation and higher-variability temperature regimes to the Northeastern United States. The interactive effects of GCC with anthropogenic land use and land cover changes (LULCCs) are unknown for watershed level hydrological dynamics and nutrient fluxes to freshwater lakes. Increased nutrient fluxes can promote harmful algal blooms, also exacerbated by warmer water temperatures due to GCC. To address the complex interactions of climate, land and humans, we developed a cascading integrated assessment model to test the impacts of GCC and LULCC on the hydrological regime, water temperature, water quality, bloom duration and severity through 2040 in transnational Lake Champlain’s Missisquoi Bay. Temperature and precipitation inputs were statistically downscaled from four global circulation models (GCMs) for three Representative Concentration Pathways. An agent-based model was used to generate four LULCC scenarios. Combined climate and LULCC scenarios drove a distributed hydrological model to estimate river discharge and nutrient input to the lake. Lake nutrient dynamics were simulated with a 3D hydrodynamic-biogeochemical model. We find accelerated GCC could drastically limit land management options to maintain water quality, but the nature and severity of this impact varies dramatically by GCM and GCC scenario.

Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe

DOE PAGES

De Kauwe, M. G.; Zhou, S. -X.; Medlyn, B. E.; ...

2015-12-21

Future climate change has the potential to increase drought in many regions of the globe, making it essential that land surface models (LSMs) used in coupled climate models realistically capture the drought responses of vegetation. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art LSMs currently assume the same drought sensitivity for all vegetation. We tested whether variable drought sensitivities are needed to explain the observed large-scale patterns of drought impact on the carbon, water and energy fluxes. We implemented data-driven drought sensitivities in the Community Atmosphere Biosphere Land Exchange (CABLE) LSMmore » and evaluated alternative sensitivities across a latitudinal gradient in Europe during the 2003 heatwave. The model predicted an overly abrupt onset of drought unless average soil water potential was calculated with dynamic weighting across soil layers. We found that high drought sensitivity at the most mesic sites, and low drought sensitivity at the most xeric sites, was necessary to accurately model responses during drought. Furthermore, our results indicate that LSMs will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.« less

From the Last Interglacial to the Anthropocene: Modelling a Complete Glacial Cycle (PalMod)

NASA Astrophysics Data System (ADS)

Brücher, Tim; Latif, Mojib

2017-04-01

We will give a short overview and update on the current status of the national climate modelling initiative PalMod (Paleo Modelling, www.palmod.de). PalMod focuses on the understanding of the climate system dynamics and its variability during the last glacial cycle. The initiative is funded by the German Federal Ministry of Education and Research (BMBF) and its specific topics are: (i) to identify and quantify the relative contributions of the fundamental processes which determined the Earth's climate trajectory and variability during the last glacial cycle, (ii) to simulate with comprehensive Earth System Models (ESMs) the climate from the peak of the last interglacial - the Eemian warm period - up to the present, including the changes in the spectrum of variability, and (iii) to assess possible future climate trajectories beyond this century during the next millennia with sophisticated ESMs tested in such a way. The research is intended to be conducted over a period of 10 years, but with shorter funding cycles. PalMod kicked off in February 2016. The first phase focuses on the last deglaciation (app. the last 23.000 years). From the ESM perspective PalMod pushes forward model development by coupling ESM with dynamical ice sheet models. Computer scientists work on speeding up climate models using different concepts (like parallelisation in time) and one working group is dedicated to perform a comprehensive data synthesis to validate model performance. The envisioned approach is innovative in three respects. First, the consortium aims at simulating a full glacial cycle in transient mode and with comprehensive ESMs which allow full interactions between the physical and biogeochemical components of the Earth system, including ice sheets. Second, we shall address climate variability during the last glacial cycle on a large range of time scales, from interannual to multi-millennial, and attempt to quantify the relative contributions of external forcing and processes internal to the Earth system to climate variability at different time scales. Third, in order to achieve a higher level of understanding of natural climate variability at time scales of millennia, its governing processes and implications for the future climate, we bring together three different research communities: the Earth system modeling community, the proxy data community and the computational science community. The consortium consists of 18 partners including all major modelling centers within Germany. The funding comprises approximately 65 PostDoc positions and more than 120 scientists are involved. PalMod is coordinated at the Helmholtz Centre for Ocean Research Kiel (GEOMAR).

Confronting species distribution model predictions with species functional traits.

PubMed

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

2016-02-01

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

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Progress and Preliminary Results

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2011-12-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a distributed climate-scenario simulation exercise for historical model intercomparison and future climate change conditions with participation of multiple crop and agricultural trade modeling groups around the world. The goals of AgMIP are to improve substantially the characterization of risk of hunger and world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Recent progress and the current status of AgMIP will be presented, highlighting three areas of activity: preliminary results from crop pilot studies, outcomes from regional workshops, and emerging scientific challenges. AgMIP crop modeling efforts are being led by pilot studies, which have been established for wheat, maize, rice, and sugarcane. These crop-specific initiatives have proven instrumental in testing and contributing to AgMIP protocols, as well as creating preliminary results for aggregation and input to agricultural trade models. Regional workshops are being held to encourage collaborations and set research activities in motion for key agricultural areas. The first of these workshops was hosted by Embrapa and UNICAMP and held in Campinas, Brazil. Outcomes from this meeting have informed crop modeling research activities within South America, AgMIP protocols, and future regional workshops. Several scientific challenges have emerged and are currently being addressed by AgMIP researchers. Areas of particular interest include geospatial weather generation, ensemble methods for climate scenarios and crop models, spatial aggregation of field-scale yields to regional and global production, and characterization of future changes in climate variability.

A Caveat Note on Tuning in the Development of Coupled Climate Models

NASA Astrophysics Data System (ADS)

Dommenget, Dietmar; Rezny, Michael

2018-01-01

State-of-the-art coupled general circulation models (CGCMs) have substantial errors in their simulations of climate. In particular, these errors can lead to large uncertainties in the simulated climate response (both globally and regionally) to a doubling of CO2. Currently, tuning of the parameterization schemes in CGCMs is a significant part of the developed. It is not clear whether such tuning actually improves models. The tuning process is (in general) neither documented, nor reproducible. Alternative methods such as flux correcting are not used nor is it clear if such methods would perform better. In this study, ensembles of perturbed physics experiments are performed with the Globally Resolved Energy Balance (GREB) model to test the impact of tuning. The work illustrates that tuning has, in average, limited skill given the complexity of the system, the limited computing resources, and the limited observations to optimize parameters. While tuning may improve model performance (such as reproducing observed past climate), it will not get closer to the "true" physics nor will it significantly improve future climate change projections. Tuning will introduce artificial compensating error interactions between submodels that will hamper further model development. In turn, flux corrections do perform well in most, but not all aspects. A main advantage of flux correction is that it is much cheaper, simpler, more transparent, and it does not introduce artificial error interactions between submodels. These GREB model experiments should be considered as a pilot study to motivate further CGCM studies that address the issues of model tuning.

Dormancy release of Norway spruce under climatic warming: testing ecophysiological models of bud burst with a whole-tree chamber experiment.

PubMed

Hänninen, Heikki; Slaney, Michelle; Linder, Sune

2007-02-01

Ecophysiological models predicting timing of bud burst were tested with data gathered from 40-year-old Norway spruce (Picea abies (L.) Karst.) trees growing in northern Sweden in whole-tree chambers under climatic conditions predicted to prevail in 2100. Norway spruce trees, with heights between 5 and 7 m, were enclosed in individual chambers that provided a factorial combination of ambient (365 micromol mol-1) or elevated (700 micromol mol-1) atmospheric CO2 concentration, [CO2], and ambient or elevated air temperature. Temperature elevation above ambient ranged from +2.8 degrees C in summer to +5.6 degrees C in winter. Compared with control trees, elevated air temperature hastened bud burst by 2 to 3 weeks, whereas elevated [CO2] had no effect on the timing of bud burst. A simple model based on the assumption that bud rest completion takes place on a fixed calendar day predicted timing of bud burst more accurately than two more complicated models in which bud rest completion is caused by accumulated chilling. Together with some recent studies, the results suggest that, in adult trees, some additional environmental cues besides chilling are required for bud rest completion. Although it appears that these additional factors will protect trees under predicted climatic warming conditions, increased risk of frost damage associated with earlier bud burst cannot be ruled out. Inconsistent and partially anomalous results obtained in the model fitting show that, in addition to phenological data gathered under field conditions, more specific data from growth chamber and greenhouse experiments are needed for further development and testing of the models.

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

Red River barrier and Pleistocene climatic fluctuations shaped the genetic structure of Microhyla fissipes complex (Anura: Microhylidae) in southern China and Indochina

PubMed Central

Yuan, Zhi-Yong; Suwannapoom, Chatmongkon; Yan, Fang; Poyarkov, Nikolay A.; Nguyen, Sang Ngoc; Chen, Hong-man; Chomdej, Siriwadee; Murphy, Robert W.

2016-01-01

South China and Indochina host striking species diversity and endemism. Complex tectonic and climatic evolutions appear to be the main drivers of the biogeographic patterns. In this study, based on the geologic history of this region, we test 2 hypotheses using the evolutionary history of Microhyla fissipes species complex. Using DNA sequence data from both mitochondrial and nuclear genes, we first test the hypothesis that the Red River is a barrier to gene flow and dispersal. Second, we test the hypothesis that Pleistocene climatic cycling affected the genetic structure and population history of these frogs. We detect 2 major genetic splits that associate with the Red River. Time estimation suggests that late Miocene tectonic movement associated with the Red River drove their diversification. Species distribution modeling (SDM) resolves significant ecological differences between sides of the Red River. Thus, ecological divergence also probably promoted and maintained the diversification. Genogeography, historical demography, and SDM associate patterns in southern China with climate changes of the last glacial maximum (LGM), but not Indochina. Differences in geography and climate between the 2 areas best explain the discovery. Responses to the Pleistocene glacial–interglacial cycling vary among species and regions. PMID:29491943

Red River barrier and Pleistocene climatic fluctuations shaped the genetic structure of Microhyla fissipes complex (Anura: Microhylidae) in southern China and Indochina.

PubMed

Yuan, Zhi-Yong; Suwannapoom, Chatmongkon; Yan, Fang; Poyarkov, Nikolay A; Nguyen, Sang Ngoc; Chen, Hong-Man; Chomdej, Siriwadee; Murphy, Robert W; Che, Jing

2016-12-01

South China and Indochina host striking species diversity and endemism. Complex tectonic and climatic evolutions appear to be the main drivers of the biogeographic patterns. In this study, based on the geologic history of this region, we test 2 hypotheses using the evolutionary history of Microhyla fissipes species complex. Using DNA sequence data from both mitochondrial and nuclear genes, we first test the hypothesis that the Red River is a barrier to gene flow and dispersal. Second, we test the hypothesis that Pleistocene climatic cycling affected the genetic structure and population history of these frogs. We detect 2 major genetic splits that associate with the Red River. Time estimation suggests that late Miocene tectonic movement associated with the Red River drove their diversification. Species distribution modeling (SDM) resolves significant ecological differences between sides of the Red River. Thus, ecological divergence also probably promoted and maintained the diversification. Genogeography, historical demography, and SDM associate patterns in southern China with climate changes of the last glacial maximum (LGM), but not Indochina. Differences in geography and climate between the 2 areas best explain the discovery. Responses to the Pleistocene glacial-interglacial cycling vary among species and regions.

Signs of the Land: Reaching Arctic Communities Facing Climate Change

NASA Astrophysics Data System (ADS)

Sparrow, E. B.; Chase, M. J.; Demientieff, S.; Pfirman, S. L.; Brunacini, J.

2014-12-01

In July 2014, a diverse and intergenerational group of Alaskan Natives came together on Howard Luke's Galee'ya Camp by the Tanana River in Fairbanks, Alaska to talk about climate change and it's impacts on local communities. Over a period of four days, the Signs of the Land Climate Change Camp wove together traditional knowledge, local observations, Native language, and climate science through a mix of storytelling, presentations, dialogue, and hands-on, community-building activities. This camp adapted the model developed several years ago under the Association for Interior Native Educators (AINE)'s Elder Academy. Part of the Polar Learning and Responding Climate Change Education Partnership, the Signs of the Land Climate Change Camp was developed and conducted collaboratively with multiple partners to test a model for engaging indigenous communities in the co-production of climate change knowledge, communication tools, and solutions-building. Native Alaskans have strong subsistence and cultural connections to the land and its resources, and, in addition to being keen observers of their environment, have a long history of adapting to changing conditions. Participants in the camp included Elders, classroom teachers, local resource managers and planners, community members, and climate scientists. Based on their experiences during the camp, participants designed individualized outreach plans for bringing culturally-responsive climate learning to their communities and classrooms throughout the upcoming year. Plans included small group discussions, student projects, teacher training, and conference presentations.

Air-Quality and Climate Coupling in High Resolution for Urban Heat Island Study

NASA Astrophysics Data System (ADS)

Halenka, T.; Huszar, P.; Belda, M.

2012-04-01

Recent studies show considerable effect of atmospheric chemistry and aerosols on climate on regional and local scale. For the purpose of qualifying and quantifying the magnitude of climate forcing due to atmospheric chemistry/aerosols on regional scale and climate change effects on air-quality the regional climate model RegCM and chemistry/aerosol model CAMx was coupled. Climate change impacts on air-quality have been studied in high resolution of 10km with interactive two-way coupling of the effects of air-quality on climate. The experiments with the couple were performed for EC FP7 project MEGAPOLI assessing the impact of the megacities and industrialized areas on climate. New experiments in high resolution are prepared andsimulated for Urban Heat Island studies within the OP Central Europe Project UHI. Meteorological fields generated by RCM drive CAMx transport, chemistry and a dry/wet deposition. A preprocessor utility was developed for transforming RegCM provided fields to CAMx input fields and format. There is critical issue of the emission inventories available for 10km resolution including the urban hot-spots, TNO emissions are adopted for the experiments. Sensitivity tests switching on/off urban areas emissions are analysed as well. The results for year 2005 are presented and discussed, interactive coupling is compared to study the potential of possible impact of urban air-pollution to the urban area climate.

Modeling the Impacts of Global Climate and Regional Land Use Change on Regional Climate, Air Quality and Public Health in the New York Metropolitan Region

NASA Astrophysics Data System (ADS)

Rosenthal, J. E.; Knowlton, K. M.; Kinney, P. L.

2002-12-01

There is an imminent need to downscale the global climate models used by international consortiums like the IPCC (Intergovernmental Panel on Climate Change) to predict the future regional impacts of climate change. To meet this need, a "place-based" climate model that makes specific regional projections about future environmental conditions local inhabitants could face is being created by the Mailman School of Public Health at Columbia University, in collaboration with other researchers and universities, for New York City and the 31 surrounding counties. This presentation describes the design and initial results of this modeling study, aimed at simulating the effects of global climate change and regional land use change on climate and air quality over the northeastern United States in order to project the associated public health impacts in the region. Heat waves and elevated concentrations of ozone and fine particles are significant current public health stressors in the New York metropolitan area. The New York Climate and Health Project is linking human dimension and natural sciences models to assess the potential for future public health impacts from heat stress and air quality, and yield improved tools for assessing climate change impacts. The model will be applied to the NY metropolitan east coast region. The following questions will be addressed: 1. What changes in the frequency and severity of extreme heat events are likely to occur over the next 80 years due to a range of possible scenarios of land use and land cover (LU/LC) and climate change in the region? 2. How might the frequency and severity of episodic concentrations of ozone (O3) and airborne particulate matter smaller than 2.5 æm in diameter (PM2.5) change over the next 80 years due to a range of possible scenarios of land use and climate change in the metropolitan region? 3. What is the range of possible human health impacts of these changes in the region? 4. How might projected future human exposures and responses to heat stress and air quality differ as a function of socio-economic status and race/ethnicity across the region? The model systems used for this study are the Goddard Institute for Space Studies (GISS) Global Atmosphere-Ocean Model; the Regional Atmospheric Modeling System (RAMS) and PennState/NCAR MM5 mesoscale meteorological models; the SLEUTH land use model; the Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE); the Community Multiscale Air Quality (CMAQ) and Comprehensive Air Quality Model with Extensions (CAMx) models for simulating regional air quality; and exposure-risk coefficients for assessing population health impacts based on exposure to extreme heat, fine particulates (PM2.5) and ozone. Two different IPCC global emission scenarios and two different regional land use growth scenarios are considered in the simulations, spanning a range of possible futures. In addition to base simulations for selected time periods in the decade 1990 - 2000, the integrated model is used to simulate future scenarios in the 2020s, 2050s, and 2080s. Predictions from both the meteorological models and the air quality models are compared against available observations for the simulations in the 1990s to establish baseline model performance. A series of sensitivity tests will address whether changes in meteorology due to global climate change, changes in regional land use, or changes in emissions have the largest impact on predicted ozone and particulate matter concentrations.

Comparative Assessment of a New Hydrological Modelling Approach for Prediction of Runoff in Gauged and Ungauged Basins, and Climate Change Impacts Assessment: A Case Study from Benin.

NASA Astrophysics Data System (ADS)

GABA, C. O. U.; Alamou, E.; Afouda, A.; Diekkrüger, B.

2016-12-01

Assessing water resources is still an important challenge especially in the context of climatic changes. Although numerous hydrological models exist, new approaches are still under investigation. In this context, we investigate a new modelling approach based on the Physics Principle of Least Action which was first applied to the Bétérou catchment in Benin and gave very good results. The study presents new hypotheses to go further in the model development with a view of widening its application. The improved version of the model MODHYPMA was applied to sixteen (16) subcatchments in Bénin, West Africa. Its performance was compared to two well-known lumped conceptual models, the GR4J and HBV models. The model was successfully calibrated and validated and showed a good performance in most catchments. The analysis revealed that the three models have similar performance and timing errors. But in contrary to other models, MODHYMA is subject to a less loss of performance from calibration to validation. In order to evaluate the usefulness of our model for the prediction of runoff in ungauged basins, model parameters were estimated from the physical catchments characteristics. We relied on statistical methods applied on calibrated model parameters to deduce relationships between parameters and physical catchments characteristics. These relationships were further tested and validated on gauged basins that were considered ungauged. This regionalization was also performed for GR4J model.We obtained NSE values greater than 0.7 for MODHYPMA while the NSE values for GR4J were inferior to 0.5. In the presented study, the effects of climate change on water resources in the Ouémé catchment at the outlet of Savè (about 23 500 km2) are quantified. The output of a regional climate model was used as input to the hydrological models.Computed within the GLOWA-IMPETUS project, the future climate projections (describing a rainfall reduction of up to 15%) are derived from the regional climate model REMO driven by the global ECHAM model.The results reveal a significant decrease in future water resources (of -66% to -53% for MODHYPMA and of -59% to -46% for GR4J) for the IPCC climate scenarios A1B and B1.

Importance of Sea Ice for Validating Global Climate Models

NASA Technical Reports Server (NTRS)

Geiger, Cathleen A.

1997-01-01

Reproduction of current day large-scale physical features and processes is a critical test of global climate model performance. Without this benchmark, prognoses of future climate conditions are at best speculation. A fundamental question relevant to this issue is, which processes and observations are both robust and sensitive enough to be used for model validation and furthermore are they also indicators of the problem at hand? In the case of global climate, one of the problems at hand is to distinguish between anthropogenic and naturally occuring climate responses. The polar regions provide an excellent testing ground to examine this problem because few humans make their livelihood there, such that anthropogenic influences in the polar regions usually spawn from global redistribution of a source originating elsewhere. Concomitantly, polar regions are one of the few places where responses to climate are non-anthropogenic. Thus, if an anthropogenic effect has reached the polar regions (e.g. the case of upper atmospheric ozone sensitivity to CFCs), it has most likely had an impact globally but is more difficult to sort out from local effects in areas where anthropogenic activity is high. Within this context, sea ice has served as both a monitoring platform and sensitivity parameter of polar climate response since the time of Fridtjof Nansen. Sea ice resides in the polar regions at the air-sea interface such that changes in either the global atmospheric or oceanic circulation set up complex non-linear responses in sea ice which are uniquely determined. Sea ice currently covers a maximum of about 7% of the earth's surface but was completely absent during the Jurassic Period and far more extensive during the various ice ages. It is also geophysically very thin (typically <10 m in Arctic, <3 m in Antarctic) compared to the troposphere (roughly 10 km) and deep ocean (roughly 3 to 4 km). Because of these unique conditions, polar researchers regard sea ice as one of the more important features to monitor in terms of heat, mass, and momentum transfer between the air and sea and furthermore, the impact of such responses to global climate.

Constructing Scientific Arguments Using Evidence from Dynamic Computational Climate Models

NASA Astrophysics Data System (ADS)

Pallant, Amy; Lee, Hee-Sun

2015-04-01

Modeling and argumentation are two important scientific practices students need to develop throughout school years. In this paper, we investigated how middle and high school students ( N = 512) construct a scientific argument based on evidence from computational models with which they simulated climate change. We designed scientific argumentation tasks with three increasingly complex dynamic climate models. Each scientific argumentation task consisted of four parts: multiple-choice claim, openended explanation, five-point Likert scale uncertainty rating, and open-ended uncertainty rationale. We coded 1,294 scientific arguments in terms of a claim's consistency with current scientific consensus, whether explanations were model based or knowledge based and categorized the sources of uncertainty (personal vs. scientific). We used chi-square and ANOVA tests to identify significant patterns. Results indicate that (1) a majority of students incorporated models as evidence to support their claims, (2) most students used model output results shown on graphs to confirm their claim rather than to explain simulated molecular processes, (3) students' dependence on model results and their uncertainty rating diminished as the dynamic climate models became more and more complex, (4) some students' misconceptions interfered with observing and interpreting model results or simulated processes, and (5) students' uncertainty sources reflected more frequently on their assessment of personal knowledge or abilities related to the tasks than on their critical examination of scientific evidence resulting from models. These findings have implications for teaching and research related to the integration of scientific argumentation and modeling practices to address complex Earth systems.

Historical and Future Projected Hydrologic Extremes over the Midwest and Great Lakes Region

NASA Astrophysics Data System (ADS)

Byun, K.; Hamlet, A. F.; Chiu, C. M.

2016-12-01

There is an increasing body of evidence from observed data that climate variability combined with regional climate change has had a significant impact on hydrologic cycles, including both seasonal patterns of runoff and altered hydrologic extremes (e.g. floods and extreme stormwater events). To better understand changing patterns of extreme high flows in Midwest and Great Lakes region, we analyzed long-term historical observations of peak streamflow at different gaging stations. We also conducted hydrologic model experiments using the Variable Infiltration Capacity (VIC) at 1/16 degree resolution in order to explore sensitivity of annual peak streamflow, both historically and under temperature and precipitation changes for several future periods. For future projections, the Hybrid Delta statistical downscaling approach applied to the Coupled Model Inter-comparison, Phase5 (CMIP5) Global Climate Model (GCM) scenarios was used to produce driving data for the VIC hydrologic model. Preliminary results for several test basins in the Midwest support the hypothesis that there are consistent and statistically significant changes in the mean annual flood starting before and after about 1975. Future projections using hydrologic model simulations support the hypothesis of higher peak flows due to warming and increasing precipitation projected for the 21st century. We will extend this preliminary analysis using observed data and simulations from 40 river basins in the Midwest to further test these hypotheses.

Comparative evaluation of the IPCC AR5 CMIP5 versus the AR4 CMIP3 model ensembles for regional precipitation and their extremes over South America

NASA Astrophysics Data System (ADS)

Tolen, J.; Kodra, E. A.; Ganguly, A. R.

2011-12-01

The assertion that higher-resolution experiments or more sophisticated process models within the IPCC AR5 CMIP5 suite of global climate model ensembles improves precipitation projections over the IPCC AR4 CMIP3 suite remains a hypothesis that needs to be rigorously tested. The questions are particularly important for local to regional assessments at scales relevant for the management of critical infrastructures and key resources, particularly for the attributes of sever precipitation events, for example, the intensity, frequency and duration of extreme precipitation. Our case study is South America, where precipitation and their extremes play a central role in sustaining natural, built and human systems. To test the hypothesis that CMIP5 improves over CMIP3 in this regard, spatial and temporal measures of prediction skill are constructed and computed by comparing climate model hindcasts with the NCEP-II reanalysis data, considered here as surrogate observations, for the entire globe and for South America. In addition, gridded precipitation observations over South America based on rain gage measurements are considered. The results suggest that the utility of the next-generation of global climate models over the current generation needs to be carefully evaluated on a case-by-case basis before communicating to resource managers and policy makers.

Accounting for observation uncertainties in an evaluation metric of low latitude turbulent air-sea fluxes: application to the comparison of a suite of IPSL model versions

NASA Astrophysics Data System (ADS)

Servonnat, Jérôme; Găinuşă-Bogdan, Alina; Braconnot, Pascale

2017-09-01

Turbulent momentum and heat (sensible heat and latent heat) fluxes at the air-sea interface are key components of the whole energetic of the Earth's climate. The evaluation of these fluxes in the climate models is still difficult because of the large uncertainties associated with the reference products. In this paper we present an objective metric accounting for reference uncertainties to evaluate the annual cycle of the low latitude turbulent fluxes of a suite of IPSL climate models. This metric consists in a Hotelling T 2 test between the simulated and observed field in a reduce space characterized by the dominant modes of variability that are common to both the model and the reference, taking into account the observational uncertainty. The test is thus more severe when uncertainties are small as it is the case for sea surface temperature (SST). The results of the test show that for almost all variables and all model versions the model-reference differences are not zero. It is not possible to distinguish between model versions for sensible heat and meridional wind stress, certainly due to the large observational uncertainties. All model versions share similar biases for the different variables. There is no improvement between the reference versions of the IPSL model used for CMIP3 and CMIP5. The test also reveals that the higher horizontal resolution fails to improve the representation of the turbulent surface fluxes compared to the other versions. The representation of the fluxes is further degraded in a version with improved atmospheric physics with an amplification of some of the biases in the Indian Ocean and in the intertropical convergence zone. The ranking of the model versions for the turbulent fluxes is not correlated with the ranking found for SST. This highlights that despite the fact that SST gradients are important for the large-scale atmospheric circulation patterns, other factors such as wind speed, and air-sea temperature contrast play an important role in the representation of turbulent fluxes.

PALM-USM v1.0: A new urban surface model integrated into the PALM large-eddy simulation model

NASA Astrophysics Data System (ADS)

Resler, Jaroslav; Krč, Pavel; Belda, Michal; Juruš, Pavel; Benešová, Nina; Lopata, Jan; Vlček, Ondřej; Damašková, Daša; Eben, Kryštof; Derbek, Přemysl; Maronga, Björn; Kanani-Sühring, Farah

2017-10-01

Urban areas are an important part of the climate system and many aspects of urban climate have direct effects on human health and living conditions. This implies that reliable tools for local urban climate studies supporting sustainable urban planning are needed. However, a realistic implementation of urban canopy processes still poses a serious challenge for weather and climate modelling for the current generation of numerical models. To address this demand, a new urban surface model (USM), describing the surface energy processes for urban environments, was developed and integrated as a module into the PALM large-eddy simulation model. The development of the presented first version of the USM originated from modelling the urban heat island during summer heat wave episodes and thus implements primarily processes important in such conditions. The USM contains a multi-reflection radiation model for shortwave and longwave radiation with an integrated model of absorption of radiation by resolved plant canopy (i.e. trees, shrubs). Furthermore, it consists of an energy balance solver for horizontal and vertical impervious surfaces, and thermal diffusion in ground, wall, and roof materials, and it includes a simple model for the consideration of anthropogenic heat sources. The USM was parallelized using the standard Message Passing Interface and performance testing demonstrates that the computational costs of the USM are reasonable on typical clusters for the tested configurations. The module was fully integrated into PALM and is available via its online repository under the GNU General Public License (GPL). The USM was tested on a summer heat-wave episode for a selected Prague crossroads. The general representation of the urban boundary layer and patterns of surface temperatures of various surface types (walls, pavement) are in good agreement with in situ observations made in Prague. Additional simulations were performed in order to assess the sensitivity of the results to uncertainties in the material parameters, the domain size, and the general effect of the USM itself. The first version of the USM is limited to the processes most relevant to the study of summer heat waves and serves as a basis for ongoing development which will address additional processes of the urban environment and lead to improvements to extend the utilization of the USM to other environments and conditions.

Development of an analytical model for estimating global terrestrial carbon assimilation using a rate-limitation framework

NASA Astrophysics Data System (ADS)

Donohue, Randall; Yang, Yuting; McVicar, Tim; Roderick, Michael

2016-04-01

A fundamental question in climate and ecosystem science is "how does climate regulate the land surface carbon budget?" To better answer that question, here we develop an analytical model for estimating mean annual terrestrial gross primary productivity (GPP), which is the largest carbon flux over land, based on a rate-limitation framework. Actual GPP (climatological mean from 1982 to 2010) is calculated as a function of the balance between two GPP potentials defined by the climate (i.e., precipitation and solar radiation) and a third parameter that encodes other environmental variables and modifies the GPP-climate relationship. The developed model was tested at three spatial scales using different GPP sources, i.e., (1) observed GPP from 94 flux-sites, (2) modelled GPP (using the model-tree-ensemble approach) at 48654 (0.5 degree) grid-cells and (3) at 32 large catchments across the globe. Results show that the proposed model could account for the spatial GPP patterns, with a root-mean-square error of 0.70, 0.65 and 0.3 g C m-2 d-1 and R2 of 0.79, 0.92 and 0.97 for the flux-site, grid-cell and catchment scales, respectively. This analytical GPP model shares a similar form with the Budyko hydroclimatological model, which opens the possibility of a general analytical framework to analyze the linked carbon-water-energy cycles.

Constraining Centennial-Scale Ecosystem-Climate Interactions with a Pre-colonial Forest Reconstruction across the Upper Midwest and Northeastern United States

NASA Astrophysics Data System (ADS)

Matthes, J. H.; Dietze, M.; Fox, A. M.; Goring, S. J.; McLachlan, J. S.; Moore, D. J.; Poulter, B.; Quaife, T. L.; Schaefer, K. M.; Steinkamp, J.; Williams, J. W.

2014-12-01

Interactions between ecological systems and the atmosphere are the result of dynamic processes with system memories that persist from seconds to centuries. Adequately capturing long-term biosphere-atmosphere exchange within earth system models (ESMs) requires an accurate representation of changes in plant functional types (PFTs) through time and space, particularly at timescales associated with ecological succession. However, most model parameterization and development has occurred using datasets than span less than a decade. We tested the ability of ESMs to capture the ecological dynamics observed in paleoecological and historical data spanning the last millennium. Focusing on an area from the Upper Midwest to New England, we examined differences in the magnitude and spatial pattern of PFT distributions and ecotones between historic datasets and the CMIP5 inter-comparison project's large-scale ESMs. We then conducted a 1000-year model inter-comparison using six state-of-the-art biosphere models at sites that bridged regional temperature and precipitation gradients. The distribution of ecosystem characteristics in modeled climate space reveals widely disparate relationships between modeled climate and vegetation that led to large differences in long-term biosphere-atmosphere fluxes for this region. Model simulations revealed that both the interaction between climate and vegetation and the representation of ecosystem dynamics within models were important controls on biosphere-atmosphere exchange.

The Influence of Test-Based Accountability Policies on Early Elementary Teachers: School Climate, Environmental Stress, and Teacher Stress

ERIC Educational Resources Information Center

Saeki, Elina; Segool, Natasha; Pendergast, Laura; von der Embse, Nathaniel

2018-01-01

This study examined the potential influence of test-based accountability policies on school environment and teacher stress among early elementary teachers. Structural equation modeling of data from 541 kindergarten through second grade teachers across three states found that use of student performance on high-stakes tests to evaluate teachers…

External validity of a generic safety climate scale for lone workers across different industries and companies.

PubMed

Lee, Jin; Huang, Yueng-hsiang; Robertson, Michelle M; Murphy, Lauren A; Garabet, Angela; Chang, Wen-Ruey

2014-02-01

The goal of this study was to examine the external validity of a 12-item generic safety climate scale for lone workers in order to evaluate the appropriateness of generalized use of the scale in the measurement of safety climate across various lone work settings. External validity evidence was established by investigating the measurement equivalence (ME) across different industries and companies. Confirmatory factor analysis (CFA)-based and item response theory (IRT)-based perspectives were adopted to examine the ME of the generic safety climate scale for lone workers across 11 companies from the trucking, electrical utility, and cable television industries. Fairly strong evidence of ME was observed for both organization- and group-level generic safety climate sub-scales. Although significant invariance was observed in the item intercepts across the different lone work settings, absolute model fit indices remained satisfactory in the most robust step of CFA-based ME testing. IRT-based ME testing identified only one differentially functioning item from the organization-level generic safety climate sub-scale, but its impact was minimal and strong ME was supported. The generic safety climate scale for lone workers reported good external validity and supported the presence of a common feature of safety climate among lone workers. The scale can be used as an effective safety evaluation tool in various lone work situations. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

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.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Numerical modeling of Drangajökull Ice Cap, NW Iceland

NASA Astrophysics Data System (ADS)

Anderson, Leif S.; Jarosch, Alexander H.; Flowers, Gwenn E.; Aðalgeirsdóttir, Guðfinna; Magnússon, Eyjólfur; Pálsson, Finnur; Muñoz-Cobo Belart, Joaquín; Þorsteinsson, Þorsteinn; Jóhannesson, Tómas; Sigurðsson, Oddur; Harning, David; Miller, Gifford H.; Geirsdóttir, Áslaug

2016-04-01

Over the past century the Arctic has warmed twice as fast as the global average. This discrepancy is likely due to feedbacks inherent to the Arctic climate system. These Arctic climate feedbacks are currently poorly quantified, but are essential to future climate predictions based on global circulation modeling. Constraining the magnitude and timing of past Arctic climate changes allows us to test climate feedback parameterizations at different times with different boundary conditions. Because Holocene Arctic summer temperature changes have been largest in the North Atlantic (Kaufman et al., 2004) we focus on constraining the paleoclimate of Iceland. Glaciers are highly sensitive to changes in temperature and precipitation amount. This sensitivity allows for the estimation of paleoclimate using glacier models, modern glacier mass balance data, and past glacier extents. We apply our model to the Drangajökull ice cap (~150 sq. km) in NW Iceland. Our numerical model is resolved in two-dimensions, conserves mass, and applies the shallow-ice-approximation. The bed DEM used in the model runs was constructed from radio echo data surveyed in spring 2014. We constrain the modern surface mass balance of Drangajökull using: 1) ablation and accumulation stakes; 2) ice surface digital elevation models (DEMs) from satellite, airborne LiDAR, and aerial photographs; and 3) full-stokes model-derived vertical ice velocities. The modeled vertical ice velocities and ice surface DEMs are combined to estimate past surface mass balance. We constrain Holocene glacier geometries using moraines and trimlines (e.g., Brynjolfsson, etal, 2014), proglacial-lake cores, and radiocarbon-dated dead vegetation emerging from under the modern glacier. We present a sensitivity analysis of the model to changes in parameters and show the effect of step changes of temperature and precipitation on glacier extent. Our results are placed in context with local lacustrine and marine climate proxies as well as with glacier extent and volume changes across the North Atlantic.

New developments on the homogenization of Canadian daily temperature data

NASA Astrophysics Data System (ADS)

Vincent, Lucie A.; Wang, Xiaolan L.

2010-05-01

Long-term and homogenized surface air temperature datasets had been prepared for the analysis of climate trends in Canada (Vincent and Gullett 1999). Non-climatic steps due to instruments relocation/changes and changes in observing procedures were identified in the annual mean of the daily maximum and minimum temperatures using a technique based on regression models (Vincent 1998). Monthly adjustments were derived from the regression models and daily adjustments were obtained from an interpolation procedure using the monthly adjustments (Vincent et al. 2002). Recently, new statistical tests have been developed to improve the power of detecting changepoints in climatological data time series. The penalized maximal t (PMT) test (Wang et al. 2007) and the penalized maximal F (PMF) test (Wang 2008b) were developed to take into account the position of each changepoint in order to minimize the effect of unequal and small sample size. A software package RHtestsV3 (Wang and Feng 2009) has also been developed to implement these tests to homogenize climate data series. A recursive procedure was developed to estimate the annual cycle, linear trend, and lag-1 autocorrelation of the base series in tandem, so that the effect of lag-1 autocorrelation is accounted for in the tests. A Quantile Matching (QM) algorithm (Wang 2009) was also developed for adjusting Gaussian daily data so that the empirical distributions of all segments of the detrended series match each other. The RHtestsV3 package was used to prepare a second generation of homogenized temperatures in Canada. Both the PMT test and the PMF test were applied to detect shifts in monthly mean temperature series. Reference series was used in conducting a PMT test. Whenever possible, the main causes of the shifts were retrieved through historical evidence such as the station inspection reports. Finally, the QM algorithm was used to adjust the daily temperature series for the artificial shifts identified from the respective monthly mean series. These procedures were applied to homogenize daily maximum and minimum temperatures recorded at 336 stations across Canada. During the presentation, the procedures will be summarized and their application will be illustrated throughout the provision of selected examples. References Vincent, L.A., X. Zhang, B.R. Bonsal and W.D. Hogg, 2002: Homogenization of daily temperatures over Canada. J. Climate, 15, 1322-1334. Vincent, L.A., and D.W. Gullett, 1999: Canadian historical and homogeneous temperature datasets for climate change analyses. Int. J. Climatol., 19, 1375-1388. Vincent, L.A., 1998: A technique for the identification of inhomogeneities in Canadian temperature series. J. Climate, 11, 1094-1104. Wang, X. L., 2009: A quantile matching adjustment algorithm for Gaussian data series. Climate Research Division, Atmospheric Science and Technology Directorate, Science and Technology Branch, Environment Canada. 5 pp. [Available online at http://cccma.seos.uvic.ca/ETCCDMI/software.shtml]. Wang X. L. and Y. Feng, 2009: RHtestsV3 User Manual. Climate Research Division, Atmospheric Science and Technology Directorate, Science and Technology Branch, Environment Canada. 26 pp. [Available online at http://cccma.seos.uvic.ca/ETCCDMI/software.shtml]. Wang, X. L., 2008a: Accounting for autocorrelation in detecting mean-shifts in climate data series using the penalized maximal t or F test. J. Appl. Meteor. Climatol., 47, 2423-2444. Wang, X. L., 2008b: Penalized maximal F-test for detecting undocumented mean-shifts without trend-change. J. Atmos. Oceanic Tech., 25 (No. 3), 368-384. DOI:10.1175/2007/JTECHA982.1. Wang, X. L., Q. H. Wen, and Y. Wu, 2007: Penalized maximal t test for detecting undocumented mean change in climate data series. J. Appl. Meteor. Climatol., 46 (No. 6), 916-931. DOI:10.1175/JAM2504.1

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

NASA Astrophysics Data System (ADS)

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

2015-10-01

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

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

PubMed

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

2015-10-27

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

Dew point temperature affects ascospore release of allergenic genus Leptosphaeria

NASA Astrophysics Data System (ADS)

Sadyś, Magdalena; Kaczmarek, Joanna; Grinn-Gofron, Agnieszka; Rodinkova, Victoria; Prikhodko, Alex; Bilous, Elena; Strzelczak, Agnieszka; Herbert, Robert J.; Jedryczka, Malgorzata

2018-06-01

The genus Leptosphaeria contains numerous fungi that cause the symptoms of asthma and also parasitize wild and crop plants. In search of a robust and universal forecast model, the ascospore concentration in air was measured and weather data recorded from 1 March to 31 October between 2006 and 2012. The experiment was conducted in three European countries of the temperate climate, i.e., Ukraine, Poland, and the UK. Out of over 150 forecast models produced using artificial neural networks (ANNs) and multivariate regression trees (MRTs), we selected the best model for each site, as well as for joint two-site combinations. The performance of all computed models was tested against records from 1 year which had not been used for model construction. The statistical analysis of the fungal spore data was supported by a comprehensive study of both climate and land cover within a 30-km radius from the air sampler location. High-performance forecasting models were obtained for individual sites, showing that the local micro-climate plays a decisive role in biology of the fungi. Based on the previous epidemiological studies, we hypothesized that dew point temperature (DPT) would be a critical factor in the models. The impact of DPT was confirmed only by one of the final best neural models, but the MRT analyses, similarly to the Spearman's rank test, indicated the importance of DPT in all but one of the studied cases and in half of them ranked it as a fundamental factor. This work applies artificial neural modeling to predict the Leptosphaeria airborne spore concentration in urban areas for the first time.

Dew point temperature affects ascospore release of allergenic genus Leptosphaeria

NASA Astrophysics Data System (ADS)

Sadyś, Magdalena; Kaczmarek, Joanna; Grinn-Gofron, Agnieszka; Rodinkova, Victoria; Prikhodko, Alex; Bilous, Elena; Strzelczak, Agnieszka; Herbert, Robert J.; Jedryczka, Malgorzata

2018-01-01

The genus Leptosphaeria contains numerous fungi that cause the symptoms of asthma and also parasitize wild and crop plants. In search of a robust and universal forecast model, the ascospore concentration in air was measured and weather data recorded from 1 March to 31 October between 2006 and 2012. The experiment was conducted in three European countries of the temperate climate, i.e., Ukraine, Poland, and the UK. Out of over 150 forecast models produced using artificial neural networks (ANNs) and multivariate regression trees (MRTs), we selected the best model for each site, as well as for joint two-site combinations. The performance of all computed models was tested against records from 1 year which had not been used for model construction. The statistical analysis of the fungal spore data was supported by a comprehensive study of both climate and land cover within a 30-km radius from the air sampler location. High-performance forecasting models were obtained for individual sites, showing that the local micro-climate plays a decisive role in biology of the fungi. Based on the previous epidemiological studies, we hypothesized that dew point temperature (DPT) would be a critical factor in the models. The impact of DPT was confirmed only by one of the final best neural models, but the MRT analyses, similarly to the Spearman's rank test, indicated the importance of DPT in all but one of the studied cases and in half of them ranked it as a fundamental factor. This work applies artificial neural modeling to predict the Leptosphaeria airborne spore concentration in urban areas for the first time.

Dew point temperature affects ascospore release of allergenic genus Leptosphaeria.

PubMed

Sadyś, Magdalena; Kaczmarek, Joanna; Grinn-Gofron, Agnieszka; Rodinkova, Victoria; Prikhodko, Alex; Bilous, Elena; Strzelczak, Agnieszka; Herbert, Robert J; Jedryczka, Malgorzata

2018-06-01

The genus Leptosphaeria contains numerous fungi that cause the symptoms of asthma and also parasitize wild and crop plants. In search of a robust and universal forecast model, the ascospore concentration in air was measured and weather data recorded from 1 March to 31 October between 2006 and 2012. The experiment was conducted in three European countries of the temperate climate, i.e., Ukraine, Poland, and the UK. Out of over 150 forecast models produced using artificial neural networks (ANNs) and multivariate regression trees (MRTs), we selected the best model for each site, as well as for joint two-site combinations. The performance of all computed models was tested against records from 1 year which had not been used for model construction. The statistical analysis of the fungal spore data was supported by a comprehensive study of both climate and land cover within a 30-km radius from the air sampler location. High-performance forecasting models were obtained for individual sites, showing that the local micro-climate plays a decisive role in biology of the fungi. Based on the previous epidemiological studies, we hypothesized that dew point temperature (DPT) would be a critical factor in the models. The impact of DPT was confirmed only by one of the final best neural models, but the MRT analyses, similarly to the Spearman's rank test, indicated the importance of DPT in all but one of the studied cases and in half of them ranked it as a fundamental factor. This work applies artificial neural modeling to predict the Leptosphaeria airborne spore concentration in urban areas for the first time.

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post do data transfer from Gaea to Vapor; DTN (Nwave) has set up for all users but wants one user to test numerous cpu intensive scripts? Click here to view more information Open Effects of the problem: NCEP pre

Forest dynamics in Oregon landscapes: Evaluation and application of an individual-based model

USGS Publications Warehouse

Busing, R.T.; Solomon, A.M.; McKane, R.B.; Burdick, C.A.

2007-01-01

The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range. It was then applied to simulate present and future (1990-2050) forest landscape dynamics of a watershed in the west Cascades. Various regimes of climate change and harvesting in the watershed were considered in the landscape application. The model was able to capture much of the variation in forest basal area and composition in western Oregon even though temperature and precipitation were the only inputs that were varied among simulated sites. The measured decline in total basal area from tall coastal forests eastward to interior steppe was matched by simulations. Changes in simulated forest dominants also approximated those in the actual data. Simulated abundances of a few minor species did not match actual abundances, however. Subsequent projections of climate change and harvest effects in a west Cascades landscape indicated no change in forest dominance as of 2050. Yet, climate-driven shifts in the distributions of some species were projected. The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data. Simulations with fire as an agent of partial disturbance suggested that frequent fires of low severity can alter forest composition and structure as much or more than severe fires at historic frequencies. ?? 2007 by the Ecological Society of America.

Climate change streamflow scenarios designed for critical period water resources planning studies

NASA Astrophysics Data System (ADS)

Hamlet, A. F.; Snover, A. K.; Lettenmaier, D. P.

2003-04-01

Long-range water planning in the United States is usually conducted by individual water management agencies using a critical period planning exercise based on a particular period of the observed streamflow record and a suite of internally-developed simulation tools representing the water system. In the context of planning for climate change, such an approach is flawed in that it assumes that the future climate will be like the historic record. Although more sophisticated planning methods will probably be required as time goes on, a short term strategy for incorporating climate uncertainty into long-range water planning as soon as possible is to create alternate inputs to existing planning methods that account for climate uncertainty as it affects both supply and demand. We describe a straight-forward technique for constructing streamflow scenarios based on the historic record that include the broad-based effects of changed regional climate simulated by several global climate models (GCMs). The streamflow scenarios are based on hydrologic simulations driven by historic climate data perturbed according to regional climate signals from four GCMs using the simple "delta" method. Further data processing then removes systematic hydrologic model bias using a quantile-based bias correction scheme, and lastly, the effects of random errors in the raw hydrologic simulations are removed. These techniques produce streamflow scenarios that are consistent in time and space with the historic streamflow record while incorporating fundamental changes in temperature and precipitation from the GCM scenarios. Planning model simulations based on these climate change streamflow scenarios can therefore be compared directly to planning model simulations based on the historic record of streamflows to help planners understand the potential impacts of climate uncertainty. The methods are currently being tested and refined in two large-scale planning exercises currently being conducted in the Pacific Northwest (PNW) region of the US, and the resulting streamflow scenarios will be made freely available on the internet for a large number of sites in the PNW to help defray the costs of including climate change information in other studies.

Clime: analyzing and producing climate data in GIS environment

NASA Astrophysics Data System (ADS)

Cattaneo, Luigi; Rillo, Valeria; Mercogliano, Paola

2014-05-01

In the last years, Impacts on Soil and Coasts Division (ISC) of CMCC (Euro-Mediterranean Center on Climate Change) had several collaboration experiences with impact communities, including IS-ENES (FP7-INF) and SafeLand (FP7-ENV) projects, which involved a study of landslide risk in Europe, and is currently active in GEMINA (FIRB) and ORIENTGATE (SEE Transnational Cooperation Programme) research projects. As a result, it has brought research activities about different impact of climate changes as flood and landslide hazards, based on climate simulation obtained from the high resolution regional climate models COSMO CLM, developed at CMCC as member of the consortium CLM Assembly. ISC-Capua also collaborates with local institutions interested in atmospherical climate change and also of their impacts on the soil, such as river basin authorities in the Campania region, ARPA Emilia Romagna and ARPA Calabria. Impact models (e.g. hydraulic or stability models) are usually developed in a GIS environment, since they need an accurate territory description, so Clime has been designed to bridge the usually existing gap between climate data - both observed and simulated - gathered from different sources, and impact communities. The main goal of Clime, special purpose Geographic Information System (GIS) software integrated in ESRI ArcGIS Desktop 10, is to easily evaluate multiple climate features and study climate changes over specific geographical domains with their related effects on environment, including impacts on soil. Developed as an add-in tool, this software has been conceived for research activities of ISC Division in order to provide a substantial contribution during post-processing and validation phase. Therefore, it is possible to analyze and compare multiple datasets (observations, climate simulations, etc.) through processes involving statistical functions, percentiles, trends test and evaluation of extreme events with a flexible system of temporal and spatial filtering, and to represent results as maps, temporal and statistic plots (time series, seasonal cycles, PDFs, scatter plots, Taylor diagrams) or Excel tables; in addition, it features bias correction techniques for climate model results. Summarizing, Clime is able to provide users a simple and fast way to retrieve analysis over simulated climate data and observations within any geographical site of interest (provinces, regions, countries, etc.).

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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